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O’Donovan SD, Rundle M, Thomas EL, Bell JD, Frost G, Jacobs DM, Wanders A, de Vries R, Mariman EC, van Baak MA, Sterkman L, Nieuwdorp M, Groen AK, Arts IC, van Riel NA, Afman LA. Quantifying the effect of nutritional interventions on metabolic resilience using personalized computational models. iScience 2024; 27:109362. [PMID: 38500825 PMCID: PMC10946327 DOI: 10.1016/j.isci.2024.109362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 10/27/2023] [Accepted: 02/26/2024] [Indexed: 03/20/2024] Open
Abstract
The manifestation of metabolic deteriorations that accompany overweight and obesity can differ greatly between individuals, giving rise to a highly heterogeneous population. This inter-individual variation can impede both the provision and assessment of nutritional interventions as multiple aspects of metabolic health should be considered at once. Here, we apply the Mixed Meal Model, a physiology-based computational model, to characterize an individual's metabolic health in silico. A population of 342 personalized models were generated using data for individuals with overweight and obesity from three independent intervention studies, demonstrating a strong relationship between the model-derived metric of insulin resistance (ρ = 0.67, p < 0.05) and the gold-standard hyperinsulinemic-euglycemic clamp. The model is also shown to quantify liver fat accumulation and β-cell functionality. Moreover, we show that personalized Mixed Meal Models can be used to evaluate the impact of a dietary intervention on multiple aspects of metabolic health at the individual level.
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Affiliation(s)
- Shauna D. O’Donovan
- Division of Human Nutrition and Health, Wageningen University, Wageningen, the Netherlands
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
- Eindhoven Artificial Intelligence Systems Institute (EAISI), Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Milena Rundle
- Division of Diabetes, Endocrinology, and Metabolism, Department of Medicine, Imperial College London, London, UK
| | - E. Louise Thomas
- Research Center for Optimal Health, School of Life Sciences, University of Westminster, London, the United Kingdom
| | - Jimmy D. Bell
- Research Center for Optimal Health, School of Life Sciences, University of Westminster, London, the United Kingdom
| | - Gary Frost
- Division of Diabetes, Endocrinology, and Metabolism, Department of Medicine, Imperial College London, London, UK
| | - Doris M. Jacobs
- Science & Technology, Unilever Foods Innovation Center, Wageningen, the Netherlands
| | - Anne Wanders
- Science & Technology, Unilever Foods Innovation Center, Wageningen, the Netherlands
| | - Ryan de Vries
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Edwin C.M. Mariman
- Department of Human Biology, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, the Netherlands
| | - Marleen A. van Baak
- Department of Human Biology, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, the Netherlands
| | - Luc Sterkman
- Caelus Pharmaceuticals, Zegveld, the Netherlands
| | - Max Nieuwdorp
- Vascular Medicine, Amsterdam UMC Locatie, AMC, Amsterdam, the Netherlands
| | - Albert K. Groen
- Vascular Medicine, Amsterdam UMC Locatie, AMC, Amsterdam, the Netherlands
| | - Ilja C.W. Arts
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, the Netherlands
| | - Natal A.W. van Riel
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
- Eindhoven Artificial Intelligence Systems Institute (EAISI), Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Lydia A. Afman
- Division of Human Nutrition and Health, Wageningen University, Wageningen, the Netherlands
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Abraham A, Cule M, Thanaj M, Basty N, Hashemloo MA, Sorokin EP, Whitcher B, Burgess S, Bell JD, Sattar N, Thomas EL, Yaghootkar H. Genetic evidence for distinct biological mechanisms that link adiposity to type 2 diabetes: towards precision medicine. Diabetes 2024:db231005. [PMID: 38530928 DOI: 10.2337/db23-1005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Accepted: 03/22/2024] [Indexed: 03/28/2024]
Abstract
We aimed to unravel the mechanisms connecting adiposity to type 2 diabetes. We employed 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 Magnetic Resonance Imaging-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, 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.
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Affiliation(s)
- Angela Abraham
- College of Health and Science, University of Lincoln, Joseph Banks Laboratories, Green Lane, Lincoln, UK
| | | | - Marjola Thanaj
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | - Nicolas Basty
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | - M Amin Hashemloo
- Department of Life Sciences, Brunel University London, Uxbridge, United Kingdom
| | | | - Brandon Whitcher
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
- Department of Radiology, MRI Unit, The Royal Marsden NHS Foundation Trust, London, UK
| | - Stephen Burgess
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - Jimmy D Bell
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | - Naveed Sattar
- School of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow, UK
| | - E Louise Thomas
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | - Hanieh Yaghootkar
- College of Health and Science, University of Lincoln, Joseph Banks Laboratories, Green Lane, Lincoln, UK
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Mould RR, Mackenzie AM, Kalampouka I, Nunn AVW, Thomas EL, Bell JD, Botchway SW. Ultra weak photon emission-a brief review. Front Physiol 2024; 15:1348915. [PMID: 38420619 PMCID: PMC10899412 DOI: 10.3389/fphys.2024.1348915] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2023] [Accepted: 01/29/2024] [Indexed: 03/02/2024] Open
Abstract
Cells emit light at ultra-low intensities: photons which are produced as by-products of cellular metabolism, distinct from other light emission processes such as delayed luminescence, bioluminescence, and chemiluminescence. The phenomenon is known by a large range of names, including, but not limited to, biophotons, biological autoluminescence, metabolic photon emission and ultraweak photon emission (UPE), the latter of which shall be used for the purposes of this review. It is worth noting that the photons when produced are neither 'weak' nor specifically biological in characteristics. Research of UPE has a long yet tattered past, historically hamstrung by a lack of technology sensitive enough to detect it. Today, as technology progresses rapidly, it is becoming easier to detect and image these photons, as well as to describe their function. In this brief review we will examine the history of UPE research, their proposed mechanism, possible biological role, the detection of the phenomenon, and the potential medical applications.
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Affiliation(s)
- Rhys R Mould
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, United Kingdom
| | - Alasdair M Mackenzie
- OCTOPUS, Central Laser Facility, Science and Technology Facilities Council, Didcot, United Kingdom
| | - Ifigeneia Kalampouka
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, United Kingdom
| | - Alistair V W Nunn
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, United Kingdom
- The Guy Foundation, Beaminster, United Kingdom
| | - E Louise Thomas
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, United Kingdom
| | - Jimmy D Bell
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, United Kingdom
| | - Stanley W Botchway
- OCTOPUS, Central Laser Facility, Science and Technology Facilities Council, Didcot, United Kingdom
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Thanaj M, Basty N, Whitcher B, Sorokin EP, Liu Y, Srinivasan R, Cule M, Thomas EL, Bell JD. Precision MRI phenotyping of muscle volume and quality at a population scale. Front Physiol 2024; 15:1288657. [PMID: 38370011 PMCID: PMC10869600 DOI: 10.3389/fphys.2024.1288657] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 01/09/2024] [Indexed: 02/20/2024] Open
Abstract
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.
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Affiliation(s)
- Marjola Thanaj
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, United Kingdom
| | - Nicolas Basty
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, United Kingdom
| | - Brandon Whitcher
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, United Kingdom
| | - Elena P. Sorokin
- Calico Life Sciences LLC, South San Francisco, CA, United States
| | - Yi Liu
- Calico Life Sciences LLC, South San Francisco, CA, United States
| | | | - Madeleine Cule
- Calico Life Sciences LLC, South San Francisco, CA, United States
| | - E. Louise Thomas
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, United Kingdom
| | - Jimmy D. Bell
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, United Kingdom
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Thanaj M, Basty N, Cule M, Sorokin EP, Whitcher B, Bell JD, Thomas EL. Liver shape analysis using statistical parametric maps at population scale. BMC Med Imaging 2024; 24:15. [PMID: 38195400 PMCID: PMC10775563 DOI: 10.1186/s12880-023-01149-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 10/31/2023] [Indexed: 01/11/2024] Open
Abstract
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.
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Affiliation(s)
- Marjola Thanaj
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK.
| | - Nicolas Basty
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | | | | | - Brandon Whitcher
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | - Jimmy D Bell
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | - E Louise Thomas
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
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Thanaj M, Basty N, Cule M, Sorokin EP, Whitcher B, Srinivasan R, Lennon R, Bell JD, Thomas EL. Kidney shape statistical analysis: associations with disease and anthropometric factors. BMC Nephrol 2023; 24:362. [PMID: 38057740 PMCID: PMC10698953 DOI: 10.1186/s12882-023-03407-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Accepted: 11/22/2023] [Indexed: 12/08/2023] Open
Abstract
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.
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Affiliation(s)
- Marjola Thanaj
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK.
| | - Nicolas Basty
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | | | | | - Brandon Whitcher
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | | | - Rachel Lennon
- Wellcome Centre for Cell-Matrix Research, Division of Cell-Matrix Biology and Regenerative Medicine, School of Biological Sciences, Faculty of Biology Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK
- Department of Paediatric Nephrology, Royal Manchester Children's Hospital, Manchester University Hospitals NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
| | - Jimmy D Bell
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | - E Louise Thomas
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
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Mould RR, Kalampouka I, Thomas EL, Guy GW, Nunn AVW, Bell JD. Non-chemical signalling between mitochondria. Front Physiol 2023; 14:1268075. [PMID: 37811497 PMCID: PMC10560087 DOI: 10.3389/fphys.2023.1268075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 09/12/2023] [Indexed: 10/10/2023] Open
Abstract
A wide variety of studies have reported some form of non-chemical or non-aqueous communication between physically isolated organisms, eliciting changes in cellular proliferation, morphology, and/or metabolism. The sources and mechanisms of such signalling pathways are still unknown, but have been postulated to involve vibration, volatile transmission, or light through the phenomenon of ultraweak photon emission. Here, we report non-chemical communication between isolated mitochondria from MCF7 (cancer) and MCF10A (non-cancer) cell lines. We found that mitochondria in one cuvette stressed by an electron transport chain inhibitor, antimycin, alters the respiration of mitochondria in an adjacent, but chemically and physically separate cuvette, significantly decreasing the rate of oxygen consumption compared to a control (p = <0.0001 in MCF7 and MCF10A mitochondria). Moreover, the changes in O2-consumption were dependent on the origin of mitochondria (cancer vs. non-cancer) as well as the presence of "ambient" light. Our results support the existence of non-chemical signalling between isolated mitochondria. The experimental design suggests that the non-chemical communication is light-based, although further work is needed to fully elucidate its nature.
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Affiliation(s)
- Rhys R. Mould
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, United Kingdom
| | - Ifigeneia Kalampouka
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, United Kingdom
| | - E. Louise Thomas
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, United Kingdom
| | | | - Alistair V. W. Nunn
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, United Kingdom
- The Guy Foundation, Dorset, United Kingdom
| | - Jimmy D. Bell
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, United Kingdom
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Rundle M, Fiamoncini J, Thomas EL, Wopereis S, Afman LA, Brennan L, Drevon CA, Gundersen TE, Daniel H, Perez IG, Posma JM, Ivanova DG, Bell JD, van Ommen B, Frost G. Diet-induced Weight Loss and Phenotypic Flexibility Among Healthy Overweight Adults: A Randomized Trial. Am J Clin Nutr 2023; 118:591-604. [PMID: 37661105 PMCID: PMC10517213 DOI: 10.1016/j.ajcnut.2023.07.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 06/28/2023] [Accepted: 07/03/2023] [Indexed: 09/05/2023] Open
Abstract
BACKGROUND The capacity of an individual to respond to changes in food intake so that postprandial metabolic perturbations are resolved, and metabolism returns to its pre-prandial state, is called phenotypic flexibility. This ability may be a more important indicator of current health status than metabolic markers in a fasting state. AIM In this parallel randomized controlled trial study, an energy-restricted healthy diet and 2 dietary challenges were used to assess the effect of weight loss on phenotypic flexibility. METHODS Seventy-two volunteers with overweight and obesity underwent a 12-wk dietary intervention. The participants were randomized to a weight loss group (WLG) with 20% less energy intake or a weight-maintenance group (WMG). At weeks 1 and 12, participants were assessed for body composition by MRI. Concurrently, markers of metabolism and insulin sensitivity were obtained from the analysis of plasma metabolome during 2 different dietary challenges-an oral glucose tolerance test (OGTT) and a mixed-meal tolerance test. RESULTS Intended weight loss was achieved in the WLG (-5.6 kg, P < 0.0001) and induced a significant reduction in total and regional adipose tissue as well as ectopic fat in the liver. Amino acid-based markers of insulin action and resistance such as leucine and glutamate were reduced in the postprandial phase of the OGTT in the WLG by 11.5% and 28%, respectively, after body weight reduction. Weight loss correlated with the magnitude of changes in metabolic responses to dietary challenges. Large interindividual variation in metabolic responses to weight loss was observed. CONCLUSION Application of dietary challenges increased sensitivity to detect metabolic response to weight loss intervention. Large interindividual variation was observed across a wide range of measurements allowing the identification of distinct responses to the weight loss intervention and mechanistic insight into the metabolic response to weight loss.
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Affiliation(s)
- Milena Rundle
- Section of Nutrition, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Jarlei Fiamoncini
- Food Research Center, Department of Food Science and Experimental Nutrition, School of Pharmaceutical Sciences, University of São Paulo, São Paulo, Brazil
| | - E Louise Thomas
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, United Kingdom
| | - Suzan Wopereis
- Department of Microbiology and Systems Biology, Netherlands Organization for Applied Scientific Research, Hague, The Netherlands
| | - Lydia A Afman
- Division of Human Nutrition and Health, Wageningen University, Wageningen, The Netherlands
| | - Lorraine Brennan
- UCD School of Agriculture and Food Science, Institute of Food and Health, University College Dublin, Belfield, Dublin, Ireland
| | - Christian A Drevon
- Department of Nutrition, Institute of Basic Medical Sciences, Faculty of Medicine, University of Oslo, Oslo, Norway; Vitas Ltd, Oslo Science Park, Oslo, Norway
| | | | - Hannelore Daniel
- Hannelore Daniel, Molecular Nutrition Unit, Technische Universität München, München, Germany
| | - Isabel Garcia Perez
- Section of Nutrition, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Joram M Posma
- Section of Bioinformatics, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Diana G Ivanova
- Department of Biochemistry, Molecular Medicine and Nutrigenomics, Faculty of Pharmacy, Medical University, Varna, Bulgaria
| | - Jimmy D Bell
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, United Kingdom
| | - Ben van Ommen
- Department of Microbiology and Systems Biology, Netherlands Organization for Applied Scientific Research, Hague, The Netherlands
| | - Gary Frost
- Section of Nutrition, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, United Kingdom.
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Allesøe RL, Lundgaard AT, Hernández Medina R, Aguayo-Orozco A, Johansen J, Nissen JN, Brorsson C, Mazzoni G, Niu L, Biel JH, Leal Rodríguez C, Brasas V, Webel H, Benros ME, Pedersen AG, Chmura PJ, Jacobsen UP, Mari A, Koivula R, Mahajan A, Vinuela A, Tajes JF, Sharma S, Haid M, Hong MG, Musholt PB, De Masi F, Vogt J, Pedersen HK, Gudmundsdottir V, Jones A, Kennedy G, Bell J, Thomas EL, Frost G, Thomsen H, Hansen E, Hansen TH, Vestergaard H, Muilwijk M, Blom MT, 't Hart LM, Pattou F, Raverdy V, Brage S, Kokkola T, Heggie A, McEvoy D, Mourby M, Kaye J, Hattersley A, McDonald T, Ridderstråle M, Walker M, Forgie I, Giordano GN, Pavo I, Ruetten H, Pedersen O, Hansen T, Dermitzakis E, Franks PW, Schwenk JM, Adamski J, McCarthy MI, Pearson E, Banasik K, Rasmussen S, Brunak S. Author Correction: Discovery of drug-omics associations in type 2 diabetes with generative deep-learning models. Nat Biotechnol 2023; 41:1026. [PMID: 37130959 PMCID: PMC10344774 DOI: 10.1038/s41587-023-01805-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Affiliation(s)
- Rosa Lundbye Allesøe
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
- Copenhagen Research Centre for Mental Health, Mental Health Centre Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark
| | - Agnete Troen Lundgaard
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Ricardo Hernández Medina
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Alejandro Aguayo-Orozco
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Joachim Johansen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Jakob Nybo Nissen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Caroline Brorsson
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Gianluca Mazzoni
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Lili Niu
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Jorge Hernansanz Biel
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Cristina Leal Rodríguez
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Valentas Brasas
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Henry Webel
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Michael Eriksen Benros
- Copenhagen Research Centre for Mental Health, Mental Health Centre Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark
- Department of Immunology and Microbiology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Anders Gorm Pedersen
- Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Piotr Jaroslaw Chmura
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Ulrik Plesner Jacobsen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Andrea Mari
- C.N.R. Institute of Neuroscience, Padova, Italy
| | - Robert Koivula
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Anubha Mahajan
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Ana Vinuela
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland
- Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle, UK
| | | | - Sapna Sharma
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Bavaria, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Bavaria, Germany
- Chair of Food Chemistry and Molecular and Sensory Science, Technical University of Munich, Freising, Germany
| | - Mark Haid
- Metabolomics and Proteomics Core, Helmholtz Zentrum Muenchen, German Research Center for Environmental Health, Neuherberg, Germany
| | - Mun-Gwan Hong
- Affinity Proteomics, Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Solna, Sweden
| | - Petra B Musholt
- Research and Development Global Development, Translational Medicine and Clinical Pharmacology, Sanofi-Aventis Deutschland, Frankfurt, Germany
| | - Federico De Masi
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Josef Vogt
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Helle Krogh Pedersen
- Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Valborg Gudmundsdottir
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Angus Jones
- University of Exeter Medical School, Exeter, UK
| | - Gwen Kennedy
- The Immunoassay Biomarker Core Laboratory, School of Medicine, University of Dundee, Dundee, UK
| | - Jimmy Bell
- Research Centre for Optimal Health, Department of Life Sciences, University of Westminster, London, UK
| | - E Louise Thomas
- Research Centre for Optimal Health, Department of Life Sciences, University of Westminster, London, UK
| | - Gary Frost
- Section for Nutrition Research, Faculty of Medicine, Imperial College London, London, UK
| | - Henrik Thomsen
- Department of Radiology, Copenhagen University Hospital Herlev-Gentofte, Herlev, Denmark
| | - Elizaveta Hansen
- Department of Radiology, Copenhagen University Hospital Herlev-Gentofte, Herlev, Denmark
| | - Tue Haldor Hansen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Henrik Vestergaard
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Mirthe Muilwijk
- Department of Epidemiology and Data Science, Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Marieke T Blom
- Department of General Practice, Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Leen M 't Hart
- Department of Epidemiology and Data Science, Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Department of Biomedical Data Science, Section Molecular Epidemiology, Leiden University Medical Center, Leiden, the Netherlands
- Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, the Netherlands
| | - Francois Pattou
- Inserm, Univ Lille, CHU Lille, Lille Pasteur Institute, EGID, Lille, France
| | - Violeta Raverdy
- Inserm, Univ Lille, CHU Lille, Lille Pasteur Institute, EGID, Lille, France
| | - Soren Brage
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Tarja Kokkola
- Department of Medicine, University of Eastern Finland, Kuopio, Finland
| | - Alison Heggie
- Institute of Cellular Medicine, Newcastle University, Newcastle, UK
| | - Donna McEvoy
- Diabetes Research Network, Royal Victoria Infirmary, Newcastle, UK
| | - Miranda Mourby
- Centre for Health, Law and Emerging Technologies (HeLEX), Faculty of Law, University of Oxford, Oxford, UK
| | - Jane Kaye
- Centre for Health, Law and Emerging Technologies (HeLEX), Faculty of Law, University of Oxford, Oxford, UK
| | | | | | - Martin Ridderstråle
- Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Mark Walker
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle, UK
| | - Ian Forgie
- Division of Population Health & Genomics, School of Medicine, University of Dundee, Dundee, UK
| | - Giuseppe N Giordano
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Sciences, CRC, Lund University, SUS, Malmö, Sweden
| | - Imre Pavo
- Eli Lilly Regional Operations, Vienna, Austria
| | - Hartmut Ruetten
- Research and Development Global Development, Translational Medicine and Clinical Pharmacology, Sanofi-Aventis Deutschland, Frankfurt, Germany
| | - Oluf Pedersen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Torben Hansen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Emmanouil Dermitzakis
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland
| | - Paul W Franks
- Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, Malmö, Sweden
- Harvard T.H. Chan School of Public Health, Boston, MA, USA
- OCDEM, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Jochen M Schwenk
- Affinity Proteomics, Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Solna, Sweden
| | - Jerzy Adamski
- Institute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Mark I McCarthy
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK
- Genentech, South San Francisco, CA, USA
| | - Ewan Pearson
- Division of Population Health & Genomics, School of Medicine, University of Dundee, Dundee, UK
| | - Karina Banasik
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Simon Rasmussen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
| | - Søren Brunak
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
- Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark.
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10
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Basty N, Sorokin EP, Thanaj M, Srinivasan R, Whitcher B, Bell JD, Cule M, Thomas EL. Abdominal imaging associates body composition with COVID-19 severity. PLoS One 2023; 18:e0283506. [PMID: 37053189 PMCID: PMC10101472 DOI: 10.1371/journal.pone.0283506] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 03/10/2023] [Indexed: 04/14/2023] Open
Abstract
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.
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Affiliation(s)
- Nicolas Basty
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, United Kingdom
| | - Elena P. Sorokin
- Calico Life Sciences LLC, South San Francisco, California, United States of America
| | - Marjola Thanaj
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, United Kingdom
| | | | - Brandon Whitcher
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, United Kingdom
| | - Jimmy D. Bell
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, United Kingdom
| | - Madeleine Cule
- Calico Life Sciences LLC, South San Francisco, California, United States of America
| | - E. Louise Thomas
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, United Kingdom
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11
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Allesøe RL, Lundgaard AT, Hernández Medina R, Aguayo-Orozco A, Johansen J, Nissen JN, Brorsson C, Mazzoni G, Niu L, Biel JH, Brasas V, Webel H, Benros ME, Pedersen AG, Chmura PJ, Jacobsen UP, Mari A, Koivula R, Mahajan A, Vinuela A, Tajes JF, Sharma S, Haid M, Hong MG, Musholt PB, De Masi F, Vogt J, Pedersen HK, Gudmundsdottir V, Jones A, Kennedy G, Bell J, Thomas EL, Frost G, Thomsen H, Hansen E, Hansen TH, Vestergaard H, Muilwijk M, Blom MT, 't Hart LM, Pattou F, Raverdy V, Brage S, Kokkola T, Heggie A, McEvoy D, Mourby M, Kaye J, Hattersley A, McDonald T, Ridderstråle M, Walker M, Forgie I, Giordano GN, Pavo I, Ruetten H, Pedersen O, Hansen T, Dermitzakis E, Franks PW, Schwenk JM, Adamski J, McCarthy MI, Pearson E, Banasik K, Rasmussen S, Brunak S, Thomas CE, Haussler R, Beulens J, Rutters F, Nijpels G, van Oort S, Groeneveld L, Elders P, Giorgino T, Rodriquez M, Nice R, Perry M, Bianzano S, Graefe-Mody U, Hennige A, Grempler R, Baum P, Stærfeldt HH, Shah N, Teare H, Ehrhardt B, Tillner J, Dings C, Lehr T, Scherer N, Sihinevich I, Cabrelli L, Loftus H, Bizzotto R, Tura A, Dekkers K, van Leeuwen N, Groop L, Slieker R, Ramisch A, Jennison C, McVittie I, Frau F, Steckel-Hamann B, Adragni K, Thomas M, Pasdar NA, Fitipaldi H, Kurbasic A, Mutie P, Pomares-Millan H, Bonnefond A, Canouil M, Caiazzo R, Verkindt H, Holl R, Kuulasmaa T, Deshmukh H, Cederberg H, Laakso M, Vangipurapu J, Dale M, Thorand B, Nicolay C, Fritsche A, Hill A, Hudson M, Thorne C, Allin K, Arumugam M, Jonsson A, Engelbrechtsen L, Forman A, Dutta A, Sondertoft N, Fan Y, Gough S, Robertson N, McRobert N, Wesolowska-Andersen A, Brown A, Davtian D, Dawed A, Donnelly L, Palmer C, White M, Ferrer J, Whitcher B, Artati A, Prehn C, Adam J, Grallert H, Gupta R, Sackett PW, Nilsson B, Tsirigos K, Eriksen R, Jablonka B, Uhlen M, Gassenhuber J, Baltauss T, de Preville N, Klintenberg M, Abdalla M. Discovery of drug-omics associations in type 2 diabetes with generative deep-learning models. Nat Biotechnol 2023; 41:399-408. [PMID: 36593394 PMCID: PMC10017515 DOI: 10.1038/s41587-022-01520-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Accepted: 09/20/2022] [Indexed: 01/03/2023]
Abstract
The application of multiple omics technologies in biomedical cohorts has the potential to reveal patient-level disease characteristics and individualized response to treatment. However, the scale and heterogeneous nature of multi-modal data makes integration and inference a non-trivial task. We developed a deep-learning-based framework, multi-omics variational autoencoders (MOVE), to integrate such data and applied it to a cohort of 789 people with newly diagnosed type 2 diabetes with deep multi-omics phenotyping from the DIRECT consortium. Using in silico perturbations, we identified drug-omics associations across the multi-modal datasets for the 20 most prevalent drugs given to people with type 2 diabetes with substantially higher sensitivity than univariate statistical tests. From these, we among others, identified novel associations between metformin and the gut microbiota as well as opposite molecular responses for the two statins, simvastatin and atorvastatin. We used the associations to quantify drug-drug similarities, assess the degree of polypharmacy and conclude that drug effects are distributed across the multi-omics modalities.
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Affiliation(s)
- Rosa Lundbye Allesøe
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.,Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark.,Copenhagen Research Centre for Mental Health, Mental Health Centre Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark
| | - Agnete Troen Lundgaard
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.,Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Ricardo Hernández Medina
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Alejandro Aguayo-Orozco
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.,Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Joachim Johansen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.,Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Jakob Nybo Nissen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Caroline Brorsson
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.,Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Gianluca Mazzoni
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.,Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Lili Niu
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Jorge Hernansanz Biel
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.,Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Valentas Brasas
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Henry Webel
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Michael Eriksen Benros
- Copenhagen Research Centre for Mental Health, Mental Health Centre Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark.,Department of Immunology and Microbiology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Anders Gorm Pedersen
- Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Piotr Jaroslaw Chmura
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.,Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Ulrik Plesner Jacobsen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.,Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Andrea Mari
- C.N.R. Institute of Neuroscience, Padova, Italy
| | - Robert Koivula
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Anubha Mahajan
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Ana Vinuela
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland.,Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle, UK
| | | | - Sapna Sharma
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Bavaria, Germany.,Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Bavaria, Germany.,Chair of Food Chemistry and Molecular and Sensory Science, Technical University of Munich, Freising, Germany
| | - Mark Haid
- Metabolomics and Proteomics Core, Helmholtz Zentrum Muenchen, German Research Center for Environmental Health, Neuherberg, Germany
| | - Mun-Gwan Hong
- Affinity Proteomics, Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Solna, Sweden
| | - Petra B Musholt
- Research and Development Global Development, Translational Medicine and Clinical Pharmacology, Sanofi-Aventis Deutschland, Frankfurt, Germany
| | - Federico De Masi
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.,Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Josef Vogt
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Helle Krogh Pedersen
- Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark.,Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Valborg Gudmundsdottir
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.,Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Angus Jones
- University of Exeter Medical School, Exeter, UK
| | - Gwen Kennedy
- The Immunoassay Biomarker Core Laboratory, School of Medicine, University of Dundee, Dundee, UK
| | - Jimmy Bell
- Research Centre for Optimal Health, Department of Life Sciences, University of Westminster, London, UK
| | - E Louise Thomas
- Research Centre for Optimal Health, Department of Life Sciences, University of Westminster, London, UK
| | - Gary Frost
- Section for Nutrition Research, Faculty of Medicine, Imperial College London, London, UK
| | - Henrik Thomsen
- Department of Radiology, Copenhagen University Hospital Herlev-Gentofte, Herlev, Denmark
| | - Elizaveta Hansen
- Department of Radiology, Copenhagen University Hospital Herlev-Gentofte, Herlev, Denmark
| | - Tue Haldor Hansen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Henrik Vestergaard
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Mirthe Muilwijk
- Department of Epidemiology and Data Science, Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Marieke T Blom
- Department of General Practice, Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Leen M 't Hart
- Department of Epidemiology and Data Science, Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.,Department of Biomedical Data Science, Section Molecular Epidemiology, Leiden University Medical Center, Leiden, the Netherlands.,Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, the Netherlands
| | - Francois Pattou
- Inserm, Univ Lille, CHU Lille, Lille Pasteur Institute, EGID, Lille, France
| | - Violeta Raverdy
- Inserm, Univ Lille, CHU Lille, Lille Pasteur Institute, EGID, Lille, France
| | - Soren Brage
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Tarja Kokkola
- Department of Medicine, University of Eastern Finland, Kuopio, Finland
| | - Alison Heggie
- Institute of Cellular Medicine, Newcastle University, Newcastle, UK
| | - Donna McEvoy
- Diabetes Research Network, Royal Victoria Infirmary, Newcastle, UK
| | - Miranda Mourby
- Centre for Health, Law and Emerging Technologies (HeLEX), Faculty of Law, University of Oxford, Oxford, UK
| | - Jane Kaye
- Centre for Health, Law and Emerging Technologies (HeLEX), Faculty of Law, University of Oxford, Oxford, UK
| | | | | | - Martin Ridderstråle
- Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Mark Walker
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle, UK
| | - Ian Forgie
- Division of Population Health & Genomics, School of Medicine, University of Dundee, Dundee, UK
| | - Giuseppe N Giordano
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Sciences, CRC, Lund University, SUS, Malmö, Sweden
| | - Imre Pavo
- Eli Lilly Regional Operations, Vienna, Austria
| | - Hartmut Ruetten
- Research and Development Global Development, Translational Medicine and Clinical Pharmacology, Sanofi-Aventis Deutschland, Frankfurt, Germany
| | - Oluf Pedersen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Torben Hansen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Emmanouil Dermitzakis
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland
| | - Paul W Franks
- Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, Malmö, Sweden.,Harvard T.H. Chan School of Public Health, Boston, MA, USA.,OCDEM, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Jochen M Schwenk
- Affinity Proteomics, Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Solna, Sweden
| | - Jerzy Adamski
- Institute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.,Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.,Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Mark I McCarthy
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK.,Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK.,Genentech, South San Francisco, CA, USA
| | - Ewan Pearson
- Division of Population Health & Genomics, School of Medicine, University of Dundee, Dundee, UK
| | - Karina Banasik
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.,Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Simon Rasmussen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
| | - Søren Brunak
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark. .,Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark.
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12
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Basty N, Thanaj M, Cule M, Sorokin EP, Liu Y, Thomas EL, Bell JD, Whitcher B. Artifact-free fat-water separation in Dixon MRI using deep learning. J Big Data 2023; 10:4. [PMID: 36686622 PMCID: PMC9835035 DOI: 10.1186/s40537-022-00677-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 12/25/2022] [Indexed: 06/17/2023]
Abstract
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.
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Affiliation(s)
- Nicolas Basty
- Research Centre for Optimal Health, University of Westminster, London, UK
| | - Marjola Thanaj
- Research Centre for Optimal Health, University of Westminster, London, UK
| | | | | | - Yi Liu
- Calico Life Sciences LLC, South San Francisco, USA
| | - E. Louise Thomas
- Research Centre for Optimal Health, University of Westminster, London, UK
| | - Jimmy D. Bell
- Research Centre for Optimal Health, University of Westminster, London, UK
| | - Brandon Whitcher
- Research Centre for Optimal Health, University of Westminster, London, UK
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13
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O'Donovan SD, Erdős B, Jacobs DM, Wanders AJ, Thomas EL, Bell JD, Rundle M, Frost G, Arts ICW, Afman LA, van Riel NAW. Quantifying the contribution of triglycerides to metabolic resilience through the mixed meal model. iScience 2022; 25:105206. [PMID: 36281448 DOI: 10.1016/j.isci.2022.105206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Revised: 09/01/2022] [Accepted: 09/22/2022] [Indexed: 11/26/2022] Open
Abstract
Despite the pivotal role played by elevated circulating triglyceride levels in the pathophysiology of cardio-metabolic diseases many of the indices used to quantify metabolic health focus on deviations in glucose and insulin alone. We present the Mixed Meal Model, a computational model describing the systemic interplay between triglycerides, free fatty acids, glucose, and insulin. We show that the Mixed Meal Model can capture deviations in the post-meal excursions of plasma glucose, insulin, and triglyceride that are indicative of features of metabolic resilience; quantifying insulin resistance and liver fat; validated by comparison to gold-standard measures. We also demonstrate that the Mixed Meal Model is generalizable, applying it to meals with diverse macro-nutrient compositions. In this way, by coupling triglycerides to the glucose-insulin system the Mixed Meal Model provides a more holistic assessment of metabolic resilience from meal response data, quantifying pre-clinical metabolic deteriorations that drive disease development in overweight and obesity.
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Affiliation(s)
- Shauna D O'Donovan
- Division of Human Nutrition and Health, Wageningen University, Wageningen, the Netherlands.,Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands.,Eindhoven Artifical Intelligence Systems Institute (EAISI), Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Balázs Erdős
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, the Netherlands
| | - Doris M Jacobs
- Unilever Global Food Innovation Centre, Bronland 14, 6708WH Wageningen, the Netherlands
| | - Anne J Wanders
- Unilever Global Food Innovation Centre, Bronland 14, 6708WH Wageningen, the Netherlands
| | - E Louise Thomas
- Research Center for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | - Jimmy D Bell
- Research Center for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | - Milena Rundle
- Division of Diabetes, Endocrinology, and Metabolism, Department of Medicine, Imperial College London, London, UK
| | - Gary Frost
- Division of Diabetes, Endocrinology, and Metabolism, Department of Medicine, Imperial College London, London, UK
| | - Ilja C W Arts
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, the Netherlands
| | - Lydia A Afman
- Division of Human Nutrition and Health, Wageningen University, Wageningen, the Netherlands
| | - Natal A W van Riel
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands.,Eindhoven Artifical Intelligence Systems Institute (EAISI), Eindhoven University of Technology, Eindhoven, the Netherlands
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14
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Asaturyan HA, Basty N, Thanaj M, Whitcher B, Thomas EL, Bell JD. Improving the accuracy of fatty liver index to reflect liver fat content with predictive regression modelling. PLoS One 2022; 17:e0273171. [PMID: 36099244 PMCID: PMC9469950 DOI: 10.1371/journal.pone.0273171] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Accepted: 08/03/2022] [Indexed: 11/18/2022] Open
Abstract
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.
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Affiliation(s)
- Hykoush A. Asaturyan
- Research Centre for Optimal Health, University of Westminster, London, United Kingdom
| | - Nicolas Basty
- Research Centre for Optimal Health, University of Westminster, London, United Kingdom
| | - Marjola Thanaj
- Research Centre for Optimal Health, University of Westminster, London, United Kingdom
| | - Brandon Whitcher
- Research Centre for Optimal Health, University of Westminster, London, United Kingdom
| | - E. Louise Thomas
- Research Centre for Optimal Health, University of Westminster, London, United Kingdom
| | - Jimmy D. Bell
- Research Centre for Optimal Health, University of Westminster, London, United Kingdom
- * E-mail:
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15
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Sorokin EP, Basty N, Whitcher B, Liu Y, Bell JD, Cohen RL, Cule M, Thomas EL. Analysis of MRI-derived spleen iron in the UK Biobank identifies genetic variation linked to iron homeostasis and hemolysis. Am J Hum Genet 2022; 109:1092-1104. [PMID: 35568031 PMCID: PMC9247824 DOI: 10.1016/j.ajhg.2022.04.013] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Accepted: 04/19/2022] [Indexed: 11/25/2022] Open
Abstract
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 by using magnetic resonance imaging and provide a 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 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 that 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 the lytic phase of the erythrocyte life cycle and iron reuptake in the spleen.
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Affiliation(s)
| | - Nicolas Basty
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | - Brandon Whitcher
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | - Yi Liu
- Calico Life Sciences LLC, South San Francisco, CA, USA
| | - Jimmy D Bell
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | | | | | - E Louise Thomas
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK.
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16
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Martin S, Tyrrell J, Thomas EL, Bown MJ, Wood AR, Beaumont RN, Tsoi LC, Stuart PE, Elder JT, Law P, Houlston R, Kabrhel C, Papadimitriou N, Gunter M, Bull C, Bell JA, Vincent EE, Sattar N, Dunlop MG, Tomlinson IPM, Lindström S, Bell JD, Frayling T, Yaghootkar H. Correction: Disease consequences of higher adiposity uncoupled from its adverse metabolic effects using Mendelian randomisation. eLife 2022; 11:e80233. [PMID: 35583923 PMCID: PMC9116939 DOI: 10.7554/elife.80233] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 03/28/2022] [Indexed: 11/13/2022] Open
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17
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Osimo EF, Brugger SP, Thomas EL, Howes OD. A cross-sectional MR study of body fat volumes and distribution in chronic schizophrenia. Schizophrenia (Heidelb) 2022; 8:24. [PMID: 35304889 PMCID: PMC8933542 DOI: 10.1038/s41537-022-00233-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Accepted: 02/01/2022] [Indexed: 11/20/2022]
Abstract
People with schizophrenia show higher risk for abdominal obesity than the general population, which could contribute to excess mortality. However, it is unclear whether this is driven by alterations in abdominal fat partitioning. Here, we test the hypothesis that individuals with schizophrenia show a higher proportion of visceral to total body fat measured using magnetic resonance imaging (MRI). We recruited 38 participants with schizophrenia and 38 healthy controls matched on age, sex, ethnicity, and body mass index. We found no significant differences in body fat distribution between groups, suggesting that increased abdominal obesity in schizophrenia is not associated with altered fat distribution.
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Affiliation(s)
- Emanuele F Osimo
- MRC London Institute of Medical Sciences, Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, Hammersmith Hospital Campus, London, UK. .,Department of Psychiatry, University of Cambridge, Cambridge, UK. .,Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK. .,South London and Maudsley NHS Foundation Trust, London, UK.
| | - Stefan P Brugger
- Cardiff University Brain Research and Imaging Centre, School of Psychology, Cardiff University, London, UK
| | - E Louise Thomas
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | - Oliver D Howes
- MRC London Institute of Medical Sciences, Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, Hammersmith Hospital Campus, London, UK. .,South London and Maudsley NHS Foundation Trust, London, UK. .,Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
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18
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Whitcher B, Thanaj M, Cule M, Liu Y, Basty N, Sorokin EP, Bell JD, Thomas EL. Precision MRI phenotyping enables detection of small changes in body composition for longitudinal cohorts. Sci Rep 2022; 12:3748. [PMID: 35260612 PMCID: PMC8904801 DOI: 10.1038/s41598-022-07556-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2021] [Accepted: 02/18/2022] [Indexed: 12/23/2022] Open
Abstract
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.
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Affiliation(s)
- Brandon Whitcher
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | - Marjola Thanaj
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | - Madeleine Cule
- Calico Life Sciences LLC, South San Francisco, California, USA
| | - Yi Liu
- Calico Life Sciences LLC, South San Francisco, California, USA
| | - Nicolas Basty
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | - Elena P Sorokin
- Calico Life Sciences LLC, South San Francisco, California, USA
| | - Jimmy D Bell
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | - E Louise Thomas
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK.
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19
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Martin S, Sorokin EP, Thomas EL, Sattar N, Cule M, Bell JD, Yaghootkar H. Estimating the Effect of Liver and Pancreas Volume and Fat Content on Risk of Diabetes: A Mendelian Randomization Study. Diabetes Care 2022; 45:460-468. [PMID: 34983059 DOI: 10.2337/dc21-1262] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Accepted: 11/05/2021] [Indexed: 02/03/2023]
Abstract
OBJECTIVE Fat content and volume of liver and pancreas are associated with risk of diabetes in observational studies; whether these associations are causal is unknown. We conducted a Mendelian randomization (MR) study to examine causality of such associations. RESEARCH DESIGN AND METHODS We used genetic variants associated (P < 5 × 10-8) with the exposures (liver and pancreas volume and fat content) using MRI scans of UK Biobank participants (n = 32,859). We obtained summary-level data for risk of type 1 (9,358 cases) and type 2 (55,005 cases) diabetes from the largest available genome-wide association studies. We performed inverse-variance weighted MR as main analysis and several sensitivity analyses to assess pleiotropy and to exclude variants with potential pleiotropic effects. RESULTS Observationally, liver fat and volume were associated with type 2 diabetes (odds ratio per 1 SD higher exposure 2.16 [2.02, 2.31] and 2.11 [1.96, 2.27], respectively). Pancreatic fat was associated with type 2 diabetes (1.42 [1.34, 1.51]) but not type 1 diabetes, and pancreas volume was negatively associated with type 1 diabetes (0.42 [0.36, 0.48]) and type 2 diabetes (0.73 [0.68, 0.78]). MR analysis provided evidence only for a causal role of liver fat and pancreas volume in risk of type 2 diabetes (1.27 [1.08, 1.49] or 27% increased risk and 0.76 [0.62, 0.94] or 24% decreased risk per 1SD, respectively) and no causal associations with type 1 diabetes. CONCLUSIONS Our findings assist in understanding the causal role of ectopic fat in the liver and pancreas and of organ volume in the pathophysiology of type 1 and type 2 diabetes.
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Affiliation(s)
- Susan Martin
- Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Royal Devon & Exeter Hospital, Exeter, U.K
| | | | - E Louise Thomas
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, U.K
| | - Naveed Sattar
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, U.K
| | | | - Jimmy D Bell
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, U.K
| | - Hanieh Yaghootkar
- Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Royal Devon & Exeter Hospital, Exeter, U.K.,Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, U.K.,Department of Life Sciences, Centre for Inflammation Research and Translational Medicine, Brunel University London, London, U.K
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20
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Martin S, Tyrrell J, Thomas EL, Bown MJ, Wood AR, Beaumont RN, Tsoi LC, Stuart PE, Elder JT, Law P, Houlston R, Kabrhel C, Papadimitriou N, Gunter MJ, Bull CJ, Bell JA, Vincent EE, Sattar N, Dunlop MG, Tomlinson IPM, Lindström S, Bell JD, Frayling TM, Yaghootkar H. Disease consequences of higher adiposity uncoupled from its adverse metabolic effects using Mendelian randomisation. eLife 2022; 11:e72452. [PMID: 35074047 PMCID: PMC8789289 DOI: 10.7554/elife.72452] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Accepted: 12/21/2021] [Indexed: 12/13/2022] Open
Abstract
Background Some individuals living with obesity may be relatively metabolically healthy, whilst others suffer from multiple conditions that may be linked to adverse metabolic effects or other factors. The extent to which the adverse metabolic component of obesity contributes to disease compared to the non-metabolic components is often uncertain. We aimed to use Mendelian randomisation (MR) and specific genetic variants to separately test the causal roles of higher adiposity with and without its adverse metabolic effects on diseases. Methods We selected 37 chronic diseases associated with obesity and genetic variants associated with different aspects of excess weight. These genetic variants included those associated with metabolically 'favourable adiposity' (FA) and 'unfavourable adiposity' (UFA) that are both associated with higher adiposity but with opposite effects on metabolic risk. We used these variants and two sample MR to test the effects on the chronic diseases. Results MR identified two sets of diseases. First, 11 conditions where the metabolic effect of higher adiposity is the likely primary cause of the disease. Here, MR with the FA and UFA genetics showed opposing effects on risk of disease: coronary artery disease, peripheral artery disease, hypertension, stroke, type 2 diabetes, polycystic ovary syndrome, heart failure, atrial fibrillation, chronic kidney disease, renal cancer, and gout. Second, 9 conditions where the non-metabolic effects of excess weight (e.g. mechanical effect) are likely a cause. Here, MR with the FA genetics, despite leading to lower metabolic risk, and MR with the UFA genetics, both indicated higher disease risk: osteoarthritis, rheumatoid arthritis, osteoporosis, gastro-oesophageal reflux disease, gallstones, adult-onset asthma, psoriasis, deep vein thrombosis, and venous thromboembolism. Conclusions Our results assist in understanding the consequences of higher adiposity uncoupled from its adverse metabolic effects, including the risks to individuals with high body mass index who may be relatively metabolically healthy. Funding Diabetes UK, UK Medical Research Council, World Cancer Research Fund, National Cancer Institute.
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Affiliation(s)
- Susan Martin
- Institute of Biomedical and Clinical Science, University of Exeter Medical School, Research, Innovation, Learning and Development building, Royal Devon & Exeter HospitalExeterUnited Kingdom
| | - Jessica Tyrrell
- Institute of Biomedical and Clinical Science, University of Exeter Medical School, Research, Innovation, Learning and Development building, Royal Devon & Exeter HospitalExeterUnited Kingdom
| | - E Louise Thomas
- Research Centre for Optimal Health, School of Life Sciences, University of WestminsterLondonUnited Kingdom
| | - Matthew J Bown
- Department of Cardiovascular Sciences, University of LeicesterLeicesterUnited Kingdom
- NIHR Leicester Biomedical Research CentreLeicesterUnited Kingdom
| | - Andrew R Wood
- Institute of Biomedical and Clinical Science, University of Exeter Medical School, Research, Innovation, Learning and Development building, Royal Devon & Exeter HospitalExeterUnited Kingdom
| | - Robin N Beaumont
- Institute of Biomedical and Clinical Science, University of Exeter Medical School, Research, Innovation, Learning and Development building, Royal Devon & Exeter HospitalExeterUnited Kingdom
| | - Lam C Tsoi
- Department of Dermatology, University of MichiganAnn ArborUnited States
| | - Philip E Stuart
- Department of Dermatology, University of MichiganAnn ArborUnited States
| | - James T Elder
- Department of Dermatology, University of MichiganAnn ArborUnited States
- Ann Arbor Veterans Affairs HospitalAnn ArborUnited States
| | - Philip Law
- The Institute of Cancer ResearchLondonUnited Kingdom
| | | | - Christopher Kabrhel
- Department of Emergency Medicine, Massachusetts General HospitalBostonUnited States
- Department of Emergency Medicine, Harvard Medical SchoolBostonUnited States
| | - Nikos Papadimitriou
- Nutrition and Metabolism Branch, International Agency for Research on CancerLyonFrance
| | - Marc J Gunter
- Nutrition and Metabolism Branch, International Agency for Research on CancerLyonFrance
| | - Caroline J Bull
- MRC Integrative Epidemiology Unit at the University of BristolBristolUnited Kingdom
- Population Health Sciences, Bristol Medical School, University of BristolBristolUnited Kingdom
- School of Cellular and Molecular Medicine, University of BristolBristolUnited Kingdom
| | - Joshua A Bell
- MRC Integrative Epidemiology Unit at the University of BristolBristolUnited Kingdom
- Population Health Sciences, Bristol Medical School, University of BristolBristolUnited Kingdom
| | - Emma E Vincent
- MRC Integrative Epidemiology Unit at the University of BristolBristolUnited Kingdom
- Population Health Sciences, Bristol Medical School, University of BristolBristolUnited Kingdom
- School of Cellular and Molecular Medicine, University of BristolBristolUnited Kingdom
| | - Naveed Sattar
- Institute of Cardiovascular and Medical Sciences, University of GlasgowGlasgowUnited Kingdom
| | - Malcolm G Dunlop
- University of EdinburghEdinburghUnited Kingdom
- Western General HospitalEdinburghUnited Kingdom
| | - Ian PM Tomlinson
- Edinburgh Cancer Research Centre, IGMM, University of EdinburghEdinburghUnited Kingdom
| | - Sara Lindström
- Department of Epidemiology, University of WashingtonSeattleUnited States
- Division of Public Health Sciences, Fred Hutchinson Cancer Research CenterSeattleUnited States
| | | | - Jimmy D Bell
- Research Centre for Optimal Health, School of Life Sciences, University of WestminsterLondonUnited Kingdom
| | - Timothy M Frayling
- Institute of Biomedical and Clinical Science, University of Exeter Medical School, Research, Innovation, Learning and Development building, Royal Devon & Exeter HospitalExeterUnited Kingdom
| | - Hanieh Yaghootkar
- Institute of Biomedical and Clinical Science, University of Exeter Medical School, Research, Innovation, Learning and Development building, Royal Devon & Exeter HospitalExeterUnited Kingdom
- Research Centre for Optimal Health, School of Life Sciences, University of WestminsterLondonUnited Kingdom
- Centre for Inflammation Research and Translational Medicine (CIRTM), Department of Life Sciences, Brunel University LondonUxbridgeUnited Kingdom
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21
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Osimo EF, Sweeney M, de Marvao A, Berry A, Statton B, Perry BI, Pillinger T, Whitehurst T, Cook SA, O'Regan DP, Thomas EL, Howes OD. Adipose tissue dysfunction, inflammation, and insulin resistance: alternative pathways to cardiac remodelling in schizophrenia. A multimodal, case-control study. Transl Psychiatry 2021; 11:614. [PMID: 34873143 PMCID: PMC8648771 DOI: 10.1038/s41398-021-01741-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 11/17/2021] [Accepted: 11/23/2021] [Indexed: 12/12/2022] Open
Abstract
Cardiovascular diseases are the leading cause of death in schizophrenia. Patients with schizophrenia show evidence of concentric cardiac remodelling (CCR), defined as an increase in left-ventricular mass over end-diastolic volumes. CCR is a predictor of cardiac disease, but the molecular pathways leading to this in schizophrenia are unknown. We aimed to explore the relevance of hypertensive and non-hypertensive pathways to CCR and their potential molecular underpinnings in schizophrenia. In this multimodal case-control study, we collected cardiac and whole-body fat magnetic resonance imaging (MRI), clinical measures, and blood levels of several cardiometabolic biomarkers known to potentially cause CCR from individuals with schizophrenia, alongside healthy controls (HCs) matched for age, sex, ethnicity, and body surface area. Of the 50 participants, 34 (68%) were male. Participants with schizophrenia showed increases in cardiac concentricity (d = 0.71, 95% CI: 0.12, 1.30; p = 0.01), indicative of CCR, but showed no differences in overall content or regional distribution of adipose tissue compared to HCs. Despite the cardiac changes, participants with schizophrenia did not demonstrate activation of the hypertensive CCR pathway; however, they showed evidence of adipose dysfunction: adiponectin was reduced (d = -0.69, 95% CI: -1.28, -0.10; p = 0.02), with evidence of activation of downstream pathways, including hypertriglyceridemia, elevated C-reactive protein, fasting glucose, and alkaline phosphatase. In conclusion, people with schizophrenia showed adipose tissue dysfunction compared to body mass-matched HCs. The presence of non-hypertensive CCR and a dysmetabolic phenotype may contribute to excess cardiovascular risk in schizophrenia. If our results are confirmed, acting on this pathway could reduce cardiovascular risk and resultant life-years lost in people with schizophrenia.
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Affiliation(s)
- Emanuele F Osimo
- MRC London Institute of Medical Sciences and Imperial College London Institute of Clinical Sciences, Hammersmith Campus, Du Cane Road, London, W12 0NN, UK. .,Department of Psychiatry, University of Cambridge, Cambridge, UK. .,Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK. .,South London and Maudsley NHS Foundation Trust, London, UK.
| | - Mark Sweeney
- MRC London Institute of Medical Sciences and Imperial College London Institute of Clinical Sciences, Hammersmith Campus, Du Cane Road, London, W12 0NN, UK.,Imperial College London, London, UK
| | - Antonio de Marvao
- MRC London Institute of Medical Sciences and Imperial College London Institute of Clinical Sciences, Hammersmith Campus, Du Cane Road, London, W12 0NN, UK
| | - Alaine Berry
- MRC London Institute of Medical Sciences and Imperial College London Institute of Clinical Sciences, Hammersmith Campus, Du Cane Road, London, W12 0NN, UK
| | - Ben Statton
- MRC London Institute of Medical Sciences and Imperial College London Institute of Clinical Sciences, Hammersmith Campus, Du Cane Road, London, W12 0NN, UK
| | - Benjamin I Perry
- Department of Psychiatry, University of Cambridge, Cambridge, UK.,Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
| | - Toby Pillinger
- MRC London Institute of Medical Sciences and Imperial College London Institute of Clinical Sciences, Hammersmith Campus, Du Cane Road, London, W12 0NN, UK.,South London and Maudsley NHS Foundation Trust, London, UK.,Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Thomas Whitehurst
- MRC London Institute of Medical Sciences and Imperial College London Institute of Clinical Sciences, Hammersmith Campus, Du Cane Road, London, W12 0NN, UK
| | - Stuart A Cook
- MRC London Institute of Medical Sciences and Imperial College London Institute of Clinical Sciences, Hammersmith Campus, Du Cane Road, London, W12 0NN, UK
| | - Declan P O'Regan
- MRC London Institute of Medical Sciences and Imperial College London Institute of Clinical Sciences, Hammersmith Campus, Du Cane Road, London, W12 0NN, UK
| | - E Louise Thomas
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | - Oliver D Howes
- MRC London Institute of Medical Sciences and Imperial College London Institute of Clinical Sciences, Hammersmith Campus, Du Cane Road, London, W12 0NN, UK. .,South London and Maudsley NHS Foundation Trust, London, UK. .,Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
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22
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Martin S, Cule M, Basty N, Tyrrell J, Beaumont RN, Wood AR, Frayling TM, Sorokin E, Whitcher B, Liu Y, Bell JD, Thomas EL, Yaghootkar H. Genetic Evidence for Different Adiposity Phenotypes and Their Opposing Influences on Ectopic Fat and Risk of Cardiometabolic Disease. Diabetes 2021; 70:1843-1856. [PMID: 33980691 DOI: 10.2337/db21-0129] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Accepted: 05/06/2021] [Indexed: 11/13/2022]
Abstract
To understand the causal role of adiposity and ectopic fat in type 2 diabetes and cardiometabolic diseases, we aimed to identify two clusters of adiposity genetic variants: one with "adverse" metabolic effects (UFA) and the other with, paradoxically, "favorable" metabolic effects (FA). We performed a multivariate genome-wide association study using body fat percentage and metabolic biomarkers from UK Biobank and identified 38 UFA and 36 FA variants. Adiposity-increasing alleles were associated with an adverse metabolic profile, higher risk of disease, higher CRP, and higher fat in subcutaneous and visceral adipose tissue, liver, and pancreas for UFA and a favorable metabolic profile, lower risk of disease, higher CRP and higher subcutaneous adipose tissue but lower liver fat for FA. We detected no sexual dimorphism. The Mendelian randomization studies provided evidence for a risk-increasing effect of UFA and protective effect of FA for type 2 diabetes, heart disease, hypertension, stroke, nonalcoholic fatty liver disease, and polycystic ovary syndrome. FA is distinct from UFA by its association with lower liver fat and protection from cardiometabolic diseases; it was not associated with visceral or pancreatic fat. Understanding the difference in FA and UFA may lead to new insights in preventing, predicting, and treating cardiometabolic diseases.
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Affiliation(s)
- Susan Martin
- Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Royal Devon & Exeter Hospital, Exeter, U.K
| | | | - Nicolas Basty
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, U.K
| | - Jessica Tyrrell
- Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Royal Devon & Exeter Hospital, Exeter, U.K
| | - Robin N Beaumont
- Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Royal Devon & Exeter Hospital, Exeter, U.K
| | - Andrew R Wood
- Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Royal Devon & Exeter Hospital, Exeter, U.K
| | - Timothy M Frayling
- Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Royal Devon & Exeter Hospital, Exeter, U.K
| | | | - Brandon Whitcher
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, U.K
| | - Yi Liu
- Calico Life Sciences LLC, South San Francisco, CA
| | - Jimmy D Bell
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, U.K
| | - E Louise Thomas
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, U.K
| | - Hanieh Yaghootkar
- Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Royal Devon & Exeter Hospital, Exeter, U.K.
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, U.K
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23
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Liu Y, Basty N, Whitcher B, Bell JD, Sorokin EP, van Bruggen N, Thomas EL, Cule M. Genetic architecture of 11 organ traits derived from abdominal MRI using deep learning. eLife 2021; 10:e65554. [PMID: 34128465 PMCID: PMC8205492 DOI: 10.7554/elife.65554] [Citation(s) in RCA: 71] [Impact Index Per Article: 23.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Accepted: 05/09/2021] [Indexed: 12/24/2022] Open
Abstract
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.
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Affiliation(s)
- Yi Liu
- Calico Life Sciences LLCSouth San FranciscoUnited States
| | - Nicolas Basty
- Research Centre for Optimal Health, School of Life Sciences, University of WestminsterLondonUnited Kingdom
| | - Brandon Whitcher
- Research Centre for Optimal Health, School of Life Sciences, University of WestminsterLondonUnited Kingdom
| | - Jimmy D Bell
- Research Centre for Optimal Health, School of Life Sciences, University of WestminsterLondonUnited Kingdom
| | | | | | - E Louise Thomas
- Research Centre for Optimal Health, School of Life Sciences, University of WestminsterLondonUnited Kingdom
| | - Madeleine Cule
- Calico Life Sciences LLCSouth San FranciscoUnited States
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24
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Villarini B, Asaturyan H, Kurugol S, Afacan O, Bell JD, Thomas EL. 3D Deep Learning for Anatomical Structure Segmentation in Multiple Imaging Modalities. Proc IEEE Int Symp Comput Based Med Syst 2021; 2021:166-171. [PMID: 35224185 PMCID: PMC8867534 DOI: 10.1109/cbms52027.2021.00066] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Accurate, quantitative segmentation of anatomical structures in radiological scans, such as Magnetic Resonance Imaging (MRI) and Computer Tomography (CT), can produce significant biomarkers and can be integrated into computer-aided assisted diagnosis (CADx) systems to support the interpretation of medical images from multi-protocol scanners. However, there are serious challenges towards developing robust automated segmentation techniques, including high variations in anatomical structure and size, the presence of edge-based artefacts, and heavy un-controlled breathing that can produce blurred motion-based artefacts. This paper presents a novel computing approach for automatic organ and muscle segmentation in medical images from multiple modalities by harnessing the advantages of deep learning techniques in a two-part process. (1) a 3D encoder-decoder, Rb-UNet, builds a localisation model and a 3D Tiramisu network generates a boundary-preserving segmentation model for each target structure; (2) the fully trained Rb-UNet predicts a 3D bounding box encapsulating the target structure of interest, after which the fully trained Tiramisu model performs segmentation to reveal detailed organ or muscle boundaries. The proposed approach is evaluated on six different datasets, including MRI, Dynamic Contrast Enhanced (DCE) MRI and CT scans targeting the pancreas, liver, kidneys and psoas-muscle and achieves quantitative measures of mean Dice similarity coefficient (DSC) that surpass or are comparable with the state-of-the-art. A qualitative evaluation performed by two independent radiologists verified the preservation of detailed organ and muscle boundaries.
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Affiliation(s)
| | - Hykoush Asaturyan
- School of Computer Science, University of Westminster, London, United Kingdom
| | - Sila Kurugol
- Department of Radiology, Boston Children’s Hospital & Harvard Medical School, Boston, Massachusetts, USA
| | - Onur Afacan
- Department of Radiology Boston Children’s Hospital & Harvard Medical School, Boston, Massachusetts, USA
| | - Jimmy D. Bell
- School of Life Sciences, University of Westminster, London, United Kingdom
| | - E. Louise Thomas
- School of Life Sciences, University of Westminster, London, United Kingdom
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25
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Krishnan A, Kogan C, Peters RT, Thomas EL, Critzer F. Microbial and physicochemical assessment of irrigation water treatment methods. J Appl Microbiol 2021; 131:1555-1562. [PMID: 33594789 DOI: 10.1111/jam.15043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 02/09/2021] [Accepted: 02/14/2021] [Indexed: 12/01/2022]
Abstract
AIMS The presence of foodborne pathogens in preharvest agricultural water has been identified as a potential contamination source in outbreak investigations, driving markets and auditing bodies to begin requiring water treatment for high-risk produce. Therefore, it is essential that we identify water treatment methods which are effective as well as practical in their application on farm. METHODS AND RESULTS In this work, we evaluated two sanitizers which are most prominent in preharvest agricultural water treatment (calcium hypochlorite (free chlorine: 3-5 ppm) and peracetic acid (PAA: 5 ppm)), an EPA registered antimicrobial device (ultraviolet light (UV)), in addition to a combination approach (chlorine + UV, PAA + UV). Treatments were evaluated for their ability to inactivate total coliforms and generic Escherichia coli and consistency in treatment efficacy over 1 h of operation. Physicochemical variables were measured along with microbial populations at 0, 5, 15, 30, 45 and 60 min of operation. Escherichia coli and coliform counts showed a significant (P < 0·05) reduction after treatment, with combination and singular treatments equally effective at inactivating E. coli and coliforms. A significant increase (P < 0·05) in oxidation-reduction potential was seen during water treatment (Chlorine; UV + Chlorine), and a significant reduction (P < 0·05) in pH was seen after PAA and PAA + UV treatments (60 min). CONCLUSION Overall, the results indicate that all treatments evaluated are equally efficacious for inactivating E. coli and coliforms present in surface agricultural water. SIGNIFICANCE AND IMPACT OF THE STUDY This information when paired with challenge studies targeting foodborne pathogens of interest can be used to support grower decisions when selecting and validating a preharvest agricultural water treatment programme.
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Affiliation(s)
- A Krishnan
- School of Food Science and Irrigated Agriculture Research and Extension Center, Washington State University, Prosser, WA, USA
| | - C Kogan
- Department of Mathematics, Washington State University, Pullman, WA, USA
| | - R T Peters
- Department of Biosystems Engineering and Irrigated Agriculture Research and Extension Center, Washington State University, Prosser, WA, USA
| | - E L Thomas
- Department of Biosystems Engineering and Irrigated Agriculture Research and Extension Center, Washington State University, Prosser, WA, USA
| | - F Critzer
- School of Food Science and Irrigated Agriculture Research and Extension Center, Washington State University, Prosser, WA, USA
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26
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Bizzotto R, Jennison C, Jones AG, Kurbasic A, Tura A, Kennedy G, Bell JD, Thomas EL, Frost G, Eriksen R, Koivula RW, Brage S, Kaye J, Hattersley AT, Heggie A, McEvoy D, 't Hart LM, Beulens JW, Elders P, Musholt PB, Ridderstråle M, Hansen TH, Allin KH, Hansen T, Vestergaard H, Lundgaard AT, Thomsen HS, De Masi F, Tsirigos KD, Brunak S, Viñuela A, Mahajan A, McDonald TJ, Kokkola T, Forgie IM, Giordano GN, Pavo I, Ruetten H, Dermitzakis E, McCarthy MI, Pedersen O, Schwenk JM, Adamski J, Franks PW, Walker M, Pearson ER, Mari A. Processes Underlying Glycemic Deterioration in Type 2 Diabetes: An IMI DIRECT Study. Diabetes Care 2021; 44:511-518. [PMID: 33323478 DOI: 10.2337/dc20-1567] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Accepted: 10/31/2020] [Indexed: 02/03/2023]
Abstract
OBJECTIVE We investigated the processes underlying glycemic deterioration in type 2 diabetes (T2D). RESEARCH DESIGN AND METHODS A total of 732 recently diagnosed patients with T2D from the Innovative Medicines Initiative Diabetes Research on Patient Stratification (IMI DIRECT) study were extensively phenotyped over 3 years, including measures of insulin sensitivity (OGIS), β-cell glucose sensitivity (GS), and insulin clearance (CLIm) from mixed meal tests, liver enzymes, lipid profiles, and baseline regional fat from MRI. The associations between the longitudinal metabolic patterns and HbA1c deterioration, adjusted for changes in BMI and in diabetes medications, were assessed via stepwise multivariable linear and logistic regression. RESULTS Faster HbA1c progression was independently associated with faster deterioration of OGIS and GS and increasing CLIm; visceral or liver fat, HDL-cholesterol, and triglycerides had further independent, though weaker, roles (R 2 = 0.38). A subgroup of patients with a markedly higher progression rate (fast progressors) was clearly distinguishable considering these variables only (discrimination capacity from area under the receiver operating characteristic = 0.94). The proportion of fast progressors was reduced from 56% to 8-10% in subgroups in which only one trait among OGIS, GS, and CLIm was relatively stable (odds ratios 0.07-0.09). T2D polygenic risk score and baseline pancreatic fat, glucagon-like peptide 1, glucagon, diet, and physical activity did not show an independent role. CONCLUSIONS Deteriorating insulin sensitivity and β-cell function, increasing insulin clearance, high visceral or liver fat, and worsening of the lipid profile are the crucial factors mediating glycemic deterioration of patients with T2D in the initial phase of the disease. Stabilization of a single trait among insulin sensitivity, β-cell function, and insulin clearance may be relevant to prevent progression.
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Affiliation(s)
| | | | - Angus G Jones
- Institute of Biomedical and Clinical Science, University of Exeter Medical School, Exeter, U.K.,Diabetes and Endocrinology, Royal Devon and Exeter NHS Foundation Trust, Exeter, U.K
| | - Azra Kurbasic
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Sciences, Clinical Research Centre, Lund University, Skåne University Hospital, Malmö, Malmö, Sweden
| | - Andrea Tura
- CNR Institute of Neuroscience, Padova, Italy
| | - Gwen Kennedy
- Immunoassay Biomarker Core Laboratory, School of Medicine, Ninewells Hospital, Dundee, U.K
| | - Jimmy D Bell
- School of Life Sciences, Research Centre for Optimal Health, University of Westminster, London, U.K
| | - E Louise Thomas
- School of Life Sciences, Research Centre for Optimal Health, University of Westminster, London, U.K
| | - Gary Frost
- Section for Nutrition Research, Faculty of Medicine, Hammersmith Campus, Imperial College London, London, U.K
| | - Rebeca Eriksen
- Section for Nutrition Research, Faculty of Medicine, Hammersmith Campus, Imperial College London, London, U.K
| | - Robert W Koivula
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Sciences, Clinical Research Centre, Lund University, Skåne University Hospital, Malmö, Malmö, Sweden.,Radcliffe Department of Medicine, Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, U.K
| | - Soren Brage
- MRC Epidemiology Unit, University of Cambridge, Cambridge, U.K
| | - Jane Kaye
- Faculty of Law, Centre for Health, Law and Emerging Technologies, University of Oxford, Oxford, U.K.,Melbourne Law School, Centre for Health, Law and Emerging Technologies, University of Melbourne, Carlton, Victoria, Australia
| | - Andrew T Hattersley
- Institute of Biomedical and Clinical Science, University of Exeter Medical School, Exeter, U.K.,Diabetes and Endocrinology, Royal Devon and Exeter NHS Foundation Trust, Exeter, U.K
| | - Alison Heggie
- Institute of Cellular Medicine, Newcastle University, Newcastle upon Tyne, U.K
| | - Donna McEvoy
- Diabetes Research Network, Royal Victoria Infirmary, Newcastle upon Tyne, U.K
| | - Leen M 't Hart
- Department of Epidemiology and Data Science, Amsterdam UMC-Location VUmc, Amsterdam Public Health Research Institute, Amsterdam, the Netherlands.,Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, the Netherlands.,Department of Biomedical Data Sciences, Molecular Epidemiology Section, Leiden University Medical Center, Leiden, the Netherlands
| | - Joline W Beulens
- Department of Epidemiology and Data Science, Amsterdam UMC-Location VUmc, Amsterdam Public Health Research Institute, Amsterdam, the Netherlands
| | - Petra Elders
- Department of General Practice, Amsterdam UMC-Location VUmc, Amsterdam Public Health Research Institute, Amsterdam, the Netherlands
| | - Petra B Musholt
- R&D Global Development, Translational Medicine & Clinical Pharmacology, Sanofi Deutschland GmbH, Frankfurt, Germany
| | - Martin Ridderstråle
- Clinical Pharmacology and Translational Medicine, Novo Nordisk A/S, Søborg, Denmark
| | - Tue H Hansen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Kristine H Allin
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Torben Hansen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Henrik Vestergaard
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.,Bornholms Hospital, Rønne, Denmark
| | - Agnete T Lundgaard
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark.,Department of Systems Biology, Center for Biological Sequence Analysis, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Henrik S Thomsen
- Faculty of Medical and Health Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Federico De Masi
- Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Konstantinos D Tsirigos
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark.,Department of Systems Biology, Center for Biological Sequence Analysis, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Søren Brunak
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark.,Department of Systems Biology, Center for Biological Sequence Analysis, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Ana Viñuela
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland.,Faculty of Medical Sciences, Biosciences Institute, Newcastle University, Newcastle, U.K
| | - Anubha Mahajan
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, U.K
| | - Timothy J McDonald
- Institute of Biomedical and Clinical Science, University of Exeter Medical School, Exeter, U.K.,Blood Sciences, Royal Devon and Exeter NHS Foundation Trust, Exeter, U.K
| | - Tarja Kokkola
- Department of Medicine, University of Eastern Finland, Kuopio, Finland
| | - Ian M Forgie
- Population Health and Genomics, School of Medicine, University of Dundee, Dundee, U.K
| | - Giuseppe N Giordano
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Sciences, Clinical Research Centre, Lund University, Skåne University Hospital, Malmö, Malmö, Sweden
| | - Imre Pavo
- Eli Lilly Regional Operations GmbH, Vienna, Austria
| | - Hartmut Ruetten
- R&D Global Development, Translational Medicine & Clinical Pharmacology, Sanofi Deutschland GmbH, Frankfurt, Germany
| | - Emmanouil Dermitzakis
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland
| | - Mark I McCarthy
- Radcliffe Department of Medicine, Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, U.K.,Wellcome Centre for Human Genetics, University of Oxford, Oxford, U.K.,Oxford NIHR Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, U.K
| | - Oluf Pedersen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Jochen M Schwenk
- Affinity Proteomics, Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Solna, Sweden
| | - Jerzy Adamski
- Research Unit of Molecular Endocrinology and Metabolism, Helmholtz Zentrum München, Neuherberg, Germany.,Lehrstuhl für Experimentelle Genetik, Technische Universität München, Freising-Weihenstephan, Germany.,Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Paul W Franks
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Sciences, Clinical Research Centre, Lund University, Skåne University Hospital, Malmö, Malmö, Sweden
| | - Mark Walker
- Faculty of Medical Sciences, Translational and Clinical Research Institute, Newcastle University, Newcastle, U.K
| | - Ewan R Pearson
- Population Health and Genomics, School of Medicine, University of Dundee, Dundee, U.K
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27
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Koivula RW, Atabaki-Pasdar N, Giordano GN, White T, Adamski J, Bell JD, Beulens J, Brage S, Brunak S, De Masi F, Dermitzakis ET, Forgie IM, Frost G, Hansen T, Hansen TH, Hattersley A, Kokkola T, Kurbasic A, Laakso M, Mari A, McDonald TJ, Pedersen O, Rutters F, Schwenk JM, Teare HJA, Thomas EL, Vinuela A, Mahajan A, McCarthy MI, Ruetten H, Walker M, Pearson E, Pavo I, Franks PW. Correction to: The role of physical activity in metabolic homeostasis before and after the onset of type 2 diabetes: an IMI DIRECT study. Diabetologia 2021; 64:260-261. [PMID: 33184701 PMCID: PMC7852866 DOI: 10.1007/s00125-020-05311-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Unfortunately, 'Present address' was omitted from one of the addresses provided for Mark I. McCarthy (#26).
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Affiliation(s)
- Robert W Koivula
- Department of Clinical Sciences, Lund University, Genetic and Molecular Epidemiology, CRC, Skåne University Hospital Malmö, Building 91, Level 12, Jan Waldenströms gata 35, SE-205 02, Malmö, Sweden.
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK.
| | - Naeimeh Atabaki-Pasdar
- Department of Clinical Sciences, Lund University, Genetic and Molecular Epidemiology, CRC, Skåne University Hospital Malmö, Building 91, Level 12, Jan Waldenströms gata 35, SE-205 02, Malmö, Sweden
| | - Giuseppe N Giordano
- Department of Clinical Sciences, Lund University, Genetic and Molecular Epidemiology, CRC, Skåne University Hospital Malmö, Building 91, Level 12, Jan Waldenströms gata 35, SE-205 02, Malmö, Sweden
| | - Tom White
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Jerzy Adamski
- Research Unit Molecular Endocrinology and Metabolism, Genome Analysis Center, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Lehrstuhl für Experimentelle Genetik, Technische Universität München, Freising-Weihenstephan, Germany
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Republic of Singapore
| | - Jimmy D Bell
- Research Centre for Optimal Health, Department of Life Sciences, University of Westminister, London, UK
| | - Joline Beulens
- Department of Epidemiology and Biostatistics, Amsterdam Public Health Research Institute, Amsterdam University Medical Centre, Location VU University Medical Center, Amsterdam, the Netherlands
| | - Søren Brage
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, UK
- Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark
| | - Søren Brunak
- The Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
- Department of Bio and Health Informatics, Technical University of Denmark, Lyngby, Denmark
| | - Federico De Masi
- The Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
- Department of Bio and Health Informatics, Technical University of Denmark, Lyngby, Denmark
| | - Emmanouil T Dermitzakis
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland
- Institute of Genetics and Genomics in Geneva (iGE3), University of Geneva, Geneva, Switzerland
- Swiss Institute of Bioinformatics, Geneva, Switzerland
| | - Ian M Forgie
- Population Health & Genomics, School of Medicine, University of Dundee, Ninewells Hospital, Dundee, UK
| | - Gary Frost
- Nutrition and Dietetics Research Group, Department of Medicine, Division of Diabetes, Endocrinology and Metabolism, Imperial College London, Hammersmith Campus, London, UK
| | - Torben Hansen
- Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, Denmark
| | - Tue H Hansen
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, Denmark
| | - Andrew Hattersley
- NIHR Exeter Clinical Research Facility, University of Exeter Medical School, Exeter, UK
- Institute of Biomedical and Clinical Science, University of Exeter Medical School, Exeter, UK
| | - Tarja Kokkola
- Department of Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | - Azra Kurbasic
- Department of Clinical Sciences, Lund University, Genetic and Molecular Epidemiology, CRC, Skåne University Hospital Malmö, Building 91, Level 12, Jan Waldenströms gata 35, SE-205 02, Malmö, Sweden
| | - Markku Laakso
- Department of Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | - Andrea Mari
- Institute of Neurosciences, National Research Council, Padova, Italy
| | - Timothy J McDonald
- NIHR Exeter Clinical Research Facility, University of Exeter Medical School, Exeter, UK
| | - Oluf Pedersen
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, Denmark
| | - Femke Rutters
- Department of Epidemiology and Biostatistics, Amsterdam Public Health Research Institute, Amsterdam University Medical Centre, Location VU University Medical Center, Amsterdam, the Netherlands
| | - Jochen M Schwenk
- Affinity Proteomics, Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Harriet J A Teare
- HeLEX, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Headington, Oxford, UK
| | - E Louise Thomas
- Research Centre for Optimal Health, Department of Life Sciences, University of Westminister, London, UK
| | - Ana Vinuela
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland
- Institute of Genetics and Genomics in Geneva (iGE3), University of Geneva, Geneva, Switzerland
- Swiss Institute of Bioinformatics, Geneva, Switzerland
| | - Anubha Mahajan
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Mark I McCarthy
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
- NIHR Oxford Biomedical Research Centre, Churchill Hospital, Oxford, UK
- Human Genetics, Genentech, South San Francisco, CA, USA
| | - Hartmut Ruetten
- Sanofi-Aventis Deutschland GmbH, R&D, Frankfurt am Main, Germany
| | - Mark Walker
- Institute of Cellular Medicine (Diabetes), Newcastle University, Newcastle upon Tyne, UK
| | - Ewan Pearson
- Population Health & Genomics, School of Medicine, University of Dundee, Ninewells Hospital, Dundee, UK
| | - Imre Pavo
- Eli Lilly Regional Operations GmbH, Vienna, Austria
| | - Paul W Franks
- Department of Clinical Sciences, Lund University, Genetic and Molecular Epidemiology, CRC, Skåne University Hospital Malmö, Building 91, Level 12, Jan Waldenströms gata 35, SE-205 02, Malmö, Sweden
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- Department of Nutrition, Harvard School of Public Health, Boston, MA, USA
- Department of Public Health & Clinical Medicine, Section for Medicine, Umeå University Hospital, Umeå, Sweden
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28
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Parisinos CA, Wilman HR, Thomas EL, Kelly M, Nicholls RC, McGonigle J, Neubauer S, Hingorani AD, Patel RS, Hemingway H, Bell JD, Banerjee R, Yaghootkar H. Corrigendum to: "Genome-wide and Mendelian randomisation studies of liver MRI yield insights into the pathogenesis of steatohepatitis" [J Hepatol (2020) 241-251]. J Hepatol 2020; 73:1594-1595. [PMID: 32951912 PMCID: PMC8055539 DOI: 10.1016/j.jhep.2020.08.030] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/04/2022]
Affiliation(s)
- Constantinos A. Parisinos
- Institute of Health Informatics, Faculty of Population Health Sciences, University College London, London, UK,Corresponding authors. Addresses: Institute of Health Informatics, Faculty of Population Health Sciences, University College London, NW12DA. Tel.: +(44)7899786998
| | - Henry R. Wilman
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK,Perspectum Diagnostics Ltd., Oxford, UK
| | - E. Louise Thomas
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | | | | | | | - Stefan Neubauer
- Perspectum Diagnostics Ltd., Oxford, UK,Oxford Centre for Clinical Magnetic Resonance Research, Division of Cardiovascular Medicine, Oxford NIHR Biomedical Research Centre, University of Oxford, Oxford, UK
| | - Aroon D. Hingorani
- Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, UK
| | - Riyaz S. Patel
- Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, UK
| | - Harry Hemingway
- Health Data Research UK London, Institute of Health Informatics, Faculty of Population Health Sciences, University College London, London, UK
| | - Jimmy D. Bell
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | | | - Hanieh Yaghootkar
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK; Genetics of Complex Traits, College of Medicine and Health, University of Exeter, Exeter, UK; Division of Medical Sciences, Department of Health Sciences, Luleå University of Technology, Luleå, Sweden.
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Alenaini W, Parkinson JRC, McCarthy JP, Goldstone AP, Wilman HR, Banerjee R, Yaghootkar H, Bell JD, Thomas EL. Ethnic Differences in Body Fat Deposition and Liver Fat Content in Two UK-Based Cohorts. Obesity (Silver Spring) 2020; 28:2142-2152. [PMID: 32939982 DOI: 10.1002/oby.22948] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/11/2019] [Revised: 06/15/2020] [Accepted: 06/16/2020] [Indexed: 02/04/2023]
Abstract
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.
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Affiliation(s)
- Wareed Alenaini
- Research Centre for Optimal Health, School of Life Sciences, College of Liberal Arts and Sciences, University of Westminster, London, UK
| | - James R C Parkinson
- Research Centre for Optimal Health, School of Life Sciences, College of Liberal Arts and Sciences, University of Westminster, London, UK
| | - John P McCarthy
- School of Healthcare Practice, University of Bedfordshire, Luton, Bedfordshire, UK
| | - Anthony P Goldstone
- PsychoNeuroEndocrinology Research Group, Neuro-psychopharmacology Unit, Centre for Psychiatry, Division of Brain Sciences, Imperial College London-Hammersmith Hospital, London, UK
| | - Henry R Wilman
- Research Centre for Optimal Health, School of Life Sciences, College of Liberal Arts and Sciences, University of Westminster, London, UK
- Perspectum Diagnostics, Oxford, UK
| | | | - Hanieh Yaghootkar
- Research Centre for Optimal Health, School of Life Sciences, College of Liberal Arts and Sciences, University of Westminster, London, UK
- Genetics of Complex Traits, Medical School, University of Exeter-Royal Devon & Exeter Hospital, Exeter, UK
- Division of Medical Sciences, Department of Health Sciences, Luleå University of Technology, Luleå, Sweden
| | - Jimmy D Bell
- Research Centre for Optimal Health, School of Life Sciences, College of Liberal Arts and Sciences, University of Westminster, London, UK
| | - E Louise Thomas
- Research Centre for Optimal Health, School of Life Sciences, College of Liberal Arts and Sciences, University of Westminster, London, UK
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30
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Abstract
Type 2 diabetes is more common in non-Europeans and starts at a younger age and at lower BMI cut-offs. This review discusses the insights from genetic studies about pathophysiological mechanisms which determine risk of disease with a focus on the role of adiposity and body fat distribution in ethnic disparity in risk of type 2 diabetes. During the past decade, genome-wide association studies (GWAS) have identified more than 400 genetic variants associated with the risk of type 2 diabetes. The Eurocentric nature of these genetic studies has made them less effective in identifying mechanisms that make non-Europeans more susceptible to higher risk of disease. One possible mechanism suggested by epidemiological studies is the role of ethnic difference in body fat distribution. Using genetic variants associated with an ability to store extra fat in a safe place, which is subcutaneous adipose tissue, we discuss how different ethnic groups could be genetically less susceptible to type 2 diabetes by developing a more favourable fat distribution.
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Affiliation(s)
- H Yaghootkar
- From the, Genetics of Complex Traits, University of Exeter Medical School, Royal Devon & Exeter Hospital, Exeter, UK.,School of Life Sciences, College of Liberal Arts and Science, University of Westminster, London, UK.,Division of Medical Sciences, Department of Health Sciences, Luleå University of Technology, Luleå, Sweden
| | - B Whitcher
- School of Life Sciences, College of Liberal Arts and Science, University of Westminster, London, UK
| | - J D Bell
- School of Life Sciences, College of Liberal Arts and Science, University of Westminster, London, UK
| | - E L Thomas
- School of Life Sciences, College of Liberal Arts and Science, University of Westminster, London, UK
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31
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Parisinos CA, Wilman HR, Thomas EL, Kelly M, Nicholls RC, McGonigle J, Neubauer S, Hingorani AD, Patel RS, Hemingway H, Bell JD, Banerjee R, Yaghootkar H. Genome-wide and Mendelian randomisation studies of liver MRI yield insights into the pathogenesis of steatohepatitis. J Hepatol 2020; 73:241-251. [PMID: 32247823 PMCID: PMC7372222 DOI: 10.1016/j.jhep.2020.03.032] [Citation(s) in RCA: 62] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/04/2019] [Revised: 03/03/2020] [Accepted: 03/19/2020] [Indexed: 02/06/2023]
Abstract
BACKGROUND & AIMS MRI-based corrected T1 (cT1) is a non-invasive method to grade the severity of steatohepatitis and liver fibrosis. We aimed to identify genetic variants influencing liver cT1 and use genetics to understand mechanisms underlying liver fibroinflammatory disease and its link with other metabolic traits and diseases. METHODS First, we performed a genome-wide association study (GWAS) in 14,440 Europeans, with liver cT1 measures, from the UK Biobank. Second, we explored the effects of the cT1 variants on liver blood tests, and a range of metabolic traits and diseases. Third, we used Mendelian randomisation to test the causal effects of 24 predominantly metabolic traits on liver cT1 measures. RESULTS We identified 6 independent genetic variants associated with liver cT1 that reached the GWAS significance threshold (p <5×10-8). Four of the variants (rs759359281 in SLC30A10, rs13107325 in SLC39A8, rs58542926 in TM6SF2, rs738409 in PNPLA3) were also associated with elevated aminotransferases and had variable effects on liver fat and other metabolic traits. Insulin resistance, type 2 diabetes, non-alcoholic fatty liver and body mass index were causally associated with elevated cT1, whilst favourable adiposity (instrumented by variants associated with higher adiposity but lower risk of cardiometabolic disease and lower liver fat) was found to be protective. CONCLUSION The association between 2 metal ion transporters and cT1 indicates an important new mechanism in steatohepatitis. Future studies are needed to determine whether interventions targeting the identified transporters might prevent liver disease in at-risk individuals. LAY SUMMARY We estimated levels of liver inflammation and scarring based on magnetic resonance imaging of 14,440 UK Biobank participants. We performed a genetic study and identified variations in 6 genes associated with levels of liver inflammation and scarring. Participants with variations in 4 of these genes also had higher levels of markers of liver cell injury in blood samples, further validating their role in liver health. Two identified genes are involved in the transport of metal ions in our body. Further investigation of these variations may lead to better detection, assessment, and/or treatment of liver inflammation and scarring.
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Affiliation(s)
- Constantinos A Parisinos
- Institute of Health Informatics, Faculty of Population Health Sciences, University College London, London, UK.
| | - Henry R Wilman
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK; Perspectum Diagnostics Ltd., Oxford, UK
| | - E Louise Thomas
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | | | | | | | - Stefan Neubauer
- Perspectum Diagnostics Ltd., Oxford, UK; Oxford Centre for Clinical Magnetic Resonance Research, Division of Cardiovascular Medicine, Oxford NIHR Biomedical Research Centre, University of Oxford, Oxford, UK
| | - Aroon D Hingorani
- Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, UK
| | - Riyaz S Patel
- Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, UK
| | - Harry Hemingway
- Health Data Research UK London, Institute of Health Informatics, Faculty of Population Health Sciences, University College London, London, UK
| | - Jimmy D Bell
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | | | - Hanieh Yaghootkar
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK; Genetics of Complex Traits, College of Medicine and Health, University of Exeter, Exeter, UK; Division of Medical Sciences, Department of Health Sciences, Luleå University of Technology, Luleå, Sweden.
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32
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Machann J, Stefan N, Wagner R, Fritsche A, Bell JD, Whitcher B, Häring HU, Birkenfeld AL, Nikolaou K, Schick F, Thomas EL. Normalized Indices Derived from Visceral Adipose Mass Assessed by Magnetic Resonance Imaging and Their Correlation with Markers for Insulin Resistance and Prediabetes. Nutrients 2020; 12:nu12072064. [PMID: 32664590 PMCID: PMC7400828 DOI: 10.3390/nu12072064] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2020] [Revised: 07/08/2020] [Accepted: 07/09/2020] [Indexed: 12/30/2022] Open
Abstract
Visceral adipose tissue (VAT) plays an important role in the pathogenesis of insulin resistance (IR), prediabetes and type 2 diabetes. However, VAT volume alone might not be the best marker for insulin resistance and prediabetes or diabetes, as a given VAT volume may impact differently on these metabolic traits based on body height, gender, age and ethnicity. In a cohort of 1295 subjects from the Tübingen Diabetes Family Study (TDFS) and in 9978 subjects from the UK Biobank (UKBB) undergoing magnetic resonance imaging for quantification of VAT volume, total adipose tissue (TAT) in the TDFS, total abdominal adipose tissue (TAAT) in the UKBB, and total lean tissue (TLT), VAT volume and several VAT-indices were investigated for their relationships with insulin resistance and glycemic traits. VAT-related indices were calculated by correcting for body height (VAT/m:VAT/body height; VAT/m2:VAT/(body height)2, and VAT/m3:VAT/(body height)3), TAT (%VAT), TLT (VAT/TLT) and weight (VAT/WEI), with closest equivalents used within the UKBB dataset. Prognostic values of VAT and VAT-related indices for insulin sensitivity, HbA1c levels and prediabetes/diabetes were analyzed for males and females. Males had higher VAT volume and VAT-related indices than females in both cohorts (p < 0.0001) and VAT volume has shown to be a stronger determinant for insulin sensitivity than anthropometric variables. Among the parameters uncorrected VAT and derived indices, VAT/m3 most strongly correlated negatively with insulin sensitivity and positively with HbA1c levels and prediabetes/diabetes in the TDFS (R2 = 0.375/0.305 for females/males for insulin sensitivity, 0.178/0.148 for HbA1c levels vs., e.g., 0.355/0.293 and 0.144/0.133 for VAT, respectively) and positively with HbA1c (R2 = 0.046/0.042) in the UKBB for females and males. Furthermore, VAT/m3 was found to be a significantly better determinant of insulin resistance or prediabetes than uncorrected VAT volume (p < 0.001/0.019 for females/males regarding insulin sensitivity, p < 0.001/< 0.001 for females/males regarding HbA1c). Evaluation of several indices derived from VAT volume identified VAT/m3 to correlate most strongly with insulin sensitivity and glucose metabolism. Thus, VAT/m3 appears to provide better indications of metabolic characteristics (insulin sensitivity and pre-diabetes/diabetes) than VAT volume alone.
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Affiliation(s)
- Jürgen Machann
- Institute for Diabetes Research and Metabolic Diseases (IDM) of the Helmholtz Center Munich at the University of Tübingen, 72076 Tübingen, Germany; (N.S.); (R.W.); (A.F.); (A.L.B.); (F.S.)
- German Center for Diabetes Research (DZD), 72076 Tübingen, Germany;
- Section on Experimental Radiology, Department of Diagnostic and Interventional Radiology, University Hospital Tübingen, Hoppe-Seyler-Str. 3, 72076 Tübingen, Germany
- Correspondence:
| | - Norbert Stefan
- Institute for Diabetes Research and Metabolic Diseases (IDM) of the Helmholtz Center Munich at the University of Tübingen, 72076 Tübingen, Germany; (N.S.); (R.W.); (A.F.); (A.L.B.); (F.S.)
- German Center for Diabetes Research (DZD), 72076 Tübingen, Germany;
- Department of Endocrinology, Diabetology and Nephrology, University Hospital Tübingen, 72076 Tübingen, Germany
| | - Robert Wagner
- Institute for Diabetes Research and Metabolic Diseases (IDM) of the Helmholtz Center Munich at the University of Tübingen, 72076 Tübingen, Germany; (N.S.); (R.W.); (A.F.); (A.L.B.); (F.S.)
- German Center for Diabetes Research (DZD), 72076 Tübingen, Germany;
- Department of Endocrinology, Diabetology and Nephrology, University Hospital Tübingen, 72076 Tübingen, Germany
| | - Andreas Fritsche
- Institute for Diabetes Research and Metabolic Diseases (IDM) of the Helmholtz Center Munich at the University of Tübingen, 72076 Tübingen, Germany; (N.S.); (R.W.); (A.F.); (A.L.B.); (F.S.)
- German Center for Diabetes Research (DZD), 72076 Tübingen, Germany;
- Department of Endocrinology, Diabetology and Nephrology, University Hospital Tübingen, 72076 Tübingen, Germany
| | - Jimmy D. Bell
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London W1W 6UW, UK; (J.D.B.); (B.W.); (E.L.T)
| | - Brandon Whitcher
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London W1W 6UW, UK; (J.D.B.); (B.W.); (E.L.T)
| | | | - Andreas L. Birkenfeld
- Institute for Diabetes Research and Metabolic Diseases (IDM) of the Helmholtz Center Munich at the University of Tübingen, 72076 Tübingen, Germany; (N.S.); (R.W.); (A.F.); (A.L.B.); (F.S.)
- German Center for Diabetes Research (DZD), 72076 Tübingen, Germany;
- Department of Endocrinology, Diabetology and Nephrology, University Hospital Tübingen, 72076 Tübingen, Germany
| | - Konstantin Nikolaou
- Department of Diagnostic and Interventional Radiology, University Hospital Tübingen, 72076 Tübingen, Germany;
| | - Fritz Schick
- Institute for Diabetes Research and Metabolic Diseases (IDM) of the Helmholtz Center Munich at the University of Tübingen, 72076 Tübingen, Germany; (N.S.); (R.W.); (A.F.); (A.L.B.); (F.S.)
- German Center for Diabetes Research (DZD), 72076 Tübingen, Germany;
- Section on Experimental Radiology, Department of Diagnostic and Interventional Radiology, University Hospital Tübingen, Hoppe-Seyler-Str. 3, 72076 Tübingen, Germany
| | - E. Louise Thomas
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London W1W 6UW, UK; (J.D.B.); (B.W.); (E.L.T)
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Whyte MB, Shojaee-Moradie F, Sharaf SE, Cuthbertson DJ, Kemp GJ, Barrett M, Jackson NC, Herring RA, Wright J, Thomas EL, Bell J, Umpleby AM. HDL-apoA-I kinetics in response to 16 wk of exercise training in men with nonalcoholic fatty liver disease. Am J Physiol Endocrinol Metab 2020; 318:E839-E847. [PMID: 32286882 DOI: 10.1152/ajpendo.00019.2020] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
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 by the lack of effect on plasma and VLDL1-TAG.
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Affiliation(s)
- Martin B Whyte
- Faculty of Health and Medical Sciences, University of Surrey, Guildford, Surrey, United Kingdom
| | - Fariba Shojaee-Moradie
- Faculty of Health and Medical Sciences, University of Surrey, Guildford, Surrey, United Kingdom
| | - Sharaf E Sharaf
- Faculty of Health and Medical Sciences, University of Surrey, Guildford, Surrey, United Kingdom
| | - Daniel J Cuthbertson
- Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool, United Kingdom
| | - Graham J Kemp
- Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool, United Kingdom
| | - Mark Barrett
- Faculty of Health and Medical Sciences, University of Surrey, Guildford, Surrey, United Kingdom
| | - Nicola C Jackson
- Faculty of Health and Medical Sciences, University of Surrey, Guildford, Surrey, United Kingdom
| | - Roselle A Herring
- Centre for Diabetes, Endocrinology, and Research, Royal Surrey County Hospital, Guildford, United Kingdom
| | - John Wright
- Centre for Diabetes, Endocrinology, and Research, Royal Surrey County Hospital, Guildford, United Kingdom
| | - E Louise Thomas
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, United Kingdom
| | - Jimmy Bell
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, United Kingdom
| | - A Margot Umpleby
- Faculty of Health and Medical Sciences, University of Surrey, Guildford, Surrey, United Kingdom
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Mani BK, Puzziferri N, He Z, Rodriguez JA, Osborne-Lawrence S, Metzger NP, Chhina N, Gaylinn B, Thorner MO, Thomas EL, Bell JD, Williams KW, Goldstone AP, Zigman JM. LEAP2 changes with body mass and food intake in humans and mice. J Clin Invest 2020; 129:3909-3923. [PMID: 31424424 DOI: 10.1172/jci125332] [Citation(s) in RCA: 109] [Impact Index Per Article: 27.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2018] [Accepted: 06/11/2019] [Indexed: 12/11/2022] Open
Abstract
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.
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Affiliation(s)
- Bharath K Mani
- Division of Hypothalamic Research.,Division of Endocrinology & Metabolism, Department of Internal Medicine.,Department of Psychiatry, and
| | - Nancy Puzziferri
- Department of Surgery, UT Southwestern Medical Center, Dallas, Texas, USA.,Department of Surgery, Veterans Administration North Texas Heath Care System, Dallas, Texas, USA
| | | | - Juan A Rodriguez
- Division of Hypothalamic Research.,Division of Endocrinology & Metabolism, Department of Internal Medicine.,Department of Psychiatry, and
| | - Sherri Osborne-Lawrence
- Division of Hypothalamic Research.,Division of Endocrinology & Metabolism, Department of Internal Medicine.,Department of Psychiatry, and
| | - Nathan P Metzger
- Division of Hypothalamic Research.,Division of Endocrinology & Metabolism, Department of Internal Medicine.,Department of Psychiatry, and
| | - Navpreet Chhina
- PsychoNeuroEndocrinology Research Group, Neuropsychopharmacology Unit, Centre for Psychiatry, and.,Computational, Cognitive and Clinical Neuroimaging Laboratory, Division of Brain Sciences, Imperial College London, Hammersmith Hospital, London, United Kingdom
| | - Bruce Gaylinn
- Department of Endocrinology, University of Virginia, Charlottesville, Virginia, USA
| | - Michael O Thorner
- Department of Endocrinology, University of Virginia, Charlottesville, Virginia, USA
| | - E Louise Thomas
- Research Centre for Optimal Health, University of Westminster, London, United Kingdom
| | - Jimmy D Bell
- Research Centre for Optimal Health, University of Westminster, London, United Kingdom
| | | | - Anthony P Goldstone
- PsychoNeuroEndocrinology Research Group, Neuropsychopharmacology Unit, Centre for Psychiatry, and.,Computational, Cognitive and Clinical Neuroimaging Laboratory, Division of Brain Sciences, Imperial College London, Hammersmith Hospital, London, United Kingdom
| | - Jeffrey M Zigman
- Division of Hypothalamic Research.,Division of Endocrinology & Metabolism, Department of Internal Medicine.,Department of Psychiatry, and
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35
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Koivula RW, Atabaki-Pasdar N, Giordano GN, White T, Adamski J, Bell JD, Beulens J, Brage S, Brunak S, De Masi F, Dermitzakis ET, Forgie IM, Frost G, Hansen T, Hansen TH, Hattersley A, Kokkola T, Kurbasic A, Laakso M, Mari A, McDonald TJ, Pedersen O, Rutters F, Schwenk JM, Teare HJA, Thomas EL, Vinuela A, Mahajan A, McCarthy MI, Ruetten H, Walker M, Pearson E, Pavo I, Franks PW. The role of physical activity in metabolic homeostasis before and after the onset of type 2 diabetes: an IMI DIRECT study. Diabetologia 2020; 63:744-756. [PMID: 32002573 PMCID: PMC7054368 DOI: 10.1007/s00125-019-05083-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/29/2019] [Accepted: 11/29/2019] [Indexed: 11/17/2022]
Abstract
AIMS/HYPOTHESIS It 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). METHODS We 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. RESULTS The 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 with glycaemic control was primarily mediated by variables in the liver fat cycle. CONCLUSIONS/INTERPRETATION These analyses partially support the mechanisms proposed in the twin-cycle model and highlight mechanistic pathways through which insulin sensitivity and liver fat mediate the association between physical activity and glycaemic control.
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Affiliation(s)
- Robert W Koivula
- Department of Clinical Sciences, Lund University, Genetic and Molecular Epidemiology, CRC, Skåne University Hospital Malmö, Building 91, Level 12, Jan Waldenströms gata 35, SE-205 02, Malmö, Sweden.
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK.
| | - Naeimeh Atabaki-Pasdar
- Department of Clinical Sciences, Lund University, Genetic and Molecular Epidemiology, CRC, Skåne University Hospital Malmö, Building 91, Level 12, Jan Waldenströms gata 35, SE-205 02, Malmö, Sweden
| | - Giuseppe N Giordano
- Department of Clinical Sciences, Lund University, Genetic and Molecular Epidemiology, CRC, Skåne University Hospital Malmö, Building 91, Level 12, Jan Waldenströms gata 35, SE-205 02, Malmö, Sweden
| | - Tom White
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Jerzy Adamski
- Research Unit Molecular Endocrinology and Metabolism, Genome Analysis Center, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Lehrstuhl für Experimentelle Genetik, Technische Universität München, Freising-Weihenstephan, Germany
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Republic of Singapore
| | - Jimmy D Bell
- Research Centre for Optimal Health, Department of Life Sciences, University of Westminister, London, UK
| | - Joline Beulens
- Department of Epidemiology and Biostatistics, Amsterdam Public Health Research Institute, Amsterdam University Medical Centre, location VU University Medical Center, Amsterdam, the Netherlands
| | - Søren Brage
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, UK
- Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark
| | - Søren Brunak
- The Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
- Department of Bio and Health Informatics, Technical University of Denmark, Lyngby, Denmark
| | - Federico De Masi
- The Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
- Department of Bio and Health Informatics, Technical University of Denmark, Lyngby, Denmark
| | - Emmanouil T Dermitzakis
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland
- Institute of Genetics and Genomics in Geneva (iGE3), University of Geneva, Geneva, Switzerland
- Swiss Institute of Bioinformatics, Geneva, Switzerland
| | - Ian M Forgie
- Population Health & Genomics, School of Medicine, University of Dundee, Ninewells Hospital, Dundee, UK
| | - Gary Frost
- Nutrition and Dietetics Research Group, Department of Medicine, Division of Diabetes, Endocrinology and Metabolism, Imperial College London, Hammersmith Campus, London, UK
| | - Torben Hansen
- Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, Denmark
| | - Tue H Hansen
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, Denmark
| | - Andrew Hattersley
- NIHR Exeter Clinical Research Facility, University of Exeter Medical School, Exeter, UK
- Institute of Biomedical and Clinical Science, University of Exeter Medical School, Exeter, UK
| | - Tarja Kokkola
- Department of Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | - Azra Kurbasic
- Department of Clinical Sciences, Lund University, Genetic and Molecular Epidemiology, CRC, Skåne University Hospital Malmö, Building 91, Level 12, Jan Waldenströms gata 35, SE-205 02, Malmö, Sweden
| | - Markku Laakso
- Department of Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | - Andrea Mari
- Institute of Neurosciences, National Research Council, Padova, Italy
| | - Timothy J McDonald
- NIHR Exeter Clinical Research Facility, University of Exeter Medical School, Exeter, UK
| | - Oluf Pedersen
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, Denmark
| | - Femke Rutters
- Department of Epidemiology and Biostatistics, Amsterdam Public Health Research Institute, Amsterdam University Medical Centre, location VU University Medical Center, Amsterdam, the Netherlands
| | - Jochen M Schwenk
- Affinity Proteomics, Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Harriet J A Teare
- HeLEX, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Headington, Oxford, UK
| | - E Louise Thomas
- Research Centre for Optimal Health, Department of Life Sciences, University of Westminister, London, UK
| | - Ana Vinuela
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland
- Institute of Genetics and Genomics in Geneva (iGE3), University of Geneva, Geneva, Switzerland
- Swiss Institute of Bioinformatics, Geneva, Switzerland
| | - Anubha Mahajan
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Mark I McCarthy
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
- NIHR Oxford Biomedical Research Centre, Churchill Hospital, Oxford, UK
- Human Genetics, Genentech, South San Francisco, CA, USA
| | - Hartmut Ruetten
- Sanofi-Aventis Deutschland GmbH, R&D, Frankfurt am Main, Germany
| | - Mark Walker
- Institute of Cellular Medicine (Diabetes), Newcastle University, Newcastle upon Tyne, UK
| | - Ewan Pearson
- Population Health & Genomics, School of Medicine, University of Dundee, Ninewells Hospital, Dundee, UK
| | - Imre Pavo
- Eli Lilly Regional Operations GmbH, Vienna, Austria
| | - Paul W Franks
- Department of Clinical Sciences, Lund University, Genetic and Molecular Epidemiology, CRC, Skåne University Hospital Malmö, Building 91, Level 12, Jan Waldenströms gata 35, SE-205 02, Malmö, Sweden
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- Department of Nutrition, Harvard School of Public Health, Boston, MA, USA
- Department of Public Health & Clinical Medicine, Section for Medicine, Umeå University Hospital, Umeå, Sweden
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Wilman HR, Parisinos CA, Atabaki-Pasdar N, Kelly M, Thomas EL, Neubauer S, Mahajan A, Hingorani AD, Patel RS, Hemingway H, Franks PW, Bell JD, Banerjee R, Yaghootkar H. Genetic studies of abdominal MRI data identify genes regulating hepcidin as major determinants of liver iron concentration. J Hepatol 2019; 71:594-602. [PMID: 31226389 PMCID: PMC6694204 DOI: 10.1016/j.jhep.2019.05.032] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/18/2019] [Revised: 05/27/2019] [Accepted: 05/29/2019] [Indexed: 01/09/2023]
Abstract
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 disease. We identified 3 genetic variants that are linked to an increased risk of developing higher liver iron content. We show that the same genetic variants are linked to higher risk of many diseases, but they may also be associated with some health advantages. Finally, we use genetic variants associated with waist-to-hip ratio as a tool to show that central obesity is causally associated with increased liver iron content.
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Affiliation(s)
- Henry R Wilman
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK; Perspectum Diagnostics Ltd., Oxford, UK
| | - Constantinos A Parisinos
- Institute of Health Informatics, Faculty of Population Health Sciences, University College London, London, UK.
| | - Naeimeh Atabaki-Pasdar
- Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University, Skåne University Hospital Malmö, Malmö, Sweden
| | | | - E Louise Thomas
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | - Stefan Neubauer
- Perspectum Diagnostics Ltd., Oxford, UK; Oxford Centre for Clinical Magnetic Resonance Research, Division of Cardiovascular Medicine, Oxford NIHR Biomedical Research Centre, University of Oxford, Oxford, UK
| | - Anubha Mahajan
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Aroon D Hingorani
- Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, UK
| | - Riyaz S Patel
- Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, UK
| | - Harry Hemingway
- Institute of Health Informatics, Faculty of Population Health Sciences, University College London, London, UK
| | - Paul W Franks
- Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University, Skåne University Hospital Malmö, Malmö, Sweden
| | - Jimmy D Bell
- Oxford Centre for Clinical Magnetic Resonance Research, Division of Cardiovascular Medicine, Oxford NIHR Biomedical Research Centre, University of Oxford, Oxford, UK
| | | | - Hanieh Yaghootkar
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK; Genetics of Complex Traits, College of Medicine and Health, University of Exeter, Exeter, UK.
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Koivula RW, Forgie IM, Kurbasic A, Viñuela A, Heggie A, Giordano GN, Hansen TH, Hudson M, Koopman ADM, Rutters F, Siloaho M, Allin KH, Brage S, Brorsson CA, Dawed AY, De Masi F, Groves CJ, Kokkola T, Mahajan A, Perry MH, Rauh SP, Ridderstråle M, Teare HJA, Thomas EL, Tura A, Vestergaard H, White T, Adamski J, Bell JD, Beulens JW, Brunak S, Dermitzakis ET, Froguel P, Frost G, Gupta R, Hansen T, Hattersley A, Jablonka B, Kaye J, Laakso M, McDonald TJ, Pedersen O, Schwenk JM, Pavo I, Mari A, McCarthy MI, Ruetten H, Walker M, Pearson E, Franks PW. 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 2019; 62:1601-1615. [PMID: 31203377 PMCID: PMC6677872 DOI: 10.1007/s00125-019-4906-1] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/13/2018] [Accepted: 04/10/2019] [Indexed: 12/12/2022]
Abstract
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 enrolment. Fifty-eight per cent of participants in cohort 2 was male. Cohort 2 participants had the following characteristics at baseline: age 62 (8.1) years; BMI 30.5 (5.0) kg/m2; fasting glucose 7.2 (1.4) mmol/l; 2 h glucose 8.6 (2.8) mmol/l. At the final follow-up examination, the participants' clinical characteristics were as follows: fasting glucose 7.9 (2.0) mmol/l; 2 h mixed-meal tolerance test glucose 9.9 (3.4) mmol/l. CONCLUSIONS/INTERPRETATION The IMI DIRECT cohorts are intensely characterised, with a wide-variety of metabolically relevant measures assessed prospectively. We anticipate that the cohorts, made available through managed access, will provide a powerful resource for biomarker discovery, multivariate aetiological analyses and reclassification of patients for the prevention and treatment of type 2 diabetes.
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Affiliation(s)
- Robert W Koivula
- Department of Clinical Sciences, Lund University Diabetes Centre, Genetic and Molecular Epidemiology Unit, CRC, Skåne University Hospital Malmö, Building 91, Level 10, Jan Waldenströms gata 35, SE-205 02, Malmö, Sweden
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Ian M Forgie
- Population Health & Genomics, Medical Research Institute, University of Dundee, Dundee, DD1 9SY, UK
| | - Azra Kurbasic
- Department of Clinical Sciences, Lund University Diabetes Centre, Genetic and Molecular Epidemiology Unit, CRC, Skåne University Hospital Malmö, Building 91, Level 10, Jan Waldenströms gata 35, SE-205 02, Malmö, Sweden
| | - Ana Viñuela
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland
- Institute of Genetics and Genomics in Geneva (iGE3), University of Geneva, Geneva, Switzerland
- Swiss Institute of Bioinformatics, Geneva, Switzerland
| | - Alison Heggie
- Institute of Cellular Medicine (Diabetes), Newcastle University, Newcastle upon Tyne, UK
| | - Giuseppe N Giordano
- Department of Clinical Sciences, Lund University Diabetes Centre, Genetic and Molecular Epidemiology Unit, CRC, Skåne University Hospital Malmö, Building 91, Level 10, Jan Waldenströms gata 35, SE-205 02, Malmö, Sweden
| | - Tue H Hansen
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, Denmark
| | - Michelle Hudson
- NIHR Exeter Clinical Research Facility, University of Exeter Medical School, Exeter, UK
| | - Anitra D M Koopman
- Department of Epidemiology and Biostatistics, Amsterdam Public Health Research Institute, VU University Medical Center, Amsterdam, the Netherlands
| | - Femke Rutters
- Department of Epidemiology and Biostatistics, Amsterdam Public Health Research Institute, VU University Medical Center, Amsterdam, the Netherlands
| | - Maritta Siloaho
- Department of Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | - Kristine H Allin
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Epidemiology, Bispebjerg and Frederiksberg Hospital, the Capital Region, Copenhagen, Denmark
| | - Søren Brage
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, UK
- Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark
| | - Caroline A Brorsson
- Department of Bio and Health Informatics, Technical University of Denmark, Lyngby, Denmark
| | - Adem Y Dawed
- Population Health & Genomics, Medical Research Institute, University of Dundee, Dundee, DD1 9SY, UK
| | - Federico De Masi
- Department of Bio and Health Informatics, Technical University of Denmark, Lyngby, Denmark
| | - Christopher J Groves
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Tarja Kokkola
- Department of Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | - Anubha Mahajan
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Mandy H Perry
- NIHR Exeter Clinical Research Facility, University of Exeter Medical School, Exeter, UK
| | - Simone P Rauh
- Department of Epidemiology and Biostatistics, Amsterdam Public Health Research Institute, VU University Medical Center, Amsterdam, the Netherlands
| | - Martin Ridderstråle
- Department of Clinical Sciences, Clinical Obesity, Skåne University Hospital Malmö, Lund University, Malmö, Sweden
- Novo Nordisk A/S, Søborg, Denmark
| | - Harriet J A Teare
- HeLEX, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Headington, Oxford, UK
| | - E Louise Thomas
- Research Centre for Optimal Health, Department of Life Sciences, University of Westminster, London, UK
| | - Andrea Tura
- Institute of Neurosciences, National Research Council, Padova, Italy
| | - Henrik Vestergaard
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, Denmark
| | - Tom White
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Jerzy Adamski
- Institute of Epidemiology II, Helmholtz Zentrum Muenchen, German Research Center for Environmental Health (GmbH), Neuherberg, Germany
| | - Jimmy D Bell
- Research Centre for Optimal Health, Department of Life Sciences, University of Westminster, London, UK
| | - Joline W Beulens
- Department of Epidemiology and Biostatistics, Amsterdam Public Health Research Institute, VU University Medical Center, Amsterdam, the Netherlands
| | - Søren Brunak
- Department of Bio and Health Informatics, Technical University of Denmark, Lyngby, Denmark
- The Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
| | - Emmanouil T Dermitzakis
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland
- Institute of Genetics and Genomics in Geneva (iGE3), University of Geneva, Geneva, Switzerland
- Swiss Institute of Bioinformatics, Geneva, Switzerland
| | - Philippe Froguel
- Department of Genomics of Common Disease, School of Public Health, Imperial College London, London, UK
- CNRS, Pasteur Institute of Lille, University of Lille, Lille, France
| | - Gary Frost
- Nutrition and Dietetics Research Group, Department of Medicine, Division of Diabetes, Endocrinology and Metabolism, Imperial College London, Hammersmith Campus, London, UK
| | - Ramneek Gupta
- Department of Bio and Health Informatics, Technical University of Denmark, Lyngby, Denmark
| | - Torben Hansen
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, Denmark
- Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark
| | - Andrew Hattersley
- NIHR Exeter Clinical Research Facility, University of Exeter Medical School, Exeter, UK
- Institute of Biomedical and Clinical Science, University of Exeter Medical School, Exeter, UK
| | - Bernd Jablonka
- Sanofi-Aventis Deutschland GmbH, R&D, Frankfurt am Main, Germany
| | - Jane Kaye
- HeLEX, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Headington, Oxford, UK
| | - Markku Laakso
- Department of Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | - Timothy J McDonald
- NIHR Exeter Clinical Research Facility, University of Exeter Medical School, Exeter, UK
| | - Oluf Pedersen
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, Denmark
| | - Jochen M Schwenk
- Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Imre Pavo
- Eli Lilly Regional Operations GmbH, Vienna, Austria
| | - Andrea Mari
- Institute of Neurosciences, National Research Council, Padova, Italy
| | - Mark I McCarthy
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
- NIHR Oxford Biomedical Research Centre, Churchill Hospital, Oxford, UK
| | - Hartmut Ruetten
- Sanofi-Aventis Deutschland GmbH, R&D, Frankfurt am Main, Germany
| | - Mark Walker
- Institute of Cellular Medicine (Diabetes), Newcastle University, Newcastle upon Tyne, UK
| | - Ewan Pearson
- Population Health & Genomics, Medical Research Institute, University of Dundee, Dundee, DD1 9SY, UK.
| | - Paul W Franks
- Department of Clinical Sciences, Lund University Diabetes Centre, Genetic and Molecular Epidemiology Unit, CRC, Skåne University Hospital Malmö, Building 91, Level 10, Jan Waldenströms gata 35, SE-205 02, Malmö, Sweden.
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK.
- Department of Nutrition, Harvard School of Public Health, Boston, MA, USA.
- Department of Public Health & Clinical Medicine, Section for Medicine, Umeå University, Umeå, Sweden.
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Ji Y, Yiorkas AM, Frau F, Mook-Kanamori D, Staiger H, Thomas EL, Atabaki-Pasdar N, Campbell A, Tyrrell J, Jones SE, Beaumont RN, Wood AR, Tuke MA, Ruth KS, Mahajan A, Murray A, Freathy RM, Weedon MN, Hattersley AT, Hayward C, Machann J, Häring HU, Franks P, de Mutsert R, Pearson E, Stefan N, Frayling TM, Allebrandt KV, Bell JD, Blakemore AI, Yaghootkar H. 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 2019; 68:207-219. [PMID: 30352878 DOI: 10.2337/db18-0708] [Citation(s) in RCA: 58] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/28/2018] [Accepted: 10/12/2018] [Indexed: 11/13/2022]
Abstract
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.
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Affiliation(s)
- Yingjie Ji
- Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Royal Devon and Exeter Hospital, Exeter, U.K
| | - Andrianos M Yiorkas
- Section of Investigative Medicine, Imperial College London, London, U.K
- Department of Life Sciences, Brunel University London, Uxbridge, U.K
| | - Francesca Frau
- Translational Medicine and Early Development, TMED Translational Informatics, Sanofi, Frankfurt am Main, Germany
| | - Dennis Mook-Kanamori
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands
- Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, the Netherlands
| | - Harald Staiger
- Institute for Diabetes Research and Metabolic Diseases of the Helmholtz Center Munich at the University of Tübingen, Tübingen, Germany
- German Center for Diabetes Research (DZD), Tübingen, Germany
- Institute of Pharmaceutical Sciences, Department of Pharmacy and Biochemistry, Eberhard Karls University Tübingen, Tübingen, Germany
| | - E Louise Thomas
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, U.K
| | - Naeimeh Atabaki-Pasdar
- Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University, Skåne University Hospital Malmö, Malmö, Sweden
| | - Archie Campbell
- Generation Scotland, Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, U.K
- Usher Institute for Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, U.K
| | - Jessica Tyrrell
- Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Royal Devon and Exeter Hospital, Exeter, U.K
| | - Samuel E Jones
- Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Royal Devon and Exeter Hospital, Exeter, U.K
| | - Robin N Beaumont
- Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Royal Devon and Exeter Hospital, Exeter, U.K
| | - Andrew R Wood
- Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Royal Devon and Exeter Hospital, Exeter, U.K
| | - Marcus A Tuke
- Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Royal Devon and Exeter Hospital, Exeter, U.K
| | - Katherine S Ruth
- Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Royal Devon and Exeter Hospital, Exeter, U.K
| | - Anubha Mahajan
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, U.K
| | - Anna Murray
- Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Royal Devon and Exeter Hospital, Exeter, U.K
| | - Rachel M Freathy
- Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Royal Devon and Exeter Hospital, Exeter, U.K
| | - Michael N Weedon
- Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Royal Devon and Exeter Hospital, Exeter, U.K
| | - Andrew T Hattersley
- Institute of Biomedical and Clinical Science, University of Exeter Medical School, Exeter, U.K
| | - Caroline Hayward
- Medical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, U.K
| | - Jürgen Machann
- Institute for Diabetes Research and Metabolic Diseases of the Helmholtz Center Munich at the University of Tübingen, Tübingen, Germany
- German Center for Diabetes Research (DZD), Tübingen, Germany
| | - Hans-Ulrich Häring
- Institute for Diabetes Research and Metabolic Diseases of the Helmholtz Center Munich at the University of Tübingen, Tübingen, Germany
- German Center for Diabetes Research (DZD), Tübingen, Germany
- Department of Internal Medicine IV, Division of Endocrinology, Diabetology, Angiology, Nephrology and Clinical Chemistry, Eberhard Karls University Tübingen, Tübingen, Germany
| | - Paul Franks
- Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University, Skåne University Hospital Malmö, Malmö, Sweden
- Department of Public Health & Clinical Medicine, Umeå University, Umeå, Sweden
- Department of Nutrition, Harvard School of Public Health, Boston, MA
| | - Renée de Mutsert
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Ewan Pearson
- Division of Molecular and Clinical Medicine, University of Dundee, Ninewells Hospital, Dundee, U.K
| | - Norbert Stefan
- Institute for Diabetes Research and Metabolic Diseases of the Helmholtz Center Munich at the University of Tübingen, Tübingen, Germany
- German Center for Diabetes Research (DZD), Tübingen, Germany
- Department of Internal Medicine IV, Division of Endocrinology, Diabetology, Angiology, Nephrology and Clinical Chemistry, Eberhard Karls University Tübingen, Tübingen, Germany
| | - Timothy M Frayling
- Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Royal Devon and Exeter Hospital, Exeter, U.K
| | - Karla V Allebrandt
- Translational Medicine and Early Development, TMED Translational Informatics, Sanofi, Frankfurt am Main, Germany
| | - Jimmy D Bell
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, U.K
| | - Alexandra I Blakemore
- Section of Investigative Medicine, Imperial College London, London, U.K
- Department of Life Sciences, Brunel University London, Uxbridge, U.K
| | - Hanieh Yaghootkar
- Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Royal Devon and Exeter Hospital, Exeter, U.K.
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Mojtahed A, Kelly CJ, Herlihy AH, Kin S, Wilman HR, McKay A, Kelly M, Milanesi M, Neubauer S, Thomas EL, Bell JD, Banerjee R, Harisinghani M. Reference range of liver corrected T1 values in a population at low risk for fatty liver disease-a UK Biobank sub-study, with an appendix of interesting cases. Abdom Radiol (NY) 2019; 44:72-84. [PMID: 30032383 PMCID: PMC6348264 DOI: 10.1007/s00261-018-1701-2] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Purpose Corrected T1 (cT1) value is a novel MRI-based quantitative metric for assessing a composite of liver inflammation and fibrosis. It has been shown to distinguish between non-alcoholic fatty liver disease (NAFL) and non-alcoholic steatohepatitis. However, these studies were conducted in patients at high risk for liver disease. This study establishes the normal reference range of cT1 values for a large UK population, and assesses interactions of age and gender. Methods MR data were acquired on a 1.5 T system as part of the UK Biobank Imaging Enhancement study. Measures for Proton Density Fat Fraction and cT1 were calculated from the MRI data using a multiparametric MRI software application. Data that did not meet quality criteria were excluded from further analysis. Inter and intra-reader variability was estimated in a set of data. A cohort at low risk for NAFL was identified by excluding individuals with BMI ≥ 25 kg/m2 and PDFF ≥ 5%. Of the 2816 participants with data of suitable quality, 1037 (37%) were classified as at low risk. Results The cT1 values in the low-risk population ranged from 573 to 852 ms with a median of 666 ms and interquartile range from 643 to 694 ms. Iron correction of T1 was necessary in 36.5% of this reference population. Age and gender had minimal effect on cT1 values. Conclusion The majority of cT1 values are tightly clustered in a population at low risk for NAFL, suggesting it has the potential to serve as a new quantitative imaging biomarker for studies of liver health and disease.
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Affiliation(s)
- A Mojtahed
- Division of Abdominal Imaging, Massachusetts General Hospital, Boston, MA, USA.
| | | | | | - S Kin
- Perspectum Diagnostics, Oxford, UK
| | - H R Wilman
- Perspectum Diagnostics, Oxford, UK
- Department of Life Sciences, University of Westminster, London, UK
| | - A McKay
- Perspectum Diagnostics, Oxford, UK
| | - M Kelly
- Perspectum Diagnostics, Oxford, UK
| | | | - S Neubauer
- Perspectum Diagnostics, Oxford, UK
- Oxford Centre for Clinical Magnetic Resonance Research, University of Oxford, Oxford, UK
| | - E L Thomas
- Department of Life Sciences, University of Westminster, London, UK
| | - J D Bell
- Department of Life Sciences, University of Westminster, London, UK
| | | | - M Harisinghani
- Division of Abdominal Imaging, Massachusetts General Hospital, Boston, MA, USA
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40
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Kosgodage US, Uysal-Onganer P, MacLatchy A, Mould R, Nunn AV, Guy GW, Kraev I, Chatterton NP, Thomas EL, Inal JM, Bell JD, Lange S. Cannabidiol Affects Extracellular Vesicle Release, miR21 and miR126, and Reduces Prohibitin Protein in Glioblastoma Multiforme Cells. Transl Oncol 2018; 12:513-522. [PMID: 30597288 PMCID: PMC6314156 DOI: 10.1016/j.tranon.2018.12.004] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2018] [Revised: 12/10/2018] [Accepted: 12/10/2018] [Indexed: 12/16/2022] Open
Abstract
Glioblastoma multiforme (GBM) is the most common and aggressive form of primary malignant brain tumor in adults, with poor prognosis. Extracellular vesicles (EVs) are key-mediators for cellular communication through transfer of proteins and genetic material. Cancers, such as GBM, use EV release for drug-efflux, pro-oncogenic signaling, invasion and immunosuppression; thus the modulation of EV release and cargo is of considerable clinical relevance. As EV-inhibitors have been shown to increase sensitivity of cancer cells to chemotherapy, and we recently showed that cannabidiol (CBD) is such an EV-modulator, we investigated whether CBD affects EV profile in GBM cells in the presence and absence of temozolomide (TMZ). Compared to controls, CBD-treated cells released EVs containing lower levels of pro-oncogenic miR21 and increased levels of anti-oncogenic miR126; these effects were greater than with TMZ alone. In addition, prohibitin (PHB), a multifunctional protein with mitochondrial protective properties and chemoresistant functions, was reduced in GBM cells following 1 h CBD treatment. This data suggests that CBD may, via modulation of EVs and PHB, act as an adjunct to enhance treatment efficacy in GBM, supporting evidence for efficacy of cannabinoids in GBM.
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Affiliation(s)
- Uchini S Kosgodage
- Cellular and Molecular Immunology Research Centre, School of Human Sciences, London Metropolitan University, London, UK.
| | - Pinar Uysal-Onganer
- Cancer Research Group, School of Life Sciences, University of Westminster, London, UK.
| | - Amy MacLatchy
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK.
| | - Rhys Mould
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK.
| | - Alistair V Nunn
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK.
| | - Geoffrey W Guy
- GW Research, Sovereign House, Vision Park, Cambridge, CB24 9BZ, UK.
| | - Igor Kraev
- The Open University, Walton Hall, Milton Keynes, UK.
| | | | - E Louise Thomas
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK.
| | - Jameel M Inal
- Extracellular Vesicle Research Unit and Biosciences Research Group, School of Life and Medical Sciences, University of Hertfordshire, College Lane, Hatfield, UK.
| | - Jimmy D Bell
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK.
| | - Sigrun Lange
- Tissue Architecture and Regeneration Research Group, School of Life Sciences, University of Westminster, London, UK.
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41
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Linge J, Borga M, West J, Tuthill T, Miller MR, Dumitriu A, Thomas EL, Romu T, Tunón P, Bell JD, Dahlqvist Leinhard O. Body Composition Profiling in the UK Biobank Imaging Study. Obesity (Silver Spring) 2018; 26:1785-1795. [PMID: 29785727 PMCID: PMC6220857 DOI: 10.1002/oby.22210] [Citation(s) in RCA: 109] [Impact Index Per Article: 18.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/12/2018] [Revised: 04/17/2018] [Accepted: 04/20/2018] [Indexed: 12/20/2022]
Abstract
OBJECTIVE This study aimed to investigate the value of imaging-based multivariable body composition profiling by describing its association with coronary heart disease (CHD), type 2 diabetes (T2D), and metabolic health on individual and population levels. METHODS The first 6,021 participants scanned by UK Biobank were included. Body composition profiles (BCPs) were calculated, including abdominal subcutaneous adipose tissue, visceral adipose tissue (VAT), thigh muscle volume, liver fat, and muscle fat infiltration (MFI), determined using magnetic resonance imaging. Associations between BCP and metabolic status were investigated using matching procedures and multivariable statistical modeling. RESULTS Matched control analysis showed that higher VAT and MFI were associated with CHD and T2D (P < 0.001). Higher liver fat was associated with T2D (P < 0.001) and lower liver fat with CHD (P < 0.05), matching on VAT. Multivariable modeling showed that lower VAT and MFI were associated with metabolic health (P < 0.001), and liver fat was nonsignificant. Associations remained significant adjusting for sex, age, BMI, alcohol, smoking, and physical activity. CONCLUSIONS Body composition profiling enabled an intuitive visualization of body composition and showed the complexity of associations between fat distribution and metabolic status, stressing the importance of a multivariable approach. Different diseases were linked to different BCPs, which could not be described by a single fat compartment alone.
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Affiliation(s)
| | - Magnus Borga
- AMRA Medical ABLinköpingSweden
- Centre for Medical Image Science and VisualizationLinköping UniversityLinköpingSweden
- Department of Biomedical EngineeringLinköping UniversityLinköpingSweden
| | - Janne West
- AMRA Medical ABLinköpingSweden
- Centre for Medical Image Science and VisualizationLinköping UniversityLinköpingSweden
- Department of Medical and Health SciencesLinköping UniversityLinköpingSweden
| | - Theresa Tuthill
- Imaging, Precision Medicine, Pfizer Inc.Cambridge MassachusettsUSA
| | - Melissa R. Miller
- WRD Genome Sciences & Technologies, Pfizer Inc.Cambridge, MassachusettsUSA
| | - Alexandra Dumitriu
- WRD Genome Sciences & Technologies, Pfizer Inc.Cambridge, MassachusettsUSA
| | - E. Louise Thomas
- Research Centre for Optimal Health, School of Life SciencesUniversity of WestminsterLondonUK
| | - Thobias Romu
- AMRA Medical ABLinköpingSweden
- Centre for Medical Image Science and VisualizationLinköping UniversityLinköpingSweden
- Department of Biomedical EngineeringLinköping UniversityLinköpingSweden
| | | | - Jimmy D. Bell
- Research Centre for Optimal Health, School of Life SciencesUniversity of WestminsterLondonUK
| | - Olof Dahlqvist Leinhard
- AMRA Medical ABLinköpingSweden
- Centre for Medical Image Science and VisualizationLinköping UniversityLinköpingSweden
- Department of Medical and Health SciencesLinköping UniversityLinköpingSweden
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Buckley AJ, Thomas EL, Lessan N, Trovato FM, Trovato GM, Taylor-Robinson SD. Non-alcoholic fatty liver disease: Relationship with cardiovascular risk markers and clinical endpoints. Diabetes Res Clin Pract 2018; 144:144-152. [PMID: 30170074 DOI: 10.1016/j.diabres.2018.08.011] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/18/2018] [Revised: 08/08/2018] [Accepted: 08/14/2018] [Indexed: 02/08/2023]
Abstract
Non-alcoholic fatty liver disease (NAFLD) is a common diagnosis and is increasing in prevalence worldwide. NAFLD is usually asymptomatic at presentation; progression of the disease is unpredictable, leading to the development of a variety of techniques for screening, diagnosis and risk stratification. Clinical methods in current use include serum biomarker panels, hepatic ultrasound, magnetic resonance imaging, and liver biopsy. NAFLD is strongly associated with the metabolic syndrome, and the most common cause of death for people with the condition is cardiovascular disease. Whether NAFLD is an independent cardiovascular risk factor needs exploration. NAFLD has been associated with surrogate markers of cardiovascular disease such as carotid intima-media thickness, the presence of carotid plaque, brachial artery vasodilatory responsiveness and CT coronary artery calcification score. There is no effective medical treatment for NAFLD and evidence is lacking regarding the efficacy of interventions in mitigating cardiovascular risk. Health care professionals managing patients with NAFLD should tackle the issue with early identification of risk factors and aggressive modification. Current management strategies therefore comprise lifestyle change, with close attention to known cardiovascular risk factors.
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Affiliation(s)
- Adam J Buckley
- Imperial College London, Division of Diabetes, Endocrinology and Metabolism, Department of Medicine London, United Kingdom; Imperial College London Diabetes Centre, Research Department, Abu Dhabi, United Arab Emirates.
| | - E Louise Thomas
- University of Westminster, Department of Life Sciences, London, United Kingdom.
| | - Nader Lessan
- Imperial College London, Division of Diabetes, Endocrinology and Metabolism, Department of Medicine London, United Kingdom; Imperial College London Diabetes Centre, Research Department, Abu Dhabi, United Arab Emirates.
| | - Francesca M Trovato
- University of Catania, Department of Clinical and Experimental Medicine, Catania, Italy
| | - Guglielmo M Trovato
- University of Catania, Department of Clinical and Experimental Medicine, Catania, Italy
| | - Simon D Taylor-Robinson
- Imperial College London, Division of Integrative Systems Medicine and Digestive Health, Department of Surgery and Cancer, London, United Kingdom.
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43
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Kosgodage US, Mould R, Henley AB, Nunn AV, Guy GW, Thomas EL, Inal JM, Bell JD, Lange S. Cannabidiol (CBD) Is a Novel Inhibitor for Exosome and Microvesicle (EMV) Release in Cancer. Front Pharmacol 2018; 9:889. [PMID: 30150937 PMCID: PMC6099119 DOI: 10.3389/fphar.2018.00889] [Citation(s) in RCA: 89] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2017] [Accepted: 07/23/2018] [Indexed: 01/05/2023] Open
Abstract
Exosomes and microvesicles (EMV) are lipid bilayer-enclosed structures, released by cells and involved in intercellular communication through transfer of proteins and genetic material. EMV release is also associated with various pathologies, including cancer, where increased EMV release is amongst other associated with chemo-resistance and active transfer of pro-oncogenic factors. Recent studies show that EMV-inhibiting agents can sensitize cancer cells to chemotherapeutic agents and reduce cancer growth in vivo. Cannabidiol (CBD), a phytocannabinoid derived from Cannabis sativa, has anti-inflammatory and anti-oxidant properties, and displays anti-proliferative activity. Here we report a novel role for CBD as a potent inhibitor of EMV release from three cancer cell lines: prostate cancer (PC3), hepatocellular carcinoma (HEPG2) and breast adenocarcinoma (MDA-MB-231). CBD significantly reduced exosome release in all three cancer cell lines, and also significantly, albeit more variably, inhibited microvesicle release. The EMV modulating effects of CBD were found to be dose dependent (1 and 5 μM) and cancer cell type specific. Moreover, we provide evidence that this may be associated with changes in mitochondrial function, including modulation of STAT3 and prohibitin expression, and that CBD can be used to sensitize cancer cells to chemotherapy. We suggest that the known anti-cancer effects of CBD may partly be due to the regulatory effects on EMV biogenesis, and thus CBD poses as a novel and safe modulator of EMV-mediated pathological events.
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Affiliation(s)
- Uchini S Kosgodage
- Cellular and Molecular Immunology Research Centre, School of Human Sciences, London Metropolitan University, London, United Kingdom
| | - Rhys Mould
- Research Centre for Optimal Health, Department of Life Sciences, University of Westminster, London, United Kingdom
| | - Aine B Henley
- Research Centre for Optimal Health, Department of Life Sciences, University of Westminster, London, United Kingdom
| | - Alistair V Nunn
- Research Centre for Optimal Health, Department of Life Sciences, University of Westminster, London, United Kingdom
| | - Geoffrey W Guy
- GW Research, Sovereign House Vision Park, Cambridge, United Kingdom
| | - E L Thomas
- Research Centre for Optimal Health, Department of Life Sciences, University of Westminster, London, United Kingdom
| | - Jameel M Inal
- School of Life and Medical Sciences, University of Hertfordshire, Hatfield, United Kingdom
| | - Jimmy D Bell
- Research Centre for Optimal Health, Department of Life Sciences, University of Westminster, London, United Kingdom
| | - Sigrun Lange
- Tissue Architecture and Regeneration Research Group, Department of Biomedical Sciences, University of Westminster, London, United Kingdom.,Department of Pharmacology, University College London School of Pharmacy, London, United Kingdom
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44
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Fiamoncini J, Rundle M, Gibbons H, Thomas EL, Geillinger-Kästle K, Bunzel D, Trezzi JP, Kiselova-Kaneva Y, Wopereis S, Wahrheit J, Kulling SE, 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. FASEB J 2018; 32:5447-5458. [PMID: 29718708 DOI: 10.1096/fj.201800330r] [Citation(s) in RCA: 49] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
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.
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Affiliation(s)
- Jarlei Fiamoncini
- Department of Food and Nutrition, Technische Universität München, Freising-Weihenstephan, Germany
| | - Milena Rundle
- Division of Diabetes, Endocrinology, and Metabolism, Department of Medicine, Imperial College London, London, United Kingdom
| | - Helena Gibbons
- University College Dublin (UCD) School of Agriculture and Food Science, Institute of Food and Health, Dublin, Ireland
| | - E Louise Thomas
- Research Centre for Optimal Health, Department of Life Sciences, University of Westminster, London, United Kingdom
| | | | - Diana Bunzel
- Department of Safety and Quality of Fruit and Vegetables, Max Rubner Institut, Karlsruhe, Germany
| | - Jean-Pierre Trezzi
- Integrated Biobank of Luxembourg, Dudelange, Luxembourg.,Centre for Systems Biomedicine, Esch-sur-Alzette, Luxembourg
| | - Yoana Kiselova-Kaneva
- Department of Biochemistry, Molecular Medicine, and Nutrigenomics, Medical University-Varna, Varna, Bulgaria
| | - Suzan Wopereis
- Department of Microbiology and Systems Biology, Netherlands Organization for Applied Scientific Research (TNO), Zeist, The Netherlands
| | | | - Sabine E Kulling
- Department of Safety and Quality of Fruit and Vegetables, Max Rubner Institut, Karlsruhe, Germany
| | - Karsten Hiller
- Braunschweig Integrated Centre of Systems Biology, University of Braunschweig, Braunschweig, Germany.,Department of Computational Biology of Infection Research, Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - Denise Sonntag
- Department of Microbiology and Systems Biology, Netherlands Organization for Applied Scientific Research (TNO), Zeist, The Netherlands
| | - Diana Ivanova
- Department of Biochemistry, Molecular Medicine, and Nutrigenomics, Medical University-Varna, Varna, Bulgaria
| | - Ben van Ommen
- Department of Microbiology and Systems Biology, Netherlands Organization for Applied Scientific Research (TNO), Zeist, The Netherlands
| | - Gary Frost
- Division of Diabetes, Endocrinology, and Metabolism, Department of Medicine, Imperial College London, London, United Kingdom
| | - Lorraine Brennan
- University College Dublin (UCD) School of Agriculture and Food Science, Institute of Food and Health, Dublin, Ireland
| | - Jimmy Bell
- Research Centre for Optimal Health, Department of Life Sciences, University of Westminster, London, United Kingdom
| | - Hannelore Daniel
- Department of Food and Nutrition, Technische Universität München, Freising-Weihenstephan, Germany
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45
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Cobbold JFL, Atkinson S, Marchesi JR, Smith A, Wai SN, Stove J, Shojaee-Moradie F, Jackson N, Umpleby AM, Fitzpatrick J, Thomas EL, Bell JD, Holmes E, Taylor-Robinson SD, Goldin RD, Yee MS, Anstee QM, Thursz MR. Rifaximin in non-alcoholic steatohepatitis: An open-label pilot study. Hepatol Res 2018; 48:69-77. [PMID: 28425154 DOI: 10.1111/hepr.12904] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/07/2017] [Revised: 04/04/2017] [Accepted: 04/08/2017] [Indexed: 12/31/2022]
Abstract
AIM Gut microbial dysbiosis is implicated in the pathogenesis of non-alcoholic steatohepatitis (NASH). We investigated downstream effects of gut microbiota modulation on markers of hepatic inflammation, steatosis, and hepatic and peripheral insulin sensitivity in patients with NASH using rifaximin therapy. METHODS Patients with biopsy-proven NASH and elevated aminotransferase values were included in this open-label pilot study, all receiving 6 weeks rifaximin 400 mg twice daily, followed by a 6-week observation period. The primary endpoint was change in alanine aminotransferase (ALT) after 6 weeks of rifaximin. Secondary endpoints were change in hepatic lipid content and insulin sensitivity measured with a hyperinsulinemic-euglycemic clamp. RESULTS Fifteen patients (13 men and 2 women) with a median (range) age of 46 (32-63) years were included. Seven had diabetes on oral hypoglycemic medications and 8 had no diabetes. After 6 weeks of therapy, no differences were seen in ALT (55 [33-191] vs. 63 [41-218] IU/L, P = 0.41), peripheral glucose uptake (28.9 [19.4-48.3] to 25.5 [17.7-47.9] μmol/kg/min, P = 0.30), hepatic insulin sensitivity (35.2 [15.3-51.7]% vs. 30.0 [10.8-50.5]%, P = 0.47), or hepatic lipid content (21.6 [2.2-46.2]% vs. 24.8 [1.7-59.3]%, P = 0.59) before and after rifaximin treatment. After 12 weeks from baseline, serum ALT increased to 83 (30-217) IU/L, P = 0.02. There was a significant increase in the homeostasis model assessment-estimated insulin resistance index (P = 0.05). The urinary metabolic profile indicated a significant reduction in urinary hippurate with treatment, which reverted to baseline after cessation of rifaximin, although there was no consistent difference in relative abundance of fecal microbiota with treatment. CONCLUSION These data do not indicate a beneficial effect of rifaximin in patients with NASH.
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Affiliation(s)
- Jeremy F L Cobbold
- Department of Medicine, Imperial College London, London, UK.,Translational Gastroenterology Unit, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | | | - Julian R Marchesi
- Department of Surgery and Cancer, Imperial College London, London, UK.,School of Biosciences, Cardiff University, Cardiff, UK
| | - Ann Smith
- Department of Surgery and Cancer, Imperial College London, London, UK
| | - Sann N Wai
- Department of Medicine, Imperial College London, London, UK
| | - Julie Stove
- Department of Medicine, Imperial College London, London, UK
| | - Fariba Shojaee-Moradie
- Diabetes and Metabolic Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK
| | - Nicola Jackson
- Diabetes and Metabolic Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK
| | - A Margot Umpleby
- Diabetes and Metabolic Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK
| | | | - E Louise Thomas
- Institute of Clinical Science, Imperial College London, London, UK
| | - Jimmy D Bell
- Institute of Clinical Science, Imperial College London, London, UK
| | - Elaine Holmes
- Department of Surgery and Cancer, Imperial College London, London, UK
| | | | | | - Michael S Yee
- Department of Endocrinology and Diabetic Medicine, Imperial College Healthcare NHS Trust, London, UK
| | - Quentin M Anstee
- Institute of Cellular Medicine, Newcastle University, Newcastle-Upon-Tyne, UK
| | - Mark R Thursz
- Department of Medicine, Imperial College London, London, UK
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46
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Lee S, Norheim F, Langleite TM, Noreng HJ, Storås TH, Afman LA, Frost G, Bell JD, Thomas EL, Kolnes KJ, Tangen DS, Stadheim HK, Gilfillan GD, Gulseth HL, Birkeland KI, Jensen J, Drevon CA, Holen T. Effect of energy restriction and physical exercise intervention on phenotypic flexibility as examined by transcriptomics analyses of mRNA from adipose tissue and whole body magnetic resonance imaging. Physiol Rep 2017; 4:4/21/e13019. [PMID: 27821717 PMCID: PMC5112497 DOI: 10.14814/phy2.13019] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2016] [Revised: 09/09/2016] [Accepted: 10/03/2016] [Indexed: 12/11/2022] Open
Abstract
Overweight and obesity lead to changes in adipose tissue such as inflammation and reduced insulin sensitivity. The aim of this study was to assess how altered energy balance by reduced food intake or enhanced physical activity affect these processes. We studied sedentary subjects with overweight/obesity in two intervention studies, each lasting 12 weeks affecting energy balance either by energy restriction (~20% reduced intake of energy from food) in one group, or by enhanced energy expenditure due to physical exercise (combined endurance‐ and strength‐training) in the other group. We monitored mRNA expression by microarray and mRNA sequencing from adipose tissue biopsies. We also measured several plasma parameters as well as fat distribution with magnetic resonance imaging and spectroscopy. Comparison of microarray and mRNA sequencing showed strong correlations, which were also confirmed using RT‐PCR. In the energy restricted subjects (body weight reduced by 5% during a 12 weeks intervention), there were clear signs of enhanced lipolysis as monitored by mRNA in adipose tissue as well as plasma concentration of free‐fatty acids. This increase was strongly related to increased expression of markers for M1‐like macrophages in adipose tissue. In the exercising subjects (glucose infusion rate increased by 29% during a 12‐week intervention), there was a marked reduction in the expression of markers of M2‐like macrophages and T cells, suggesting that physical exercise was especially important for reducing inflammation in adipose tissue with insignificant reduction in total body weight. Our data indicate that energy restriction and physical exercise affect energy‐related pathways as well as inflammatory processes in different ways, probably related to macrophages in adipose tissue.
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Affiliation(s)
- Sindre Lee
- Department of Nutrition, Institute of Basic Medical Sciences Faculty of Medicine University of Oslo, Oslo, Norway
| | - Frode Norheim
- Department of Nutrition, Institute of Basic Medical Sciences Faculty of Medicine University of Oslo, Oslo, Norway.,Division of Cardiology, Department of Medicine, University of California at Los Angeles, Los Angeles, California
| | - Torgrim M Langleite
- Department of Nutrition, Institute of Basic Medical Sciences Faculty of Medicine University of Oslo, Oslo, Norway
| | - Hans J Noreng
- The Intervention Centre, Oslo University Hospital Oslo, Oslo, Norway
| | - Trygve H Storås
- The Intervention Centre, Oslo University Hospital Oslo, Oslo, Norway
| | - Lydia A Afman
- Nutrition, Metabolism and Genomics Group, Division of Human Nutrition, Wageningen University, Wageningen, The Netherlands
| | - Gary Frost
- Division of Diabetes, Endocrinology and Metabolism, Dietetics, Imperial College Hammersmith Campus, London, UK
| | - Jimmy D Bell
- Research Centre for Optimal Health, Department of Life Sciences, University of Westminster, London, UK
| | - E Louise Thomas
- Research Centre for Optimal Health, Department of Life Sciences, University of Westminster, London, UK
| | - Kristoffer J Kolnes
- Department of Physical Performance, Norwegian School of Sport Sciences, Oslo, Norway
| | - Daniel S Tangen
- Department of Physical Performance, Norwegian School of Sport Sciences, Oslo, Norway
| | - Hans K Stadheim
- Department of Physical Performance, Norwegian School of Sport Sciences, Oslo, Norway
| | | | - Hanne L Gulseth
- Department of Endocrinology, Morbid Obesity and Preventive Medicine, Oslo University Hospital, Oslo, Norway.,Institute of Clinical Medicine, Faculty of medicine, University of Oslo, Oslo, Norway
| | - Kåre I Birkeland
- Department of Endocrinology, Morbid Obesity and Preventive Medicine, Oslo University Hospital, Oslo, Norway.,Institute of Clinical Medicine, Faculty of medicine, University of Oslo, Oslo, Norway
| | - Jørgen Jensen
- Department of Physical Performance, Norwegian School of Sport Sciences, Oslo, Norway
| | - Christian A Drevon
- Department of Nutrition, Institute of Basic Medical Sciences Faculty of Medicine University of Oslo, Oslo, Norway
| | - Torgeir Holen
- Department of Nutrition, Institute of Basic Medical Sciences Faculty of Medicine University of Oslo, Oslo, Norway
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47
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Brody LP, Sahuri-Arisoylu M, Parkinson JR, Parkes HG, So PW, Hajji N, Thomas EL, Frost GS, Miller AD, Bell JD. Cationic lipid-based nanoparticles mediate functional delivery of acetate to tumor cells in vivo leading to significant anticancer effects. Int J Nanomedicine 2017; 12:6677-6685. [PMID: 28932113 PMCID: PMC5598551 DOI: 10.2147/ijn.s135968] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
Metabolic reengineering using nanoparticle delivery represents an innovative therapeutic approach to normalizing the deregulation of cellular metabolism underlying many diseases, including cancer. Here, we demonstrated a unique and novel application to the treatment of malignancy using a short-chain fatty acid (SCFA)-encapsulated lipid-based delivery system – liposome-encapsulated acetate nanoparticles for cancer applications (LITA-CAN). We assessed chronic in vivo administration of our nanoparticle in three separate murine models of colorectal cancer. We demonstrated a substantial reduction in tumor growth in the xenograft model of colorectal cancer cell lines HT-29, HCT-116 p53+/+ and HCT-116 p53−/−. Nanoparticle-induced reductions in histone deacetylase gene expression indicated a potential mechanism for these anti-proliferative effects. Together, these results indicated that LITA-CAN could be used as an effective direct or adjunct therapy to treat malignant transformation in vivo.
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Affiliation(s)
- Leigh P Brody
- Department of Life Sciences, Faculty of Science and Technology, University of Westminster
| | - Meliz Sahuri-Arisoylu
- Department of Life Sciences, Faculty of Science and Technology, University of Westminster
| | - James R Parkinson
- Department of Life Sciences, Faculty of Science and Technology, University of Westminster
| | - Harry G Parkes
- CR-UK Clinical MR Research Group, Institute of Cancer Research, Sutton, Surrey
| | - Po Wah So
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London
| | - Nabil Hajji
- Department of Medicine, Division of Experimental Medicine, Centre for Pharmacology & Therapeutics, Toxicology Unit, Imperial College London
| | - E Louise Thomas
- Department of Life Sciences, Faculty of Science and Technology, University of Westminster
| | - Gary S Frost
- Faculty of Medicine, Nutrition and Dietetic Research Group, Division of Diabetes, Endocrinology and Metabolism, Department of Investigative Medicine, Imperial College London, Hammersmith Hospital
| | - Andrew D Miller
- Institute of Pharmaceutical Science, King's College London, London, UK
| | - Jimmy D Bell
- Department of Life Sciences, Faculty of Science and Technology, University of Westminster
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Wilman HR, Kelly M, Garratt S, Matthews PM, Milanesi M, Herlihy A, Gyngell M, Neubauer S, Bell JD, Banerjee R, Thomas EL. Characterisation of liver fat in the UK Biobank cohort. PLoS One 2017; 12:e0172921. [PMID: 28241076 PMCID: PMC5328634 DOI: 10.1371/journal.pone.0172921] [Citation(s) in RCA: 64] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2016] [Accepted: 02/10/2017] [Indexed: 12/14/2022] Open
Abstract
Non-alcoholic fatty liver disease and the risk of progression to steatohepatitis, cirrhosis and hepatocellular carcinoma have been identified as major public health concerns. We have demonstrated the feasibility and potential value of measuring liver fat content by magnetic resonance imaging (MRI) in a large population in this study of 4,949 participants (aged 45–73 years) in the UK Biobank imaging enhancement. Despite requirements for only a single (≤3min) scan of each subject, liver fat was able to be measured as the MRI proton density fat fraction (PDFF) with an overall success rate of 96.4%. The overall hepatic fat distribution was centred between 1–2%, and was highly skewed towards higher fat content. The mean PDFF was 3.91%, and median 2.11%. Analysis of PDFF in conjunction with other data fields available from the UK Biobank Resource showed associations of increased liver fat with greater age, BMI, weight gain, high blood pressure and Type 2 diabetes. Subjects with BMI less than 25 kg/m2 had a low risk (5%) of high liver fat (PDFF > 5.5%), whereas in the higher BMI population (>30 kg/m2) the prevalence of high liver fat was approximately 1 in 3. These data suggest that population screening to identify people with high PDFF is possible and could be cost effective. MRI based PDFF is an effective method for this. Finally, although cross sectional, this study suggests the utility of the PDFF measurement within UK Biobank, particularly for applications to elucidating risk factors through associations with prospectively acquired data on clinical outcomes of liver diseases, including non-alcoholic fatty liver disease.
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Affiliation(s)
- Henry R. Wilman
- Perspectum Diagnostics, Oxford, United Kingdom
- Department of Life Sciences, University of Westminster, London, United Kingdom
| | - Matt Kelly
- Perspectum Diagnostics, Oxford, United Kingdom
| | | | - Paul M. Matthews
- Division of Brain Sciences and Centre for Neurotechnology, Imperial College London, United Kingdom
| | | | - Amy Herlihy
- Perspectum Diagnostics, Oxford, United Kingdom
| | | | - Stefan Neubauer
- Perspectum Diagnostics, Oxford, United Kingdom
- OCMR, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Jimmy D. Bell
- Department of Life Sciences, University of Westminster, London, United Kingdom
| | | | - E. Louise Thomas
- Department of Life Sciences, University of Westminster, London, United Kingdom
- * E-mail:
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Shojaee-Moradie F, Cuthbertson DJ, Barrett M, Jackson NC, Herring R, Thomas EL, Bell J, Kemp GJ, Wright J, Umpleby AM. Exercise Training Reduces Liver Fat and Increases Rates of VLDL Clearance But Not VLDL Production in NAFLD. J Clin Endocrinol Metab 2016; 101:4219-4228. [PMID: 27583475 DOI: 10.1210/jc.2016-2353] [Citation(s) in RCA: 65] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/10/2023]
Abstract
CONTEXT Randomized controlled trials in nonalcoholic fatty liver disease (NAFLD) have shown that regular exercise, even without calorie restriction, reduces liver steatosis. A previous study has shown that 16 weeks of supervised exercise training in NAFLD did not affect total very low-density lipoprotein (VLDL) kinetics. OBJECTIVE The objective of the study was to determine the effect of exercise training on intrahepatocellular fat (IHCL) and the kinetics of large triglyceride (TG)-rich VLDL1 and smaller denser VLDL2, which has a lower TG content. DESIGN This was a 16-week randomized controlled trial. PATIENTS A total of 27 sedentary patients with NAFLD participated in the trial. INTERVENTION The intervention was composed of supervised exercise with moderate-intensity aerobic exercise or conventional lifestyle advice (control). MAIN OUTCOME VLDL1 and VLDL2-TG and apolipoprotein B (apoB) kinetics were investigated using stable isotopes before and after the intervention. RESULTS In the exercise group, maximal oxygen uptake increased by 31% ± 6% (mean ± SEM) and IHCL decreased from 19.6% (14.8%, 30.0%) to 8.9% (5.4%, 17.3%) (median [interquartile range]) with no significant change in maximal oxygen uptake or IHCL in the control group (change between groups, P < .001 and P = .02, respectively). Exercise training increased VLDL1-TG and apoB fractional catabolic rates, a measure of clearance, (change between groups, P = .02 and P = .01, respectively), and VLDL1-apoB production rate (change between groups, P = .006), with no change in VLDL1-TG production rate. Plasma TG did not change in either group. CONCLUSION An increased clearance of VLDL1 may contribute to the significant decrease in liver fat after 16 weeks of exercise in NAFLD. A longer duration or higher-intensity exercise interventions may be needed to lower the plasma TG and VLDL production rate.
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Affiliation(s)
- F Shojaee-Moradie
- Department of Diabetes and Metabolic Medicine (F.S.-M., M.B., N.C.J., A.M.U.), Faculty of Health and Medical Sciences, University of Surrey, Guildford GU2 7WG, United Kingdom; Metabolism and Nutrition Research Group (D.J.C., G.J.K.), Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool L69 7ZX, United Kingdom; Section of Investigative Medicine, Endocrinology, and Metabolism (E.L.T., J.B.), University of Westminster, London W1B2UW United Kingdom; and Centre for Diabetes, Endocrinology, and Research (R.H., J.W.), Royal Surrey County Hospital, Guildford GU2 7XX, United Kingdom
| | - D J Cuthbertson
- Department of Diabetes and Metabolic Medicine (F.S.-M., M.B., N.C.J., A.M.U.), Faculty of Health and Medical Sciences, University of Surrey, Guildford GU2 7WG, United Kingdom; Metabolism and Nutrition Research Group (D.J.C., G.J.K.), Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool L69 7ZX, United Kingdom; Section of Investigative Medicine, Endocrinology, and Metabolism (E.L.T., J.B.), University of Westminster, London W1B2UW United Kingdom; and Centre for Diabetes, Endocrinology, and Research (R.H., J.W.), Royal Surrey County Hospital, Guildford GU2 7XX, United Kingdom
| | - M Barrett
- Department of Diabetes and Metabolic Medicine (F.S.-M., M.B., N.C.J., A.M.U.), Faculty of Health and Medical Sciences, University of Surrey, Guildford GU2 7WG, United Kingdom; Metabolism and Nutrition Research Group (D.J.C., G.J.K.), Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool L69 7ZX, United Kingdom; Section of Investigative Medicine, Endocrinology, and Metabolism (E.L.T., J.B.), University of Westminster, London W1B2UW United Kingdom; and Centre for Diabetes, Endocrinology, and Research (R.H., J.W.), Royal Surrey County Hospital, Guildford GU2 7XX, United Kingdom
| | - N C Jackson
- Department of Diabetes and Metabolic Medicine (F.S.-M., M.B., N.C.J., A.M.U.), Faculty of Health and Medical Sciences, University of Surrey, Guildford GU2 7WG, United Kingdom; Metabolism and Nutrition Research Group (D.J.C., G.J.K.), Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool L69 7ZX, United Kingdom; Section of Investigative Medicine, Endocrinology, and Metabolism (E.L.T., J.B.), University of Westminster, London W1B2UW United Kingdom; and Centre for Diabetes, Endocrinology, and Research (R.H., J.W.), Royal Surrey County Hospital, Guildford GU2 7XX, United Kingdom
| | - R Herring
- Department of Diabetes and Metabolic Medicine (F.S.-M., M.B., N.C.J., A.M.U.), Faculty of Health and Medical Sciences, University of Surrey, Guildford GU2 7WG, United Kingdom; Metabolism and Nutrition Research Group (D.J.C., G.J.K.), Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool L69 7ZX, United Kingdom; Section of Investigative Medicine, Endocrinology, and Metabolism (E.L.T., J.B.), University of Westminster, London W1B2UW United Kingdom; and Centre for Diabetes, Endocrinology, and Research (R.H., J.W.), Royal Surrey County Hospital, Guildford GU2 7XX, United Kingdom
| | - E L Thomas
- Department of Diabetes and Metabolic Medicine (F.S.-M., M.B., N.C.J., A.M.U.), Faculty of Health and Medical Sciences, University of Surrey, Guildford GU2 7WG, United Kingdom; Metabolism and Nutrition Research Group (D.J.C., G.J.K.), Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool L69 7ZX, United Kingdom; Section of Investigative Medicine, Endocrinology, and Metabolism (E.L.T., J.B.), University of Westminster, London W1B2UW United Kingdom; and Centre for Diabetes, Endocrinology, and Research (R.H., J.W.), Royal Surrey County Hospital, Guildford GU2 7XX, United Kingdom
| | - J Bell
- Department of Diabetes and Metabolic Medicine (F.S.-M., M.B., N.C.J., A.M.U.), Faculty of Health and Medical Sciences, University of Surrey, Guildford GU2 7WG, United Kingdom; Metabolism and Nutrition Research Group (D.J.C., G.J.K.), Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool L69 7ZX, United Kingdom; Section of Investigative Medicine, Endocrinology, and Metabolism (E.L.T., J.B.), University of Westminster, London W1B2UW United Kingdom; and Centre for Diabetes, Endocrinology, and Research (R.H., J.W.), Royal Surrey County Hospital, Guildford GU2 7XX, United Kingdom
| | - G J Kemp
- Department of Diabetes and Metabolic Medicine (F.S.-M., M.B., N.C.J., A.M.U.), Faculty of Health and Medical Sciences, University of Surrey, Guildford GU2 7WG, United Kingdom; Metabolism and Nutrition Research Group (D.J.C., G.J.K.), Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool L69 7ZX, United Kingdom; Section of Investigative Medicine, Endocrinology, and Metabolism (E.L.T., J.B.), University of Westminster, London W1B2UW United Kingdom; and Centre for Diabetes, Endocrinology, and Research (R.H., J.W.), Royal Surrey County Hospital, Guildford GU2 7XX, United Kingdom
| | - J Wright
- Department of Diabetes and Metabolic Medicine (F.S.-M., M.B., N.C.J., A.M.U.), Faculty of Health and Medical Sciences, University of Surrey, Guildford GU2 7WG, United Kingdom; Metabolism and Nutrition Research Group (D.J.C., G.J.K.), Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool L69 7ZX, United Kingdom; Section of Investigative Medicine, Endocrinology, and Metabolism (E.L.T., J.B.), University of Westminster, London W1B2UW United Kingdom; and Centre for Diabetes, Endocrinology, and Research (R.H., J.W.), Royal Surrey County Hospital, Guildford GU2 7XX, United Kingdom
| | - A M Umpleby
- Department of Diabetes and Metabolic Medicine (F.S.-M., M.B., N.C.J., A.M.U.), Faculty of Health and Medical Sciences, University of Surrey, Guildford GU2 7WG, United Kingdom; Metabolism and Nutrition Research Group (D.J.C., G.J.K.), Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool L69 7ZX, United Kingdom; Section of Investigative Medicine, Endocrinology, and Metabolism (E.L.T., J.B.), University of Westminster, London W1B2UW United Kingdom; and Centre for Diabetes, Endocrinology, and Research (R.H., J.W.), Royal Surrey County Hospital, Guildford GU2 7XX, United Kingdom
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50
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Jadoon KA, Ratcliffe SH, Barrett DA, Thomas EL, Stott C, Bell JD, O'Sullivan SE, Tan GD. Efficacy and Safety of Cannabidiol and Tetrahydrocannabivarin on Glycemic and Lipid Parameters in Patients With Type 2 Diabetes: A Randomized, Double-Blind, Placebo-Controlled, Parallel Group Pilot Study. Diabetes Care 2016; 39:1777-86. [PMID: 27573936 DOI: 10.2337/dc16-0650] [Citation(s) in RCA: 150] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/24/2016] [Accepted: 07/21/2016] [Indexed: 02/03/2023]
Abstract
OBJECTIVE Cannabidiol (CBD) and Δ(9)-tetrahydrocannabivarin (THCV) are nonpsychoactive phytocannabinoids affecting lipid and glucose metabolism in animal models. This study set out to examine the effects of these compounds in patients with type 2 diabetes. RESEARCH DESIGN AND METHODS In this randomized, double-blind, placebo-controlled study, 62 subjects with noninsulin-treated type 2 diabetes were randomized to five treatment arms: CBD (100 mg twice daily), THCV (5 mg twice daily), 1:1 ratio of CBD and THCV (5 mg/5 mg, twice daily), 20:1 ratio of CBD and THCV (100 mg/5 mg, twice daily), or matched placebo for 13 weeks. The primary end point was a change in HDL-cholesterol concentrations from baseline. Secondary/tertiary end points included changes in glycemic control, lipid profile, insulin sensitivity, body weight, liver triglyceride content, adipose tissue distribution, appetite, markers of inflammation, markers of vascular function, gut hormones, circulating endocannabinoids, and adipokine concentrations. Safety and tolerability end points were also evaluated. RESULTS Compared with placebo, THCV significantly decreased fasting plasma glucose (estimated treatment difference [ETD] = -1.2 mmol/L; P < 0.05) and improved pancreatic β-cell function (HOMA2 β-cell function [ETD = -44.51 points; P < 0.01]), adiponectin (ETD = -5.9 × 10(6) pg/mL; P < 0.01), and apolipoprotein A (ETD = -6.02 μmol/L; P < 0.05), although plasma HDL was unaffected. Compared with baseline (but not placebo), CBD decreased resistin (-898 pg/ml; P < 0.05) and increased glucose-dependent insulinotropic peptide (21.9 pg/ml; P < 0.05). None of the combination treatments had a significant impact on end points. CBD and THCV were well tolerated. CONCLUSIONS THCV could represent a new therapeutic agent in glycemic control in subjects with type 2 diabetes.
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Affiliation(s)
- Khalid A Jadoon
- Division of Medical Sciences & Graduate Entry Medicine, School of Medicine, University of Nottingham, Derby, U.K
| | | | - David A Barrett
- School of Pharmacy, Centre for Analytical Bioscience, University of Nottingham, Nottingham, U.K
| | - E Louise Thomas
- Department of Life Sciences, Research Centre for Optimal Health, University of Westminster, London, U.K
| | | | - Jimmy D Bell
- Department of Life Sciences, Research Centre for Optimal Health, University of Westminster, London, U.K
| | - Saoirse E O'Sullivan
- Division of Medical Sciences & Graduate Entry Medicine, School of Medicine, University of Nottingham, Derby, U.K.
| | - Garry D Tan
- NIHR Oxford Biomedical Research Centre, Oxford Centre for Diabetes, Endocrinology & Metabolism, Churchill Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, U.K
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