270 results found
Nunn AVW, Guy GW, Bell JD, 2022, Bioelectric Fields at the Beginnings of Life, Bioelectricity, ISSN: 2576-3105
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
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
<jats:p>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
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
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
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.
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
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
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.
Koivula RW, Atabaki-Pasdar N, Giordano GN, et al., 2020, The role of physical activity in metabolic homeostasis before and after the onset of type 2 diabetes: an IMI DIRECT study, Diabetologia, Vol: 63, Pages: 744-756, ISSN: 0012-186X
Aims/hypothesisIt is well established that physical activity, abdominal ectopic fat and glycaemic regulation are related but the underlying structure of these relationships is unclear. The previously proposed twin-cycle hypothesis (TC) provides a mechanistic basis for impairment in glycaemic control through the interactions of substrate availability, substrate metabolism and abdominal ectopic fat accumulation. Here, we hypothesise that the effect of physical activity in glucose regulation is mediated by the twin-cycle. We aimed to examine this notion in the Innovative Medicines Initiative Diabetes Research on Patient Stratification (IMI DIRECT) Consortium cohorts comprised of participants with normal or impaired glucose regulation (cohort 1: N ≤ 920) or with recently diagnosed type 2 diabetes (cohort 2: N ≤ 435).MethodsWe defined a structural equation model that describes the TC and fitted this within the IMI DIRECT dataset. A second model, twin-cycle plus physical activity (TC-PA), to assess the extent to which the effects of physical activity in glycaemic regulation are mediated by components in the twin-cycle, was also fitted. Beta cell function, insulin sensitivity and glycaemic control were modelled from frequently sampled 75 g OGTTs (fsOGTTs) and mixed-meal tolerance tests (MMTTs) in participants without and with diabetes, respectively. Abdominal fat distribution was assessed using MRI, and physical activity through wrist-worn triaxial accelerometry. Results are presented as standardised beta coefficients, SE and p values, respectively.ResultsThe TC and TC-PA models showed better fit than null models (TC: χ2 = 242, p = 0.004 and χ2 = 63, p = 0.001 in cohort 1 and 2, respectively; TC-PA: χ2 = 180, p = 0.041 and χ2 = 60, p = 0.008 in cohort 1 and 2, respectively). The association of physical activity wi
Aldraimli M, Soria D, Parkinson J, et al., 2020, Machine Learning Classification of Females Susceptibility to Visceral Fat Associated Diseases, Editors: Henriques, Neves, DeCarvalho, Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 679-693, ISBN: 978-3-030-31634-1
Mani B, Puzziferri N, He Z, et al., 2019, LEAP2 changes with body mass and food intake in humans and mice, Journal of Clinical Investigation, Vol: 129, Pages: 3909-3923, ISSN: 0021-9738
Acyl-ghrelin administration increases food intake, body weight, and blood glucose. In contrast, mice lacking ghrelin or ghrelin receptors (GHSRs) exhibit life-threatening hypoglycemia during starvation-like conditions, but do not consistently exhibit overt metabolic phenotypes when given ad libitum food access. These results, and findings of ghrelin resistance in obese states, imply nutritional state dependence of ghrelin’s metabolic actions. Here, we hypothesized that liver-enriched antimicrobial peptide-2 (LEAP2), a recently characterized endogenous GHSR antagonist, blunts ghrelin action during obese states and postprandially. To test this hypothesis, we determined changes in plasma LEAP2 and acyl-ghrelin due to fasting, eating, obesity, Roux-en-Y gastric bypass (RYGB), vertical sleeve gastrectomy (VSG), oral glucose administration, and type 1 diabetes mellitus (T1DM) using humans and/or mice. Our results suggest that plasma LEAP2 is regulated by metabolic status: its levels increased with body mass and blood glucose and decreased with fasting, RYGB, and in postprandial states following VSG. These changes were mostly opposite of those of acyl-ghrelin. Furthermore, using electrophysiology, we showed that LEAP2 both hyperpolarizes and prevents acyl-ghrelin from activating arcuate NPY neurons. We predict that the plasma LEAP2/acyl-ghrelin molar ratio may be a key determinant modulating acyl-ghrelin activity in response to body mass, feeding status, and blood glucose.
Wilman HR, Parisinos CA, Atabaki-Pasdar N, et al., 2019, Genetic studies of abdominal MRI data identify genes regulating hepcidin as major determinants of liver iron concentration, Journal of Hepatology, Vol: 71, Pages: 594-602, ISSN: 0168-8278
BACKGROUND & AIMS: Excess liver iron content is common and is linked to the risk of hepatic and extrahepatic diseases. We aimed to identify genetic variants influencing liver iron content and use genetics to understand its link to other traits and diseases. METHODS: First, we performed a genome-wide association study (GWAS) in 8,289 individuals from UK Biobank, whose liver iron level had been quantified by magnetic resonance imaging, before validating our findings in an independent cohort (n = 1,513 from IMI DIRECT). Second, we used Mendelian randomisation to test the causal effects of 25 predominantly metabolic traits on liver iron content. Third, we tested phenome-wide associations between liver iron variants and 770 traits and disease outcomes. RESULTS: We identified 3 independent genetic variants (rs1800562 [C282Y] and rs1799945 [H63D] in HFE and rs855791 [V736A] in TMPRSS6) associated with liver iron content that reached the GWAS significance threshold (p <5 × 10-8). The 2 HFE variants account for ∼85% of all cases of hereditary haemochromatosis. Mendelian randomisation analysis provided evidence that higher central obesity plays a causal role in increased liver iron content. Phenome-wide association analysis demonstrated shared aetiopathogenic mechanisms for elevated liver iron, high blood pressure, cirrhosis, malignancies, neuropsychiatric and rheumatological conditions, while also highlighting inverse associations with anaemias, lipidaemias and ischaemic heart disease. CONCLUSION: Our study provides genetic evidence that mechanisms underlying higher liver iron content are likely systemic rather than organ specific, that higher central obesity is causally associated with higher liver iron, and that liver iron shares common aetiology with multiple metabolic and non-metabolic diseases. LAY SUMMARY: Excess liver iron content is common and is associated with liver diseases and metabolic diseases including diabetes, high blood pressure, and heart
Koivula RW, Forgie IM, Kurbasic A, et al., 2019, Discovery of biomarkers for glycaemic deterioration before and after the onset of type 2 diabetes: descriptive characteristics of the epidemiological studies within the IMI DIRECT Consortium., Diabetologia, Vol: 62, Pages: 1601-1615, ISSN: 0012-186X
AIMS/HYPOTHESIS: Here, we describe the characteristics of the Innovative Medicines Initiative (IMI) Diabetes Research on Patient Stratification (DIRECT) epidemiological cohorts at baseline and follow-up examinations (18, 36 and 48 months of follow-up). METHODS: From a sampling frame of 24,682 adults of European ancestry enrolled in population-based cohorts across Europe, participants at varying risk of glycaemic deterioration were identified using a risk prediction algorithm (based on age, BMI, waist circumference, use of antihypertensive medication, smoking status and parental history of type 2 diabetes) and enrolled into a prospective cohort study (n = 2127) (cohort 1, prediabetes risk). We also recruited people from clinical registries with type 2 diabetes diagnosed 6-24 months previously (n = 789) into a second cohort study (cohort 2, diabetes). Follow-up examinations took place at ~18 months (both cohorts) and at ~48 months (cohort 1) or ~36 months (cohort 2) after baseline examinations. The cohorts were studied in parallel using matched protocols across seven clinical centres in northern Europe. RESULTS: Using ADA 2011 glycaemic categories, 33% (n = 693) of cohort 1 (prediabetes risk) had normal glucose regulation and 67% (n = 1419) had impaired glucose regulation. Seventy-six per cent of participants in cohort 1 was male. Cohort 1 participants had the following characteristics (mean ± SD) at baseline: age 62 (6.2) years; BMI 27.9 (4.0) kg/m2; fasting glucose 5.7 (0.6) mmol/l; 2 h glucose 5.9 (1.6) mmol/l. At the final follow-up examination the participants' clinical characteristics were as follows: fasting glucose 6.0 (0.6) mmol/l; 2 h OGTT glucose 6.5 (2.0) mmol/l. In cohort 2 (diabetes), 66% (n = 517) were treated by lifestyle modification and 34% (n = 272) were treated with metformin plus lifestyle modification at enro
Evangelou E, Gao H, Blakeley P, et al., 2019, New alcohol-related genes suggest shared genetic mechanisms with neuropsychiatric disorders, Nature Human Behaviour, Vol: 3, Pages: 950-961, ISSN: 2397-3374
Excessive alcohol consumption is one of the main causes of death and disability worldwide. Alcohol consumption is a heritable complex trait. Here we conducted a meta-analysis of genome-wide association studies of alcohol consumption (g d−1) from the UK Biobank, the Alcohol Genome-Wide Consortium and the Cohorts for Heart and Aging Research in Genomic Epidemiology Plus consortia, collecting data from 480,842 people of European descent to decipher the genetic architecture of alcohol intake. We identified 46 new common loci and investigated their potential functional importance using magnetic resonance imaging data and gene expression studies. We identify genetic pathways associated with alcohol consumption and suggest genetic mechanisms that are shared with neuropsychiatric disorders such as schizophrenia.
Chambers E, Byrne C, Rugyendo A, et al., 2019, The effects of dietary supplementation with inulin and inulin-propionate ester on hepatic steatosis in adults with non-alcoholic fatty liver disease, Diabetes, Obesity and Metabolism, Vol: 21, Pages: 372-376, ISSN: 1462-8902
The short chain fatty acid (SCFA) propionate, produced through fermentation of dietary fibre by the gut microbiota, has been shown to alter hepatic metabolic processes that reduce lipid storage. We aimed to investigate the impact of raising colonic propionate production on hepatic steatosis in adults with non‐alcoholic fatty liver disease (NAFLD). Eighteen adults were randomised to receive 20g/day of an inulin‐propionate ester (IPE), designed to deliver propionate to the colon, or an inulin‐control for 42‐days in a parallel design. The change in intrahepatocellular lipid (IHCL) following the supplementation period was not different between groups (P=0.082), however IHCL significantly increased within the inulin‐control group (20.9±2.9 to 26.8±3.9%; P=0.012; n=9), which was not observed within the IPE group (22.6±6.9 to 23.5±6.8%; P=0.635; n=9). The predominant SCFA from colonic fermentation of inulin is acetate, which in a background of NAFLD and a hepatic metabolic profile that promotes fat accretion, may provide surplus lipogenic substrate to the liver. The increased colonic delivery of propionate from IPE appears to attenuate this acetate‐mediated increase in IHCL.
So P-W, Ekonomou A, Galley K, et al., 2019, Intraperitoneal delivery of acetate-encapsulated liposomal nanoparticles for neuroprotection of the penumbra in a rat model of ischemic stroke, International Journal of Nanomedicine, Vol: 14, Pages: 1979-1991, ISSN: 1176-9114
Background: Ischemic stroke is a devastating condition, with metabolic derangement and persistent inflammation enhancing the initial insult of ischaemia. Recombinant tissue plasminogen remains the only effective treatment but limited as therapy must commence soon after the onset of symptoms.Purpose: We investigated whether acetate, which modulates many pathways including inflammation, may attenuate brain injury in stroke. As acetate has a short blood half-life and high amounts irritate the gastrointestinal tract, acetate was administered encapsulated in a liposomal nanoparticle (liposomal-encapsulated acetate, LITA).Methods: Transient ischemia was induced by 90 mins middle-cerebral artery occlusion (MCAO) in Sprague-Dawley rats, and LITA or control liposomes given intraperitoneally at occlusion and daily for up to two weeks post-MCAO. Magnetic resonance imaging (MRI) was used to estimate lesion volume at 24 h, 1 and 2 weeks post-MCAO and anterior lateral ventricular volume (ALVv) at 2 weeks post-MCAO. Locomotive behaviour was tested prior to the final MRI scan. After the final scan, brains were collected, and immunohistochemistry was performed.Results: Lesion volumes were decreased by ~80% from 24 h to one-week post-MCAO, in both control and LITA groups (P<0.05). However, the lesion was increased by ~50% over the subsequent 1 to 2 weeks after MCAO in the control group (from 24.1±10.0 to 58.7±28.6 mm3; P<0.05) but remained unchanged in the LITA group. ALVv were also attenuated by LITA treatment at 2 weeks post-MCAO (177.2±11.9% and 135.3±10.9% of contralateral ALVv for control and LITA groups, respectively; P<0.05). LITA-treated animals also appeared to have improved motor activity, moving with greater average velocity than control animals. Microglial immunoreactivity was ~40% lower in the LITA group compared to the control group (P<0.05), but LITA did not modulate neurogenesis, apoptosis, histone acetylation and lipid peroxida
Ji Y, Yiorkas AM, Frau F, et al., 2019, Genome-wide and abdominal MRI data provide evidence that a genetically determined favorable adiposity phenotype is characterized by lower ectopic liver fat and lower risk of type 2 diabetes, heart disease, and hypertension, Diabetes, Vol: 68, Pages: 207-219, ISSN: 0012-1797
Recent genetic studies have identified alleles associated with opposite effects on adiposity and risk of type 2 diabetes. We aimed to identify more of these variants and test the hypothesis that such favorable adiposity alleles are associated with higher subcutaneous fat and lower ectopic fat. We combined MRI data with genome-wide association studies of body fat percentage (%) and metabolic traits. We report 14 alleles, including 7 newly characterized alleles, associated with higher adiposity but a favorable metabolic profile. Consistent with previous studies, individuals carrying more favorable adiposity alleles had higher body fat % and higher BMI but lower risk of type 2 diabetes, heart disease, and hypertension. These individuals also had higher subcutaneous fat but lower liver fat and a lower visceral-to-subcutaneous adipose tissue ratio. Individual alleles associated with higher body fat % but lower liver fat and lower risk of type 2 diabetes included those in PPARG, GRB14, and IRS1, whereas the allele in ANKRD55 was paradoxically associated with higher visceral fat but lower risk of type 2 diabetes. Most identified favorable adiposity alleles are associated with higher subcutaneous and lower liver fat, a mechanism consistent with the beneficial effects of storing excess triglycerides in metabolically low-risk depots.
Fiamoncini J, Rundle M, Gibbons H, et al., 2018, Plasma metabolome analysis identifies distinct human metabotypes in the postprandial state with different susceptibility to weight loss-mediated metabolic improvements, FASEB Journal, Vol: 32, Pages: 5447-5458, ISSN: 0892-6638
Health has been defined as the capability of the organism to adapt to challenges. In this study, we tested to what extent comprehensively phenotyped individuals reveal differences in metabolic responses to a standardized mixed meal tolerance test (MMTT) and how these responses change when individuals experience moderate weight loss. Metabolome analysis was used in 70 healthy individuals. with profiling of ∼300 plasma metabolites during an MMTT over 8 h. Multivariate analysis of plasma markers of fatty acid catabolism identified 2 distinct metabotype clusters (A and B). Individuals from metabotype B showed slower glucose clearance, had increased intra-abdominal adipose tissue mass and higher hepatic lipid levels when compared with individuals from metabotype A. An NMR-based urine analysis revealed that these individuals also to have a less healthy dietary pattern. After a weight loss of ∼5.6 kg over 12 wk, only the subjects from metabotype B showed positive changes in the glycemic response during the MMTT and in markers of metabolic diseases. Our study in healthy individuals demonstrates that more comprehensive phenotyping can reveal discrete metabotypes with different outcomes in a dietary intervention and that markers of lipid catabolism in plasma could allow early detection of the metabolic syndrome.—Fiamoncini, J., Rundle, M., Gibbons, H., Thomas, E. L., Geillinger-Kästle, K., Bunzel, D., Trezzi, J.-P., Kiselova-Kaneva, Y., Wopereis, S., Wahrheit, J., Kulling, S. E., Hiller, K., Sonntag, D., Ivanova, D., van Ommen, B., Frost, G., Brennan, L., Bell, J. Daniel, H. Plasma metabolome analysis identifies distinct human metabotypes in the postprandial state with different susceptibility to weight loss–mediated metabolic improvements.One of the key features of human metabolism is its plasticity and capacity to regain homeostasis upon a disturbance such as acute stress, starvation, or food intake (1). In this regard it has been proposed that heal
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