Dr. Pletser’s experience includes 30 years of working with the European Space Agency as a Senior Physicist/Engineer and coordinating their parabolic flight campaigns, and he is the Guinness World Record holder for the most number of aircraft flown (12) in parabolas, personally logging more than 7,300 parabolas.
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Seeing the 5,000th book published makes us at the same time proud, happy, humble, and grateful. This is a great opportunity to stop and celebrate what we have done so far, but is also an opportunity to engage even more, grow, and succeed. It wouldn't be possible to get here without the synergy of team members’ hard work and authors and editors who devote time and their expertise into Open Access book publishing with us.
\\n\\n
Over these years, we have gone from pioneering the scientific Open Access book publishing field to being the world’s largest Open Access book publisher. Nonetheless, our vision has remained the same: to meet the challenges of making relevant knowledge available to the worldwide community under the Open Access model.
\\n\\n
We are excited about the present, and we look forward to sharing many more successes in the future.
\\n\\n
Thank you all for being part of the journey. 5,000 times thank you!
\\n\\n
Now with 5,000 titles available Open Access, which one will you read next?
Preparation of Space Experiments edited by international leading expert Dr. Vladimir Pletser, Director of Space Training Operations at Blue Abyss is the 5,000th Open Access book published by IntechOpen and our milestone publication!
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"This book presents some of the current trends in space microgravity research. The eleven chapters introduce various facets of space research in physical sciences, human physiology and technology developed using the microgravity environment not only to improve our fundamental understanding in these domains but also to adapt this new knowledge for application on earth." says the editor. Listen what else Dr. Pletser has to say...
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Dr. Pletser’s experience includes 30 years of working with the European Space Agency as a Senior Physicist/Engineer and coordinating their parabolic flight campaigns, and he is the Guinness World Record holder for the most number of aircraft flown (12) in parabolas, personally logging more than 7,300 parabolas.
\n\n
Seeing the 5,000th book published makes us at the same time proud, happy, humble, and grateful. This is a great opportunity to stop and celebrate what we have done so far, but is also an opportunity to engage even more, grow, and succeed. It wouldn't be possible to get here without the synergy of team members’ hard work and authors and editors who devote time and their expertise into Open Access book publishing with us.
\n\n
Over these years, we have gone from pioneering the scientific Open Access book publishing field to being the world’s largest Open Access book publisher. Nonetheless, our vision has remained the same: to meet the challenges of making relevant knowledge available to the worldwide community under the Open Access model.
\n\n
We are excited about the present, and we look forward to sharing many more successes in the future.
\n\n
Thank you all for being part of the journey. 5,000 times thank you!
\n\n
Now with 5,000 titles available Open Access, which one will you read next?
\n'}],latestNews:[{slug:"webinar-introduction-to-open-science-wednesday-18-may-1-pm-cest-20220518",title:"Webinar: Introduction to Open Science | Wednesday 18 May, 1 PM CEST"},{slug:"step-in-the-right-direction-intechopen-launches-a-portfolio-of-open-science-journals-20220414",title:"Step in the Right Direction: IntechOpen Launches a Portfolio of Open Science Journals"},{slug:"let-s-meet-at-london-book-fair-5-7-april-2022-olympia-london-20220321",title:"Let’s meet at London Book Fair, 5-7 April 2022, Olympia London"},{slug:"50-books-published-as-part-of-intechopen-and-knowledge-unlatched-ku-collaboration-20220316",title:"50 Books published as part of IntechOpen and Knowledge Unlatched (KU) Collaboration"},{slug:"intechopen-joins-the-united-nations-sustainable-development-goals-publishers-compact-20221702",title:"IntechOpen joins the United Nations Sustainable Development Goals Publishers Compact"},{slug:"intechopen-signs-exclusive-representation-agreement-with-lsr-libros-servicios-y-representaciones-s-a-de-c-v-20211123",title:"IntechOpen Signs Exclusive Representation Agreement with LSR Libros Servicios y Representaciones S.A. de C.V"},{slug:"intechopen-expands-partnership-with-research4life-20211110",title:"IntechOpen Expands Partnership with Research4Life"},{slug:"introducing-intechopen-book-series-a-new-publishing-format-for-oa-books-20210915",title:"Introducing IntechOpen Book Series - A New Publishing Format for OA Books"}]},book:{item:{type:"book",id:"7556",leadTitle:null,fullTitle:"Dyslipidemia",title:"Dyslipidemia",subtitle:null,reviewType:"peer-reviewed",abstract:"Dyslipidemia is a major risk factor for cardiovascular disease, which is the leading cause of morbidity and mortality around the globe, particularly among aging populations. Lipoprotein disorders, frequently encountered by clinicians, require early recognition and treatment. In this book, we assembled a group of world-renowned scholars in their field to address major areas in lipoprotein disorders that are imminently relevant to clinicians and other healthcare providers. Areas discussed include an overview of lipid metabolism, a complex topic, presented in a simplified and rational way. We also highlight recent developments in the field including dyslipidemias characterized by nontraditional lipid biomarkers. Furthermore, we discuss the pathogenesis of atherosclerosis and the role of dyslipidemia. Other chapters include the assessment of primary and secondary causes of dyslipidemia. Targets for treatment as well as the role of major therapeutic agents including statins and PCSK9 inhibitors are also discussed in light of the most recent guidelines by major international organizations. This is in addition to an overview of lifestyle and dietary modification as well as alternative options for dyslipidemia management. Furthermore, dyslipidemia in special populations is emphasized including various ethnic groups as well as those with HIV disease, chronic kidney disease, among others. The role of adiposity including brown fat together with highlights on lipidomics and dyslipidemias characterized by nontraditional lipid biomarkers is also highlighted. We believe that this volume will serve as a valuable resource, not only for clinicians and other healthcare providers, but for students and research scholars as well.",isbn:"978-1-83968-004-5",printIsbn:"978-1-83968-003-8",pdfIsbn:"978-1-83968-005-2",doi:"10.5772/intechopen.76825",price:119,priceEur:129,priceUsd:155,slug:"dyslipidemia",numberOfPages:180,isOpenForSubmission:!1,isInWos:1,isInBkci:!1,hash:"dfd1faefe925f0f8335c42cdb36256c1",bookSignature:"Samy I. McFarlane",publishedDate:"December 18th 2019",coverURL:"https://cdn.intechopen.com/books/images_new/7556.jpg",numberOfDownloads:11258,numberOfWosCitations:8,numberOfCrossrefCitations:10,numberOfCrossrefCitationsByBook:0,numberOfDimensionsCitations:13,numberOfDimensionsCitationsByBook:0,hasAltmetrics:1,numberOfTotalCitations:31,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"April 9th 2018",dateEndSecondStepPublish:"June 18th 2018",dateEndThirdStepPublish:"August 17th 2018",dateEndFourthStepPublish:"November 5th 2018",dateEndFifthStepPublish:"January 4th 2019",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"53477",title:"Prof.",name:"Samy I.",middleName:null,surname:"McFarlane",slug:"samy-i.-mcfarlane",fullName:"Samy I. McFarlane",profilePictureURL:"https://mts.intechopen.com/storage/users/53477/images/system/53477.png",biography:"Dr. McFarlane is Distinguished Teaching Professor of Medicine/Endocrinology and Associate Dean at SUNY-Downstate, Health Sciences University, Brooklyn, New York, USA.\n\nHe has extensive experience in clinical and translational research and served as the PI for the largest center in North America in the landmark Diabetes Reduction Assessment with ramipril and rosiglitazone Medication (DREAM) trial. \n\nHe is the author of more than 400 publications with more than 10,000 citations and an h-index of 46. He also has 270 highly influential citations to his credit, including those in major guidelines by the American Heart Association (AHA), such as stroke guidelines (2018 and 2019), the Scientific Statement from the AHA on Lifestyle and Risk Factor Modification for Reduction of Atrial Fibrillation 2020, and the 2021 SHNE/HRS/EHRA/APHRS Expert Collaborative Statement. He is also the editor of several books on diabetes, hypertension, cardiovascular disease, and related topics.\n\nDr. McFarlane is a three-term member of the National Institutes of Health’s National Institute of Diabetes and Digestive and Kidney Diseases (NIH-NIDDK) and served twice as chair of the NIH-NIDDK U01 committee. He also served as chair of the clinical sub-committee of the National Kidney Foundation (NKF) Kidney Early Evaluation Program. Locally, he held several leadership positions including Medical Director of Clinical Research and Program Director and Chief of Endocrinology. He also served as District President of the American College of Physicians. Dr. McFarlane is the recipient of numerous awards and honors including recognition from the United States Army, the US Congress, and the Gold Foundation for Humanism in Medicine.",institutionString:"State University of New York",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"3",totalChapterViews:"0",totalEditedBooks:"2",institution:null}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"170",title:"Cardiology and Cardiovascular Medicine",slug:"cardiology-and-cardiovascular-medicine"}],chapters:[{id:"66265",title:"Introductory Chapter: Overview of Lipoprotein Metabolism",doi:"10.5772/intechopen.85094",slug:"introductory-chapter-overview-of-lipoprotein-metabolism",totalDownloads:1512,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:0,abstract:null,signatures:"Angelina Zhyvotovska, Denis Yusupov and Samy I. McFarlane",downloadPdfUrl:"/chapter/pdf-download/66265",previewPdfUrl:"/chapter/pdf-preview/66265",authors:[{id:"53477",title:"Prof.",name:"Samy I.",surname:"McFarlane",slug:"samy-i.-mcfarlane",fullName:"Samy I. McFarlane"},{id:"298681",title:"Dr.",name:"Denis",surname:"Yusupov",slug:"denis-yusupov",fullName:"Denis Yusupov"},{id:"298682",title:"Dr.",name:"Angelina",surname:"Zhyvotovska",slug:"angelina-zhyvotovska",fullName:"Angelina Zhyvotovska"}],corrections:null},{id:"66725",title:"Dyslipidemia and Its Role in the Pathogenesis of Atherosclerotic Cardiovascular Disease: Implications for Evaluation and Targets for Treatment of Dyslipidemia Based on Recent Guidelines",doi:"10.5772/intechopen.85772",slug:"dyslipidemia-and-its-role-in-the-pathogenesis-of-atherosclerotic-cardiovascular-disease-implications",totalDownloads:2178,totalCrossrefCites:5,totalDimensionsCites:7,hasAltmetrics:1,abstract:"The clinical presentations of atherosclerotic disease are the result of a constellation of diverse metabolic and immunologic mechanisms ultimately set into motion by the formation of fatty acid streaks and the accompanying inflammatory cell activation, endothelial damage, smooth muscle proliferation, vascular fibrosis, and end-organ infarction and necrosis. At the heart of atherosclerosis are the byproducts of lipid metabolism, lipoproteins containing triglycerides, phospholipids, and cholesterol, and the changes they undergo that eventually lead to macrophage activation, foam cell formation, and other downstream atherosclerotic changes. Understanding the functionality of cholesterol, triglycerides, and lipoproteins in the cascade of atherosclerotic pathways has tremendous implications on current guidelines for the evaluation and targets in the management of dyslipidemia, and serves as the foundation for future investigations into targets of atherosclerotic therapies.",signatures:"Perry Wengrofsky, Justin Lee and Amgad N. Makaryus",downloadPdfUrl:"/chapter/pdf-download/66725",previewPdfUrl:"/chapter/pdf-preview/66725",authors:[{id:"106536",title:"Dr.",name:"Amgad N.",surname:"Makaryus",slug:"amgad-n.-makaryus",fullName:"Amgad N. Makaryus"},{id:"292156",title:"Dr.",name:"Perry",surname:"Wengrofsky",slug:"perry-wengrofsky",fullName:"Perry Wengrofsky"},{id:"292157",title:"Dr.",name:"Justin",surname:"Lee",slug:"justin-lee",fullName:"Justin Lee"}],corrections:null},{id:"64903",title:"Dyslipidemia in Special Populations, the Elderly, Women, HIV, Chronic Kidney Disease and ESRD, and Minority Groups",doi:"10.5772/intechopen.82831",slug:"dyslipidemia-in-special-populations-the-elderly-women-hiv-chronic-kidney-disease-and-esrd-and-minori",totalDownloads:858,totalCrossrefCites:2,totalDimensionsCites:2,hasAltmetrics:0,abstract:"This chapter discusses the management of dyslipidemia in special patient populations: the elderly, woman and pregnancy, renal disease, human immunodeficiency virus (HIV), and different racial/ethnic groups. In the elderly, dyslipidemia is often underdiagnosed and undertreated. Consideration for potential atherosclerotic risk-reduction benefits, risk of adverse effects, drug-drug interactions, and patient preferences should precede the initiation of statin therapy. Data on pregnant women are lacking and need future research. Dyslipidemia and its effects on the cardiovascular system in chronic kidney disease (CKD), end-stage renal disease (ESRD), and HIV are dynamic and multimodal. These conditions are states of chronic inflammation, where it is difficult to associate quantities of cholesterol types with outcomes. Among all racial groups, Asian Indians, Filipinos, and Hispanics are at a higher risk for dyslipidemia. Genetic differences in statin metabolism may explain this racial/ethnic difference.",signatures:"Amarpali Brar, Jeans M. Santana, Moro O. Salifu and Clinton D. Brown",downloadPdfUrl:"/chapter/pdf-download/64903",previewPdfUrl:"/chapter/pdf-preview/64903",authors:[{id:"261968",title:"M.D.",name:"Amarpali",surname:"Brar",slug:"amarpali-brar",fullName:"Amarpali Brar"},{id:"263925",title:"Dr.",name:"Clinton D.",surname:"Brown",slug:"clinton-d.-brown",fullName:"Clinton D. Brown"},{id:"263926",title:"Dr.",name:"Moro O.",surname:"Salifu",slug:"moro-o.-salifu",fullName:"Moro O. Salifu"},{id:"271942",title:"Dr.",name:"Jeans M.",surname:"Santana",slug:"jeans-m.-santana",fullName:"Jeans M. Santana"}],corrections:null},{id:"65757",title:"Prevalence of Dyslipidemia and Goal Attainment with Lipid-Lowering Therapy: Insights from Thai Multicenter Study and Overview of the Major Guidelines",doi:"10.5772/intechopen.82355",slug:"prevalence-of-dyslipidemia-and-goal-attainment-with-lipid-lowering-therapy-insights-from-thai-multic",totalDownloads:2241,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Background Since the release in Thailand in 2001 of the Third Guidelines by the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation and Treatment of High Blood Cholesterol in Adults or the Adult Treatment Panel (ATP III), there have been no nationwide studies on the proportion of dyslipidaemic patients who have achieved the low-density lipoprotein cholesterol (LDL-C) goals. The authors therefore aimed to estimate the percentage achievement of LDL-C goals based on the modified NCEP ATP III guidelines in intermediate- to high-risk patients. Methods The authors conducted a hospital-based, cross-sectional, epidemiological survey. Patients (1240) were selected consecutively from 50 hospitals across Thailand. Patients were included if they had been treated with statins for at least 3 months. Results Two-thirds were female, and the mean age was 61.7+69.5 years. The median duration of statin treatment was 21 months. Half (633/1240) of the patients achieved the LDL-C goal levels as defined by the NCEP guidelines (51.1%, 95% CI 48.3% to 53.8%). The very high-risk group had the lowest percentage achievement (11.6%; 95% CI 1.6% to 21.6%), compared with 54.2% (95% CI 50.9% to 57.4%) for the high-risk group and 47.0% (95% CI 41.1% to 52.8%) for the moderate-risk group. More males achieved the LDL-C goals than females (55.6% vs. 48.9%; P = 0.029). Conclusions Overall, 51.1% of the patients with cardiovascular risk, on statins treatment, achieved the NCEP ATP III LDL-C goal levels.",signatures:"Songkwan Silaruks, Charn Sriratanasathavorn, Petch Rawdaree, Rapeephon Kunjara-Na-Ayudhaya, Bandit Thinkhamrop and Piyamitr Sritara",downloadPdfUrl:"/chapter/pdf-download/65757",previewPdfUrl:"/chapter/pdf-preview/65757",authors:[{id:"256769",title:"Prof.",name:"Songkwan",surname:"Silaruks",slug:"songkwan-silaruks",fullName:"Songkwan Silaruks"},{id:"283455",title:"Dr.",name:"Charn",surname:"Sriratanasathavorn",slug:"charn-sriratanasathavorn",fullName:"Charn Sriratanasathavorn"},{id:"283456",title:"Dr.",name:"Petch",surname:"Rawdaree",slug:"petch-rawdaree",fullName:"Petch Rawdaree"},{id:"283457",title:"Dr.",name:"Rapeephon",surname:"Kunjara-Na-Ayudhaya",slug:"rapeephon-kunjara-na-ayudhaya",fullName:"Rapeephon Kunjara-Na-Ayudhaya"},{id:"283458",title:"Dr.",name:"Bandit",surname:"Thinkhamrop",slug:"bandit-thinkhamrop",fullName:"Bandit Thinkhamrop"},{id:"283459",title:"Prof.",name:"Piyamitr",surname:"Sritara",slug:"piyamitr-sritara",fullName:"Piyamitr Sritara"}],corrections:null},{id:"69909",title:"Postprandial Lipemia as Cardiovascular Disease Risk Factor",doi:"10.5772/intechopen.89933",slug:"postprandial-lipemia-as-cardiovascular-disease-risk-factor",totalDownloads:724,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Postprandial lipemia (PPL) is characterized by prolonged and increased levels of lipids especially triglycerides (TG) and triglyceride-rich lipoprotein levels after a meal. There are an increasing number of evidence that postprandial lipemia is a significant risk factor for cardiovascular disease because of its causative role in atherosclerosis and endothelial dysfunction. This has serious implications because common dietary patterns are characterized by high fat content and meal consumption; hence, most will be in a postprandial state resulting to frequent and prolonged exposure to high lipid levels. The review will present the current evidences for the role of postprandial lipemia as a risk factor for cardiovascular disease and its association with other cardiovascular risk factors, namely, diabetes and obesity. We will also present recommendations on the diagnosis and management of postprandial lipemia.",signatures:"Neil Francis Amba and Leilani B. Mercado-Asis",downloadPdfUrl:"/chapter/pdf-download/69909",previewPdfUrl:"/chapter/pdf-preview/69909",authors:[{id:"299157",title:"Prof.",name:"Leilani B.",surname:"Mercado-Asis",slug:"leilani-b.-mercado-asis",fullName:"Leilani B. Mercado-Asis"},{id:"309025",title:"Dr.",name:"Neil Francis",surname:"Amba",slug:"neil-francis-amba",fullName:"Neil Francis Amba"}],corrections:null},{id:"65816",title:"Alternative Natural Management of Dyslipidemia",doi:"10.5772/intechopen.82430",slug:"alternative-natural-management-of-dyslipidemia",totalDownloads:835,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"In hypercholesterolemic patients, besides therapeutic treatments, alternative treatments can be used such as lifestyle changes, e.g. avoiding smoking, regular exercise, and consuming a diet rich in fiber and low in trans saturated and saturated fats. There are also certain plant products, such as the gum residue guggulipid, that are used in India as a traditional medicine to reduce blood cholesterol levels. Similarly, red yeast rice and rice bran oil have been observed to reduce elevated cholesterol levels. Other herbal products have also been investigated for their role in lowering cholesterol levels, as well as various other herbs and spices such as ginger and turmeric. Another herbal remedy available for reducing high cholesterol levels is the leaf extract of Cynara scolymus, commonly known as artichoke thistle. Cynara cardunculus var. scolymus, or globe artichoke, is mainly cultivated as a food crop. It has an important effect on reducing plasma cholesterol and low-density lipoprotein levels.",signatures:"Abdullah Glil Alkushi",downloadPdfUrl:"/chapter/pdf-download/65816",previewPdfUrl:"/chapter/pdf-preview/65816",authors:[{id:"261081",title:"Prof.",name:"Abdullah",surname:"Glil Alkushi",slug:"abdullah-glil-alkushi",fullName:"Abdullah Glil Alkushi"}],corrections:null},{id:"64081",title:"The Role of Niacin in the Management of Dyslipidemia",doi:"10.5772/intechopen.81725",slug:"the-role-of-niacin-in-the-management-of-dyslipidemia",totalDownloads:1314,totalCrossrefCites:2,totalDimensionsCites:2,hasAltmetrics:1,abstract:"Niacin or nicotinic acid has been used for the management of dyslipidemia for over 50 years, and it is the first medication that has been shown to reduce both coronary disease events and mortality. It is unique among the various lipid therapies in that it can not only reduce all of atherogenic lipid fractions (total cholesterol, low-density lipoprotein, very low-density lipoprotein, non-HDL lipoproteins, and triglycerides), but is also the most effective agent for raising high-density lipoprotein (specifically Apolipoprotein A-1). It is also the only lipid therapy that can lower lipoprotein (a). Niacin also has non-lipid benefits that improve vascular health and reduce atherogenesis. Niacin therapy was initially hampered by a high incidence of side effects, especially flushing, but this has largely been overcome by extended-release formulations and dosing and administering properly. Despite the failure of two recent clinical trials to show benefit of combining niacin with statins, there are many trials that support using niacin as monotherapy or in combination with other lipid agents including statins. Niacin is also the cheapest lipid agent available, and with the epidemic of cardiovascular disease in the world, it offers great value in the population-wide management of this health problem.",signatures:"Joseph M. Keenan",downloadPdfUrl:"/chapter/pdf-download/64081",previewPdfUrl:"/chapter/pdf-preview/64081",authors:[{id:"255775",title:"Emeritus Prof.",name:"Joseph M.",surname:"Keenan",slug:"joseph-m.-keenan",fullName:"Joseph M. Keenan"}],corrections:null},{id:"69010",title:"Novel Therapies for Dyslipidemia",doi:"10.5772/intechopen.88477",slug:"novel-therapies-for-dyslipidemia",totalDownloads:886,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Multiple studies have shown a strong correlation between low-density lipoprotein cholesterol (LDL-C) concentration and development as well as progression of atherosclerosis and cardiovascular disorders. Thus, the decrease of the LDL-C burden through lifestyle modification and/or pharmacological interventions unanimously demonstrated a decrease in cardiovascular events and mortality. To date, statins are considered the cornerstone of lipid-lowering therapy. The Cholesterol Treatment Trialists’ (CTT) Collaboration has shown consistency of treatment benefits across a wide patient population. However, new data are now revealing that a considerate patient population failed to achieve lipid goals solely on statins and a significant percentage cannot tolerate treatment. Therefore, extensive work has recently been done in generating novel LDL-C-lowering agents that would act through mechanisms different from statins. Among others, monoclonal antibodies to protein convertase subtilisin/kexin type 9 (PCSK9) and ezetimibe seem particularly promising. Both PCSK9 monoclonal antibodies and ezetimibe have shown to be well tolerated and very effective at lowering LDL-C.",signatures:"Olta Tafaj Reddy",downloadPdfUrl:"/chapter/pdf-download/69010",previewPdfUrl:"/chapter/pdf-preview/69010",authors:[{id:"295020",title:"Dr.",name:"Olta",surname:"Tafaj Reddy",slug:"olta-tafaj-reddy",fullName:"Olta Tafaj Reddy"}],corrections:null},{id:"68179",title:"Adipose Tissue Complexities in Dyslipidemias",doi:"10.5772/intechopen.87439",slug:"adipose-tissue-complexities-in-dyslipidemias",totalDownloads:716,totalCrossrefCites:0,totalDimensionsCites:1,hasAltmetrics:0,abstract:"Adipose tissue is the largest organ in the human body and, in excess, contributes to dyslipidemias and the dysregulation of other vascular and metabolic processes. Adipose tissue is heterogeneous, comprised of several cell types based on morphology, cellular age, and endocrine and paracrine function. Adipose tissue depots are also regional, primarily due to sex differences and genetic variation. Adipose tissue is also characterized as subcutaneous vs. visceral. In addition, fatty deposits exist outside of adipose tissue, such as those surrounding the heart, or as infiltration of skeletal muscle. This review focuses on adipose tissue and its contribution to dyslipidemias. Dyslipidemias are defined as circulating blood lipid levels that are too high or altered. Lipids include both traditional and nontraditional species. Leaving aside traditional definitions, adipose tissue contributes to dyslipidemias in a myriad of ways. To address a small portion of this topic, we reviewed (a) adipose tissue location and cell types, (b) body composition, (c) endocrine adipose, (d) the fat-brain axis, and (e) genetic susceptibility. The influence of these complex aspects of adipose tissue on dyslipidemias and human health, illustrating that, once again, that adipose tissue is a quintessential, multifunctional tissue of the human body, will be summarized.",signatures:"Deborah R. Gustafson",downloadPdfUrl:"/chapter/pdf-download/68179",previewPdfUrl:"/chapter/pdf-preview/68179",authors:[{id:"294635",title:"Dr.",name:"Deborah R.",surname:"Gustafson",slug:"deborah-r.-gustafson",fullName:"Deborah R. Gustafson"}],corrections:null}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},subseries:null,tags:null},relatedBooks:[{type:"book",id:"10488",title:"Renin-Angiotensin Aldosterone System",subtitle:null,isOpenForSubmission:!1,hash:"5815b21958b2b2d5b653771c3f0cc35c",slug:"renin-angiotensin-aldosterone-system",bookSignature:"Samy I. McFarlane",coverURL:"https://cdn.intechopen.com/books/images_new/10488.jpg",editedByType:"Edited by",editors:[{id:"53477",title:"Prof.",name:"Samy I.",surname:"McFarlane",slug:"samy-i.-mcfarlane",fullName:"Samy I. 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1. Introduction
Many problems in the field of artificial intelligence can be modeled as constraint satisfaction problems (CSP). A CSP is a tuple XDC where, X=x1x2…xn is a finite set of variables, D=Dx1Dx2…Dxn is a finite set of domains. Thus each variable x∈X has a corresponding discrete domain Dx from which it can be instantiated, and C=C1C2…Ck is a finite set of constraints. Each k-ary constraint restricts a k-tuple of variables, x1x2…xk and specifies a subset of D1×…×Dk, each element of which are values that the variables cannot take simultaneously. A solution to a CSP requires the assignment of values to each of the variables from their domains such that all the constraints on the variables are satisfied. The maximum constraint satisfaction problem (Max-CSP) aims at finding an assignment so as to maximize the number of satisfied constraints. Max-CSP can be regarded as the generalization of CSP; the solution maximizes the number of satisfied constraints. In this chapter, attention is focused on binary CSPs, where all constraints are binary, that is, they are based on the cartesian product of the domains of two variables. However, any non-binary CSP can theoretically be converted to a binary CSP [1]. Algorithms for solving CSPs apply the so-called 1-exchange neighborhood under, which two solutions are direct neighbors if, and only if, they differ at most in the value assigned to one variable. Examples include the minimum conflict heuristic MCH [2], the break method for escaping from local minima [3], and various enhanced MCH (e.g., randomized iterative improvement of MCH called WMCH [4], MCH with tabu search [5], and evolutionary algorithms [6]). Algorithms based on assigning weights on constraints are techniques that work by introducing weights on variables or constraints in order to avoid local minima. Methods belonging to this category include genet [7], guided local search [8], the exponentiated subgradient [9], discrete Lagrangian search [10], the scaling and probabilistic smoothing [11], evolutionary algorithms combined with stepwise adaptation of weights [12], methods based on dynamically adapting weights on variables [13], or both (i.e., variables and constraints) [14]. Methods based on large neighborhood search have recently attracted several researchers for solving the CSP [15]. The central idea is to reduce the size of local search space relying on a continual relaxation (removing elements from the solution) and re-optimization (re-inserting the removed elements). Finally, the work introduced in [16] introduces a variable depth metaheuristic combing a greedy local search with a self-adaptive weighting strategy on the constraints weights.
2. Algorithm
2.1. Multilevel context
The multilevel paradigm is a simple technique, which at its core involves recursive coarsening to produce smaller and smaller problems that are easier to solve than the original one. Multilevel techniques have been developed in the period after 1960 and are among the most efficient techniques used for solving large algebraic systems arising from the discretization of partial differential equations. In recent years, it has been recognized that an effective way of enhancing metaheuristics is to use them in the multilevel context. The pseudo-code of the multilevel genetic algorithm is shown in Algorithm 1. Figure 1 illustrates the multilevel paradigm used for six variables and two coarsening levels. The multilevel paradigm consists of four phases: coarsening, initial solution, uncoarsening, and refinement. The coarsening phase aims at merging the variables associated with the problem to form clusters. The clusters are used in a recursive manner to construct a hierarchy of problems each representing the original problem but with fewer degrees of freedom. The coarsest level can then be used to compute an initial solution. The solution found at the coarsest level is uncoarsened (extended to give an initial solution for the parent level) and then improved using a chosen optimization algorithm. A common feature that characterizes multilevel algorithms, is that any solution in any of the coarsened problems is a legitimate solution to the original one. Optimization algorithms using the multilevel paradigm draw their strength from coupling the refinement process across different levels.
Algorithm 1.
The multilevel genetic algorithm.
Figure 1.
The different steps of the multilevel paradigm.
2.2. Multilevel genetic algorithm (GA)
GAs [17] are stochastic methods for global search and optimization and belong to the group of nature-inspired metaheuristics leading to the so-called natural computing. It is a fast-growing interdisciplinary field in which a range of techniques and methods are studied for dealing with large, complex, and dynamic problems with various sources of potential uncertainties. GAs simultaneously examine and manipulate a set of possible solutions. A gene is a part of a chromosome (solution), which is the smallest unit of genetic information. Every gene is able to assume different values called allele. All genes of an organism form a genome, which affects the appearance of an organism called phenotype. The chromosomes are encoded using a chosen representation and each can be thought of as a point in the search space of candidate solutions. Each individual is assigned a score (fitness) value that allows assessing its quality. The members of the initial population may be randomly generated or by using sophisticated mechanisms by means of which an initial population of high-quality chromosomes is produced. The reproduction operator selects (randomly or based on the individual’s fitness) chromosomes from the population to be parents and enter them in a mating pool. Parent individuals are drawn from the mating pool and combined so that information is exchanged and passed to off-springs depending on the probability of the crossover operator. The new population is then subjected to mutation and enters into an intermediate population. The mutation operator acts as an element of diversity into the population and is generally applied with a low-probability to avoid disrupting crossover results. Finally, a selection scheme is used to update the population giving rise to a new generation. The individuals from the set of solutions, which is called population will evolve from generation to generation by repeated applications of an evaluation procedure that is based on genetic operators. Over many generations, the population becomes increasingly uniform until it ultimately converges to optimal or near-optimal solutions. The different steps of the multilevel weighted genetic algorithm are described as follows:
construction of levels: let G0=V0E0 be an undirected graph of vertices V and edges E. The set V denotes variables and each edge xixj∈E implies a constraint joining the variables xi and xj. Given the initial graph G0, the graph is repeatedly transformed into smaller and smaller graphs G1, G2, …, Gm such that ∣V0∣>, ∣V1∣>, … >∣Vm∣. To coarsen a graph from Gj to Gj+1, a number of different techniques may be used. In this chapter, when combining a set of variables into clusters, the variables are visited in a random order. If a variable xi has not been matched yet, then the algorithms randomly select one of its neighboring unmatched variable xj, and a new cluster consisting of these two variables is created. Its neighbors are the combined neighbors of the merged variables xi and xj. Unmatched variables are simply left unmatched and copied to the next level.
initial assignment: the process of constructing a hierarchy of graphs ceases as soon as the size of the coarsest graphs reaches some desired threshold. A random initial population is generated at the lowest level Gk=VkEk. The chromosomes, which are assignments of values to the variables are encoded as strings of bits, the length of which is the number of variables. At the lowest level, the length of the chromosome is equal to the number of clusters. The initial solution is simply constructed by assigning to all variable in a cluster, a random value vi. In this work, it is assumed that all variables have the same domain (i.e., same set of values), otherwise different random values should be assigned to each variable in the cluster. All the individuals of the initial population are evaluated and assigned a fitness expressed in Eq. (1), which counts the number of constraint violations where <xisi,xjsj> denotes the constraint between the variables xi and xj where xi is assigned the value si from Dxi and xj is assigned the value sj from Dxj.
Fitness=∑i=1n−1∑j=i+1nViolationWi,j<xisixjsj>E1
initial weights: the next step of the algorithm assigns a fixed amount of weight equal to 1 across all the constraints. The distribution of weights to constraints aims at forcing hard constraints with large weights to be satisfied thereby preventing the algorithm at a later stage from getting stuck at a local optimum.
optimization: having computed an initial solution at the coarsest graph, GA starts the search process from the coarsest level Gk=(Vk,Ek) and continues to move toward smaller levels. The motivation behind this strategy is that the order in which the levels are traversed offers a better mechanism for performing diversification and intensification. The coarsest level allows GA to view any cluster of variables as a single entity leading the search to become guided in faraway regions of the solution space and restricted to only those configurations in the solution space in which the variables grouped within a cluster are assigned the same value. As the switch from one level to another implies a decrease in the size of the neighborhood, the search is intensified around solutions from previous levels in order to reach better ones.
parent selection: during the optimization, new solutions are created by combining pairs of individuals in the population and then applying a crossover operator to each chosen pair. Combining pairs of individuals can be viewed as a matching process. In the version of GA used in this work, the individuals are visited in random order. An unmatched individual ik is matched randomly with an unmatched individual il.
genetic operators: the task of the crossover operator is to reach regions of the search space with higher average quality. The two-point crossover operator is applied to each matched pair of individuals. The two-point crossover selects two randomly points within a chromosome and then interchanges the two parent chromosomes between these points to generate two new offspring.
survivor selection: the selection acts on individuals in the current population. Based on each individual quality (fitness), it determines the next population. In the roulette method, the selection is stochastic and biased toward the best individuals. The first step is to calculate the cumulative fitness of the whole population through the sum of the fitness of all individuals. After that, the probability of selection is calculated for each individual as being PSelectioni=fi/∑1Nfi, where fi is the fitness of individual i.
updating weights: the weights of each current violated constraint is then increased by one, whereas the newly satisfied constraints will have their weights decreased by one before the start of new generation.
termination condition: the convergence of GA is supposed to be reached if the best individual remains unchanged during five consecutive generations.
projection: once GA has reached the convergence criterion with respect to a child level graph Gk=VkEk, the assignment reached on that level must be projected on its parent graph Gk−1=Vk−1Ek−1. The projection algorithm is simple; if a cluster belongs to Gk=VkEk is assigned the value vli, the merged pair of clusters that it represents belonging to Gk−1=Vk−1Ek−1 are also assigned the value vli,
3. Experimental results
3.1. Experimental setup
The benchmark instances were generated using model A [18] as follows: each instance is defined by the 4-tuple n,m,pd,pt, where n is the number of variables; m is the size of each variable’s domain; pd, the constraint density, is the proportion of pairs of variables, which have a constraint between them; and pt, the constraint tightness, is the probability that a pair of values is inconsistent. From the n×n−1/2 possible constraints, each one is independently chosen to be added in the constraint graph with the probability pd. Given a constraint, we select with the probability pt, which value pairs become no-goods. The model A will on average have pd×n−1/2 constraints, each of which has on average pt×m2 inconsistent pairs of values. For each pair of density tightness, we generate one soluble instance (i.e., at least one solution exists). Because of the stochastic nature of GA, we let each algorithm do 100 independent runs, each run with a different random seed. Many NP-complete or NP-hard problems show a phase transition point that marks the spot where we go from problems that are under-constrained and so relatively easy to solve, to problems that are over-constrained and so relatively easy to prove insoluble. Problems that are on average harder to solve occur between these two types of relatively easy problem. The values of pd and pt are chosen in such a way that the instances generated are within the phase transition. In order to predict the phase transition region, a formula for the constrainedness [19] of binary CSPs was defined by:
κ=n−12pdlogm11−pt.E2
The tests were carried out on a DELL machine with 800 MHz CPU and 2 GB of memory. The code was written in C and compiled with the GNU C compiler version 4.6. The following parameters have been fixed experimentally and are listed below:
Population size = 50
Stopping criteria for the coarsening phase: the reduction process stops as soon as the number of levels reaches 3. At this level, MLV-WGA generates an initial population.
Convergence during the optimization phase: if there is no observable improvement of the fitness function of the best individual during five consecutive generations, MLV-WGA is assumed to have reached convergence and moves to a higher level.
3.2. Results
The plots in Figures 2 and 3 compare the WGA with its multilevel variant MLV-WGA. The improvement in quality imparted by the multilevel context is immediately clear. Both WGA and MLV-WGA exhibit what is called a plateau region. A plateau region spans a region in the search space where crossover and mutation operators leave the best solution or the mean solution unchanged. However, the length of this region is shorter with MLV-WGA compared to that of WGA. The multilevel context uses the projected solution obtained at Gm+1Vm+1Em+1 as the initial solution for GmVmEm for further refinement. Even though the solution at Gm+1Vm+1Em+1 is at a local minimum, the projected solution may not be at a local optimum with respect to GmVmEm. The projected assignment is already a good solution leading WGA to converge quicker within few generations to a better solution. Tables 1–3 show a comparison of the two algorithms. For each algorithm, the best (Min) and the worst (Max) results are given, while mean represents the average solution. MLV-WGA outperforms WGA in 53 cases out of 96, gives similar results in 20 cases, and was beaten in 23 cases. The performance of both algorithms differs significantly. The difference for the total performance is between 25 and 70% in the advantage of MLV-GA. Comparing the worst performances of both algorithms, MLV-WGA gave bad results in 15 cases, both algorithms give similar results in 8 cases, and MLV-WGA was able to perform better than WGA in 73 cases. Looking at the average results, MLV-WGA does between 16 and 41% better than WGA in 84 cases, while the differences are very marginal in the remaining cases where WGA beats MLV-WGA.
Figure 2.
MLV-GA vs. GA: evolution of the mean unsatisfied constraints as a function of time. Csp-N30-DS40-C125-cd026ct063.
Figure 3.
MLV-GA vs. GA: evolution of the mean unsatisfied constraints as a function of time. Csp-N35-DS20-C562-cd094-ct017.
MLV-WGA
WGA
Instance
Min
Max
Mean
REav
Min
Max
Mean
REav
N25-DS20-C36-cd-014-ct083
3
7
4.58
0.128
3
8
5.41
0.151
N25-DS20-C44-cd012-ct087
6
10
8.04
0.183
8
14
9.91
0.226
N25-DS20-C54-cd018-ct075
3
7
5.37
0.100
4
9
6.91
0.128
N25-DS20-C78-cd026-ct061
2
8
4.33
0.056
2
10
5.79
0.073
N25-DS20-C225-cd078-ct027
3
8
4.16
0.019
3
9
5.66
0.026
N25-DS20-C229-cd072-ct029
4
9
6.04
0.014
4
11
8.16
0.036
N25-DS20-C242-cd086-ct025
1
6
3.5
0.015
3
10
5.70
0.024
N25-DS20-C269-cd086-ct025
4
10
5.66
0.022
4
10
7.54
0.029
N25-DS20-C279-cd094-ct023
2
7
4.75
0.018
4
9
6.75
0.025
N25-DS40-C53-cd016-ct085
6
11
8.91
0.169
8
13
10.70
0.202
N25-DS40-C70-cd026-ct069
2
6
4.25
0.061
3
8
5.75
0.083
N25-DS40-C72-cd022-ct075
6
12
9
0.125
6
15
10.45
0.146
N25-DS40-C102-cd032-ct061
5
12
8.12
0.080
7
14
10.33
0.102
N25-DS40-C103-cd034-ct059
5
9
6.83
0.067
4
12
8.79
0.086
N25-DS40-C237-cd082-ct031
3
8
5.66
0.024
5
10
7.87
0.034
N25-DS40-C253-cd088-ct029
3
7
4.95
0.020
5
12
8.04
0.032
N25-DS40-C264-cd088-ct029
5
10
6.91
0.027
6
16
8.91
0.034
N25-DS40-C281-cd096-ct027
3
9
5.62
0.020
4
12
8.54
0.031
N25-DS40-C290-cd096-ct027
4
10
7.08
0.025
6
14
9
0.032
Table 1.
MLV-WGA vs. WGA: number of variables: 25.
REav denotes the relative error in percent. The value in bold shows the algorithm with the lowest RE.
MLV-WGA
WGA
Instance
Min
Max
Mean
REav
Min
Max
Mean
REav
N30-DS20-C41-cd012-ct083
2
6
3.70
0.026
3
7
5.08
0.124
N30-DS20-C71-cd018-ct069
1
7
3.37
0.048
3
10
5.66
0.080
N30-DS20-C85-cd020-ct065
3
9
6
0.071
5
12
8.37
0.099
N30-DS20-C119-cd028-ct053
3
10
5.70
0.048
6
12
8.83
0.075
N30-DS20-C334-cd074-ct025
6
13
8.16
0.025
6
14
9.87
0.030
N30-DS20-C387-cd090-ct021
3
9
6.66
0.018
5
13
8.70
0.033
N30-DS20-C389-cd090-ct021
2
9
6.08
0.016
4
14
8.95
0.024
N30-DS20-C392-cd090-ct021
3
10
7.08
0.019
5
15
9.16
0.024
N30-DS20-C399-cd090-ct021
5
13
7.70
0.020
6
14
9.79
0.025
N30-DS40-C85-cd020-ct073
5
11
7.75
0.092
7
14
10.87
0.152
N30-DS40-C96-cd020-ct073
8
12
16
0.167
11
19
14.58
0.015
N30-DS40-C121-cd026-ct063
8
14
10.5
0.087
9
19
14.33
0.152
N30-DS40-C125-cd026-ct063
8
18
12.20
0.098
10
19
15.58
0.125
N30-DS40-C173-cd044-ct045
4
10
6.41
0.038
6
14
9.20
0.054
N30-DS40-C312-cd070-ct031
7
14
10.5
0.033
7
19
13.33
0.025
N30-DS40-C328-cd076-ct029
6
13
10.37
0.032
10
18
13.45
0.042
N30-DS40-C333-cd076-ct029
7
13
10.25
0.031
9
18
12.62
0.038
N30-DS40-C389-cd090-ct025
6
13
9.33
0.024
9
17
12.20
0.032
N30-DS40-C390-cd090-ct025
6
14
9.29
0.024
10
17
13
0.031
Table 2.
MLV-WGA vs. WGA: number of variables: 30.
REav denotes the relative error in percent. The value in bold shows the algorithm with the lowest RE.
MLV-WGA
WGA
Instance
Min
Max
Mean
REav
Min
Max
Mean
REav
N40-DS20-C78-cd010-ct079
6
12
8.91
0.115
5
13
9.04
0.116
N40-DS20-C80-cd010-ct079
7
13
9.62
0.121
7
13
10.04
0.153
N40-DS20-C82-cd012-ct073
4
9
6.25
0.073
4
11
6.95
0.085
N40-DS20-C95-cd014-ct067
2
8
4.45
0.047
2
7
4.12
0.044
N40-DS20-C653-cd084-ct017
2
14
9.37
0.015
6
16
10.62
0.018
N40-DS20-C660-cd084-ct017
6
14
9.12
0.014
7
6
9.75
0.015
N40-DS20-C751-cd096-ct015
6
13
9.91
0.014
5
13
9.83
0.014
N40-DS20-C752-cd096-ct015
5
17
9.29
0.013
3
13
9.20
0.013
N40-DS20-C756-cd096-ct015
6
15
9.95
0.014
5
16
8.75
0.012
N40-DS40-C106-cd014-ct075
7
14
11.08
0.105
7
16
11.5
0.109
N40-DS40-C115-cd014-ct075
12
20
15.5
0.135
11
20
15.5
0.135
N40-DS40-C181-cd024-ct055
6
17
12.04
0.067
7
17
11.75
0.065
N40-DS40-C196-cd024-ct055
11
12
16.58
0.085
12
20
15.54
0.080
N40-DS40-C226-cd030-ct047
7
14
10.91
0.051
7
16
11.16
0.050
N40-DS40-C647-cd082-ct021
11
23
15.66
0.025
11
20
15.20
0.024
N40-DS40-C658-cd082-ct021
11
22
16.33
0.025
13
21
16.70
0.026
N40-DS40-C703-cd092-ct019
9
21
13.41
0.020
8
20
13.58
0.020
N40-DS40-C711-cd092-ct019
12
23
15.75
0.023
8
20
14.87
0.021
N40-DS40-C719-cd092-ct019
8
21
16.54
0.024
10
20
15.16
0.022
Table 3.
MLV-WGA vs. WGA: number of variables 40.
REav denotes the relative error in percent. The value in bold shows the algorithm with the lowest RE.
4. Conclusion
In this work, a multilevel weighted based-genetic algorithm is introduced for MAX-CSP. The results have shown that the multilevel genetic algorithm returns a better solution for the equivalent run-time for most cases compared to the standard genetic algorithm. The multilevel paradigm offeres a better strategy for performing diversification and intensification. This is achieved by allowing GA to view a cluster of variables as a single entity thereby leading the search becoming guided and restricted to only those assignments in the solution space in which the variables grouped within a cluster are assigned the same value. As the size of the clusters gets smaller from one level to another, the size of the neighborhood becomes adaptive, and allows the possibility of exploring different regions in the search space while intensifying the search by exploiting the solutions from previous levels in order to reach better solutions.
\n',keywords:"maximum constraint satisfaction problem, genetic algorithms, multilevel paradigm",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/61918.pdf",chapterXML:"https://mts.intechopen.com/source/xml/61918.xml",downloadPdfUrl:"/chapter/pdf-download/61918",previewPdfUrl:"/chapter/pdf-preview/61918",totalDownloads:954,totalViews:122,totalCrossrefCites:0,totalDimensionsCites:0,totalAltmetricsMentions:0,impactScore:0,impactScorePercentile:37,impactScoreQuartile:2,hasAltmetrics:0,dateSubmitted:"November 1st 2017",dateReviewed:"May 3rd 2018",datePrePublished:null,datePublished:"June 27th 2018",dateFinished:"June 5th 2018",readingETA:"0",abstract:"Genetic algorithms (GA) which belongs to the class of evolutionary algorithms are regarded as highly successful algorithms when applied to a broad range of discrete as well continuous optimization problems. This chapter introduces a hybrid approach combining genetic algorithm with the multilevel paradigm for solving the maximum constraint satisfaction problem (Max-CSP). The multilevel paradigm refers to the process of dividing large and complex problems into smaller ones, which are hopefully much easier to solve, and then work backward toward the solution of the original problem, using the solution reached from a child level as a starting solution for the parent level. The promising performances achieved by the proposed approach are demonstrated by comparisons made to solve conventional random benchmark problems.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/61918",risUrl:"/chapter/ris/61918",book:{id:"6646",slug:"artificial-intelligence-emerging-trends-and-applications"},signatures:"Noureddine Bouhmala",authors:[{id:"213633",title:"Dr.",name:"Noureddine",middleName:null,surname:"Bouhmala",fullName:"Noureddine Bouhmala",slug:"noureddine-bouhmala",email:"noureddine.bouhmala@usn.no",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:{name:"Southeast University",institutionURL:null,country:{name:"China"}}}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Algorithm",level:"1"},{id:"sec_2_2",title:"2.1. Multilevel context",level:"2"},{id:"sec_3_2",title:"2.2. Multilevel genetic algorithm (GA)",level:"2"},{id:"sec_5",title:"3. Experimental results",level:"1"},{id:"sec_5_2",title:"3.1. Experimental setup",level:"2"},{id:"sec_6_2",title:"3.2. Results",level:"2"},{id:"sec_8",title:"4. Conclusion",level:"1"}],chapterReferences:[{id:"B1",body:'Dechter R, Pearl J. Tree clustering for constraint networks. Artificial Intelligence. 1989;38:353366'},{id:"B2",body:'Minton S, Johnson M, Philips A, Laird P. Minimizing conflicts: A heuristic repair method for constraint satisfaction and scheduling scheduling problems. Artificial Intelligence. 1992;58:161-205'},{id:"B3",body:'Morris P. The breakout method for escaping from local minima. In: Proceeding AAAI’93 Proceedings of the Eleventh National Conference on Artificial Intelligence. 1993. pp. 40-45'},{id:"B4",body:'Wallace R, Freuder E. Heuristic methods for over-constrained constraint satisfaction problems. In: Over-Constrained Systems. LNCS. Vol. 1106. Berlin, Germany: Springer Verlag; 1995. pp. 207-216'},{id:"B5",body:'Galinier P, Hao, J. Tabu search for maximal constraint satisfaction problems. In: Principles and Practice of Constraint Programming CP 1997. LNCS. Vol. 1330. Berlin, Germany: Springer Verlag; 1997. pp. 196-208'},{id:"B6",body:'Zhou Y, Zhou G, Zhang J. A hybrid glowworm swarm optimization algorithm for constrained engineering design problems. Applied Mathematics and Information Sciences. 2013;7(1):379-388'},{id:"B7",body:'Davenport A, Tsang E, Wang C, Zhu K. Genet: A connectionist architecture for solving constraint satisfaction problems by iterative improvement. In: Proceedings of the Twelth National Conference on Artificial Intelligence. 1994'},{id:"B8",body:'Voudouris C, Tsang E. Guided local search: Handbook of metaheuristics. International Series in Operation Research and Management Science. 2003;57:185-218'},{id:"B9",body:'Schuurmans D, Southey F, Holte E. The exponentiated subgradient algorithm for heuristic Boolean programming. In: 17th International Joint Conference on Artificial Intelligence. San Francisco, CA, USA: Morgan Kaufmann Publishers; 2001. pp. 334-341'},{id:"B10",body:'Shang E, Wah B. A discrete Lagrangian-based global-search method for solving satisfiability problems. Journal of Global Optimization. 1998;12(1):6199'},{id:"B11",body:'Hutter F, Tompkins D, Hoos H. Scaling and probabilistic smoothing: Efficient dynamic local search for SAT. In: Principles and Practice of Constraint Programming CP 2002. LNCS. Vol. 2470. Berlin, Germany: Springer Verlag; 2002. pp. 233-248'},{id:"B12",body:'Amante D, Marin A. Adaptive penalty weights when solving congress timetabling. Advances in Artificial Intelligence, Lectures Notes in Computer Science. 2004;3315:144-153'},{id:"B13",body:'Pullan W, Mascia F, Brunato M. Cooperating local search for the maximum clique problems. Journal of Heuristics. 2011;17:181-199'},{id:"B14",body:'Fang S, Chu Y, Qiao K, Feng X, Xu K. Combining edge weight and vertex weight for minimum vertex cover problem. In: FAW 2014. 2014. pp. 71-81'},{id:"B15",body:'Lee H, Cha S, Yu Y, Jo G. Large neighborhood search using constraint satisfaction techniques in vehicle routing problem. In: Gao Y, Japkowicz N, editors. Advances in Artificial Intelligence. Lecture Notes in Computer Science. Vol. 5549. Heidelberg: Springer Berlin; 2010. pp. 229-232'},{id:"B16",body:'Bouhmala N. A variable depth search algorithm for binary constraint satisfaction problems. Mathematical Problems in Engineering. 2015;2015:Article ID 637809, 10 pages. DOI: 10.1155/2015/637809'},{id:"B17",body:'Holland J. Adaptation in Natural and Artificial Systems. Ann Arbor: The University of Michigan Press; 1975'},{id:"B18",body:'Xu W. Satisfiability transition and experiments on a random constraint satisfaction problem model. International Journal of Hybrid Information Technology. 2014;7(2):191-202'},{id:"B19",body:'Gent IP, MacIntyre E, Prosser P, Walsh T. The constrainedness of search. In: Proceedings of the AAAI-96. 1996. pp. 246-252'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Noureddine Bouhmala",address:"noureddine.bouhmala@usn.no",affiliation:'
Department of Maritime Technology and Innovation, University SouthEast, Raveien, Borre, Norway
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1. Introduction
Synthesis of metformin was reported in 1922 and its effect of lowering glucose was reported soon after. Metformin was first reported to be used for the treatment of diabetes by French physician Jean Steme in 1957. The effect of metformin on improvement of morbidity and mortality in type 2 diabetes (T2D) was confirmed in the United Kingdom Prospective Diabetes Study (UKPDS), a large clinical trial performed in 1980–1990s [1]. It was approved for T2D treatment in adults by US FDA in 1994 and for pediatric patients 10 years and older in 2000. Metformin is prescribed world-wide as the first-line oral drug for adults and children with T2D. Its physiological effects related to T2D include increase in insulin sensitivity, reduction of gluconeogenesis in the liver, enhanced glucose uptake by muscle, and reduced intestinal glucose absorption. Several molecular mechanisms of action have been proposed but more remain to be discovered. In this chapter, we will review molecular mechanisms of action of metformin and its prospect for clinical application.
2. Mechanisms of action
The potential mechanisms of metformin action involve several pathways. The AMPK-pathway plays an important role in metformin actions [2, 3]. Metformin inhibits the mitochondrial respiratory chain (complex I), which increases the AMP to ATP ratio, leading to the phosphorylation of AMP-activated protein kinase (AMPK) at Thr-172. We have demonstrated that metformin treatment increases protein level of phosphorylated AMPK in high-glucose-treated endothelial cells [4]. The phosphorylated AMPK subsequently phosphorylates multiple downstream effectors to regulate cellular metabolism and energy homeostasis [5]. These downstream effectors include thioredoxin interacting protein (TXNIP) and TBC1D1, a RAB-GTPase activating protein and a member of the tre-2/BUB2/cdc1 domain family. Phosphorylated TXNIP and TBC1D1 increase the plasma membrane localization of glucose transporter 1 (GLUT1) and GLUT4, respectively [6, 7], and regulate glycogen synthases (GYS1 and GYS2) to prevent the storage of glycogen [8]. Some actions of metformin have been found to be AMPK-independent [9].
In diabetic mice, metformin has an effect on gut microbiota by inducing a profound shift in the gut microbial community profile, resulting in an increase in the Akkermansia spp. population [10] and cAMP-induced agmatine production [11], which may decrease absorption of glucose from the gastrointestinal tract and increase lipid metabolism respectively. In addition, metformin decreases insulin-induced suppression of fatty acid oxidation and lowers lipid content of hepatic cells [12].
3. Insulin resistance
Insulin resistance (IR) is a condition in which the cellular response to insulin is decreased resulting in elevated insulin levels (hyperinsulinism). When the beta cells are not able to overcome the resistance by producing more insulin, hyperglycemia develops. Insulin resistance is more prevalent in certain racial populations suggesting a genetic basis for the resistance. The major “environmental” risk factors for insulin resistance are obesity and sedentary lifestyle. Exercise and weight loss are established approaches to improve insulin sensitivity and decrease insulin resistance [13]. Insulin resistance may also be the basis for polycystic ovary syndrome (PCOS) in women. Some studies have suggested that metabolic syndrome (insulin resistance, type 2 diabetes, obesity, hyperlipidemia, and hypertension) and PCOS (insulin resistance, hyperandrogenism, amenorrhea, non-obese) are the ends of a spectrum of insulin resistance. The loss of microvascular insulin response and reduction of muscle glucose uptake are early events in the pathogenesis of insulin resistance [14, 15].
Metformin can increase insulin receptor tyrosine kinase activity, enhance glycogen synthesis, and increase the recruitment and activity of GLUT4 glucose transporters. In high-fat-diet-fed insulin resistant rats, metformin improved the insulin sensitivity of vascular and skeletal muscle and restored glucose uptake in insulin resistant skeletal muscle [16]. In adipose tissue, metformin promoted the re-esterification of free fatty acids and inhibited lipolysis, which indirectly improved insulin sensitivity through reduced lipotoxicity [17].
Insulin resistance is a risk factor for the development of T2D [18] and occurs earlier than hyperglycemia. Blood-based biomarker that identify insulin resistance earlier than current glycemia-based approaches, including fasting glucose and HbA1C [19] might identify individual’s at risk for developing diabetes, and provide a novel tool to monitor metformin treatment in the high risk population. Several blood-based biomarkers of insulin resistance have been identified [19]. Branched-chain amino acids [20] and asymmetric dimethylarginine (ADMA) [21] show an association with insulin resistance. Metformin decreases the level of circulating branched-chain amino acids and reduces insulin resistance in a high-fat diet mouse model [22]. Metformin treatment lowers plasma ADMA which is associated with improved glycemic control in patients with T2D [23].
Recent studies indicate that phosphatidylinositol-3-kinase/protein kinase B protein (PI3K/PKB, also known as Akt) signaling pathway is associated with insulin resistance, and plays a critical role in insulin stimulation of glucose transport into cells [24, 25, 26, 27, 28, 29, 30]. The key molecules involved in this pathway are PI3K, Akt, 3-phosphoinositide-dependent protein kinase 1 (PDK1), and phosphoinositide 3.4.5 trisphosphate (PIP3).
Akt has three isoforms Akt1, Akt2 and Akt3 (also referred to as protein kinase B (PKB) α, −β and –γ, respectively). Their domain structures are similar, including a pleckstrin homology (PH) kinase domain at the amino-terminal and a hydrophobic motif (HM) domain at the carboxyl-terminal [31]. Three isoforms share many substrates, but each isoform also has specific substrate. Akt2 is specific for the insulin signaling pathway and plays a critical role in glucose homeostasis. Akt2 deficient mice have insulin resistance, hyperglycemia, and loss of pancreatic β cells while Akt1 deficient mice do not exhibit diabetes phenotypes [32, 33].
PIP3 binds to PDK1 and Akt protein and recruits Akt protein to the plasma membrane. PDK1 phosphorylates Akt at Thr308/309 of Akt1/Akt2, respectively of the kinase domain leading to partial Akt activation. PI3K might directly phosphorylate Akt1 at Thr308 [34]. Full Akt activation is associated with a second PI3K phosphorylation of Akt at Ser473/474 of Akt1/Akt2, respectively in the carboxyl-terminal hydrophobic motif [34]. Subsequently, the phosphorylated Akt2 recruits insulin-regulated GLUT1 and GLUT4 glucose transporters from the cytoplasm onto the cell membrane surface and thereby increases glucose uptake [35].
GLUT1 is an insulin independent transporter whereas GLUT4 is an insulin dependent transporter. Insulin increases GLUT4 in the cell membrane and promotes the glucose transport into muscle and fatty cells (Figure 1). Any defect in Akt pathway along with the downstream molecules could result in insulin resistance [29]. Clinical data indicate that acute myocardial insulin resistance that occurs after cardiac surgery with cardiopulmonary bypass is attributed to Akt inactivation. Inactivated Akt impairs the membrane transposition of GLUT4, which results in insulin resistance accompanied with hyperinsulinemia, hyperglycemia and cardiac dysfunction [36]. It has been reported that metformin attenuates insulin resistance by restoring PI3K/Akt/GLUT4 signaling in the hepatocytes of T2D rats [37]. Metformin combined with phloretin, a dihydrochalcone found in fruits, promoted glucose consumption and suppressed gluconeogenesis in skeletal muscle via PI3K/Akt/GlUT4 signaling pathway in T2D rat models [38].
Figure 1.
Insulin binds to insulin receptor and induces its dimerization and auto phosphorylation of tyrosine residues in two transmembrane β subunits, which further lead to the phosphorylation of tyrosine residues on the IRS protein. These molecules can further activate PI3K, resulting in activation of PDK1/2. AKT is recruited and gets phosphorylated by PDK1/2. Once activated, AKT promotes GLUT4 translocation to plasma membrane and facilitates glucose into cell. TXNIP inhibits glucose transporter by promoting GLUT4 endocytosis.
TXNIP is being considered as a novel mediator of insulin resistance [39, 40]. TXNIP induced by high-glucose concentration is a key intracellular regulator of glucose and lipid metabolism [6]. We have demonstrated that metformin improves endothelial cell function via down-regulation of high-glucose-induced TXNIP transcription [4].
Over expression of TXNIP induces apoptosis of pancreatic β cells and endothelial cells, decreases muscle and adipose insulin sensitivity, promotes GLUT4 endocytosis and reduces glucose uptake in myocytes and adipocytes [4, 41, 42, 43]. Reduction of TXNIP expression by RNA interference gene-silencing significantly improves insulin induced glucose uptake in cultured human skeletal muscle cells [41]. TXNIP knockout mice had improved insulin sensitivity and increased glucose uptake in both adipose and skeletal muscle [39]. In PCOS, metformin improved insulin resistance in a PCOS rat model via an AMPK alpha-SIRT1 pathway [44].
4. Prediabetes
New criteria defining prediabetes includes the presence of one or more of the following, impaired fasting glucose (IFG), impaired glucose tolerance (IGT) and HbA1C of 5.7–6.4% [45]. The progression from prediabetes to diabetes is related to insulin resistance and β-cell dysfunction. Prediabetes is a serious health condition which increases the risk of developing T2D, heart disease and stroke. In the US, approximately 84 million American adults (more than 1 out of 3) have prediabetes but 90% patients with prediabetes are not aware of their condition [46]. Metformin improves insulin sensitivity and provides an attractive pharmacological intervention for prediabetes [47, 48]. Results from several clinical trials in the prediabetes population, including children, adolescents and adults, have indicated that metformin can delay or halt the progression from prediabetes to diabetes [49, 50, 51]. Metformin is generally well tolerated and has no significant safety issues with long-term use for diabetes prevention [48]. In the long-term “Diabetes Prevention Program Outcomes Study (DPPOS)”, either lifestyle intervention or metformin significantly reduced diabetes development over 15 years. Lifestyle intervention has been shown similar or greater effectiveness than metformin in clinical trials [52] and remains the cornerstone of care for patients with prediabetes. However, lifestyle interventions are difficult for patients to maintain and often fail to control weight over the long term. Metformin therapy was shown to be just as effective as lifestyle intervention in individual with prediabetes <60 years of age, BMI ≥ 35 kg/m2, and in women with a history of gestational diabetes mellitus [51, 53]. A study showed that metformin was underused in patients with prediabetes and only 3.7% of adult patients with prediabetes were prescribed metformin [54]. Currently metformin is not approved by FDA for prediabetes. Overweight patients with comorbidities may be at increased risk of diabetes. New guidelines recommended that metformin therapy for T2D prevention should be considered in those with prediabetes, especially those with BMI ≥ 35 kg/m2, those aged <60 years, and women with prior gestational diabetes mellitus [55]. The combinations of metformin with lifestyle or other treatments have shown more beneficial effects in diabetes prevention [48, 49].
5. Diabetes
Metformin is approved for use in patients with T2D. It is still under debated whether metformin can be an adjunct therapy for T1D though many overweight T1D patients have been prescribed metformin due to its beneficial effects on improving insulin resistance.
5.1 Adult T2D
Metformin is considered first-line therapy to treat T2D due to its blood glucose-lowering effects, safety and relatively low cost. Metformin lowers blood glucose level by decreasing glucose production in liver, reducing intestinal glucose absorption, increasing insulin sensitivity and promoting muscle glucose uptake in muscle. Metformin treatment can be combined with lifestyle modification and other antidiabetic drugs, such as dipeptidyl peptidase-4 (DPP-4) inhibitors, glucagon-like peptide-1 (GLP-1) receptor agonists or sodium-glucose cotransporter-2 (SGLT2) [56, 57]. Combined therapy is individualized depending on effectiveness, safety, tolerability, and the characteristics of each patient [58].
Metformin is safe and tolerable with the exception of the risk of lactic acidosis in patients with risk factors for lactic acidosis [59], including impairment of renal, cardiac, and hepatic function [60, 61, 62]. Another concern is metformin-induced vitamin B12 deficiency; patients who receive long-term metformin treatment (>6 months) at large doses have developed B12 deficiency [63, 64], so that annual screening of vitamin B12 level is recommended [65].
5.2 Adult T1D
Insulin resistance in T1D patients may contribute to poor glycemic control and is associated with increased insulin dose requirement [66]. Metformin treatment has been shown to increase insulin sensitivity, improve glycemic control, and reduce cardiovascular risk in patients with T1D [67]. The studies reported that metformin used as an adjunct therapy in T1D reduced insulin dose and body weight with no improvement in HbA1c and glycemic control [68, 69]. Another short term adjunct therapy with metformin demonstrated improved glycemic control, insulin sensitivity, and quality of life without weight gain, while long-term (2 years) metformin treatment was associated with decreased BMI [70]. A 1 year retrospective investigation reported an association between metformin as adjunct therapy and decreased glucose levels, decreased prevalence metabolic syndrome traits, and decreased insulin dose [71].
5.3 Pediatric T2D
Metformin was shown to be safe and effective for treatment of pediatric patients with T2D age 10 to 16 years old [72]. Treatment Options for Type 2 Diabetes in Adolescents and Youth (TODAY) recruited 699 youth and adolescents over a 4-year period. In this cohort study, metformin was used alone or in combination with life style modification or other antidiabetics drugs [73]. Metformin treatment was associated with decreased HbA1c and improved glycemic control in more than half of the participants. Metformin plus rosiglitazone was significantly better than metformin monotherapy [74].
5.4 Pediatric T1D
Using metformin to improve glycemic control and insulin sensitivity in youth and adolescents with T1D has been reported in several clinical trials. Studies that report a positive association of metformin have reported: 1. Decreased insulin dose, BMI and waist circumference in adolescents with T1D [75]. 2. Lower daily insulin dose improved whole-body and peripheral insulin resistance in adolescents with T1D who were overweight/obese [76]. 3. Lower insulin dose and improved vascular smooth muscle function and HbA1c children with T1D [77]. 4. Decreased cardiovascular disease risk factors in youth with T1D [78]. 5. Improvement in HbA1c level in adolescents with T1D [79, 80]. In contrast, some trials did not observe improvement in HbA1c [76, 81], or glycemic control. As expected, there was an increased gastrointestinal adverse event in overweight adolescents with T1D [81].
6. Aging
Metformin has attracted interest for its potential effects on aging [82]. Metformin treatment has a positive association with reduction in the incidence of mortality from age-related diseases including diabetes, cancer, cardiovascular diseases, and neurodegenerative diseases. Metformin is reported to increase lifespan in several animal models. Cohort clinical trials, Metformin in Longevity Study (MILES) and Targeting Aging with Metformin (TAME), have been initiated to investigate metformin’s anti-aging effects in human.
In several animal models, including nematodes and rodents, metformin has been shown to delay aging. Metformin treated female outbred mice (100 mg/kg in drinking water) showed an increased mean lifespan 37.8% [83]. The effects of metformin treatment were shown to be age dependent in mice. When treatment was started at the early stage of life, middle-age and late stages of life, the mean lifespan was increased by 21%, 7% and 13% respectively compared to the controls [84]. In a mouse breast cancer model, metformin delayed the onset of mammary adenocarcinoma and increased lifespan by a mean of 8% compared to the control group [85]. Metformin prolonged the survival time of male mice with Huntington’s disease by 21.1%, but had no effects in female [86]. A recent study found that metformin reduced oxidative stress and inflammation, extended both lifespan and healthspan by 4–6% in different strains of mice, and attenuated the deleterious effects of aging in male mice [87].
Gut microbiota has been shown to affect health status and longevity and play a role in resistance to infection, inflammation, autoimmunity, and cancer, and the regulation of the brain-gut axis [88, 89]. Metformin acts directly on gut bacteria to decrease absorption of glucose, improve lipid metabolism and elevate agmatine production to extend host lifespan [10, 90].
The reported effects of metformin on microbiota and animals have promoted interest in evaluating its effects on human longevity. In 2014, Metformin in Longevity Study (MILES, NCT02432287) clinical trial was initiated to examine the effects of metformin treatment on the biology of aging in humans, and to determine if treatment with metformin (1700 mg/day) could restore more youthful gene expression in elderly people with impaired glucose tolerance. Results from MILES showed that 6-weeks of metformin treatment in older adults (~70-year-old participants) improved age-associated gene expression, and significantly influenced metabolic and non-metabolic pathways in skeletal muscle and subcutaneous adipose tissue [91]. Currently, MILES has progressed to a phase 4 trial. Targeting Aging with Metformin (TAME) is managed by America Federation for Aging Research (AFAR) to investigate metformin’s ability to delay the onset of comorbidities related to aging. The plan is to recruit 3000 older adults (aged 65–79 years old) without diabetes who will be randomly assigned to 1500 mg metformin daily or placebo for 6 years, with a mean follow-up time of more than 3–5 years (https://www.afar.org/research/TAME). These ongoing trials are expected to further evaluate and update the roles of metformin in antiaging.
7. PCOS
Polycystic ovary syndrome (PCOS) is a common endocrine disorder affecting about 5–15% of reproductive age women [92, 93]. PCOS is associated with insulin resistance and hyperinsulinemia, even in lean women. The condition puts women at risk for infertility, obesity, diabetes, as well as cardiovascular disease [94]. Metformin has been used to treat PCOS for 25 years and is currently recommended in combination with other therapy.
Clinically, metformin was first reported as a treatment for PCOS in 1994 [95]. A 6-month trial of metformin or placebo in women with PCOS found that metformin improved menstruation and insulin sensitivity, and reduced hyperinsulinemia and hyperandrogenemia [96]. In addition, metformin has been found to inhibit androgen production by repressing the steroidogenic enzymatic activities of 17α-hydroxylase/17,20 lyase (CYP17A1) and 3β-hydroxysteroid dehydrogenase type 2 (HSD3B2) in the theca cells taken from the ovaries of women with PCOS [97].
Women treated with metformin had increased rates of ovulation and pregnancy [93], reduced rates of early pregnancy loss, preterm delivery, preeclampsia, and fetal growth restriction [98, 99], and improved live birth rates [93]. There were no serious adverse effects in pregnant women with PCOS treated with metformin or their offspring [98, 99, 100]. These results indicate that the roles of metformin are not only in glucose metabolism, but also in regulating ovarian hormonal activities and functions in women with PCOS.
There is not enough evidence to recommend metformin as first-line therapy for women with PCOS but adding metformin to other PCOS treatment seems an optimal option. Gastrointestinal side effects were more common in metformin combined with clomiphene citrate than clomiphene citrate alone, but the combined therapy may have beneficial effects in the rates of ovulation and pregnancy [93, 101]. Combination of metformin with clomiphene citrate can be considered as the first line therapy in anovulatory PCOS women without other infertility factors [102]. Metformin was less effective than clomiphene citrate in obese women with PCOS [93, 102]. Combined therapy of metformin and spironolactone showed greater improvement in menstrual cycles and hyperinsulinemia. Adding metformin to ethinyl estradiol-cyproterone acetate treatment in non-obese women with PCOS resulted in significant decreases in androgen levels and increases sex hormone-binding globulin level, which confirmed that metformin also, has some beneficial effects in non-obese women with PCOS [103]. In a DHEA-induced PCOS rat animal model, metformin treatment restored ovarian angiogenesis and follicular development [104].
8. Cardiovascular diseases
Cardiovascular diseases (CVD) are the leading cause of death and disability in the world. Metformin might have sustained beneficial role on reducing CVD risk and mortality [105, 106]. The cardioprotective effects include reduction of weight gain and hyperinsulinemia, improvement of endothelial function and fibrinolysis, and reduction of low-grade inflammation, oxidative stress, and glycation.
Recent clinical studies have shown that metformin has protective effects on vascular endothelial function and angiogenesis in patients with T2D [107]. Several clinical trials have reported that metformin treatment reduced CVD risk in T2D [1, 108]. Recently the efficacy of metformin in modifying CVD outcomes has been challenged [109, 110, 111] but updated evidence support that metformin is cardiovascular protective [112]. A meta-analysis that included 40 clinical trials comprising 1,066,408 patients has shown that metformin reduced cardiovascular mortality, all-cause mortality and cardiovascular events in coronary artery disease [105].
Diabetes increases CVD risk and mortality. More than 75% of male and more than 57% female T2D patients died from cardiovascular disease. The mortality of CVD with T2D patients is twice those without T2D [113]. Patients with chronic cardiovascular disease (CVD) comorbidity are likely to benefit from metformin treatment [1, 105, 108]. Metformin is recommended to be used alone or in combination with other drugs as the first line therapy in T2D patients with high risk of CVD, including atherosclerotic cardiovascular disease [114, 115].
Several clinical trials for metformin on participants with or without T1D diabetes have been completed [106]. Trials Metformin in Insulin Resistant Left Ventricular Dysfunction (TAYSIDE, NCT00473876) and Reducing with Metformin Vascular Adverse Lesions of Type 1 Diabetes (REMOVAL, NCT01483560) have promising data. TAYSIDE found that metformin had a beneficial effect in participants with nondiabetic chronic heart failure and insulin resistance, significantly improved the secondary endpoint of the slope of the ratio of minute ventilation to carbon dioxide production, fasting insulin resistance and weight loss [116]. REMOVAL showed that metformin reduced the prespecified tertiary end point of carotid artery intima-media thickness in T1D suggesting a cardiovascular protective effect [117]. In an 8-week period of metformin treatment for nondiabetic participants with cardiac syndrome X, metformin improved endothelium-dependent microvascular response, maximal ST-segment depression, Duke score, and chest pain incidence, which suggested that metformin may improve vascular function and decrease myocardial ischemia [118]. However, several studies reported that metformin was not found to be effective in their participants [106].
Investigation of Metformin in Pre-diabetes on Atherosclerotic Cardiovascular OuTcomes (VA-IMPACT, NCT02915198) and Glucose Lowering in Non-diabetic Hyperglycemia Trial (GLINT, ISRCTN34875079) are current ongoing studies to further evaluate the effects of metformin on CVD [119]. The trials will evaluate the incidence of cardiovascular death and non-fatal myocardial infarction events. Their data will provide more insight on the association of metformin treatment on CVD.
The role of metformin in inhibiting mitochondrial enzymes and activating AMPK pathway are the most likely cellular mechanisms in cardiovascular protection. We have demonstrated that AMPK activated by metformin improved cellular function, decreased apoptosis, and reduced inflammation in vascular endothelial cells [4, 42]. TXNIP is a key regulator of cellular redox state induced by high glucose and promotes high-glucose-induced macrovascular endothelial dysfunction. We have also reported that metformin down-regulated high-glucose-induced TXNIP expression by inactivating ChREBP and Forkhead box O1 (FOXO1) through AMPK pathway (Figure 2) [4].
Figure 2.
Metformin inhibits the nuclear entry of ChREBP and FOXO1 from cytosol and their binding capacity to the TXNIP promoter, thus potently and effectively suppresses TXNIP transcription induced by high glucose at last. The inhibitory effect of metformin on nuclear translocation is AMPK-phosphorylation-dependent.
9. Cancer
Preexisting diabetes is a risk factor for cancers, including liver, pancreas, endometrium, colon, breast, and bladder cancers [120]. Epidemiological studies show that the incidence of cancer is decreased in patients with T2D treated with metformin [121]. Metformin has shown to inhibit cancer cell growth in clinical trials including cancer patients without diabetes [122, 123, 124]. Based on http://ClinicalTrials.gov in January 2020, there are more than 300 clinical trials investigating metformin in cancer treatment, more than 100 of them have been completed. The results were published or posted on http://ClinicalTrials.gov. These trials included patients with or without diabetes with different cancers using metformin treatment or combination of metformin with other anticancer drugs. Accumulating evidence from clinical trials and a national cohort study suggest that metformin treatment may improve therapeutic response and have potential beneficial effects on cancer prevention and therapy [125, 126, 127].
The effect of metformin on inhibiting cell proliferation can be classified as AMPK independent and AMPK dependent [128]. Metformin inhibits the electron transport chain, resulting in an elevated NADH/NAD+ ratio and decrease of ATP production in mitochondrial complex I ATP as well as activation of AMPK [129, 130]. AMPK activated by metformin subsequently regulates cell growth and survival by targeting metabolic enzymes and transporters [131, 132]. AMPK downregulates mTOR activity that plays a central role in the regulation of cell proliferation, growth, differentiation, migration, and survival [133, 134, 135].
Tumor protein 53 (p53) plays a central role in the cellular responses to repair of DNA damage, cell survival and apoptosis. p53 mutations occur in almost every type of human cancer cells and more than 50% of human cancers have a somatic p53 mutation [136]. AMPK activation induced phosphorylation at Ser15 of p53, leading to cell-cycle arrest [137].
Metformin was reported to inhibit melanoma cell invasion and metastasis via an AMPK/p53 dependent manner [138]. In a pre-clinical lymphoma model, metformin treatment resulted in activation of p53, leading to cell apoptosis [139]. In the prostate cancer cells, the combination of metformin and 2-deoxyglucose resulted in p53-dependent cell apoptosis [140]. Metformin has been found to inhibit human cervical cancer cell proliferation and induce apoptosis via modulating p53 and cyclin D1 expression [141].
The effect of metformin on anti-cancer also has a p53-independent mechanism. Metformin has been shown to induce G2M arrest in p53-deficient colorectal cancer cells and tumors. When combined with ionizing radiation metformin therapy enhanced antitumor effects in radioresistant p53-deficient colorectal cancer cells [142]. Treatment with metformin increased apoptosis in p53-deficient human colon cancer cell and reduced tumor growth in xenografts of p53-deficient human colon cancer cells [143].
The p53 homologs, P63 and p73 have overlapping function in tumorigenesis and development [144]. P63 and P73 mutations are rare in human tumors, but they can be overexpressed. P63 plays a critical role in development of squamous epithelium and is overexpressed in squamous cell carcinoma [145]. Metformin inhibited p63 protein expression in squamous carcinoma cell, resulting in decreased cell viability and xenographic tumor growth [146]. P73 overexpression induces apoptosis and cell cycle arrest of tumor cells [147]. AMPK activated by metformin phosphorylated Ser426 of p73 leading to p73 accumulation and cell apoptosis in human colon cancer cells [148].
Metformin may prevent tumorigenesis by inhibiting the insulin like growth factor (IGF)-1 signaling pathway and increasing insulin sensitivity. The proliferation marker Ki-67 was significantly decreased in patients with endometrial cancer cell after metformin treatment [149]. Metformin enhances cytotoxic T lymphocyte (CTL) antitumor activity via activating AMPK to phosphorylate Ser195 of PDL-1 in a murine model of breast cancer which is consistent with the finding that tumor tissues from metformin-treated breast cancer patients exhibited reduced PDL-1 level with AMPK activation [150].
These findings suggest that metformin could be a useful adjuvant agent and has therapeutic benefits in several tumor types, including colorectal, prostate and breast cancers. However, there is limited evidence in other tumor types, and further clinical investigations are needed to evaluate metformin effects in cancer therapy.
10. Neurodegenerative diseases
Metformin is described to have a beneficial effect in neurodegenerative diseases (ND), including dementia, Alzheimer’s disease, Parkinson’s disease, Huntington’s disease and mild cognitive impairment [151, 152].
Population-based studies support an association between the elevated risk of ND in patients with T2D [153, 154, 155]. A large population cohort study used Taiwan’s National Health Insurance Database to investigate the relationship between dementia, T2D, and metformin treatment. They found that the prevalence of dementia was increased in patients with T2D and that metformin therapy was associated with a 24% decrease in the incidence of dementia in patients with T2D. The combination treatment of metformin with sulfonylureas was associated with a 35% decrease in the risk of dementia in T2D patients over 8 years of observation [156]. In a recent study, long-term (>2 years) metformin therapy was associated with lower incidence of dementia among elderly adults with T2D. Longer term treatment (>4 years) was associated with reduced risk of Alzheimer’s and Parkinson’s diseases, and none with mild cognitive impairment [157]. A large T2D population cohort study found that sulfonylureas therapy increased the risk of Parkinson’s disease, but adding metformin as a co-therapy significantly reduced the risk of Parkinson’s disease in T2D [158]. Long-term (>6 years) metformin treatment significantly reduced the risk of cognitive impairment among older adults with T2D [159].
In contrast, other studies have shown that the metformin therapy of T2D is associated with: 1. a slightly higher risk of Alzheimer’s disease [160], 2. increased risk for cognitive impairment [161], and 3. no beneficial effects on preventing development of Alzheimer’s disease after adjusting for underlying risk factors and the duration of diabetes since diagnosis [162]. In addition, metformin treatment aggravated neurodegenerative process in ApoE knockout mice [163].
The current evidence suggests that the neuroprotective effects of metformin occur via activation of AMPK/mTOR pathway and inhibition of tau phosphorylation [164, 165]. In addition, it is known that metformin enhances angiogenesis and neurogenesis, induces autophagy, reduces oxidative stress, and improves neurological deficits [166, 167, 168, 169, 170].
Despite the different findings from these studies, a recent meta-analysis suggests that metformin may prevent development of dementia in patients with diabetes indicating that metformin should be continued in patients with T2D patients at risk of the dementia or Alzheimer’s disease. Use of metformin to prevent neurodegenerative diseases in people without diabetes is not supported by current evidence [152].
11. Conclusions
Metformin is currently approved and widely prescribed for patients with T2D and PCOS. The clinical trial data and clinical experience over several decades have demonstrated its safety and efficacy. The interest in metformin therapy has dramatically increased as the population-based cohort studies indicate that metformin can decrease the risk of cancer, cardiovascular and cerebral disease. Current studies indicate that metformin has potential for treatment of T1D, cancer, aging, cardiovascular and neurodegenerative diseases. Translational and clinical trials need to be continued and expanded to determine if there are indications for metformin therapy in diseases other than T2D.
Conflict of interest
The authors declare no conflict of interest.
\n',keywords:"metformin, insulin, insulin resistance, diabetes, aging, PCOS, cancer, cardiovascular, neurodegenerative",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/71037.pdf",chapterXML:"https://mts.intechopen.com/source/xml/71037.xml",downloadPdfUrl:"/chapter/pdf-download/71037",previewPdfUrl:"/chapter/pdf-preview/71037",totalDownloads:1221,totalViews:0,totalCrossrefCites:4,dateSubmitted:"May 23rd 2019",dateReviewed:"January 11th 2020",datePrePublished:"March 20th 2020",datePublished:"May 27th 2020",dateFinished:"February 11th 2020",readingETA:"0",abstract:"Metformin is the first-line medication for Type 2 diabetes (T2D) treatment, and it is the only US FDA approved oral antidiabetic medication for pediatric patients with T2D 10 years and older. Metformin is also used to treat polycystic ovary syndrome (PCOS), another condition with underlying insulin resistance. The clinical applications of metformin are continuing to expand into other fields including cancer, aging, cardiovascular diseases, and neurodegenerative diseases. Metformin modulates multiple biological pathways. Its novel properties and effects continue to evolve; however, its molecular mechanism of action remains incompletely understood. In this chapter, we focus on the recent translational research and clinical data on the molecular action of metformin and the evidence linking the effects of metformin on insulin resistance, prediabetes, diabetes, aging, cancer, PCOS, cardiovascular diseases, and neurodegenerative diseases.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/71037",risUrl:"/chapter/ris/71037",signatures:"Yun Yan, Karen L. Kover and Wayne V. Moore",book:{id:"7994",type:"book",title:"Metformin",subtitle:null,fullTitle:"Metformin",slug:"metformin",publishedDate:"May 27th 2020",bookSignature:"Anca Mihaela Pantea Stoian and Manfredi Rizzo",coverURL:"https://cdn.intechopen.com/books/images_new/7994.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",isbn:"978-1-83880-428-2",printIsbn:"978-1-83880-427-5",pdfIsbn:"978-1-83880-760-3",isAvailableForWebshopOrdering:!0,editors:[{id:"243049",title:"Dr.",name:"Anca",middleName:null,surname:"Pantea Stoian",slug:"anca-pantea-stoian",fullName:"Anca Pantea Stoian"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:null,sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Mechanisms of action",level:"1"},{id:"sec_3",title:"3. Insulin resistance",level:"1"},{id:"sec_4",title:"4. Prediabetes",level:"1"},{id:"sec_5",title:"5. Diabetes",level:"1"},{id:"sec_5_2",title:"5.1 Adult T2D",level:"2"},{id:"sec_6_2",title:"5.2 Adult T1D",level:"2"},{id:"sec_7_2",title:"5.3 Pediatric T2D",level:"2"},{id:"sec_8_2",title:"5.4 Pediatric T1D",level:"2"},{id:"sec_10",title:"6. Aging",level:"1"},{id:"sec_11",title:"7. PCOS",level:"1"},{id:"sec_12",title:"8. Cardiovascular diseases",level:"1"},{id:"sec_13",title:"9. Cancer",level:"1"},{id:"sec_14",title:"10. Neurodegenerative diseases",level:"1"},{id:"sec_15",title:"11. Conclusions",level:"1"},{id:"sec_19",title:"Conflict of interest",level:"1"}],chapterReferences:[{id:"B1",body:'UKPDS G. Effect of intensive blood-glucose control with metformin on complications in overweight patients with type 2 diabetes (UKPDS 34). UK prospective Diabetes study (UKPDS) group. Lancet. 1998;352:854-865. DOI: 10.1016/S0140-6736(98)07037-8'},{id:"B2",body:'Zhou G, Myers R, Li Y, Chen Y, Shen X, Fenyk-Melody J, et al. Role of AMP-activated protein kinase in mechanism of metformin action. 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Department of Pediatrics, Division of Endocrinology, Children’s Mercy Kansas City, School of Medicine, University of Missouri Kansas City, Kansas City, USA
'},{corresp:null,contributorFullName:"Karen L. Kover",address:null,affiliation:'
Department of Pediatrics, Division of Endocrinology, Children’s Mercy Kansas City, School of Medicine, University of Missouri Kansas City, Kansas City, USA
'},{corresp:null,contributorFullName:"Wayne V. Moore",address:null,affiliation:'
Department of Pediatrics, Division of Endocrinology, Children’s Mercy Kansas City, School of Medicine, University of Missouri Kansas City, Kansas City, USA
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Initial biochemical studies have been exclusively analytic: dissecting, purifying, and examining individual components of a biological system; in the apt words of Efraim Racker (1913 –1991), “Don’t waste clean thinking on dirty enzymes.” Today, however, biochemistry is becoming more agglomerative and comprehensive, setting out to integrate and describe entirely particular biological systems. The ‘big data’ metabolomics can define the complement of small molecules, e.g., in a soil or biofilm sample; proteomics can distinguish all the comprising proteins, e.g., serum; metagenomics can identify all the genes in a complex environment, e.g., the bovine rumen. 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Dr. Blumenberg’s research is focused on the epidermis, expression of keratin genes, transcription profiling, keratinocyte differentiation, inflammatory diseases and cancers, and most recently the effects of the microbiome on the skin. 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Other positions she has held at the university include Vice-Dean of Master Programs, Vice-Dean of the Degree in Biology and Vice-Dean for Mobility and Enterprise and Engagement at the Faculty of Science (University of Alicante). She received her Bachelor in Biology in 1998 (University of Alicante) and her PhD in 2003 (Biochemistry, University of Alicante). She undertook post-doctoral research at the University of East Anglia (Norwich, U.K. 2004-2005; 2007-2008).\nHer multidisciplinary research focuses on investigating archaea and their potential applications in biotechnology. She has an H-index of 21. She has authored one patent and has published more than 70 indexed papers and around 60 book chapters.\nShe has contributed to more than 150 national and international meetings during the last 15 years. Her research interests include archaea metabolism, enzymes purification and characterization, gene regulation, carotenoids and bioplastics production, antioxidant\ncompounds, waste water treatments, and brines bioremediation.\nRosa María’s other roles include editorial board member for several journals related\nto biochemistry, reviewer for more than 60 journals (biochemistry, molecular biology, biotechnology, chemistry and microbiology) and president of several organizing committees in international meetings related to the N-cycle or respiratory processes.",institutionString:null,institution:{name:"University of Alicante",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"15",title:"Chemical Biology",coverUrl:"https://cdn.intechopen.com/series_topics/covers/15.jpg",isOpenForSubmission:!0,editor:{id:"441442",title:"Dr.",name:"Şükrü",middleName:null,surname:"Beydemir",slug:"sukru-beydemir",fullName:"Şükrü Beydemir",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003GsUoIQAV/Profile_Picture_1634557147521",biography:"Dr. Şükrü Beydemir obtained a BSc in Chemistry in 1995 from Yüzüncü Yıl University, MSc in Biochemistry in 1998, and PhD in Biochemistry in 2002 from Atatürk University, Turkey. 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He is a member of the Turkish Biochemical Society, American Chemical Society, and German Genetics society. Dr. Ekinci published around ninety scientific papers, reviews and book chapters, and presented several conferences to scientists. He has received numerous publication awards from several scientific councils. 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He worked on the structure-function relationships of glycoconjugates and his main project was the investigations on the biological roles of the de-N-glycosylation enzymes (Endo-N-acetyl-β-D-glucosaminidase and peptide-N4-(N-acetyl-β-glucosaminyl) asparagine amidase). From 2002 he contributes to the understanding of the Blood-brain barrier functioning using proteomics approaches. He has published more than 70 papers. 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Since then, he has been working as an Adjunct Professor in the same Department at the University of Pavia. His research activity during the first years was primarily focused on the purification and structural characterization of enzymes from animal and plant sources. During this period, Prof. Iadarola familiarized himself with the conventional techniques used in column chromatography, spectrophotometry, manual Edman degradation, and electrophoresis). Since 1995, he has been working on: i) the determination in biological fluids (serum, urine, bronchoalveolar lavage, sputum) of proteolytic activities involved in the degradation processes of connective tissue matrix, and ii) on the identification of biological markers of lung diseases. In this context, he has developed and validated new methodologies (e.g., Capillary Electrophoresis coupled to Laser-Induced Fluorescence, CE-LIF) whose application enabled him to determine both the amounts of biochemical markers (Desmosines) in urine/serum of patients affected by Chronic Obstructive Pulmonary Disease (COPD) and the activity of proteolytic enzymes (Human Neutrophil Elastase, Cathepsin G, Pseudomonas aeruginosa elastase) in sputa of these patients. More recently, Prof. Iadarola was involved in developing techniques such as two-dimensional electrophoresis coupled to liquid chromatography/mass spectrometry (2DE-LC/MS) for the proteomic analysis of biological fluids aimed at the identification of potential biomarkers of different lung diseases. He is the author of about 150 publications (According to Scopus: H-Index: 23; Total citations: 1568- According to WOS: H-Index: 20; Total Citations: 1296) of peer-reviewed international journals. 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She gained considerable experience in developing and validating new methodologies whose applications allowed her to determine both the amount of biomarkers (Desmosine and Isodesmosine) in the urine of patients affected by COPD, and the activity of proteolytic enzymes (HNE, Cathepsin G, Pseudomonas aeruginosa elastase) in the sputa of these patients. Simona Viglio was also involved in research dealing with the supplementation of amino acids in patients with brain injury and chronic heart failure. She is presently engaged in the development of 2-DE and LC-MS techniques for the study of proteomics in biological fluids. The aim of this research is the identification of potential biomarkers of lung diseases. 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Waisundara",profilePictureURL:"https://mts.intechopen.com/storage/users/194281/images/system/194281.jpg",biography:"Dr. Viduranga Waisundara obtained her Ph.D. in Food Science and Technology from the Department of Chemistry, National University of Singapore, in 2010. She was a lecturer at Temasek Polytechnic, Singapore from July 2009 to March 2013. She relocated to her motherland of Sri Lanka and spearheaded the Functional Food Product Development Project at the National Institute of Fundamental Studies from April 2013 to October 2016. She was a senior lecturer on a temporary basis at the Department of Food Technology, Faculty of Technology, Rajarata University of Sri Lanka. She is currently Deputy Principal of the Australian College of Business and Technology – Kandy Campus, Sri Lanka. 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Her research interests include microalgal biotechnology with an emphasis on microalgae-based products.",institutionString:"Universidade Federal de Santa Maria",institution:{name:"Universidade Federal de Santa Maria",institutionURL:null,country:{name:"Brazil"}}}]},{type:"book",id:"7953",title:"Bioluminescence",subtitle:"Analytical Applications and Basic Biology",coverURL:"https://cdn.intechopen.com/books/images_new/7953.jpg",slug:"bioluminescence-analytical-applications-and-basic-biology",publishedDate:"September 25th 2019",editedByType:"Edited by",bookSignature:"Hirobumi Suzuki",hash:"3a8efa00b71abea11bf01973dc589979",volumeInSeries:4,fullTitle:"Bioluminescence - Analytical Applications and Basic Biology",editors:[{id:"185746",title:"Dr.",name:"Hirobumi",middleName:null,surname:"Suzuki",slug:"hirobumi-suzuki",fullName:"Hirobumi Suzuki",profilePictureURL:"https://mts.intechopen.com/storage/users/185746/images/system/185746.png",biography:"Dr. Hirobumi Suzuki received his Ph.D. in 1997 from Tokyo Metropolitan University, Japan, where he studied firefly phylogeny and the evolution of mating systems. He is especially interested in the genetic differentiation pattern and speciation process that correlate to the flashing pattern and mating behavior of some fireflies in Japan. He then worked for Olympus Corporation, a Japanese manufacturer of optics and imaging products, where he was involved in the development of luminescence technology and produced a bioluminescence microscope that is currently being used for gene expression analysis in chronobiology, neurobiology, and developmental biology. 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\r\n\tWater is not only a crucial substance needed for biological life on Earth, but it is also a basic requirement for the existence and development of the human society. Owing to the importance of water to life on Earth, early researchers conducted numerous studies and analyses on the liquid form of water from the perspectives of chemistry, physics, earth science, and biology, and concluded that Earth is a "water polo". Water covers approximately 71% of Earth's surface. However, 97.2% of this water is seawater, 21.5% is icebergs and glaciers, and only 0.65% is freshwater that can be used directly by humans. As a result, the amount of water reserves available for human consumption is limited. The development, utilization, and protection of freshwater resources has become the focus of water science research for the continued improvement of human livelihoods and society.
\r\n
\r\n\tWater exists as solid, liquid, and gas within Earth’s atmosphere, lithosphere, and biosphere. Liquid water is used for a variety of purposes besides drinking, including power generation, ecology, landscaping, and shipping. Because water is involved in various environmental hydrological processes as well as numerous aspects of the economy and human society, the study of various phenomena in the hydrosphere, the laws governing their occurrence and development, the relationship between the hydrosphere and other spheres of Earth, and the relationship between water and social development, are all part of water science. Knowledge systems for water science are improving continuously. Water science has become a specialized field concerned with the identification of its physical, chemical, and biological properties. In addition, it reveals the laws of water distribution, movement, and circulation, and proposes methods and tools for water development, utilization, planning, management, and protection. Currently, the field of water science covers research related to topics such as hydrology, water resources and water environment. It also includes research on water related issues such as safety, engineering, economy, law, culture, information, and education.
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He has chaired or acted as a technical committee member for twenty-five international forums (conferences). Dr. Shang graduated from Tsinghua University, China, in 2010 with a Ph.D. in Engineering. Prior to that, he worked as a research fellow at Harvard University from 2008 to 2009. 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