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Nádasdi Á, Gál V, Masszi T, Somogyi A, Firneisz G. PNPLA3 rs738409 risk genotype decouples TyG index from HOMA2-IR and intrahepatic lipid content. Cardiovasc Diabetol 2023; 22:64. [PMID: 36944955 PMCID: PMC10031960 DOI: 10.1186/s12933-023-01792-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Accepted: 03/06/2023] [Indexed: 03/23/2023] Open
Abstract
BACKGROUND Recent reports suggested a different predictive value for TyG index compared to HOMA-IR in coronary artery calcification (CAC) and other atherosclerotic outcomes, despite that both indices are proposed as surrogate markers of insulin resistance. We hypothesized a key role for liver pathology as an explanation and therefore assessed the relationship among the two indices and the intrahepatic lipid content stratified by PNPLA3 rs738409 genotypes as a known non-alcoholic fatty liver disease (NAFLD) genetic risk. METHODS Thirty-nine women from a prior GDM-genetic study were recalled with PNPLA3 rs738409 CC and GG genotypes for metabolic phenotyping and to assess hepatic triglyceride content (HTGC). 75 g OGTT was performed, fasting lipid, glucose, insulin levels and calculated insulin resistance indices (TyG and HOMA2-IR) were used. HTGC was measured by MR based methods. Mann-Whitney-U, χ2 and for the correlation analysis Spearman rank order tests were applied. RESULTS The PNPLA3 rs738409 genotype had a significant effect on the direct correlation between the HOMA2-IR and TyG index: the correlation (R = 0.52, p = 0.0054) found in the CC group was completely abolished in those with the GG (NAFLD) risk genotype. In addition, the HOMA2-IR correlated with HTGC in the entire study population (R = 0.69, p < 0.0001) and also separately in both genotypes (CC R = 0.62, p = 0.0006, GG: R = 0.74, p = 0.0058). In contrast, the correlation between TyG index and HTGC was only significant in rs738409 CC genotype group (R = 0.42, p = 0.0284) but not in GG group. A similar pattern was observed in the correlation between TG and HTGC (CC: R = 0.41, p = 0.0335), when the components of the TyG index were separately assessed. CONCLUSIONS PNPLA3 rs738409 risk genotype completely decoupled the direct correlation between two surrogate markers of insulin resistance: TyG and HOMA2-IR confirming our hypothesis. The liver lipid content increased in parallel with the HOMA2-IR independent of genotype, in contrast to the TyG index where the risk genotype abolished the correlation. This phenomenon seems to be related to the nature of hepatic fat accumulation and to the different concepts establishing the two insulin resistance markers.
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Affiliation(s)
- Ákos Nádasdi
- Translational Medicine Institute, Faculty of Medicine, Semmelweis University, Budapest, Hungary
- Department of Internal Medicine and Haematology, Faculty of Medicine, Semmelweis University, Budapest, Hungary
| | - Viktor Gál
- Brain Imaging Centre, Research Centre for Natural Sciences, Eötvös Loránd Research Network, Budapest, Hungary
- Medical Imaging Centre, Faculty of Medicine, Semmelweis University, Budapest, Hungary
| | - Tamás Masszi
- Department of Internal Medicine and Haematology, Faculty of Medicine, Semmelweis University, Budapest, Hungary
| | - Anikó Somogyi
- Department of Internal Medicine and Haematology, Faculty of Medicine, Semmelweis University, Budapest, Hungary
| | - Gábor Firneisz
- Translational Medicine Institute, Faculty of Medicine, Semmelweis University, Budapest, Hungary.
- Department of Internal Medicine and Haematology, Faculty of Medicine, Semmelweis University, Budapest, Hungary.
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Tschigg K, Consoli L, Biasiotto R, Mascalzoni D. Ethical, legal and social/societal implications (ELSI) of recall-by-genotype (RbG) and genotype-driven-research (GDR) approaches: a scoping review. Eur J Hum Genet 2022; 30:1000-1010. [PMID: 35705790 PMCID: PMC9437022 DOI: 10.1038/s41431-022-01120-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Revised: 03/17/2022] [Accepted: 05/05/2022] [Indexed: 11/29/2022] Open
Abstract
Recall by Genotype (RbG), Genotype-driven-recall (GDR), and Genotype-based-recall (GBR) strategies are increasingly used to conduct genomic or biobanking sub-studies that single out participants as eligible because of their specific individual genotypic information. However, existing regulatory and governance frameworks do not apply to all aspects of genotype-driven research approaches. The recall strategies disclose or withhold personal genotypic information with uncertain clinical utility. Accordingly, this scoping review aims to identify peculiar, explicit and implicit ethical, legal, and societal/social implications (ELSI) of RbG study designs. We conducted a systematic literature search of three electronic databases from November 2020 to February 2021. We investigated qualitative and quantitative research methods used to report ELSI aspects in RbG research. Congruent with other research findings, we identified a lack of qualitative research investigating the particular ELSI challenges with RbG. We included and analysed the content of twenty-five publications. We found a consensus on RbG posing significant ethical issues, dilemmas, barriers, concerns and societal challenges. However, we found that the approaches to disclosure and study-specific recall and communication strategies employed consent models and Return of Research Results (RoRR) policies varied considerably. Furthermore, we identified a high heterogeneity in perspectives of participants and experts about ELSI of study-specific RbG policies. Therefore, further fine-mapping through qualitative and empirical research is needed to draw conclusions and re-fine ELSI frameworks.
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Affiliation(s)
- Katharina Tschigg
- Department of Cellular, Computational, and Integrative Biology, University of Trento, Trento, Italy. .,Institute for Biomedicine & Affiliated Institute of the University of Lübeck, Eurac Research, Bolzano, Italy, Bozen, Italy.
| | - Luca Consoli
- Institute for Science in Society, Radboud University, Nijmegen, Netherlands
| | - Roberta Biasiotto
- Institute for Biomedicine & Affiliated Institute of the University of Lübeck, Eurac Research, Bolzano, Italy, Bozen, Italy.,Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy
| | - Deborah Mascalzoni
- Institute for Biomedicine & Affiliated Institute of the University of Lübeck, Eurac Research, Bolzano, Italy, Bozen, Italy.,Department of Public Health and Caring Sciences, Center for Research Ethics and Bioethics, Uppsala University, Uppsala, Sweden
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Brito MDF, Torre C, Silva-Lima B. Scientific Advances in Diabetes: The Impact of the Innovative Medicines Initiative. Front Med (Lausanne) 2021; 8:688438. [PMID: 34295913 PMCID: PMC8290522 DOI: 10.3389/fmed.2021.688438] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 06/02/2021] [Indexed: 12/16/2022] Open
Abstract
Diabetes Mellitus is one of the World Health Organization's priority diseases under research by the first and second programmes of Innovative Medicines Initiative, with the acronyms IMI1 and IMI2, respectively. Up to October of 2019, 13 projects were funded by IMI for Diabetes & Metabolic disorders, namely SUMMIT, IMIDIA, DIRECT, StemBANCC, EMIF, EBiSC, INNODIA, RHAPSODY, BEAT-DKD, LITMUS, Hypo-RESOLVE, IM2PACT, and CARDIATEAM. In general, a total of €447 249 438 was spent by IMI in the area of Diabetes. In order to prompt a better integration of achievements between the different projects, we perform a literature review and used three data sources, namely the official project's websites, the contact with the project's coordinators and co-coordinator, and the CORDIS database. From the 662 citations identified, 185 were included. The data collected were integrated into the objectives proposed for the four IMI2 program research axes: (1) target and biomarker identification, (2) innovative clinical trials paradigms, (3) innovative medicines, and (4) patient-tailored adherence programmes. The IMI funded projects identified new biomarkers, medical and research tools, determinants of inter-individual variability, relevant pathways, clinical trial designs, clinical endpoints, therapeutic targets and concepts, pharmacologic agents, large-scale production strategies, and patient-centered predictive models for diabetes and its complications. Taking into account the scientific data produced, we provided a joint vision with strategies for integrating personalized medicine into healthcare practice. The major limitations of this article were the large gap of data in the libraries on the official project websites and even the Cordis database was not complete and up to date.
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Affiliation(s)
| | - Carla Torre
- Faculty of Pharmacy, University of Lisbon, Lisbon, Portugal.,Laboratory of Systems Integration Pharmacology, Clinical & Regulatory Science-Research Institute for Medicines (iMED.ULisboa), Lisbon, Portugal
| | - Beatriz Silva-Lima
- Faculty of Pharmacy, University of Lisbon, Lisbon, Portugal.,Laboratory of Systems Integration Pharmacology, Clinical & Regulatory Science-Research Institute for Medicines (iMED.ULisboa), Lisbon, Portugal
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Merino J, Guasch-Ferré M, Ellervik C, Dashti HS, Sharp SJ, Wu P, Overvad K, Sarnowski C, Kuokkanen M, Lemaitre RN, Justice AE, Ericson U, Braun KVE, Mahendran Y, Frazier-Wood AC, Sun D, Chu AY, Tanaka T, Luan J, Hong J, Tjønneland A, Ding M, Lundqvist A, Mukamal K, Rohde R, Schulz CA, Franco OH, Grarup N, Chen YDI, Bazzano L, Franks PW, Buring JE, Langenberg C, Liu CT, Hansen T, Jensen MK, Sääksjärvi K, Psaty BM, Young KL, Hindy G, Sandholt CH, Ridker PM, Ordovas JM, Meigs JB, Pedersen O, Kraft P, Perola M, North KE, Orho-Melander M, Voortman T, Toft U, Rotter JI, Qi L, Forouhi NG, Mozaffarian D, Sørensen TIA, Stampfer MJ, Männistö S, Selvin E, Imamura F, Salomaa V, Hu FB, Wareham NJ, Dupuis J, Smith CE, Kilpeläinen TO, Chasman DI, Florez JC. Quality of dietary fat and genetic risk of type 2 diabetes: individual participant data meta-analysis. BMJ 2019; 366:l4292. [PMID: 31345923 PMCID: PMC6652797 DOI: 10.1136/bmj.l4292] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
OBJECTIVE To investigate whether the genetic burden of type 2 diabetes modifies the association between the quality of dietary fat and the incidence of type 2 diabetes. DESIGN Individual participant data meta-analysis. DATA SOURCES Eligible prospective cohort studies were systematically sourced from studies published between January 1970 and February 2017 through electronic searches in major medical databases (Medline, Embase, and Scopus) and discussion with investigators. REVIEW METHODS Data from cohort studies or multicohort consortia with available genome-wide genetic data and information about the quality of dietary fat and the incidence of type 2 diabetes in participants of European descent was sought. Prospective cohorts that had accrued five or more years of follow-up were included. The type 2 diabetes genetic risk profile was characterized by a 68-variant polygenic risk score weighted by published effect sizes. Diet was recorded by using validated cohort-specific dietary assessment tools. Outcome measures were summary adjusted hazard ratios of incident type 2 diabetes for polygenic risk score, isocaloric replacement of carbohydrate (refined starch and sugars) with types of fat, and the interaction of types of fat with polygenic risk score. RESULTS Of 102 305 participants from 15 prospective cohort studies, 20 015 type 2 diabetes cases were documented after a median follow-up of 12 years (interquartile range 9.4-14.2). The hazard ratio of type 2 diabetes per increment of 10 risk alleles in the polygenic risk score was 1.64 (95% confidence interval 1.54 to 1.75, I2=7.1%, τ2=0.003). The increase of polyunsaturated fat and total omega 6 polyunsaturated fat intake in place of carbohydrate was associated with a lower risk of type 2 diabetes, with hazard ratios of 0.90 (0.82 to 0.98, I2=18.0%, τ2=0.006; per 5% of energy) and 0.99 (0.97 to 1.00, I2=58.8%, τ2=0.001; per increment of 1 g/d), respectively. Increasing monounsaturated fat in place of carbohydrate was associated with a higher risk of type 2 diabetes (hazard ratio 1.10, 95% confidence interval 1.01 to 1.19, I2=25.9%, τ2=0.006; per 5% of energy). Evidence of small study effects was detected for the overall association of polyunsaturated fat with the risk of type 2 diabetes, but not for the omega 6 polyunsaturated fat and monounsaturated fat associations. Significant interactions between dietary fat and polygenic risk score on the risk of type 2 diabetes (P>0.05 for interaction) were not observed. CONCLUSIONS These data indicate that genetic burden and the quality of dietary fat are each associated with the incidence of type 2 diabetes. The findings do not support tailoring recommendations on the quality of dietary fat to individual type 2 diabetes genetic risk profiles for the primary prevention of type 2 diabetes, and suggest that dietary fat is associated with the risk of type 2 diabetes across the spectrum of type 2 diabetes genetic risk.
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Franks PW, Timpson NJ. Genotype-Based Recall Studies in Complex Cardiometabolic Traits. CIRCULATION. GENOMIC AND PRECISION MEDICINE 2018; 11:e001947. [PMID: 30354344 PMCID: PMC6813040 DOI: 10.1161/circgen.118.001947] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
In genotype-based recall (GBR) studies, people (or their biological samples) who carry genotypes of special interest for a given hypothesis test are recalled from a larger cohort (or biobank) for more detailed investigations. There are several GBR study designs that offer a range of powerful options to elucidate (1) genotype-phenotype associations (by increasing the efficiency of genetic association studies, thereby allowing bespoke phenotyping in relatively small cohorts), (2) the effects of environmental exposures (within the Mendelian randomization framework), and (3) gene-treatment interactions (within the setting of GBR interventional trials). In this review, we overview the literature on GBR studies as applied to cardiometabolic health outcomes. We also review the GBR approaches used to date and outline new methods and study designs that might enhance the utility of GBR-focused studies. Specifically, we highlight how GBR methods have the potential to augment randomized controlled trials, providing an alternative application for the now increasingly accepted Mendelian randomization methods usually applied to large-scale population-based data sets. Further to this, we consider how functional and basic science approaches alongside GBR designs offer intellectually intriguing and potentially powerful ways to explore the implications of alterations to specific (and potentially druggable) biological pathways.
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Affiliation(s)
- Paul W Franks
- Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Skåne University Hospital, SE-21741, Malmö, Sweden
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford
- Department of Nutrition, Harvard TH Chan School of Public Health, Boston, MA, USA
- Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden
| | - Nicholas J Timpson
- MRC Integrative Epidemiology Unit, Avon Longitudinal Study of Parents and Children, Population Health Science, Bristol Medical School, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
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6
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Gene-lifestyle interplay in type 2 diabetes. Curr Opin Genet Dev 2018; 50:35-40. [PMID: 29459268 DOI: 10.1016/j.gde.2018.02.001] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2018] [Revised: 02/01/2018] [Accepted: 02/01/2018] [Indexed: 12/20/2022]
Abstract
Type 2 diabetes (T2D) is widespread, affecting the health of hundreds of millions worldwide. The disease results from the complex interplay of lifestyle factors acting on a backdrop of inherited DNA risk variants. Detecting and understanding biomarkers, whether genotypes or other downstream biological features that dictate a person's phenotypic response to different lifestyle exposures, may have tremendous utility in the prevention of T2D. Here, we explore (i) evidence of how human genetic adaptation to diverse local environments might interact with lifestyle factors in T2D, (ii) the key challenges facing the research area of gene×lifestyle interactions in T2D, and (iii) the solutions that might be pursued in future studies. Overall, many preliminary examples of such interactions exist, but none is sufficient to have a major impact on clinical decision making. Future studies, integrating genetics and other biological markers into regulatory networks, are likely to be necessary to facilitate the integration of genomics into lifestyle medicine in T2D.
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Corbin LJ, Tan VY, Hughes DA, Wade KH, Paul DS, Tansey KE, Butcher F, Dudbridge F, Howson JM, Jallow MW, John C, Kingston N, Lindgren CM, O'Donavan M, O'Rahilly S, Owen MJ, Palmer CNA, Pearson ER, Scott RA, van Heel DA, Whittaker J, Frayling T, Tobin MD, Wain LV, Smith GD, Evans DM, Karpe F, McCarthy MI, Danesh J, Franks PW, Timpson NJ. Formalising recall by genotype as an efficient approach to detailed phenotyping and causal inference. Nat Commun 2018; 9:711. [PMID: 29459775 PMCID: PMC5818506 DOI: 10.1038/s41467-018-03109-y] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2017] [Accepted: 01/19/2018] [Indexed: 02/02/2023] Open
Abstract
Detailed phenotyping is required to deepen our understanding of the biological mechanisms behind genetic associations. In addition, the impact of potentially modifiable risk factors on disease requires analytical frameworks that allow causal inference. Here, we discuss the characteristics of Recall-by-Genotype (RbG) as a study design aimed at addressing both these needs. We describe two broad scenarios for the application of RbG: studies using single variants and those using multiple variants. We consider the efficacy and practicality of the RbG approach, provide a catalogue of UK-based resources for such studies and present an online RbG study planner.
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Affiliation(s)
- Laura J Corbin
- MRC Integrative Epidemiology Unit at University of Bristol, Bristol, BS8 2BN, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
| | - Vanessa Y Tan
- MRC Integrative Epidemiology Unit at University of Bristol, Bristol, BS8 2BN, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
| | - David A Hughes
- MRC Integrative Epidemiology Unit at University of Bristol, Bristol, BS8 2BN, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
| | - Kaitlin H Wade
- MRC Integrative Epidemiology Unit at University of Bristol, Bristol, BS8 2BN, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
| | - Dirk S Paul
- MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, UK
- British Heart Foundation (BHF) Centre of Excellence, Division of Cardiovascular Medicine, Addenbrooke's Hospital, Cambridge, CB2 0QQ, UK
| | - Katherine E Tansey
- Core Bioinformatics and Statistics Team, College of Biomedical & Life Sciences, Cardiff University, Cardiff, CF10 3XQ, UK
| | - Frances Butcher
- Oxford School of Public Health, University of Oxford, Oxford, OX3 7LF, UK
| | - Frank Dudbridge
- Department of Health Sciences, University of Leicester, Leicester, LE1 7RH, UK
| | - Joanna M Howson
- MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, UK
| | - Momodou W Jallow
- Department of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK
- MRC Unit The Gambia (MRCG), Atlantic Boulevard, Fajara, P.O. Box 273, Banjul, Gambia
| | - Catherine John
- Department of Health Sciences, University of Leicester, Leicester, LE1 7RH, UK
| | - Nathalie Kingston
- National Institute for Health Research (NIHR) BioResource for Translational Research in Common and Rare Diseases & NIHR BioResource Centre Cambridge, University of Cambridge, Cambridge, CB2 0QQ, UK
| | - Cecilia M Lindgren
- Big Data Institute at the Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, OX3 7FZ, UK
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, UK
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, 02142, USA
- NIHR Oxford Biomedical Research Centre, OUH Hospital, Oxford, OX4 2PG, UK
| | - Michael O'Donavan
- MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, CF24 4HQ, UK
| | - Stephen O'Rahilly
- Metabolic Research Laboratories, Institute of Metabolic Science, University of Cambridge, Cambridge, CB2 0QQ, UK
| | - Michael J Owen
- MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, CF24 4HQ, UK
| | - Colin N A Palmer
- Medical Research Institute, University of Dundee, Ninewells Hospital and Medical School, Dundee, DD1 9SY, UK
| | - Ewan R Pearson
- Medical Research Institute, University of Dundee, Ninewells Hospital and Medical School, Dundee, DD1 9SY, UK
| | - Robert A Scott
- Quantitative Sciences, GlaxoSmithKline, Stevenage, SG1 2NY, UK
| | - David A van Heel
- Blizard Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, E1 2AT, UK
| | - John Whittaker
- Department of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK
- Statistical Genetics, Projects, Clinical Platforms, and Sciences (PCPS), GlaxoSmithKline, Research Triangle Park, NC, 27709, USA
| | - Tim Frayling
- Genetics of Complex Traits, Institute of Biomedical and Clinical Science, University of Exeter Medical School, Royal Devon and Exeter Hospital, Exeter, EX1 2LU, UK
| | - Martin D Tobin
- Department of Health Sciences, University of Leicester, Leicester, LE1 7RH, UK
- NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, LE3 9QP, UK
| | - Louise V Wain
- Department of Health Sciences, University of Leicester, Leicester, LE1 7RH, UK
- NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, LE3 9QP, UK
| | - George Davey Smith
- MRC Integrative Epidemiology Unit at University of Bristol, Bristol, BS8 2BN, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
| | - David M Evans
- MRC Integrative Epidemiology Unit at University of Bristol, Bristol, BS8 2BN, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
- The University of Queensland Diamantina Institute, The University of Queensland, Translational Research Institute, Brisbane, QLD, 4072, Australia
| | - Fredrik Karpe
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, OX3 7LE, UK
- NIHR Oxford Biomedical Research Centre, Churchill Hospital, Oxford, OX3 7LE, UK
| | - Mark I McCarthy
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, UK
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, OX3 7LE, UK
- NIHR Oxford Biomedical Research Centre, Churchill Hospital, Oxford, OX3 7LE, UK
| | - John Danesh
- MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, UK
- British Heart Foundation (BHF) Centre of Excellence, Division of Cardiovascular Medicine, Addenbrooke's Hospital, Cambridge, CB2 0QQ, UK
- Department of Human Genetics, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, CB10 1HH, UK
- NIHR Blood and Transplant Research Unit in Donor Health and Genomics, Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB2 0SR, UK
| | - Paul W Franks
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, OX3 7LE, UK
- Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Clinical Research Centre, Lund University, Skåne University Hospital, Malmö, SE-205 02, Sweden
- Department of Public Health and Clinical Medicine, Section for Medicine, Umeå University, Umeå, 907 37, Sweden
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA
| | - Nicholas J Timpson
- MRC Integrative Epidemiology Unit at University of Bristol, Bristol, BS8 2BN, UK.
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK.
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Li Q, Ye Z, Zhu P, Guo D, Yang H, Huang J, Zhang W, Polli JE, Shu Y. Indinavir Alters the Pharmacokinetics of Lamivudine Partially via Inhibition of Multidrug and Toxin Extrusion Protein 1 (MATE1). Pharm Res 2018; 35:14. [PMID: 29302757 DOI: 10.1007/s11095-017-2290-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2017] [Accepted: 10/23/2017] [Indexed: 12/01/2022]
Abstract
PURPOSE Lamivudine, a characterized substrate for human multidrug and toxin extrusion protein 1 (hMATE1) in vitro, was commonly used with indinavir as a therapy against human immunodeficiency virus (HIV). We aimed to investigate whether mouse MATE1 is involved in the disposition of lamivudine in vivo, and whether there is any transporter-mediated interaction between indinavir and lamivudine. METHODS The role of MATE1 in the disposition of lamivudine was determined using Mate1 wild type (+/+) and knockout (-/-) mice. The inhibitory potencies of indinavir on lamivudine uptake mediated by OCT2 and MATE1 were determined in human embryonic kidney 293 (HEK 293) cells stably expressing these transporters. The role of MATE1 in the interaction between indinavir and lamivudine in vivo was determined using Mate1 (+/+) and Mate1 (-/-) mice. RESULTS The plasma concentrations and tissue accumulation of lamivudine were markedly elevated in Mate1 (-/-) mice as compared to those in Mate1 (+/+) mice. Indinavir significantly increased the pharmacokinetic exposure of lamivudine in mice; however, the effect by indinavir was significantly less pronounced in Mate1 (-/-) mice as compared to Mate1(+/+) mice. CONCLUSION MATE1 played an important role in lamivudine pharmacokinetics. Indinavir could cause drug-drug interaction with lamivudine in vivo via inhibition of MATE1 and additional mechanism.
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Affiliation(s)
- Qing Li
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, 110 Xiangya Road, Changsha, 410078, China.,Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland at Baltimore, 20 N Pine Street, PH N519, Baltimore, 21201, Maryland, USA
| | - Zhi Ye
- Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland at Baltimore, 20 N Pine Street, PH N519, Baltimore, 21201, Maryland, USA.,Department of Anesthesiology, Xiangya Hospital, Central South University, Changsha, 410078, China
| | - Peng Zhu
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, 110 Xiangya Road, Changsha, 410078, China
| | - Dong Guo
- Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland at Baltimore, 20 N Pine Street, PH N519, Baltimore, 21201, Maryland, USA
| | - Hong Yang
- Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland at Baltimore, 20 N Pine Street, PH N519, Baltimore, 21201, Maryland, USA
| | - Jin Huang
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, 110 Xiangya Road, Changsha, 410078, China
| | - Wei Zhang
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, 110 Xiangya Road, Changsha, 410078, China
| | - James E Polli
- Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland at Baltimore, 20 N Pine Street, PH N519, Baltimore, 21201, Maryland, USA
| | - Yan Shu
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, 110 Xiangya Road, Changsha, 410078, China. .,Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland at Baltimore, 20 N Pine Street, PH N519, Baltimore, 21201, Maryland, USA.
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