The Adipokine-Cardiovascular-Lifestyle Network · Adipokines are peptides, secreted by adipocytes and other cell types with targets in other tissues, participating in a complex network

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THE PRESENT AND FUTURE

STATE-OF-THE-ART REVIEW

The Adipokine-Cardiovascular-LifestyleNetworkTranslation to Clinical Practice

Jeffrey I. Mechanick, MD,a Shan Zhao, MD, PHD,b W. Timothy Garvey, MDc,d

ABSTRACT

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Adipokines are peptides, secreted by adipocytes and other cell types with targets in other tissues, participating in a

complex network of humoral factors involved in obesity, insulin resistance, and cardiovascular (CV) disease. This review

describes recent information about adipokine effects on the CV system. Rather than simply providing a listing of

adipokines and their respective effects, network analysis is used to enhance understanding. Various relationships and

emergent processes in the adipokine-CV system network are discussed, with the most significant interactors being

responses to hypoxia, regulation of cell migration, effects on blood coagulation, and platelet activation. Clinical trans-

lation is provided through network representations of the “obesity paradox,” “metabolically healthy obese,” “metabolic

syndrome,” and beneficial role of lifestyle medicine. As more translatable information about the larger adipokine-CV-

lifestyle network is acquired from laboratory and clinical research, the strategic and precise role of lifestyle intervention

can be fashioned to improve CV outcomes. (J Am Coll Cardiol 2016;68:1785–803) © 2016 by the American College of

Cardiology Foundation.

T he transformation of medical science from anorgan system and cellular physiology scale toone that is understood and manipulated on

the molecular scale has provided unique opportu-nities to resolve controversies and advance patientcare. From a teleological and evolutionary perspec-tive, the rapid, adverse changes in environment dur-ing human evolution outpaced the more sluggishmutation rates in the human genome. This discrep-ancy created maladapted body compositions, withgreater adiposity, localized inflammatory signaling,subsequent systemic inflammation, and eventualhazardous effects on various organ systems, such asthe cardiovascular (CV) system. Thus, within therecently discovered framework of complex adipokinesignaling and companion effects on the CV system,

m the aDivision of Endocrinology, Diabetes and Bone Disease, Icahn Scho

elequire, New York, New York; cDepartment of Nutrition Sciences and D

mingham, Birmingham, Alabama; and the dGeriatric Research Education

mingham, Alabama. Dr. Mechanick has received honoraria for lectures a

national. Dr. Garvey has received consulting fees from Novo Nordisk, Eisa

rck. Dr. Zhao has reported that he has no relationships relevant to the cont

ved as Guest Editor for this paper.

nuscript received June 2, 2016; revised manuscript received June 29, 201

there is an opportunity to explain elusive clinicalphenomena that trouble physicians today.

One recent innovation, spurred on by the advent ofsupercomputers and enhanced biocomputational re-sources, is the application of network analysis as adiscovery science. In order to adequately appreciatenetwork analysis, a basic understanding of graphanalytic methods is suggested (1,2). There are car-diometabolic networks that reveal intuitive andnonintuitive hypothesis-generating relationships(3–5). For the purpose of this review, we will focus oninteraction networks from published reports, ratherthan inference networks. To accomplish this, acompilation of recent information about adipokines,their scientifically demonstrable influence on the CVsystem, and derivation of an adipokine-CV network

ol of Medicine at Mount Sinai, New York, New York;

iabetes Research Center, University of Alabama at

and Clinical Center, Birmingham VA Medical Center,

nd program development from Abbott Nutrition In-

i, Janssen, Vivus, Takeda, AstraZeneca, Alexion, and

ents of this paper to disclose. Carl “Chip” Lavie, MD,

6, accepted June 29, 2016.

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ABBR EV I A T I ON S

AND ACRONYMS

ADIPOQ = adiponectin

BMI = body mass index

CV = cardiovascular

CVD = cardiovascular disease

IL = interleukin

MetS = metabolic syndrome

MHO = metabolically healthy

obese

OSAS = obstructive sleep

apnea syndrome

STAT3 = signal transducer and

activator of transcription 3

T2D = type 2 diabetes

TGF = transforming growth

factor

TNF = tumor necrosis fact

Mechanick et al. J A C C V O L . 6 8 , N O . 1 6 , 2 0 1 6

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are presented. Moreover, a pragmatic contextis provided wherein 3 curiosities are exam-ined: the obesity paradox; the metabolicallyhealthy obese (MHO); and the metabolicsyndrome (MetS). Finally, this information istranslated into clinical practice by showinghow various lifestyle interventions influencethe adipokine-CV network, with an ultimategoal of improving CV outcomes.

CONTEXT: OBESITY, ADIPOSITY, AND

CARDIOMETABOLIC RISK

Adipose tissue is made up of adipocytes,fibroblasts, vascular cells, lymphocytes, andmacrophages. Excess caloric intake promotesrecruitment of adipocyte precursors andresultant adipocyte hyperplasia. Beginningas early as infancy, and progressing with

advancing age, insulin resistance, or other diseasestates, this hyperplastic response is impaired. Thisresults in adipocyte hypertrophy, dysfunction,apoptosis, necrosis, inflammation, and an abnormalsecretory pattern of adipocyte-derived humoral fac-tors (6). Ultimately, this produces a complex patho-logical state of feed-forward, feedback, and otherarrays of direct and indirect cellular, humoral, andmolecular interactions with adverse cardiometabolicclinical correlates.

Obesity is a major threat affecting over 500 millionadults worldwide (7). The obese state has historicallybeen defined in terms of body mass index (BMI), eventhough ethnocultural factors (lower BMI cutoffs),changes in body composition (e.g., muscularity,sarcopenia, cachexia, increased waist circumference[visceral fat]), and curious discrepancies (e.g., pro-tective effects of mildly increased BMI) cast doubt onthe global utility of this anthropometric classifier. Theobesity problem is pervasive, affecting significantnumbers of people on a global scale, with seriousdownstream consequences on health, well-being,national security, and economics. Modern attemptsat combating this epidemic have been disappointing,having defied advances in pharmacotherapy, non-surgical or surgical bariatric procedures, and evenantiobesity legislation. However, there is consensusthat structured lifestyle interventions have not beensufficiently leveraged and are necessary to bend thecurve in obesity prevalence rates. In 2013, the Amer-ican Medical Association adopted a policy definingobesity as a chronic disease, rather than merely alifestyle choice (8). In 2014, the American Associationof Clinical Endocrinologists proposed an advancedframework for obesity diagnosis and management,

or

consisting of both anthropometric (BMI) and clinical(weight-related complications) components, whichnow places more emphasis on the complex biologicaleffects of abnormal adiposity (7).

The term “cardiometabolic” describes the inter-section of cardiovascular disease (CVD) and metabolicdisorders and is centered on the interplay amongobesity, insulin resistance, inflammation, and the CVsystem. Cardiometabolic is also used to describe risk,events, and a syndrome or disease that is essentiallythe same as the MetS. In 2014, the American Collegeof Cardiology convened a think tank to examine,clarify, and build a new care model for MetS. In short,the existence of MetS was affirmed, with differentsubtypes and severities, as well as a cluster ofmeasured and unmeasured risk factors, emphasizingspecific and residual risk factors that lifestyle in-terventions may address in a more effective andefficient public health care model (9). The take-homemessage here is that linking metabolic risk factorswith lifestyle interventions depends on complexinteractions of, among others, adiposity-relatedfactors with CVD-related targets. In other words, incontrast with pharmacotherapy or procedures, whichtarget specific nodes and have limited networkeffects, lifestyle interventions provide manifoldeffects on multiple nodes and are therefore uniquelysuited for complex network interactions.

COMPONENTS OF THE ADIPOKINE-

CARDIOVASCULAR-LIFESTYLE NETWORK

ADIPOKINES. Adipocytes can be found in variousadipose tissue depots: for example, white adiposetissue in subcutaneous (abdominal, gluteal, andfemoral), visceral (mesenteric/omental and retroper-itoneal), and intrathoracic (epicardial and pulmonary)depots; and brown/beige adipose tissue in neck,supraclavicular, perivascular/para-aortic, pericardial,and suprarenal depots (10). This contrasted withectopic fat that accumulates intracellularly in non-adipose tissue (without any adipocytes), such asheart, muscle, liver, and pancreas. Adipokines areregulatory peptides that produce a state of functionalconnectedness among the immune, endocrine-metabolic, and CV systems through autocrine/paracrine effects in adipose tissue, and endocrine ef-fects among multiple remote organs. Although origi-nally designed as an adaptive process, this complexintegrated system may cause, participate in, or reflectvarious pathological processes, including obesity,type 2 diabetes (T2D), and CVD. Currently, there are atleast 615 known adipokines representing, in aggre-gate, a portion of the adipocyte secretome (11).

TABLE 1 Adipokine–Cardiovascular System Molecular Relationships

Perturbation Factors Gene Symbol Tissue TypeDirect/Indirect

Molecular Interactors Cellular/Physiological Action Clinical Correlate

Obesity (�); insulin action(þ); proinflammatoryCK (�); autoimmune(þ), MetS (�) (121–123);COPD (þ) (124)

ADIPOQ Endothelial cells; skeletalmuscle; adipocytes

ADIPOR1; ADIPOR2; ADIPOR3; ADIPORX;CDH13; PDGFB (�); MAPK13; MAPK14;MAPK1; MAPK3; COX2; NFKB1; PRKAA1;PRKAA2; MTOR; AKT1; PIK3CA; NOS3; REN;AGT; EDN1; IL10 (þ); PTGIS (þ); PPARGC1A(þ); VCAM1 (�); ICAM1 (�); SELE (�);ABCA1 (þ); TIMP1 (þ); MSR (�); SFRP5 (51);WNT5A (50); IL6

Endothelial monocyt dhesion(�); angiogenesis (þ poptosis(�); proliferation and igration of smooth

muscle (�); myoc dial damage (�);ET-1 induced hyp rophy (�); fattyacid oxidation (þ DL oxidation (�);M2/M1 macropha (þ); CRP (�);macrophage upta of apoptotic cells(þ); angiotensin I nduced cardiachypertrophy (�); ulin action (þ)

Atherogenic (�); coronary artery disease (�);post-MI protection (þ); hypertension (�);T2D (�); cardiac ischemia/reperfusion injury(�); MetS (�) (125,126), post-MI systolicdysfunction (�), pressure overload-inducedcardiac hypertrophy (�)

Satiety (�) (127) FAM132A Adipocytes NA Inflammation (�)

Pregnancy (�) (128) CFD Adipocytes NA Alternative complem t pathway (þ) Inflammation (þ)

Coronary artery disease (þ)(129)

ANGPT2 Adipocytes TEK (130) Inflammation (þ)

Insulin (þ); dyslipidemia(�); lipid lowering (þ);hypoxia (þ); angiotensinII (�); heart failure (�);ventricular assist device(þ); cirrhosis (þ)

APLN Adipocytes; vascularsmooth muscle; CV;cardiac

NOS3; APLNR; PKC; PIK3CA; AKT1; MAPK1;MAPK3, RPS6KB1, LEP (—); MPTP (—);ADIPOQ (þ)

Triglyceride (—); imm e function;vasodilation

Satiety; inflammation (—); obesity (—); cardiaccontractility (þ); heart rate (þ); cardiacdevelopment; cardiac ischemia/reperfusioninjury (—); atherogenic (—); fluid balance

Renal disease (—) (131) BMP7 Adipocytes BMPR1 (132); ACVR1 (133) Brown adipogenesis; bone healing (þ), satiety(þ); obesity (�)

Obesity (þ);cardiomyopathy (þ);coronary artery disease(þ) (134)

CTS Adipocytes Glucose metabolism ) Atherogenic (�) (135)

MetS (þ) (136); obesity (þ);hypertension (þ);triglyceride (þ)

RARRES2 Adipocytes CMKLR1; IL2 (þ); IL6 (þ); GPR1; CCL2; PIK3CA;AKT1; MAPK1; MAPK3; NOS3; NFKB1;MAPK13; MAPK14; TNF (�); VCAM1 (�)

Glucose uptake (þ); ulin action (þ);angiogenesis (þ); acrophageattractant; adipog esis (�); MMP (þ);NO production

Adipose function (þ); vascular contractility (þ);inflammation (�); MetS (137)

Diabetes (þ) (138) DPP4 Adipocytes GLP1 (�) Blood glucose contro �) T2D (þ)

COPD (þ) (124) FABP4 Adipocytes INSR (139); LIPE (140) Blood glucose contro �) Cardiac contractility (�); CV risk; obesity (þ);T2D (þ)

Liver fat content (þ) AHSG Adipocytes INSR (141); DCN (142) Atherogenic (þ) (143)

Liver fat content (þ) FGF21 Adipocytes KLB LDL (�); triglyceride ); HDL (þ); insulinaction (þ)

Obesity (�); thermogenesis; dyslipidemia (�)

Dyslipidemia (144);heart failure (144)

IL1B Adipocytes IL1R1 (145); A2M (146); MMP2 (147) b-cell destruction T1D

Adipocyte size (þ); MetS (þ) IL6 Adipocytes IL6R; IRS1; ICAM1, VCAM1; SELE Insulin action Inflammation (þ); CV risk; atherogenic (þ)

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White adipose tissue (þ);satiety (þ); MI (þ);congestive heart failure(þ); MetS (þ) (121,148);obesity (þ)

LEP White adipose tissue;cardiac

LEPR; STAT3; JAK2; PRKAA1; PIK3CA; AKT1;MAPK1; MAPK3; SOD1; CCL2; PTPN11;PTPN1; PRKACA; AGTR1 (-); TNF (þ);IL2 (þ); IL6 (þ); SERPINE1 (þ); MPTP (�);IL4 (�); GLP1 (�); GLUT2 (�); GLUT4 (�);AT1R (þ); MCP1 (þ); NPY (�); VWF (þ);PLAT (þ); ICAM1 (þ); VCAM1 (þ); SELE (þ);RHOA; NOS (41)

Endothelial dysfunction (þ); oxidativestress (þ); metabolic rate (þ); insulinsecretion (�); insulin action (�)

Satiety (þ); cardiac ischemia/reperfusion injury(�); atherogenic (þ); hypertension, MetS(149); platelet activity (þ); inflammation (þ);cardiac output (�); cardiac lipotoxicity (�);obesity (�); T2D (�) (40,41); left ventricularfunction (þ); cardiac hypertrophy (�) (41–43);cardioprotective (44)

Liver failure (þ) (150) LCN2 Adipocytes MMP2 (151); MMP9 (152); HGF (153) Innate immune system; acute phaseresponse

Inflammation (þ)

LDL oxidation (þ) CCL2 Adipocytes; smoothmuscle; endothelialcells; T cells;macrophages

MAPK1; MAPK3; CCR2; TF (þ); SERPINE1 (þ);IL6 (þ); TNF (þ)

Adipose macrophage recruitment (þ);conversion of macrophage to foam cell(þ); scavenger receptor macrophage(þ); ox-LDL scavenging (þ); LDLreceptor macrophage (�); vascularsmooth muscle proliferation (þ); MMP(þ); glomerulosclerosing (þ); insulinaction (�)

Inflammation (þ); thrombogenic (þ); plaquedisruption (þ)

MetS (�) (154) NUCB2 Adipocytes; CNS; testes;gastrointestinal; betacells

INS (þ) Hyperglycemia (�); insulin secretion (þ) T2D (þ) (155)

Obesity (�); insulin action(þ); HDL (þ);MetS (�) (136)

ITLN1 Visceral adipose tissue;subcutaneous adiposetissue; epicardialadipose tissue

AKT1; NOS3; PRKAA1; NFKB1 (�); TNF (�);PTGS2 (�); MAPK13 (�); MAPK14 (�);SELE (�)

Glucose uptake (þ); insulin action (þ); noproduction (þ); lymphocyte toendothelial cells (�); angiogenesis (�);endothelial migration (�); vasodilation(þ)

Inflammation (�)

Obesity (þ) (156);T2D (þ) (156)

GRN Adipocytes CCNT1 (157); DLK1 (158) Adipose macrophage recruitment (þ);insulin action (�)

Inflammation (þ)

Cardiomyopathy (þ) (159) RBP4 Adipocytes ALB (160); RPE65 (161); TTR (162) Inflammation (þ); atherogenic (þ)

Obesity (þ); dyslipidemia(þ); insulin action (�)

ADSF Adipocytes, macrophages NFKB1 (þ); PIK3CA (þ); AKT1 (þ); MAPK13 (þ);MAPK14 (þ); MAPK1 (þ); MAPK2 (þ);VCAM1 (þ); ICAM1 (þ); MCP1 (þ); TNF (þ);IL1 (þ); IL6 (þ); IL12 (þ)

Glucose uptake (�); insulin action (�);aorta smooth muscle (þ); vascularsmooth muscle (þ); endothelialdysfunction; insulin action (�)

Atherogenic (þ); inflammation (þ); CV risk; T2D(þ); coronary artery disease (�); cardiacischemia/reperfusion injury (þ)

Obesity (�) SFRP5 Adipocytes MAPK8 (�); FLT1 (�); VEGF (þ) Angiogenesis (þ);Insulin action (�)

Inflammation (�); fatty liver (þ)

Atherogenic (þ) (163) TGFB1 Adipocytes TGFBR1; TGFBR2 (164) Cell differentiation; cell proliferation;apoptosis

Cardiac hypertrophy (þ) (165)

Obesity (þ); ischemia (þ);heart failure (þ); aging(þ); T2D (þ);hypertension (þ);MetS (þ) (122)

TNF Adipocytes; macrophages TNFR1; TNFR2; JAK1 (þ); STAT3 (þ); MAPK8(þ); IKBKB (þ); NFKB1 (þ); SELE (þ); ICAM1(þ); VCAM1 (þ); GLUT4 (þ); ADIPOQ (�);IRS1; LPL (�)

Angiogenesis (þ), vascular smooth musclefunction (þ), insulin action (�);apoptosis (þ)

Atherogenic (þ); tissue remodeling (þ)

MetS (þ) (166) SERPINA12 Adipocytes; stomach;hypothalamus

NA Blood glucose control (þ) Satiety (þ); MetS (137)

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Congestive heart failure (�)(167)

VEGF Adipocytes KDR; FLT1 (168) Angiogenesis (þ) Diabetic retinopathy (þ) (169)

Obesity (þ); T2D (þ); T1D(þ); hypoxia (þ); plaque(þ); MetS (þ) (170);satiety (þ) (127)

NAMPT Visceral adipose tissue;lymphocytes

NFKB1 (þ); PIK3CA (þ); AKT1 (þ); MAPK13 (þ);MAPK14 (þ); MAPK1 (þ); MAPK2 (þ); NOS3(þ); SIRT1 (þ); HIF1A; MPTP (�); VEGFA(þ); MMP2 (þ); MMP9 (þ); TNF (þ); IL1B(þ); IL6 (þ); IL10 (þ); ICAM1 (þ); VCAM1(þ)

Oxidative stress (þ); glucose metabolism(þ); insulin secretion (þ); insulin action(þ); HDL (þ); vascular smooth muscleproliferation (þ); NO production, NADbiosynthesis (þ); endothelial cellproliferation (þ); MMP (þ);vasodilation

Inflammation (þ); plaque disruption; cardiacischemia/reperfusion injury (�)

The links were derived from evidence cited in selected recent reviews (13,16–18), unless indicated by reference numbers in parentheses. These 4 reviews were selected on the basis of recent publication dates (2013 to 2015), comprehensive presentations on a molecularlevel, large adipokine portfolios, and extensive scientific referencing. All gene abbreviations are derived from their official gene symbol (120). An analysis of the information in this table was performed to determine whether emergent networking effects exist. Theaverage genetic distance (statistical measure of evolutionary divergence on the basis of genetic drift) among all of the identified adipokines is 1.246 on the basis of Molecular Evolutionary Genetics Analysis (version 7.0) (20). Ten control permutations of humanextracellular genes (GO:0005576), matched by the length of the corresponding adipokine gene to control for any dissimilarity that have arisen if chosen completely at random, were then used to approximate the distribution of genetic distance among all extracellulargenes. In this case, the cumulative distribution probability of obtaining 1.246 is 59.31% on the basis of the normal distribution approximated by the 10 control permutations. This is indicative of a diverse evolutionary origin, as the genetic distances among adipokines arenot significantly (p > 0.05) different than that of random chance, suggestive of an emergent network effect of adipokines on cardiometabolic clinical correlates.

A2M ¼ alpha-2-macroglobulin; ABCA1 ¼ ATP (adenosine triphosphate)-binding cassette transporter 1; ADIPOQ ¼ adiponectin; ADIPOR1, -2, -3, -X ¼ adiponectin receptor 1, 2, 3, or X; ADSF ¼ adipocyte-specific secretory factor/resistin; AGT ¼ angiotensinogen;AGTR1 ¼ angiotensin II receptor type 1; AHSG ¼ alpha 2-HS (Homo sapiens) glycoprotein/fetuin-A; AKT1 ¼ serine/threonine-protein kinase 1; ALB ¼ albumin; ANGPT2 ¼ angiopoietin 2; APLN ¼ apelin; APLNR ¼ apelin receptor; AT1R ¼ angiotensin II type I receptor;BMP7 ¼ bone morphogenic protein 7; BMPR1 ¼ bone morphogenetic receptor type IA; CCL2 ¼ chemokine ligand 2/monocyte chemotactic protein 1; CCNT1 ¼ cyclin T1; CCR2 ¼ chemokine receptor type 2; CDH13 ¼ cadherin 13; CFD ¼ adipsin; CK ¼ cytokine; CMKLR1 ¼chemokine-like receptor 1; CNS ¼ central nervous system; COX2 ¼ cyclooxygenase 2; CTS ¼ cathepsin; COPD ¼ chronic obstructive pulmonary disease; CRP ¼ C-reactive protein; CV ¼ cardiovascular; CVR1 ¼ activin A receptor type 1; DCN ¼ decorin; DLK1 ¼ deltahomolog 1; DPP4 ¼ dipeptidyl peptidase 4; EDN1 ¼ endothelin 1; FABP4 ¼ adipocyte protein 2; FAM132A ¼ family with sequence similarity 132; FABP4 ¼ fatty acid-binding protein 4; FGF21 ¼ fibroblast growth factor 21; FLT1 ¼ FMS (feline McDonough sarcoma viraloncogene homolog)-related tyrosine kinase 1; GLP1 ¼ glucagon-like peptide 1; GLUT2 or -4 ¼ glucose transporter 2 or 4; GPR1 ¼ G-protein coupled receptor 1; GRN ¼ granulin; HDL ¼ high-density lipoprotein; HGF ¼ hepatocyte growth factor; HIF1A ¼ hypoxia-inducible factor 1 alpha; ICAM1 ¼ intercellular adhesion molecule 1; IKBKB ¼ inhibitor of kappa light polypeptide gene enhancer in B cells, kinase beta; IL1B ¼ interleukin 1-beta; IL1R1 ¼ interleukin 1 receptor type 1; IL1, 2, -4, -6, -10, or -12 ¼ interleukin 1, 4, 6, 10, or 12;IL6R ¼ interleukin 6 receptor; INS ¼ insulin; INSR ¼ insulin receptor; IRS1 ¼ insulin receptor substrate 1; ITLN1 ¼ intelectin 1/omentin; JAK1 or -2 ¼ Janus kinase 1 or 2; KDR ¼ kinase insert domain receptor; KLB ¼ klotho beta; LCN2 ¼ lipocalin 2; LDL¼ low-densitylipoprotein; LEP ¼ leptin; LEPR ¼ leptin receptor; LIPE ¼ lipase E; LPL ¼ lipoprotein lipase; MAPK1, -2, -3, -8, -13, -14 ¼ mitogen-activated protein kinase 1, 2, 3, 8, 13, or 14; MCP1 ¼ monocyte chemoattractant protein 1; MetS ¼ metabolic syndrome; MI ¼ myocardialinfarction; MMP2 or -9 ¼ matrix metalloproteinase 2 or 9; MPTP ¼ 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine; MSR ¼ methionine sulfoxide reductase; MTOR ¼ mammalian target of rapamycin; NA ¼ not applicable; NAD ¼ nicotinamide adenine dinucleotide;NAMPT ¼ nicotinamide phosphoribosyltransferase/visfatin; NFKB1 ¼ nuclear factor kappa beta 1; NO ¼ nitric oxide; NOS ¼ nitric oxide synthase; NOS3 ¼ nitric oxide synthase 3; NPY ¼ neuropeptide Y; NUCB2 ¼ nucleobindin 2; ox-LDL ¼ oxidized low-density li-poprotein; PDGFB ¼ platelet-derived growth factor subunit B; PIK3CA ¼ phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha; PKC ¼ protein kinase C; PLAT ¼ plasminogen activator, tissue type; PPARGC1A ¼ peroxisome proliferative activatedreceptor, gamma, coactivator 1; PRKAA1 or -2 ¼ protein kinase adenosine monophosphate–activated catalytic subunit alpha 1 or 2; PRKACA ¼ protein kinase A catalytic subunit alpha; PTGIS ¼ prostaglandin I2 synthase; PTGS2 ¼ prostaglandin-endoperoxide synthase 2;PTPN1 or -11¼ protein-tyrosine phosphatase nonreceptor type 1 or 11; RARRES2 ¼ retinoic acid receptor responder 2/chemerin; RBP-4 ¼ retinol-binding protein 4; REN ¼ renin; RHOA ¼ ras homolog family member A; RPE65 ¼ retinal pigment epithelium-specific 65kDa; RPS6KB1 ¼ ribosomal protein S6 kinase B1; SELE ¼ selectin E; SERPINA12 ¼ vaspin; SERPINE1 ¼ serpin family E member 1; SFRP5 ¼ Secreted frizzled-related protein 5; SIRT1 ¼ sirtuin 1; SOD1 ¼ superoxide dismutase 1; STAT3 ¼ signal transducer and activator oftranscription 3; T1D ¼ type 1 diabetes; T2D ¼ type 2 diabetes; TEK ¼ TEK receptor tyrosine kinase; TF ¼ transferrin; TGFB1 ¼ transforming growth factor beta 1; TGFBR1 or -2 ¼ transforming growth factor beta receptor 1 or 2; TIMP1 ¼ tissue inhibitor of metal-loproteinase 1; TNF ¼ tumor necrosis factor; TNFR1 or -2 ¼ tumor necrosis factor receptor 1 or 2; TTR ¼ transthyretin; VCAM1 ¼ vascular adhesion molecule 1; VEGF ¼ vascular endothelial growth factor; VEGFA ¼ vascular endothelial growth factor A; VWF ¼ vonWillebrand factor; WNT5A ¼ Wnt family member 5A.

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However, among these adipokines, adiponectin(ADIPOQ) and leptin are 2 of the most studiedand most critical to a systems perspective of obesityand CVD.

Obesity is considered an inflammatory state andinvolves macrophage recruitment as an early step. Ithas been hypothesized that the relative contributionsof M1 (in obesity-associated adipose tissue) versus M2(in lean-associated adipose tissue) macrophagesdrive a predominantly proinflammatory or anti-inflammatory state, respectively. The M1 phenotypeis promoted by T helper-type cytokines (e.g., inter-feron gamma), as well as free fatty acids, tri-glycerides, resistin, leptin, retinol-binding protein 4,interleukins (IL)-6 and -1b, tumor necrosis factor(TNF)-alpha, inducible nitric acid synthase, reactiveoxygen species, and other factors that lead to tissuedestruction and insulin resistance (12). The M2phenotype is associated with immunosuppression,anti-inflammation, tissue remodeling, IL-10 andarginase-1 (inhibits inducible nitric acid synthase)expression, among other factors, and improves insu-lin sensitivity, wound healing, and angiogenesis(12,13).

CV SYSTEM. There are numerous cardiometabolicclinical correlates of adipokine effects (Table 1).Metabolic targets of the adipokine network affectvirtually every aspect of obesity, insulin resistance,hypertension, dyslipidemia, and inflammation. Adi-pokine CV targets are distributed among the variousanatomic components (heart and vasculature) as wellas cell types (cardiomyocytes, endothelium, and soon). The adipokine-CV interactions also influencevarious physiological processes, including car-dioprotection, reactive oxygen species scavenging,reperfusion injury, thrombosis, the renin-angiotensinsystem, cardiac lipotoxicity, and plaque disruption.Consequently, adipokine CV targets can participate inthe full spectrum of CVD.

LIFESTYLE MEDICINE. Lifestyle medicine is definedas the nonpharmacological and nonsurgical manage-ment of chronic disease (14). This is a burgeoningfield that requires formal education, high-impactclinical research, basic research into mechanisms,and relevant epidemiology. The chief components oflifestyle medicine are as follows: nutrition (specif-ically individualized and healthy eating patterns);physical activity (including both formal exercise,sports, play, and routine household and work-relatedmovements); attaining a healthy body compositionand weight; sleep hygiene (both healthy duration[6 to 9 h/night] and quality); behavioral (e.g., happi-ness and stress reduction); tobacco cessation; and

alcohol moderation. Each of these modalities canhave beneficial therapeutic effects on the adipokine-CV network.

NETWORK ANALYSES

EMERGENCE. The inherent nature of molecularphysiology is complex, voluminous, uncertain, andincomplete, making any overly detailed or overlysimplified presentation pragmatically inadequate.The challenge here is to consider an approach thatcan reveal important, accurate, and clinically relevantinformation. A bottom-up strategy (15) was selectedthat began with piecing together key physiologicalsubsystems assigned to each of the known majoradipokines, filtered by interactions with the CV sys-tem. Using the set of interactions gleaned from pub-lished reports and organized in Table 1, a networklinking adipokine perturbations and clinical corre-lates was constructed (Figure 1). The links werederived primarily from evidence cited in selectedrecent reviews (13,16–18). These 4 reviews wereselected on the basis of recent publication dates (2013to 2015), comprehensive presentations on a molecularlevel, large adipokine portfolios, and extensive sci-entific referencing. Subsequently, subnetworks forspecific clinical questions were presented to illustratethe utility of the larger adipokine-CV network.

The emergence of CV outcomes from perturbationsof adipokines is likely due to systemic networkeffects, in contrast to direct, linear effects of a singleor a few adipokines on a CV target. Furthermore,these network effects are likely a convergence ofeffects from evolutionarily distinct origins. This canbe quantified by measuring the genetic distanceamong the different adipokines (see Table 1 legend)(19,20). Systems approaches using interactionnetworks (interactome) can provide insights whentrying to understand complex physiology and caneven provide confirmations of currently held clinicalbeliefs (2,21). This type of analysis also incorporateswell-curated and accepted ontological categories(e.g., GeneOntology [22]) that map identified genesand proteins to their relevant biological process.

An important part of the analysis in this review wasto determine whether the cardiometabolic clinicalcorrelates represented emergent phenomena result-ing from networking effects. Therefore, a previouslypublished and validated protein-protein interactionnetwork was needed (23). A nearest-neighborapproach was applied to identify proteins that inter-acted with adipokines and their direct and indirecttargets on the basis of the information in Table 1 (seeTable 2 legend). The top 10 gene ontology categories

FIGURE 1 The Adipokine-Cardiovascular System Network

Diabetic Retinopathy

Adipose FunctionVascular Contractility

Thrombogenic

Plaque Disruption

Cardiac Output

Left Ventricular Function

Cardioprotective

Platelet Activity

Cardiac Lipotoxicity

Inflammation

Brown Adipogenesis

Bone Healing

Tissue RemodelingCardiac Ischemia/Reperfusion Injury

Cardiovascular Risk

Thermogenesis

Fluid BalanceCardiac Hypertrophy

Cardiac Development

Heart RateCardiac Contractility

Post–MI Protection Fatty Liver

Post–MI Systolic Dysfunction Pressure Overload Induced Cardiac Hypertrophy

Type 1 Diabetes

Metabolic Syndrome

Satiety

Type 2 Diabetes

Hypertension

Obesity Atherogenic

Dyslipidemia

Coronary Artery Disease

Congestive Heart Failure

Diabetes

LDL Oxidation VEGFA

SERPINA12 Myocardial Infarction

White Adipose TissueDPP4Plaque

Triglyceride Adipocyte Size LCN2

Pregnancy

Liver FailureFAM132A

CCL2Renal Disease

NAMPTRARRES2

NUCB2

BMP7

IL1B

LEP

GRNCFD

IL6 Insulin

ANGPT2RBP4TNF

ADSF CTS

CardiomyopathyHypoxia

Aging Heart Failure

APLN Angiotensin II

HDL ITLN1

ADIPOQVentricular Assist Device

FGF21Ischemia AHSG

FABP4

SFRP5TGFB1

Liver Fat Content

Insulin Action

Autoimmune

COPD Lipid–LoweringCirrhosis

Proinflammatory CK

This network was constructed by defining connections in Table 1 between the perturbation column and the adipokine of interest, and then between the

adipokine of interest and the cardiometabolic clinical correlates. Key for boxes and arrows: adipokines (white); cardiometabolic clinical correlates (brown);

perturbations (yellow); perturbations that are cardiometabolic clinical correlates (green); positive correlation (green arrows); negative correlation (red

arrows); and uncertain (black arrows). ADIPOQ ¼ adiponectin; ADSF ¼ adipocyte-specific secretory factor/resistin; AHSG ¼ alpha 2-HS (Homo sapiens)

glycoprotein/fetuin-A; ANGPT2 ¼ angiopoietin 2; APLN ¼ apelin; BMP7 ¼ bone morphogenic protein 7; CCL2 ¼ chemokine ligand 2/monocyte chemotactic

protein 1; CFD ¼ adipsin; CK ¼ cytokine; COPD ¼ chronic obstructive pulmonary disease; CTS ¼ cathepsin; DPP4 ¼ dipeptidyl peptidase 4; FABP4 ¼adipocyte protein 2; FAM132A ¼ family with sequence similarity 132; FGF21 ¼ fibroblast growth factor 21; GRN ¼ granulin; HDL ¼ high-density lipoprotein;

IL1B ¼ interleukin 1-beta; IL6 ¼ interleukin 6; LCN2 ¼ lipocalin 2; ITLN1 ¼ intelectin 1/omentin; LDL ¼ low-density lipoprotein; LEP ¼ leptin; MI ¼myocardial infarction; NAMPT ¼ nicotinamide phosphoribosyltransferase/visfatin; NUCB2 ¼ nucleobindin 2; RARRES2 ¼ retinoic acid receptor responder 2/

chemerin; RBP4 ¼ retinol binding protein; SERPINA12 ¼ vaspin; SFRP5 ¼ secreted frizzled-related protein 5; TGFB1 ¼ transforming growth factor beta 1;

TNF ¼ tumor necrosis factor; VEGFA ¼ vascular endothelial growth factor A.

J A C C V O L . 6 8 , N O . 1 6 , 2 0 1 6 Mechanick et al.O C T O B E R 1 8 , 2 0 1 6 : 1 7 8 5 – 8 0 3 Adipokines and CVD

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TABLE 2 Top 10 Ranked Gene Ontology Categories

GO:0001666 Response to hypoxia 0.9936 16.98

GO:0030335 Positive regulation of cell migration 0.9701 16.25

GO:0007596 Blood coagulation 0.6256 15.47

GO:0030168 Platelet activation 0.7414 15.31

GO:0032496 Response to lipopolysaccharide 0.9510 15.27

GO:0051384 Response to glucocorticoid 1.2506 14.73

GO:0017017 MAP kinase tyrosine/serine/threonine phosphataseactivity

3.4114 14.72

GO:0018108 Peptidyl-tyrosine phosphorylation 0.9008 14.55

GO:0005158 Insulin receptor binding 1.3200 14.27

GO:0006954 Inflammatory response 0.7772 14.18

These results are on the basis of the list of adipokines and their interactions in Table 1, and thenapplying a nearest-neighbor approach. The purpose of determining ontological properties ofadipokines is to logically connect proteins and protein networks with physiological function,which can subsequently be connected with cardiometabolic clinical correlates. For this geneontology analysis, we first identified the set of unknown nodes (proteins) that interacts with theset of known nodes (adipokines and their direct and indirect targets). A similar analysis on thebasis of the set of known nodes that are not adipokines was also identified. Then, the combinedset of unknown nodes and their edges (node-to-node connections, or interactions) was identified.Next, we used gene ontology to group these unknown nodes (proteins) into pre-curated onto-logical categories (or physiological functions, e.g., gluconeogenesis). On this basis, a 2 � 2 tablewas created that compares the “adipokine network” to the “nonadipokine network,” as well as the“in (specific) ontology category” versus the “not in (specific) ontology category.” As a result,adipokines are identified that are linked to specific ontology categories. The degree linkage is onthe basis of the log of the odds ratio (>0.5) and the significance is on the basis of the significanceof the odds ratio (>2). GO ¼ gene ontology; MAP ¼ mitogen-activated protein.



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are listed in Table 2; the most significant were theresponses to hypoxia, regulation of cell migration,effects on blood coagulation, and platelet activation.Other methods (24,25) are available to examine thesame data, each with their own benefits anddrawbacks.

APPLICATIONS

The application of network analysis has had a pro-found impact on society, spanning from social net-works (26) to drug discovery (27). The conceptsbehind network analysis are often simple and easy tounderstand, enabling one to capture all the details ofa large and complex system using a set of rules. Withrespect to an adipokine signaling network, networkanalysis offers many advantages, such as insight fortherapeutic targeting, despite otherwise comparableresults with traditional, correlation-drivenapproaches.

For example, Greenblum et al. (28) identified alower centrality (connectedness) in gut microbiomemetabolic networks in subjects with obesity, versusthose without obesity, suggesting that metabolictargeting in obesity may require a combination ofevents (e.g., lifestyle modification). Network analysishas also been used to integrate findings of candidategene loci relating to coronary artery disease (usinggenome-wide association study) and linking themwith lipid metabolism and inflammation (29) to un-derstand the roles of herbs (e.g., Radix curcumae) on

CVD by identifying compounds within these herbsand their connectivity to protein targets (30) andidentifying intertissue gene expression correlations,for instance, to demonstrate the effects of cardiacdipeptidyl peptidase 4 on cell proliferation-relatedgenes (particularly the chemokine regulator stromalcell-derived factor-1) in whole blood and the rele-vance of dipeptidyl peptidase 4 inhibitors (31). Genecoexpression, protein-protein interaction, andBayesian networks have been used to identify regu-latory drivers for coronary artery disease (32). Othershave used integrative genomic approaches withnetwork analysis to identify an Epstein-Barr virus-induced G-protein-coupled receptor 2 (Ebi2 orGpr183)-regulated interferon regulatory factor 7–driven inflammatory network in type 1 diabetes sus-ceptibility (33).

As a result of the insidious onset of metabolic dis-ease, such as T2D, and the numerous feedback loopswithin the adipokine network, a more comprehensiveapproach, such as network analysis, is invaluable forunderstanding and developing therapeutic options(34). For instance, a therapeutic target database wascreated on the basis of ADIPOQ applied and protein-protein interaction network analysis, along with anetwork to identify druggable interactors, such as theinsulin receptor and the signal transducer and acti-vator of transcription 3 (STAT3) (35).

CLINICAL TRANSLATION

CARDIOPROTECTION. Obesity is an independentrisk factor for CVD, including heart failure, but amongpatients with heart failure, those who are mildly tomoderately obese (classes I and II: BMI 30.0 to 39.9kg/m2) have better short- and moderate-term out-comes than those who are not obese (36,37). Thisparadox is well established using epidemiologicalstudy methods for a host of chronic diseases, espe-cially CVD. Flegal et al. (38) conducted a systematicreview of reported hazard ratios in 97 studiesinvolving 2.88 million people and found that over-weight (BMI 25.0 to 29.9 kg/m2) was associated withdecreased all-cause mortality, mild obesity (class I:BMI 30.0 to 34.9 kg/m2) with no increased all-causemortality, and moderate to severe obesity (classesII, III, and higher: BMI $35.0 kg/m2) with increasedall-cause mortality. Though very helpful in parsingout this paradox in terms of a generally acceptedclassifier (BMI), mechanisms that underpin thisfinding are best understood by challenging the centralimportance of BMI, and refocusing on the complexitythat generates a J-curve relating chronic disease riskwith body adiposity measures (39).


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Obesity leads to hypertension, insulin resistance,lipotoxicity, inflammation, atrial fibrillation, andincreased cardiac work with biventricular dysfunc-tion (37). Candidate mechanisms for the obesityparadox on the physiological scale include greatermetabolic reserve with a catabolic state, the benefitsof compulsory exercise on cardiorespiratory fitness,lower renin levels, and reverse causality (processesthat reduce body weight are associated with more CVmortality; e.g., uncontrolled diabetes, cancer, andcardiac cachexia) (36,37). An important question iswhether there are reasonable explanations and sets oftherapeutic targets that can improve CV mortality ona molecular scale that are consistent with thesephysiological mechanisms.

Leptin and ADIPOQ are 2 molecules with signalingpathways and networking that merit discussion withrespect to the obesity paradox. Even though leptinhas proatherogenic and prohypertrophic effects onthe heart, genetic mutations resulting in leptin defi-ciency states are associated with obesity and T2D(40,41). Moreover, leptin is associated with improvedleft ventricular function, raising the level of confu-sion wherein both high and low levels of leptin areassociated with cardiac hypertrophy (41–43). Thepicture is even more complicated when considering arecent study by Wannamethee et al. (44) of older men(60 to 79 years of age), in which direct and/or indirectcardioprotective effects of leptin may explain theinverse association with heart failure mortality,whereas higher muscle mass (accounting for higherweight) is associated with lower coronary heart dis-ease mortality. Leptin exerts effects on the heart viaRhoA, endothelial nitric oxide synthase, hydrogensulfide-induced endothelium-dependent hyperpolar-ization factor, and other pathways delineated inTable 1 (41). In a murine study by Leifheit-Nestleret al. (45), cardioprotection with obesity is mediatedvia cardiac leptin receptor-stimulated STAT3 activa-tion, whereas cardiac hypertrophy, antiangiogenesis,apoptosis, and fibrosis are associated with experi-mental leptin resistance.

Adiponectin is inversely associated with car-diometabolic risk, but has a direct association withcoronary heart disease and heart failure mortalities(46,47). Table 1 describes direct and indirect ADIPOQ-mediated effects on cardioprotective T-cadherin anddecreasing endothelial inflammation via vascularcell/intercellular/endothelial leukocyte adhesionmolecules (48). Interestingly, increased survivals incritical illness with obesity are thought to be associ-ated with higher leptin and lower ADIPOQ, whichseparately and together improve innate immunityand an appropriate proinflammatory response (49).

Secreted frizzled-related protein 5, another adipo-kine, has insulin-sensitizing and anti-inflammatoryroles that are similar to ADIPOQ, decreasesmacrophage-dependent adipose tissue inflammation(via WNT5A (Wnt family member 5A) [50]), and isinversely associated with coronary heart disease (51).Emergent properties of leptin-CVD and ADIPOQ-CVDsubnetworks in the obese state, filtered for car-dioprotection, revealed the importance of apelin,another adipokine, in coordinating the effects of lep-tin and ADIPOQ, through feed-forward and feedbackregulatory components, on clinical outcomes. Apelinis primarily expressed and secreted by endothelium,binding to the APJ (apelin receptor) receptor (similarto the angiotensin-II type-1 receptor) in the heart (52).This insight provided by network analysis mayprompt a shift in focus of research to apelin, ratherthan ADIPOQ or leptin, as a means of targeting theadipokine system. This network is shown in Figure 2.

A valuable clinical model for cardioprotection isthe MHO. This subset of patients with obesity isrelevant; as there may be effects of the adipokinenetwork on metabolism that directly and/or indirectlyimprove cardiovascular physiology. Depending onthe definition, approximately 16% to 37% of patientswith obesity are classified as metabolically healthy(53–55). There are many compelling argumentsagainst the true existence of the MHO on the basis ofthe durability of this state and semantics/definitionsof what actually constitutes complete or partialmetabolic health. Besides, the reprioritization ofhealth, wellness, and quality of life over mere loss ofweight and attainment of a “normal” BMI is now anevolving imperative, especially in a preventive para-digm for health care (7). Hence, the question is notonly whether the MHO exist, but more importantly,what measures can be implemented to improve CVhealth among all patients with obesity.

In the strictest sense, the MHO are those with aBMI $30.0 kg/m2 (less for other groups of people,such as south Asians) and absence of MetS compo-nents (56). However, this definition can be modulatedto suit specific clinical questions with varying differ-ences in body composition and severities of MetScomponents. Moreover, there are people with normalweights (or BMI) and high measures of body fat thathave increased CV risk factors and MetS components(57), although ADIPOQ and C-reactive protein werenot found to be different than in lean counterparts(58). Ahl et al. (59) found that, in contrast to the usualcase of lower ADIPOQ with higher CVD risk and cen-tral obesity (increased waist-to-hip ratio), higherADIPOQ levels were found in those patients withlower CVD risk and peripheral obesity. Similar results

FIGURE 2 Leptin and ADIPOQ Subnetworks

Pressure Overload Induced Cardiac Hypertrophy

Post–MI Protection

Post–MI Systolic Dysfunction

Metabolic Syndrome


Hypertension

Type 2 Diabetes


Cardioprotective

Cardiac Output

Platelet Activity


Satiety

Obesity

Cardiac Ischemia/Reperfusion Injury

Inflammation

Atherogenic

Insulin Action

COPD

Autoimmune

Proinflammatory CK

ADIPOQ

APLN

White Adipose Tissue

Congestive Heart Failure

Myocardial Infarction

LEP

SFRP5

TNF

CCL2

IL6

The leptin and ADIPOQ subnetworks were constructed on the basis of relationships defined in Table 1. A list of nodes was identified on the basis of all

perturbations and cardiometabolic clinical correlates that are immediately connected to leptin or ADIPOQ. This list was then used to filter the network in

Figure 1 into this subnetwork. Abbreviations and color explanations as in Figure 1.



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were found in an earlier study by Guenther et al. (60).However, in a prospective cohort of patients withsevere obesity before and after Roux-en-Y gastricbypass bariatric surgery, those traditionally viewed as“healthy,” with less dyslipidemia and/or insulinresistance, still had some degree of these metabolicmarkers, as well as abnormal adipokine profiles,where leptin was the most discriminating satietyparameter (61). Among Asian Indians with obesity,adipokine profiles associated with reduced numbersof MetS components included high ADIPOQ and lowvisfatin, resistin, and inflammatory cytokines (high-sensitivity C-reactive protein, TNF-a, and oxidizedlow-density lipoprotein) (53). Among Spanish pa-tients with obesity, there were nearly identical adi-pokine profiles involving leptin, ADIPOQ, and resistinbetween the MHO (<2 MetS components) and meta-bolically unhealthy obese ($2 MetS components) (62).

In defense of the concept of MHO are the findingsof Pereira et al. (63), in which certain compensatorypathways are activated in patients with severeobesity to counter chronic obesity-related inflamma-tion: 1) increased anti-inflammatory adipokines IL-10(suppresses IL-6 and TNF-a) and transforming growthfactor (TGF)-beta from subcutaneous adipose tissue;2) increased forkhead box–P3-positive regulatory Tcells (increases anti-inflammatory, IL-10–producing

macrophages); and 3) increased TGF-b and matrixmetalloproteinase-2–, -8–, and -9–dependent angio-genesis and adipogenesis. Alternatively, there aresome adipokine repertoires in the MHO that appearmore in response to acute overfeeding (e.g., ADIPOQand fibroblast growth factor-21), rather than as aresult of the chronically overfed, obese state (e.g.,endothelial plasminogen activator inhibitor 1 [SER-PINE1], fatty acid-binding protein-4, and lipocalin-2)(64). Network analysis revealed TNF-a as having thehighest closeness centrality measure (i.e., 1 dividedby the sum of distances to all other components in theobesity subnetwork) among the potential adipokinetargets downstream of obesity. This analysis can beused to prioritize treatment goals toward MetS, whichhad the highest closeness centrality measure, asopposed to the other clinical factors (Figure 3).

The working definition of MHO incorporates, at thevery least, the absence of some MetS components.But does MetS truly exist and, if so, is this existencehelpful in any way in clinical practice? This contro-versy has been previously addressed on the basis ofepidemiologic evidence of residual risk that isthought to significantly impact CVD development(9,65). However, the question here is whether resid-ual risk can be better envisioned and harnessed whenmolecular adipokine-CVD networks are considered.

FIGURE 3 Subnetwork Downstream of Obesity

Cardioprotective

Plaque Disruption

Vascular Contractility

Tissue Remodeling

Adipose Function

Cardiac Output

Satiety


Platelet Activity


Hypertension

Type 2 Diabetes

Atherogenic

Cardiac Ischemia/Reperfusion Injury

Inflammation

Metabolic Syndrome

Obesity

Fatty LiverCardiovascular Risk


Post–MI Systolic Dysfunction

Post–MI Protection

Pressure Overload Induced Cardiac Hypertrophy

VEGFA IL1B

NAMPT CCL2

LEP

RARRES2

GRN

IL6ITLN1

ADSF

SFRP5ADIPOQ

CTS

TNF

This subnetwork (2 steps or 1 intermediate node) is on the basis of relationships defined in Table 1, and then displayed in Figure 1. The importance

(connectivity) of TNF-a and the metabolic syndrome are outlined in orange for visual purposes. Obesity is in purple for visual purposes. Abbreviations and

color explanations as in Figure 1.


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Of course, the context and goal of this exercise is toidentify realistic lifestyle interventions that can notonly specifically address specific risks and residualrisk, but also result in clinically relevant benefit.

The clinical components of MetS include centralobesity and insulin resistance as primary etiologicfactors, along with hypertension, dyslipidemia, andinflammation. At a molecular level, biomarkersinclude adipokines, hormones, neurohumoral factors(e.g., ghrelin), pro- and anti-inflammatory cytokines,antioxidant status factors (e.g., paraoxonase 1) andpro-thrombotic factors (38). Thus, MetS is associated

with increased leptin, IL-6, TNF-a, oxidized low-density lipoprotein, uric acid, and plasminogenactivator inhibitor 1 [SERPINE1], and decreasedADIPOQ, IL-10, ghrelin, and paraoxonase 1 (66).López-Jaramillo et al. (67) found that the increasedleptin-ADIPOQ ratio found with MetS was also asso-ciated with increased angiotensin-II vasoconstriction.It is also an interesting question whether CVD in MetSis incited by the adipokine effects of overall fataccumulation in response to overfeeding, or the fail-ure of subcutaneous fat accumulation to buffer theinjurious effects of overfeeding (either directly or

FIGURE 4 Subnetwork on the Basis of Adipokines With Connections to or From MetS

ADIPOQ

ITLN1 SERPINA12 RARRES2 NAMPT LEP NUCB2

TNF IL6

Metabolic Syndrome

This subnetwork identifies adipokines immediately connected to the metabolic syndrome (MetS) node on the basis of relationships defined in

Table 1, and then displayed in Figure 1. Abbreviations and color explanations as in Figure 1.



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indirectly via subsequent accumulation of visceral orectopic fat) (65,66,68,69). In fact, MetS can even existwithout strictly defined obesity, such as in lean adultswith primary insulin resistance, certain genetichypertriglyceridemic conditions, South Asianethnicity (with sarcopenia), or parents with T2D (68).These caveats implicate a systems interaction of MetScomponents, instead of 1 or more MetS componentsindividually and additively driving the entire patho-physiological state. When considering the totality ofthe adipokine-CV network, a network analysis revealsnumerous positive and negative feedback loopsinvolving MetS, implicating residual risks, and sug-gesting the difficulties with a conventional pharma-cological, monotherapy approach to each MetScomponent (Figure 4).

LIFESTYLE INTERVENTIONS

HEALTHY EATING PATTERNS. One of the premisesfor healthy nutrition is that evolution favored a state ofinsulin resistance and heightened immunity, but thiswas in the setting of scarce energy supplies andincreased physical activity (70). Teleologically, ahealthy lifestyle would approximate these conditionsand is in stark contrast to the sedentary, high

processed food use, and stressful lifestyle that isprevalent today.With intensive lifestyle interventionsthat include healthy eating patterns, weight loss isassociatedwith increasedADIPOQ levels (71). Just afterweekends (on Mondays) of unhealthy lifestyle prac-tices, including poor nutritional choices, more seden-tary behaviors, and disrupted sleep, children havehigher leptin levels, compared with Fridays, whichfollow a span ofmore healthy lifestyle practices duringthe school week (72). Weight loss to improve left ven-tricular function in adult subjects with obesity wasassociated with reductions in leptin, growth differen-tiation factor-15, and matrix metalloproteinase-9 (73).Among 5 healthy women with obesity, Sahin-Efe et al.(74) found that a 10% to 15% weight loss with medicalnutrition therapy was associated with decreasedSTAT3 phosphorylation, but not with leptin-mediatedphosphorylation of STAT3, extracellular-signal-regulated kinase, Akt, or AMP-activated proteinkinase.

Specific eating patterns that are associated withcardiometabolic risk reduction (weight loss,improved blood pressure and lipids, among others)include the plant-based Mediterranean, DASH(Dietary Approaches to Stop Hypertension), and Di-etary Guidelines for Americans diets. There are


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several important nutrient-specific commonalities tothese diets that have potential molecular targets inthe adipokine-CV network, although the evidencebase is still small, inconsistent, and incomplete:

1. Polyphenols (resveratrol and quercetin attenuatedTNF-a–mediated decreases in ADIPOQ and in-creases in inflammatory adipokine levels [75,76]);

2. N-3 polyunsaturated fatty acids (increased leptin[76]; increased ADIPOQ [77], decreased inflamma-tion [76]);

3. Monounsaturated fatty acids (weight loss witha Mediterranean diet having 18.1% mono-unsaturated fatty acids associated with decreasedleptin and increased ADIPOQ [78]);

4. Low saturated fat (no effect on ADIPOQ [79]);5. Antioxidant phytochemicals (a-lipoic acid with

decreased leptin [76]);6. Vitamin D (at present, no significant effect on

adipokines [80]);7. Branched-chain amino acids (elevated branched-

chain amino acids inversely associated withADIPOQ in male, but not female adolescents[81]); and

8. Soy protein (reduces fetuin A and resistin [82]).

Furthermore, isocaloric substitution with certainnutrients (e.g., monounsaturated fatty acids, n-3polyunsaturated fatty acid, whole grains, fiber, andfresh fruits and vegetables) increase insulin sensi-tivity, whereas others (e.g., saturated fat, trans fat,refined grains, and processed foods) decrease insulinsensitivity (83). In addition, some studies havedemonstrated that a higher Mediterranean diet scorehas been inversely correlated with TNF-a (84). ThePREDIMED (PREvencion con DIeta MEDiterranea)subjects at 1 year demonstrated a reduced level ofIL-6 (85). Another group has found that patients on aMediterranean diet had decreased fasting and post-prandial gene expression levels of IL-1ß whencompared with patients on a saturated fat–rich diet(86). Finally, a healthy Nordic diet has been shown todecrease circulating cathepsin levels in patients (87).In balance, these studies illustrate the potentialbeneficial effects of adjusting eating patterns onadipokine levels.

PHYSICAL ACTIVITY. The similar mortality risks be-tween obese fit subjects and those of normal-weightfit subjects suggests the importance of physical ac-tivity on CV outcome (88,89). In a cross-sectionalstudy, Poelkens et al. (90) found an association be-tween physical activity (and cardiorespiratory fitness)and lower levels of TNF-a in obese women. In alifestyle study of children with normal weight,

overweight, and obesity, an intervention with dietarycounseling, regular physical activity, and familysupport resulted in decreased leptin in the obesegroup, and increased ADIPOQ in all groups (91).Physical activity has also been found to reduce IL-1b,-6, -8, -15, and C-reactive protein (92), and to increaseuncoupling protein 1, mitochondrial biogenesis, and“beiging” of white adipose tissue (93). In the LookAHEAD (Look Action for HEAlth in Diabetes) study,increased fitness was associated with increased ADI-POQ levels (71). Aerobic exercise was associated witha rise in ADIPOQ and resistin after 16 weeks, and coreexercise was associated with a decline in leptin after8 weeks (94). However, in a study of pre-pubertalchildren by Metcalf et al. (95), ADIPOQ was found tobe a selectively controlled modulator of insulinsensitivity. ADIPOQ has an insulin-sensitizing effect(95). However, ADIPOQ, but not insulin sensitivity,was inversely related to physical activity, an effectmost pronounced in the least active children (95).Investigators found that chemerin levels weresignificantly decreased with moderate walking exer-cise (96) and supervised exercise (97).

OTHER LIFESTYLE FACTORS. Leptin not only has ef-fects on the appetite centers in the hypothalamus, butalso on brain development, neuroplasticity, depres-sion, and cognition (98). In maltreated children (achronic stress example), leptin responses to adiposityand inflammation were blunted, potentially resultingfrom impaired stimulatory effects of glucocorticoids,leading to overeating (99). In a 105-day simulatedMarsmission (a confinementmodel of chronic stress), leptinlevels were reduced (100). Women with interpersonaltension also demonstrated lower leptin levels, alongwith unhealthy eating patterns (101). Patients withdepression had decreased dipeptidyl peptidase 4 ac-tivity, which was reversible by antidepressant treat-ment, suggesting that dipeptidyl peptidase 4 may beimplicated in stress (102). Finally, anorexia patientshad nucleobindin 2 levels that were correlated withtheir generalized anxiety disorder 7-item scale score,implicating nucleobindin 2 as a factor that is poten-tially modifiable with stress reduction (103).

Short-duration sleep in children was associatedwith increased levels of retinol-binding protein 4(104). Poor sleep quality among shift workers (e.g.,resident physicians) was associated with unhealthyeating patterns, excessive daytime sleepiness, andlower levels of leptin (105). In healthy adults, rapid eyemovement sleep was also associated with lower levelsof leptin (106). Al Mutairi et al. (107) found decreasedADIPOQ levels with progressively increased severity inobstructive sleep apnea syndrome (OSAS); however,

CENTRAL ILLUSTRATION The Impact of Lifestyle Change on the Adipokine-Cardiovascular Network

Mechanick, J.I. et al. J Am Coll Cardiol. 2016;68(16):1785–803.

The effects of lifestyle changes now identified: lifestyle modification (blue); adipokine network interactions (green); and lifestyle to adipokine interactions (orange).

ADIPOQ¼ adiponectin; ADSF¼ adipocyte-specific secretory factor/resistin; AHSG¼ alpha 2-HS (Homo sapiens) glycoprotein/fetuin-A; ANGPT2¼ angiopoietin 2; APLN¼apelin; BMP7 ¼ bone morphogenic protein 7; CCL2 ¼ chemokine ligand 2/monocyte chemotactic protein 1; CTS ¼ cathepsin; DPP4 ¼ dipeptidyl peptidase 4; FABP4 ¼adipocyte protein 2; FAM132A ¼ family with sequence similarity 132; FGF21 ¼ fibroblast growth factor 21; GRN ¼ granulin; IL1B ¼ interleukin 1-beta; IL6 ¼ interleukin 6;

ITLN1 ¼ intelectin 1/omentin; LEP ¼ leptin; NAMPT ¼ nicotinamide phosphoribosyltransferase/visfatin; NUCB2 ¼ nucleobindin 2; RARRES2 ¼ retinoic acid receptor

responder 2/chemerin; RBP4 ¼ retinol binding protein; SFRP5 ¼ secreted frizzled-related protein 5; TNF ¼ tumor necrosis factor; VEGFA ¼ vascular endothelial growth

factor A.



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leptin levels first increased with mild to moderateOSAS, and then paradoxically decreased with severeOSAS, compared with normal subjects. In anotherstudy, elevated levels of chemerin, macrophagemigratory inhibitory factor, vaspin, and chemokineCXCL5 were found with OSAS (108). Chen et al. (109)showed that continuous positive airway pressure inOSAS can significantly reduce leptin levels withoutconcomitant weight loss. Interdisciplinary lifestyleintervention with healthy nutrition, weight loss, andphysical activity was associated with improvedsleep-disordered breathing and insulin resistance,reduced leptin-ADIPOQ ratio, and lower inflammatorymarkers (110).

Leptin levels are higher among smokers attemptingto quit (an acute stress example) (111). Among thosecontinuing to smoke, there is a blunted leptin responseto pain (112). In pregnancy, tobacco smoking is asso-ciated with decreased maternal and fetal ADIPOQlevels, but not leptin levels (113). In a meta-analysis ofepidemiological studies of smokers, ADIPOQ levelswere reduced, and this was reversed with quitting(114). In patients who quit and then gained weight,ADIPOQ levels increased by 1 week, and then droppedto baseline by 9weeks, whereas in weightmaintainers,low ADIPOQ levels persisted without significantchange (115). Smokers also had increased levels ofresistin (116). Moreover, cigarette smoking is associ-ated with changes in downstream mediators of adi-pokine effects, such as increased endothelialdysfunction; oxidized low-density lipoprotein; matrixmetalloproteinase -1, -8, and -9; IL-1b, -6, and -8;reactive oxygen species; and adhesion molecules(intercellular adhesion molecules, vascular adhesionmolecules, and E-selectin), with reduced nitric oxideand tissue inhibitors of metalloproteinase (117). Thesedata in aggregate demonstrate interesting, but com-plex adipokine associations with tobacco smoking,quitting, and subsequent weight gain, which remainunclear and therefore require further study.

CONCLUSIONS

During the process of reviewing and organizing thepublished reports in Table 1, the complex nature ofadipokine interactions with the CV system wasappreciated and, at first blush, appeared to precludeany clear, translatable component analysis for clinicalpractice. However, with further thought, a networkanalysis was undertaken and the potential fordiscovering emergent effects was verified.

By examining an interactome, 4 processes werefound to be most important in mediating the effects ofadipokines on the CV system: responses to hypoxia,

regulation of cell migration, effects on blood coagula-tion, and platelet activation. Insulin receptor bindingwas ranked ninth, which is not surprising, due to theclose relationship of obesity and insulin resistance inMetS. Clearly, a more detailed and thorough analysis isrequired to clarify the networking effects of insulinresistance with adipokine-CV pathophysiology.

Next, the adipokine-CV network was used toaddress problematic issues when considering car-diometabolic risk reduction: First, that car-dioprotection in patients with obesity may bestrongly regulated by apelin as a master modulator ofleptin and ADIPOQ, and as a potential therapeutictarget. Second, that metabolic health may beimproved in patients with obesity by targeting spe-cific MetS components, primarily hypertension andT2D, and possibly inflammatory signals, primarilyTNF-a. And third, that the existence of MetS and re-sidual risk is verified, serving as tools to betterformulate cardiometabolic risk reduction strategies.Moreover, that the complex nature of car-diometabolic risk may be best addressed throughmanifold targeting, rather than the use of a single or afew agents. These findings serve not only ashypothesis-generating starting points for scientificvalidation, but they also support clinical insights.

One important extrapolation of these network-based findings is that some of the more importantlifestyle interventions (healthy eating patterns,physical activity, proper sleep, stress reduction, andtobacco cessation) can potentially exert significantsalutary effects to reduce cardiometabolic risk. In theCentral Illustration, the perturbation-adipokine-“clinical correlate” network is augmented bynetworking therapeutic lifestyle changes. In thefuture, systems pharmacology methods (2,118,119),such as computing the closeness centrality of lifestyleperturbations and utilization of subnetwork algo-rithms, may be able to personalize lifestyle medicinefor cardiometabolic risk reduction. For the entireadipokine-CV network presented here, physical ac-tivity had the greatest impact.

In short, this review demonstrates that adipokinesare responsible for significant effects on the CV sys-tem and, with the use of network analysis, importanthypotheses can be generated that not only test theefficacy of established or novel pharmaceuticals, butthe benefits of structured lifestyle change as well.

REPRINT REQUESTS AND CORRESPONDENCE: Dr.Jeffrey I. Mechanick, Division of Endocrinology, Dia-betes and Bone Disease, Icahn School of Medicine atMount Sinai, 1192 Park Avenue, New York, NewYork 10128. E-mail: [email protected].

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KEY WORDS cardiovascular disease,diabetes, hypertension, lifestyle medicine,network, obesity, systems biology







































































































































































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The Adipokine-Cardiovascular-Lifestyle Network · Adipokines are peptides, secreted by adipocytes and other cell types with targets in other tissues, participating in a complex network