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Li C, Ferro A, Mhatre SK, Lu D, Lawrance M, Li X, Li S, Allen S, Desai J, Fakih M, Cecchini M, Pedersen KS, Kim TY, Reyes-Rivera I, Segal NH, Lenain C. Hybrid-control arm construction using historical trial data for an early-phase, randomized controlled trial in metastatic colorectal cancer. Commun Med (Lond) 2022; 2:90. [PMID: 35856081 PMCID: PMC9287310 DOI: 10.1038/s43856-022-00155-y] [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: 08/12/2021] [Accepted: 06/29/2022] [Indexed: 11/13/2022] Open
Abstract
Background Treatment for metastatic colorectal cancer patients beyond the second line remains challenging, highlighting the need for early phase trials of combination therapies for patients who had disease progression during or following two prior lines of therapy. Leveraging hybrid control design in these trials may preserve the benefits of randomization while strengthening evidence by integrating historical trial data. Few examples have been established to assess the applicability of such design in supporting early phase metastatic colorectal cancer trials. Methods MORPHEUS-CRC is an umbrella, multicenter, open-label, phase Ib/II, randomized, controlled trial (NCT03555149), with active experimental arms ongoing. Patients enrolled were assigned to a control arm (regorafenib, 15 patients randomized and 13 analysed) or multiple experimental arms for immunotherapy-based treatment combinations. One experimental arm (atezolizumab + isatuximab, 15 patients randomized and analysed) was completed and included in the hybrid-control study, where the hybrid-control arm was constructed by integrating data from the IMblaze370 phase 3 trial (NCT02788279). To estimate treatment efficacy, Cox and logistic regression models were used in a frequentist framework with standardized mortality ratio weighting or in a Bayesian framework with commensurate priors. The primary endpoint is objective response rate, while disease control rate, progression-free survival, and overall survival were the outcomes assessed in the hybrid-control study. Results The experimental arm showed no efficacy signal, yet a well-tolerated safety profile in the MORPHEUS-CRC trial. Treatment effects estimated in hybrid control design were comparable to those in the MORPHEUS-CRC trial using either frequentist or Bayesian models. Conclusions Hybrid control provides comparable treatment-effect estimates with generally improved precision, and thus can be of value to inform early-phase clinical development in metastatic colorectal cancer. Treatment of patients with metastatic colorectal cancer – meaning that it has spread to other parts of the body – is difficult, and new therapies are needed for patients when standard therapies stop working. We compare a combination of drugs with a standard treatment for patients with metastatic colorectal cancer in a clinical trial, in which patients are randomly allocated to either the combination or the control (standard) treatment. We find that while the combination is safe, it isn’t effective. We also show, however, that we can combine data from our control group and the control group of a previous trial to more precisely estimate treatment effects. Statistical approaches such as this to combine data from trials may mean that fewer patients have to be recruited to control groups in future trials, to improve access to potentially effective new treatments. Li et al. report outcomes from the atezolizumab/isatuximab arm of the phase Ib/II MORPHEUS-CRC trial in patients with metastatic colorectal cancer. In addition, the authors leverage historical control data from the phase III IMblaze370 study to provide more precise treatment effect estimates.
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Affiliation(s)
- Chen Li
- Roche Products Limited, Welwyn Garden City, UK
| | - Ana Ferro
- Roche Products Limited, Welwyn Garden City, UK
| | | | - Danny Lu
- Hoffmann-La Roche Limited, Mississauga, ON Canada
| | | | - Xiao Li
- Genentech, Inc., South San Francisco, CA US
| | - Shi Li
- Genentech, Inc., South San Francisco, CA US
| | | | - Jayesh Desai
- Peter MacCallum Cancer Centre, Melbourne, VIC Australia
| | - Marwan Fakih
- City of Hope Comprehensive Cancer Center, Duarte, CA USA
| | | | | | - Tae You Kim
- Seoul National University College of Medicine, Seoul, South Korea
| | | | - Neil H Segal
- Memorial Sloan Kettering Cancer Center, New York City, NY USA
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2
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Koprulu M, Zhao Y, Wheeler E, Dong L, Rocha N, Li C, Griffin JD, Patel S, Van de Streek M, Glastonbury CA, Stewart ID, Day FR, Luan J, Bowker N, Wittemans LBL, Kerrison ND, Cai L, Lucarelli DME, Barroso I, McCarthy MI, Scott RA, Saudek V, Small KS, Wareham NJ, Semple RK, Perry JRB, O’Rahilly S, Lotta LA, Langenberg C, Savage DB. Identification of Rare Loss-of-Function Genetic Variation Regulating Body Fat Distribution. J Clin Endocrinol Metab 2022; 107:1065-1077. [PMID: 34875679 PMCID: PMC8947777 DOI: 10.1210/clinem/dgab877] [Citation(s) in RCA: 4] [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] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Indexed: 11/25/2022]
Abstract
CONTEXT Biological and translational insights from large-scale, array-based genetic studies of fat distribution, a key determinant of metabolic health, have been limited by the difficulty in linking predominantly noncoding variants to specific gene targets. Rare coding variant analyses provide greater confidence that a specific gene is involved, but do not necessarily indicate whether gain or loss of function (LoF) would be of most therapeutic benefit. OBJECTIVE This work aimed to identify genes/proteins involved in determining fat distribution. METHODS We combined the power of genome-wide analysis of array-based rare, nonsynonymous variants in 450 562 individuals in the UK Biobank with exome-sequence-based rare LoF gene burden testing in 184 246 individuals. RESULTS The data indicate that the LoF of 4 genes (PLIN1 [LoF variants, P = 5.86 × 10-7], INSR [LoF variants, P = 6.21 × 10-7], ACVR1C [LoF + moderate impact variants, P = 1.68 × 10-7; moderate impact variants, P = 4.57 × 10-7], and PDE3B [LoF variants, P = 1.41 × 10-6]) is associated with a beneficial effect on body mass index-adjusted waist-to-hip ratio and increased gluteofemoral fat mass, whereas LoF of PLIN4 (LoF variants, P = 5.86 × 10-7 adversely affects these parameters. Phenotypic follow-up suggests that LoF of PLIN1, PDE3B, and ACVR1C favorably affects metabolic phenotypes (eg, triglycerides [TGs] and high-density lipoprotein [HDL] cholesterol concentrations) and reduces the risk of cardiovascular disease, whereas PLIN4 LoF has adverse health consequences. INSR LoF is associated with lower TG and HDL levels but may increase the risk of type 2 diabetes. CONCLUSION This study robustly implicates these genes in the regulation of fat distribution, providing new and in some cases somewhat counterintuitive insight into the potential consequences of targeting these molecules therapeutically.
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Affiliation(s)
- Mine Koprulu
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, CB2 0QQ, UK
| | - Yajie Zhao
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, CB2 0QQ, UK
| | - Eleanor Wheeler
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, CB2 0QQ, UK
| | - Liang Dong
- University of Cambridge Metabolic Research Laboratories, Wellcome Trust-MRC Institute of Metabolic Science, Cambridge, CB2 0QQ, UK
| | - Nuno Rocha
- University of Cambridge Metabolic Research Laboratories, Wellcome Trust-MRC Institute of Metabolic Science, Cambridge, CB2 0QQ, UK
| | - Chen Li
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, CB2 0QQ, UK
| | - John D Griffin
- Internal Medicine Research Unit, Pfizer Worldwide Research, Development, and Medical, Cambridge, Massachusetts 02139, USA
| | - Satish Patel
- University of Cambridge Metabolic Research Laboratories, Wellcome Trust-MRC Institute of Metabolic Science, Cambridge, CB2 0QQ, UK
| | - Marcel Van de Streek
- Department of Twin Research and Genetic Epidemiology, King’s College London, St Thomas’ Campus, London, SE1 7EH, UK
| | | | - Isobel D Stewart
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, CB2 0QQ, UK
| | - Felix R Day
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, CB2 0QQ, UK
| | - Jian’an Luan
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, CB2 0QQ, UK
| | - Nicholas Bowker
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, CB2 0QQ, UK
| | - Laura B L Wittemans
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, CB2 0QQ, UK
- Big Data Institute at the Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, OX3 7LF, UK
- Nuffield Department of Women’s and Reproductive Health, Medical Sciences Division, University of Oxford, Oxford, OX3 9DU, UK
| | - Nicola D Kerrison
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, CB2 0QQ, UK
| | - Lina Cai
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, CB2 0QQ, UK
| | - Debora M E Lucarelli
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, CB2 0QQ, UK
- D.M.E.L. is currently an employee of Enhanc3D Genomics Ltd
| | - Inês Barroso
- Exeter Centre of Excellence for Diabetes Research (EXCEED), Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Exeter, EX1 2HZ, UK
| | - Mark I McCarthy
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, UK
- M.McM.’s current address is Genentech, 1 DNA Way, South San Francisco, CA 94080, USA
| | - Robert A Scott
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, CB2 0QQ, UK
| | - Vladimir Saudek
- University of Cambridge Metabolic Research Laboratories, Wellcome Trust-MRC Institute of Metabolic Science, Cambridge, CB2 0QQ, UK
| | - Kerrin S Small
- Department of Twin Research and Genetic Epidemiology, King’s College London, St Thomas’ Campus, London, SE1 7EH, UK
| | - Nicholas J Wareham
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, CB2 0QQ, UK
| | - Robert K Semple
- Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, EH16 4TJ, UK
| | - John R B Perry
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, CB2 0QQ, UK
| | - Stephen O’Rahilly
- University of Cambridge Metabolic Research Laboratories, Wellcome Trust-MRC Institute of Metabolic Science, Cambridge, CB2 0QQ, UK
| | - Luca A Lotta
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, CB2 0QQ, UK
| | - Claudia Langenberg
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, CB2 0QQ, UK
- Computational Medicine, Berlin Institute of Health at Charité–Universitätsmedizin Berlin, 10117 Berlin, Germany
- Correspondence: Claudia Langenberg, MD, Dr Med, PhD, MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Box 285, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK.
| | - David B Savage
- University of Cambridge Metabolic Research Laboratories, Wellcome Trust-MRC Institute of Metabolic Science, Cambridge, CB2 0QQ, UK
- David B. Savage, MBCHB, PhD, University of Cambridge Metabolic Research Laboratories, Wellcome Trust–MRC Institute of Metabolic Science, Box 289, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK.
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3
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Schröder C, Lawrance M, Li C, Lenain C, Mhatre SK, Fakih M, Reyes-Rivera I, Bretscher MT. Building External Control Arms From Patient-Level Electronic Health Record Data to Replicate the Randomized IMblaze370 Control Arm in Metastatic Colorectal Cancer. JCO Clin Cancer Inform 2021; 5:450-458. [PMID: 33891473 PMCID: PMC8140779 DOI: 10.1200/cci.20.00149] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [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] [Indexed: 12/03/2022] Open
Abstract
External control (EC) arms derived from electronic health records (EHRs) can provide appropriate comparison groups when randomized control arms are not feasible, but have not been explored for metastatic colorectal cancer (mCRC) trials. We constructed EC arms from two patient-level EHR-derived databases and evaluated them against the control arm from a phase III, randomized controlled mCRC trial.
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Affiliation(s)
| | | | - Chen Li
- Roche Products Ltd, Welwyn, United Kingdom
| | | | | | - Marwan Fakih
- City of Hope Comprehensive Cancer Center, Duarte, CA
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4
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Lotta LA, Pietzner M, Stewart ID, Wittemans LB, Li C, Bonelli R, Raffler J, Biggs EK, Oliver-Williams C, Auyeung VP, Luan J, Wheeler E, Paige E, Surendran P, Michelotti GA, Scott RA, Burgess S, Zuber V, Sanderson E, Koulman A, Imamura F, Forouhi NG, Khaw KT, Griffin JL, Wood AM, Kastenmüller G, Danesh J, Butterworth AS, Gribble FM, Reimann F, Bahlo M, Fauman E, Wareham NJ, Langenberg C. A cross-platform approach identifies genetic regulators of human metabolism and health. Nat Genet 2021; 53:54-64. [PMID: 33414548 PMCID: PMC7612925 DOI: 10.1038/s41588-020-00751-5] [Citation(s) in RCA: 68] [Impact Index Per Article: 34.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Accepted: 11/20/2020] [Indexed: 02/02/2023]
Abstract
In cross-platform analyses of 174 metabolites, we identify 499 associations (P < 4.9 × 10-10) characterized by pleiotropy, allelic heterogeneity, large and nonlinear effects and enrichment for nonsynonymous variation. We identify a signal at GLP2R (p.Asp470Asn) shared among higher citrulline levels, body mass index, fasting glucose-dependent insulinotropic peptide and type 2 diabetes, with β-arrestin signaling as the underlying mechanism. Genetically higher serine levels are shown to reduce the likelihood (by 95%) and predict development of macular telangiectasia type 2, a rare degenerative retinal disease. Integration of genomic and small molecule data across platforms enables the discovery of regulators of human metabolism and translation into clinical insights.
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Affiliation(s)
- Luca A. Lotta
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Maik Pietzner
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | | | - Laura B.L. Wittemans
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK,The Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford
| | - Chen Li
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Roberto Bonelli
- Population Health and Immunity Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Australia,Department of Medical Biology, The University of Melbourne, Parkville, Australia
| | - Johannes Raffler
- Institute of Computational Biology, Helmholtz Zentrum München – German Research Center for Environmental Health, Neuherberg, Germany
| | - Emma K. Biggs
- Metabolic Research Laboratories, University of Cambridge, Cambridge, United Kingdom
| | - Clare Oliver-Williams
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK,Homerton College, University of Cambridge, Cambridge, UK
| | | | - Jian’an Luan
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Eleanor Wheeler
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Ellie Paige
- National Centre for Epidemiology and Population Health, The Australian National University, Canberra, Australia
| | - Praveen Surendran
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK,British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK,Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK,Rutherford Fund Fellow, Department of Public Health and Primary Care, University of Cambridge, UK
| | | | - Robert A. Scott
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Stephen Burgess
- MRC Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom,Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | - Verena Zuber
- MRC Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom,Department of Epidemiology and Biostatistics, Imperial College London, UK
| | - Eleanor Sanderson
- MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, UK
| | - Albert Koulman
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK,Institute of Computational Biology, Helmholtz Zentrum München – German Research Center for Environmental Health, Neuherberg, Germany,NIHR BRC Nutritional Biomarker Laboratory, University of Cambridge, UK
| | - Fumiaki Imamura
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Nita G. Forouhi
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Kay-Tee Khaw
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | | | - Julian L. Griffin
- Biomolecular Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, UK
| | - Angela M. Wood
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK,British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK,Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK,National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK,The Alan Turing Institute, London, UK
| | - Gabi Kastenmüller
- Institute of Computational Biology, Helmholtz Zentrum München – German Research Center for Environmental Health, Neuherberg, Germany
| | - John Danesh
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK,British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK,Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK,National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK,National Institute for Health Research Cambridge Biomedical Research Centre, University of Cambridge and Cambridge University Hospitals, Cambridge, UK,Department of Human Genetics, Wellcome Sanger Institute, Hinxton, UK
| | - Adam S. Butterworth
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK,British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK,Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK,National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK,National Institute for Health Research Cambridge Biomedical Research Centre, University of Cambridge and Cambridge University Hospitals, Cambridge, UK,Department of Human Genetics, Wellcome Sanger Institute, Hinxton, UK
| | - Fiona M. Gribble
- Metabolic Research Laboratories, University of Cambridge, Cambridge, United Kingdom
| | - Frank Reimann
- Metabolic Research Laboratories, University of Cambridge, Cambridge, United Kingdom
| | - Melanie Bahlo
- Population Health and Immunity Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Australia,Department of Medical Biology, The University of Melbourne, Parkville, Australia
| | - Eric Fauman
- Internal Medicine Research Unit, Pfizer Worldwide Research, Cambridge, MA 02142, USA
| | | | - Claudia Langenberg
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK. .,Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK. .,Computational Medicine, Berlin Institute of Health (BIH), Charité University Medicine, Berlin, Germany.
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5
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Lotta LA, Wittemans LBL, Zuber V, Stewart ID, Sharp SJ, Luan J, Day FR, Li C, Bowker N, Cai L, De Lucia Rolfe E, Khaw KT, Perry JRB, O’Rahilly S, Scott RA, Savage DB, Burgess S, Wareham NJ, Langenberg C. Association of Genetic Variants Related to Gluteofemoral vs Abdominal Fat Distribution With Type 2 Diabetes, Coronary Disease, and Cardiovascular Risk Factors. JAMA 2018; 320:2553-2563. [PMID: 30575882 PMCID: PMC6583513 DOI: 10.1001/jama.2018.19329] [Citation(s) in RCA: 111] [Impact Index Per Article: 22.2] [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: 02/06/2023]
Abstract
IMPORTANCE Body fat distribution, usually measured using waist-to-hip ratio (WHR), is an important contributor to cardiometabolic disease independent of body mass index (BMI). Whether mechanisms that increase WHR via lower gluteofemoral (hip) or via higher abdominal (waist) fat distribution affect cardiometabolic risk is unknown. OBJECTIVE To identify genetic variants associated with higher WHR specifically via lower gluteofemoral or higher abdominal fat distribution and estimate their association with cardiometabolic risk. DESIGN, SETTING, AND PARTICIPANTS Genome-wide association studies (GWAS) for WHR combined data from the UK Biobank cohort and summary statistics from previous GWAS (data collection: 2006-2018). Specific polygenic scores for higher WHR via lower gluteofemoral or via higher abdominal fat distribution were derived using WHR-associated genetic variants showing specific association with hip or waist circumference. Associations of polygenic scores with outcomes were estimated in 3 population-based cohorts, a case-cohort study, and summary statistics from 6 GWAS (data collection: 1991-2018). EXPOSURES More than 2.4 million common genetic variants (GWAS); polygenic scores for higher WHR (follow-up analyses). MAIN OUTCOMES AND MEASURES BMI-adjusted WHR and unadjusted WHR (GWAS); compartmental fat mass measured by dual-energy x-ray absorptiometry, systolic and diastolic blood pressure, low-density lipoprotein cholesterol, triglycerides, fasting glucose, fasting insulin, type 2 diabetes, and coronary disease risk (follow-up analyses). RESULTS Among 452 302 UK Biobank participants of European ancestry, the mean (SD) age was 57 (8) years and the mean (SD) WHR was 0.87 (0.09). In genome-wide analyses, 202 independent genetic variants were associated with higher BMI-adjusted WHR (n = 660 648) and unadjusted WHR (n = 663 598). In dual-energy x-ray absorptiometry analyses (n = 18 330), the hip- and waist-specific polygenic scores for higher WHR were specifically associated with lower gluteofemoral and higher abdominal fat, respectively. In follow-up analyses (n = 636 607), both polygenic scores were associated with higher blood pressure and triglyceride levels and higher risk of diabetes (waist-specific score: odds ratio [OR], 1.57 [95% CI, 1.34-1.83], absolute risk increase per 1000 participant-years [ARI], 4.4 [95% CI, 2.7-6.5], P < .001; hip-specific score: OR, 2.54 [95% CI, 2.17-2.96], ARI, 12.0 [95% CI, 9.1-15.3], P < .001) and coronary disease (waist-specific score: OR, 1.60 [95% CI, 1.39-1.84], ARI, 2.3 [95% CI, 1.5-3.3], P < .001; hip-specific score: OR, 1.76 [95% CI, 1.53-2.02], ARI, 3.0 [95% CI, 2.1-4.0], P < .001), per 1-SD increase in BMI-adjusted WHR. CONCLUSIONS AND RELEVANCE Distinct genetic mechanisms may be linked to gluteofemoral and abdominal fat distribution that are the basis for the calculation of the WHR. These findings may improve risk assessment and treatment of diabetes and coronary disease.
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Affiliation(s)
- Luca A. Lotta
- MRC Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom
| | | | - Verena Zuber
- MRC Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom
| | - Isobel D. Stewart
- MRC Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom
| | - Stephen J. Sharp
- MRC Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom
| | - Jian’an Luan
- MRC Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom
| | - Felix R. Day
- MRC Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom
| | - Chen Li
- MRC Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom
| | - Nicholas Bowker
- MRC Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom
| | - Lina Cai
- MRC Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom
| | | | - Kay-Tee Khaw
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | - John R. B. Perry
- MRC Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom
| | - Stephen O’Rahilly
- Metabolic Research Laboratories, Institute of Metabolic Science, University of Cambridge, Cambridge, United Kingdom
| | - Robert A. Scott
- MRC Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom
| | - David B. Savage
- Metabolic Research Laboratories, Institute of Metabolic Science, University of Cambridge, Cambridge, United Kingdom
| | - Stephen Burgess
- MRC Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | | | - Claudia Langenberg
- MRC Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom
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6
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Lotta LA, Stewart ID, Sharp SJ, Day FR, Burgess S, Luan J, Bowker N, Cai L, Li C, Wittemans LBL, Kerrison ND, Khaw KT, McCarthy MI, O'Rahilly S, Scott RA, Savage DB, Perry JRB, Langenberg C, Wareham NJ. Association of Genetically Enhanced Lipoprotein Lipase-Mediated Lipolysis and Low-Density Lipoprotein Cholesterol-Lowering Alleles With Risk of Coronary Disease and Type 2 Diabetes. JAMA Cardiol 2018; 3:957-966. [PMID: 30326043 PMCID: PMC6217943 DOI: 10.1001/jamacardio.2018.2866] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Importance Pharmacological enhancers of lipoprotein lipase (LPL) are in preclinical or early clinical development for cardiovascular prevention. Studying whether these agents will reduce cardiovascular events or diabetes risk when added to existing lipid-lowering drugs would require large outcome trials. Human genetics studies can help prioritize or deprioritize these resource-demanding endeavors. Objective To investigate the independent and combined associations of genetically determined differences in LPL-mediated lipolysis and low-density lipoprotein cholesterol (LDL-C) metabolism with risk of coronary disease and diabetes. Design, Setting, and Participants In this genetic association study, individual-level genetic data from 392 220 participants from 2 population-based cohort studies and 1 case-cohort study conducted in Europe were included. Data were collected from January 1991 to July 2018, and data were analyzed from July 2014 to July 2018. Exposures Six conditionally independent triglyceride-lowering alleles in LPL, the p.Glu40Lys variant in ANGPTL4, rare loss-of-function variants in ANGPTL3, and LDL-C-lowering polymorphisms at 58 independent genomic regions, including HMGCR, NPC1L1, and PCSK9. Main Outcomes and Measures Odds ratio for coronary artery disease and type 2 diabetes. Results Of the 392 220 participants included, 211 915 (54.0%) were female, and the mean (SD) age was 57 (8) years. Triglyceride-lowering alleles in LPL were associated with protection from coronary disease (approximately 40% lower odds per SD of genetically lower triglycerides) and type 2 diabetes (approximately 30% lower odds) in people above or below the median of the population distribution of LDL-C-lowering alleles at 58 independent genomic regions, HMGCR, NPC1L1, or PCSK9. Associations with lower risk were consistent in quintiles of the distribution of LDL-C-lowering alleles and 2 × 2 factorial genetic analyses. The 40Lys variant in ANGPTL4 was associated with protection from coronary disease and type 2 diabetes in groups with genetically higher or lower LDL-C. For a genetic difference of 0.23 SDs in LDL-C, ANGPTL3 loss-of-function variants, which also have beneficial associations with LPL lipolysis, were associated with greater protection against coronary disease than other LDL-C-lowering genetic mechanisms (ANGPTL3 loss-of-function variants: odds ratio, 0.66; 95% CI, 0.52-0.83; 58 LDL-C-lowering variants: odds ratio, 0.90; 95% CI, 0.89-0.91; P for heterogeneity = .009). Conclusions and Relevance Triglyceride-lowering alleles in the LPL pathway are associated with lower risk of coronary disease and type 2 diabetes independently of LDL-C-lowering genetic mechanisms. These findings provide human genetics evidence to support the development of agents that enhance LPL-mediated lipolysis for further clinical benefit in addition to LDL-C-lowering therapy.
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Affiliation(s)
- Luca A Lotta
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, United Kingdom
| | - Isobel D Stewart
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, United Kingdom
| | - Stephen J Sharp
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, United Kingdom
| | - Felix R Day
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, United Kingdom
| | - Stephen Burgess
- MRC Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | - Jian'an Luan
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, United Kingdom
| | - Nicholas Bowker
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, United Kingdom
| | - Lina Cai
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, United Kingdom
| | - Chen Li
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, United Kingdom
| | - Laura B L Wittemans
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, United Kingdom
| | - Nicola D Kerrison
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, United Kingdom
| | - Kay-Tee Khaw
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | - Mark I McCarthy
- Oxford Centre for Diabetes, Endocrinology, and Metabolism, University of Oxford, Oxford, United Kingdom
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
- NIHR Oxford Biomedical Research Centre, Churchill Hospital, Oxford, United Kingdom
| | - Stephen O'Rahilly
- Metabolic Research Laboratories, Institute of Metabolic Science, University of Cambridge, Cambridge, United Kingdom
| | - Robert A Scott
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, United Kingdom
| | - David B Savage
- Metabolic Research Laboratories, Institute of Metabolic Science, University of Cambridge, Cambridge, United Kingdom
| | - John R B Perry
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, United Kingdom
| | - Claudia Langenberg
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, United Kingdom
| | - Nicholas J Wareham
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, United Kingdom
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7
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Li C, Li L, Lian J, Watts R, Nelson R, Goodwin B, Lehner R. Roles of Acyl-CoA:Diacylglycerol Acyltransferases 1 and 2 in Triacylglycerol Synthesis and Secretion in Primary Hepatocytes. Arterioscler Thromb Vasc Biol 2015; 35:1080-91. [PMID: 25792450 DOI: 10.1161/atvbaha.114.304584] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.0] [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: 09/03/2014] [Accepted: 03/04/2015] [Indexed: 02/06/2023]
Abstract
OBJECTIVE Very low-density lipoprotein assembly and secretion are regulated by the availability of triacylglycerol. Although compelling evidence indicates that the majority of triacylglycerol in very low-density lipoprotein is derived from re-esterification of lipolytic products released by endoplasmic reticulum-associated lipases, little is known about roles of acyl-CoA:diacylglycerol acyltransferases (DGATs) in this process. We aimed to investigate the contribution of DGAT1 and DGAT2 in lipid metabolism and lipoprotein secretion in primary mouse and human hepatocytes. APPROACH AND RESULTS We used highly selective small-molecule inhibitors of DGAT1 and DGAT2, and we tracked storage and secretion of lipids synthesized de novo from [(3)H]acetic acid and from exogenously supplied [(3)H]oleic acid. Inactivation of individual DGAT activity did not affect incorporation of either radiolabeled precursor into intracellular triacylglycerol, whereas combined inactivation of both DGATs severely attenuated triacylglycerol synthesis. However, inhibition of DGAT2 augmented fatty acid oxidation, whereas inhibition of DGAT1 increased triacylglycerol secretion, suggesting preferential channeling of separate DGAT-derived triacylglycerol pools to distinct metabolic pathways. Inactivation of DGAT2 impaired cytosolic lipid droplet expansion, whereas DGAT1 inactivation promoted large lipid droplet formation. Moreover, inactivation of DGAT2 attenuated expression of lipogenic genes. Finally, triacylglycerol secretion was significantly reduced on DGAT2 inhibition without altering extracellular apolipoprotein B levels. CONCLUSIONS Our data suggest that DGAT1 and DGAT2 can compensate for each other to synthesize triacylglycerol, but triacylglycerol synthesized by DGAT1 is preferentially channeled to oxidation, whereas DGAT2 synthesizes triacylglycerol destined for very low-density lipoprotein assembly.
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Affiliation(s)
- Chen Li
- From the Group on Molecular and Cell Biology of Lipids (C.L., L.L., J.L., R.W., R.N., R.L.), Department of Cell Biology (C.L., R.L.), Department of Pediatrics (L.L., J.L., R.W., R.N., R.L.), Mazankowski Alberta Heart Institute, University of Alberta, Edmonton, Alberta, Canada; and Pfizer Global Research and Development, Cardiovascular and Metabolic Diseases Research Unit, Cambridge, MA (B.G.)
| | - Lena Li
- From the Group on Molecular and Cell Biology of Lipids (C.L., L.L., J.L., R.W., R.N., R.L.), Department of Cell Biology (C.L., R.L.), Department of Pediatrics (L.L., J.L., R.W., R.N., R.L.), Mazankowski Alberta Heart Institute, University of Alberta, Edmonton, Alberta, Canada; and Pfizer Global Research and Development, Cardiovascular and Metabolic Diseases Research Unit, Cambridge, MA (B.G.)
| | - Jihong Lian
- From the Group on Molecular and Cell Biology of Lipids (C.L., L.L., J.L., R.W., R.N., R.L.), Department of Cell Biology (C.L., R.L.), Department of Pediatrics (L.L., J.L., R.W., R.N., R.L.), Mazankowski Alberta Heart Institute, University of Alberta, Edmonton, Alberta, Canada; and Pfizer Global Research and Development, Cardiovascular and Metabolic Diseases Research Unit, Cambridge, MA (B.G.)
| | - Russell Watts
- From the Group on Molecular and Cell Biology of Lipids (C.L., L.L., J.L., R.W., R.N., R.L.), Department of Cell Biology (C.L., R.L.), Department of Pediatrics (L.L., J.L., R.W., R.N., R.L.), Mazankowski Alberta Heart Institute, University of Alberta, Edmonton, Alberta, Canada; and Pfizer Global Research and Development, Cardiovascular and Metabolic Diseases Research Unit, Cambridge, MA (B.G.)
| | - Randal Nelson
- From the Group on Molecular and Cell Biology of Lipids (C.L., L.L., J.L., R.W., R.N., R.L.), Department of Cell Biology (C.L., R.L.), Department of Pediatrics (L.L., J.L., R.W., R.N., R.L.), Mazankowski Alberta Heart Institute, University of Alberta, Edmonton, Alberta, Canada; and Pfizer Global Research and Development, Cardiovascular and Metabolic Diseases Research Unit, Cambridge, MA (B.G.)
| | - Bryan Goodwin
- From the Group on Molecular and Cell Biology of Lipids (C.L., L.L., J.L., R.W., R.N., R.L.), Department of Cell Biology (C.L., R.L.), Department of Pediatrics (L.L., J.L., R.W., R.N., R.L.), Mazankowski Alberta Heart Institute, University of Alberta, Edmonton, Alberta, Canada; and Pfizer Global Research and Development, Cardiovascular and Metabolic Diseases Research Unit, Cambridge, MA (B.G.)
| | - Richard Lehner
- From the Group on Molecular and Cell Biology of Lipids (C.L., L.L., J.L., R.W., R.N., R.L.), Department of Cell Biology (C.L., R.L.), Department of Pediatrics (L.L., J.L., R.W., R.N., R.L.), Mazankowski Alberta Heart Institute, University of Alberta, Edmonton, Alberta, Canada; and Pfizer Global Research and Development, Cardiovascular and Metabolic Diseases Research Unit, Cambridge, MA (B.G.).
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8
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Han H, Sun D, Li W, Shen H, Zhu Y, Li C, Chen Y, Lu L, Li W, Zhang J, Tian Y, Li Y. A c-Myc-MicroRNA functional feedback loop affects hepatocarcinogenesis. Hepatology 2013; 57:2378-89. [PMID: 23389829 DOI: 10.1002/hep.26302] [Citation(s) in RCA: 72] [Impact Index Per Article: 7.2] [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: 07/18/2012] [Accepted: 01/12/2013] [Indexed: 12/24/2022]
Abstract
UNLABELLED c-Myc (Myc) plays an important role in normal liver development and tumorigenesis. We show here that Myc is pathologically activated in and essential for promoting human hepatocellular carcinoma (HCC). Myc induces HCC through a novel, microRNA (miRNA)-mediated feedback loop comprised of miR-148a-5p, miR-363-3p, and ubiquitin-specific protease 28 (USP28). Myc directly binds to conserved regions in the promoters of the two miRNAs and represses their expression. miR-148a-5p directly targets and inhibits Myc, whereas miR-363-3p destabilizes Myc by directly targeting and inhibiting USP28. Inhibition of miR-148a-5p or miR-363-3p induces hepatocellular tumorigenesis by promoting G1 to S phase progression, whereas activation of them has the opposite effects. The Myc-miRNA feedback loop is dysregulated in human HCC. CONCLUSION These results define miR-148a-5p and miR-363-3p as negative regulators of Myc, thus revealing their heretofore unappreciated roles in hepatocarcinogenesis. (HEPATOLOGY 2013;57:2378-2389).
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Affiliation(s)
- Han Han
- College of Life Sciences, State Key Laboratory of Virology, Wuhan University, Wuhan, China
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Klemm RW, Norton JP, Cole RA, Li CS, Park SH, Crane MM, Li L, Jin D, Boye-Doe A, Liu TY, Shibata Y, Lu H, Rapoport TA, Farese RV, Blackstone C, Guo Y, Mak HY. A conserved role for atlastin GTPases in regulating lipid droplet size. Cell Rep 2013; 3:1465-75. [PMID: 23684613 DOI: 10.1016/j.celrep.2013.04.015] [Citation(s) in RCA: 112] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2012] [Revised: 02/19/2013] [Accepted: 04/17/2013] [Indexed: 12/22/2022] Open
Abstract
Lipid droplets (LDs) are the major fat storage organelles in eukaryotic cells, but how their size is regulated is unknown. Using genetic screens in C. elegans for LD morphology defects in intestinal cells, we found that mutations in atlastin, a GTPase required for homotypic fusion of endoplasmic reticulum (ER) membranes, cause not only ER morphology defects, but also a reduction in LD size. Similar results were obtained after depletion of atlastin or expression of a dominant-negative mutant, whereas overexpression of atlastin had the opposite effect. Atlastin depletion in Drosophila fat bodies also reduced LD size and decreased triglycerides in whole animals, sensitizing them to starvation. In mammalian cells, co-overexpression of atlastin-1 and REEP1, a paralog of the ER tubule-shaping protein DP1/REEP5, generates large LDs. The effect of atlastin-1 on LD size correlates with its activity to promote membrane fusion in vitro. Our results indicate that atlastin-mediated fusion of ER membranes is important for LD size regulation.
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Affiliation(s)
- Robin W Klemm
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
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