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• W2963655570 abstract "HomeHypertensionVol. 74, No. 2Concept of Extremes in Vascular Aging Free AccessReview ArticlePDF/EPUBAboutView PDFView EPUBSections ToolsAdd to favoritesDownload citationsTrack citationsPermissions ShareShare onFacebookTwitterLinked InMendeleyReddit Jump toFree AccessReview ArticlePDF/EPUBConcept of Extremes in Vascular AgingFrom Early Vascular Aging to Supernormal Vascular Aging Stephane Laurent, Pierre Boutouyrie, Pedro Guimarães Cunha, Patrick Lacolley and Peter M. Nilsson Stephane LaurentStephane Laurent From the Department of Pharmacology, INSERM U970, Assistance Publique Hôpitaux de Paris, Université Paris Descartes, France (S.L., P.B.) Search for more papers by this author , Pierre BoutouyriePierre Boutouyrie From the Department of Pharmacology, INSERM U970, Assistance Publique Hôpitaux de Paris, Université Paris Descartes, France (S.L., P.B.) Search for more papers by this author , Pedro Guimarães CunhaPedro Guimarães Cunha Center for the Research and Treatment of Arterial Hypertension and Cardiovascular Risk, Serviço de Medicina Interna do Hospital da Senhora da Oliveira, Guimarães, Portugal (P.G.C.) Life and Health Science Research Institute, School of Medicine, University of Minho, Guimarães, Portugal (P.G.C.) Search for more papers by this author , Patrick LacolleyPatrick Lacolley INSERM U961, Vandoeuvre-les-Nancy cedex, France (P.L.) Search for more papers by this author and Peter M. NilssonPeter M. Nilsson Department of Clinical Sciences, Lund University, Skane University Hospital, Malmo, Sweden (P.M.N.). Search for more papers by this author Originally published17 Jun 2019https://doi.org/10.1161/HYPERTENSIONAHA.119.12655Hypertension. 2019;74:218–228Other version(s) of this articleYou are viewing the most recent version of this article. Previous versions: June 17, 2019: Ahead of Print With advancing age, changes in the arterial wall contribute to what has been called vascular aging, and in some prematurely affected subjects even early vascular aging (EVA).1–5 Several years ago,1 we listed various components of EVA, including arteriosclerosis, atherosclerosis, and excess vasoconstriction, with their clinical expression: arterial stiffening and increased central pulse pressure, carotid intima media thickening and endothelial dysfunction, and increased total peripheral resistance, respectively. In this review, we focus on arteriosclerosis, ie, arterial stiffening, for several reasons: this is the most characteristic clinical feature of the aging process of the arterial system,6 its measurement has been well standardized and referenced,7,8 and an increasing number of epidemiological studies have analyzed its independent determinants.9–11 Increased pulse wave velocity (PWV) is the established hallmark of arterial stiffening and is suggested to be one of the best biomarkers available to calculate the prospective cardiovascular risk and mortality risk of an individual.9,10,12,13 EVA can be diagnosed in subjects who present an abnormally high arterial stiffness for their age and sex.In the present review, we propose the concept of supernormal vascular aging (SUPERNOVA). SUPERNOVA can be diagnosed in subjects who present an exceptionally low arterial stiffness for their age and sex. We address the issue of the metrics and definition of EVA and SUPERNOVA as 2 extremes of the distribution of vascular aging. We discuss the concept of extremes in cardiometabolic research. Further, we review the molecular basis and mechanobiology of EVA and SUPERNOVA, in parallel with their epidemiological, genetic, and epigenetic determinants. Finally, we suggest therapeutic options and insist on the need for discovering novel molecular targets for slowing arterial aging and protecting against cardiovascular complications. By choosing the wording SUPERNOVA, we referred to the life of a supernova—a large explosion that takes place at the end of a star’s life cycle. The relationship between supernova and black hole is not firmly established, but physics theory states that in a black hole, time is slowed…like aging of arteries in SUPERNOVA subjects.Concept of Extremes in Cardiometabolic ResearchIn cardiovascular and metabolic research, we normally focus on risk and disease but not enough on protection. In the clinical perspective, focusing on risk and disease makes sense to reduce risk and treat disease manifestation, but from another perspective, it could be worth pursuing to find protective mechanisms. The ultimate reason for this is to find biomarkers (including genes) associated with protection from clinical complications to map protective mechanism as they one day could turn into novel therapeutic targets. This is much needed as the control of conventional risk factors and early intervention aimed at reducing the progress and complications of cardiometabolic disease is not able to confer the lowest attainable risk. Although this residual risk may include duration of exposure or misclassification of the measured classical risk factors, it may also include unmeasured/unknown risk factors. By contrasting extremes, it would be possible to discover novel biomarkers, leading to new therapeutic targets, for example, based on novel understanding of protection. In addition to numerous possible targets, including adipokines such as adiponectin, leptin and resistin,14 and vascular growth factors,15 new targets could be defined, leading to novel therapies. Such alternatives could hopefully be combined with the well-established drugs to control hypertension, hyperlipidemia, and hyperglycemia for synergistic effects to further reduce the residual risk.The approach using the extremes of distribution has been successfully used in genetic studies. For instance, in an extreme case-control design, Padmanabhan et al16 selected the top 2% of the blood pressure (BP) distribution in the Swedish population and contrasted them with the lower 9.2% of the BP distribution, to show the minor allele of the uromodulin gene to be protective against hypertension and associated with lower urinary uromodulin excretion.A few clinical models of cardiometabolic protection that used the extremes of the distribution were successful in determining the clinical characteristics of protected patients, with obvious clinical applications. For example, in long-standing type 1 diabetes mellitus of >40 to 50 years of duration and daily insulin regimen, there is a minority of patients who seem to escape major cardiovascular complications even if minor complications such as simplex retinopathy or mild microalbuminuria are present.17–20 Previous studies have tried to map protective factors in these patients, but to date, no conclusive genetic findings have been presented. Another model is the so-called metabolically healthy obesity—a disputed condition normally defined by the absence of variables linked to the metabolic syndrome.21,22 An alternative way to define these rare subjects is to find individuals with high body mass index, but escaping hospitalization during long periods of midlife, as was recently documented in subjects with body mass index >35 kg/m2.23 Even if metabolically healthy obesity exists, such individuals will probably not remain free of complications in the long perspective, but such events may be postponed until a higher age. A third model is represented by patients with end-stage renal disease on peritoneal dialysis for a number of years but not harmed by cardiovascular complications24 in spite of the fact that end-stage renal disease in most patients is associated with a pronounced increased risk of atherosclerosis and media sclerosis of large arteries.25 These protected patients are nondiabetic, not overweight, and rather young.25Whether these models simply result from chance, projecting some individuals at the extreme lower end of the normal distribution of risk factors or clinical parameters, or whether those individuals really possess specific protective (genetic) causative mechanisms is still an open question that further epidemiological and mechanistic studies should try to explore.EVA—New Findings and DevelopmentsDuring the last 2 decades, a growing body of evidence has accumulated that arterial stiffness, which is an intermediate end point and an independent predictor of cardiovascular disease (CVD) and cardiovascular as well as total mortality, could be used as a proxy of vascular aging.7,9,10,12,13,26 We also promoted the concept of EVA1–3,5 to show that it is possible to early identify subjects with arterial damage that if undetected otherwise would lead them into premature CVD and irrecoverable residual risk despite later therapeutic interventions. The Lancet Commission on Hypertension27 used a life course approach to better demonstrate that preventive efforts should be focused on several avoidable thresholds (elevated BP, subclinical organ damage, and cardiovascular events), with the goal to improve life course trajectory as much as possible. Figure 1 illustrates the life course concept applied to arterial stiffening. Our hypothesis is that progressive arterial wall stiffening with aging parallels incident hypertension and then subclinical target organ damage and then cardiovascular complications in a steeper way for some individuals (EVA) than in others (SUPERNOVA). EVA subjects reach each of these steps earlier than the average population. For instance, increasing blood volume, hypervolemia, and obesity are factors contributing to the upward shift from one pattern of vascular aging to another, that is, they are risk factors contributing to EVA. By contrast, SUPERNOVA subjects remain protected for a long period of time.Download figureDownload PowerPointFigure 1. This figure illustrates, in a life course approach of hypertension, our hypothesis that progressive arterial wall stiffening with aging parallels incident hypertension, and then subclinical target organ damage, and then cardiovascular (CV) complications. Early vascular aging (EVA) subjects reach each of these steps earlier than the average population, whereas supernormal vascular aging subjects remain protected for a long period of time. For clarity, healthy vascular aging subjects are not represented here. BP indicates blood pressure; CVD, cardiovascular disease; and TOD, target organ damage.EVA can be diagnosed in patients who present an abnormally high arterial stiffness for their age and sex. Thus, EVA represents an altered capacity for repairing arterial damage in response to aggressors like mechanical stress and metabolic/chemical/oxidative stresses. In other words, arterial stiffening/carotid-to-femoral PWV (cfPWV) is an integrator of all damages done to the arterial wall. Moreover, aortic stiffness/cfPWV, as a marker of arterial wall damage or arteriosclerosis, integrates both the effect of risk factors and susceptibility to those risk factors (see below the discussion on the epigenetic determinants of PWV) and duration of exposure. Thus, cfPWV measures not only the current arterial damage (a product of age, risk factors, and intrinsic susceptibility to them) but also its regression (when a therapeutic action is taken) or progression (when exposure continues or therapeutic actions fail). It differs from the usual snapshot that physicians get from their patients when they only measure BP, cholesterol, and glycemia. This is why EVA arterial stiffness has a higher predictive value for cardiovascular events than classical cardiovascular risk scores.9,10,12,13Recent cross-sectional and longitudinal studies have extended the list of the epidemiological determinants of arterial stiffness. Most of these determinants belong to the classical risk factors, either nonmodifiable such as ethnicity, sex, chronological age, family history, and personal history or modifiable such as BP, diabetes mellitus, dyslipidemia, and smoking.8 As arterial stiffness and elevated BP are entwined, it is difficult to separate these entities in EVA subjects. There is increasing evidence for a continuous loop (cross talk) between large and small arteries that will either produce increased wall damage through increasing levels of BP or produce increasing BP levels through arterial wall damage. The establishment of an extreme vascular phenotype (EVA or SUPERNOVA) is thus the product of the interaction between (1) the structural changes on arterial wall that are usually associated with age and (2) the mechanisms that accelerate or decelerate this process, respectively. Despite these mechanisms, high BP is not an exclusive condition for EVA. For example, EVA can be caused by chronic low-grade inflammation due to inflammatory bowel disease,28 in patients who exhibit high PWV despite normal BP.Additional studies underlined the role of hyperglycemia, metabolic syndrome, insulin resistance, obesity, abdominal fat, chronic kidney disease, high salt intake, chronic low-grade inflammation, oxidative stress, inadequate diet, alcohol consumption, social deprivation, perceived stress, and a number of genetic factors1–3,5 (Table). The role of the metabolic syndrome, lack of physical activity, and social stress deserves to be detailed because these factors are strongly interrelated and longitudinal data are available for analyzing trajectories. The Cardiovascular Risk in Young Finns Study29 showed that metabolic syndrome in childhood and adolescence (aged 9–18 years) predicted the level of arterial stiffness (measured as cfPWV 21 years later, then aged 30–39 years). Moreover, recovery from childhood metabolic syndrome was associated with decreased arterial stiffness in adulthood.29 These data are important because a long-lasting increased arterial stiffness predicts incident hypertension, which in turn increases arterial stiffness. This has been first showed in middle-aged adults (60 years) participating to the Framingham Heart Study30 and more recently in younger normotensive Finnish adults (30–45 years).31 In both cases, cfPWV improved the prediction of incident hypertension risk prediction beyond traditional cardiovascular risk factors.Table. Putative Determinants of EVA and SUPERNOVANonmodifiable DeterminantsDeterminants of EVADeterminants of SUPERNOVAChronological ageEthnicitySexFamily historyPrenatal fetal growthGeneticsClassical CV risk factors High BPNormal BP HyperglycemiaNormal glycemia Insulin resistance Diabetes mellitus ObesityNormal weight Abdominal fatLow-calorie diet Metabolic syndrome DyslipidemiaNormal lipids Chronic kidney disease High-salt dietLow-salt diet SmokingNo smoking Lack of physical activityIntense physical activityAdditional CV risk factors Oxidative stressInsensitivity to oxidative stressStrong protective metabolic mechanisms Alcohol consumptionNone of these risk factors Chronic low-grade inflammation Gut microbiome composition Social deprivation High perceived stress Abnormal sleep pattern Thrombogenic factors Hormonal status…This list is not exhaustive. Although subjects who have those characteristics are more likely to have SUPERNOVA, SUPERNOVA subjects do not need to fulfill all the features listed in the Table (Text). BP indicates blood pressure; CV, cardiovascular; EVA, early vascular aging; and SUPERNOVA, supernormal vascular aging.The lack of vigorous physical activity is also a major determinant of arterial stiffness trajectory. Several studies have related sedentarity to arterial stiffness in adults, but, again, trajectories are even more important from childhood to adulthood because prevention measures can be undertaken at an early stage.32–34 Finally, a few studies showed the influence of social aspects, such as occupational status, perceived stress, and social deprivation35,36 (Table).Biological aging in humans is a general phenomenon affecting all organs. This is why, for example, the Whitehall II Study has reported on associations between PWV and impaired lung function, slower gait speed, and less ability for physical functioning in everyday life.37 On a more pathophysiological aspect, all cardiovascular determinants of EVA that are listed in the Table are also possible determinants of other organs aging.From EVA to SUPERNOVA, Definition and MetricsDefining extremes implies having references, and one must define arbitrary thresholds on continuous variables. In Figures 1 and 2, we used the wording average vascular aging to describe the median field between the 2 extremes of arterial stiffness distribution. However, an average vascular aging may not be the desirable goal for patients and their physicians in a population where cardiovascular risk factors are prevalent, and an ideal aging8 or at least a healthy vascular aging (HVA)38,39 would be preferable. For sake of clarity, we will successively discuss the differences between EVA and HVA and between SUPERNOVA and HVA.MetricsArterial stiffness can be determined through 3 main approaches, by decreasing levels of physical relevance and epidemiological evidence: (1) measuring the time delay between 2 arterial sites and estimating the velocity of the pulse wave from the distance between sites divided by the time delay; (2) measuring the distension of the artery and relating it to the local pulsatile pressure; and (3) estimating arterial stiffness from cuff pressure measures through models of circulation.7Measurement of PWVSince the first reports by Bramwell and Hill in 1922,40 cfPWV methods have improved by using high precision tonometers and computing. This method has been extensively validated, standardized, and referenced.7,8 Alternative methods have been proposed by measuring transit time between the arm and the leg (brachial-ankle PWV), the heart and the leg (heart-ankle PWV) or between the finger and the toe (finger-toe PWV). These techniques are simpler, at the cost of imprecisions and ambiguities on the arterial path.Measurement of Local StiffnessMeasurement of local stiffness through distension and pulse pressure corresponds to the Hook law of elasticity and thus provides a direct measure. Local (carotid) pulse pressure is necessary to calculate the stress/strain relation and derive stiffness, which induces errors due to calibration and modeling.Single Cuff–Based Methods Are Based on Models of CirculationOne method uses overinflation of the cuff to better track the late systolic peak (reflected wave) and measures the time delay between the early and late systolic peak as the propagation time of the wave to and from a putative main reflection site. A second method uses the diastolic decay of BP, calibrated by age and BP. Both show meaningful associations with cfPWV; however, they rely on models of the circulation that are hotly discussed. Further, external calibration on age and BP questions the added value. For sake of clarity, only cfPWV will be used in the following discussion.From EVA to HVAcfPWV is currently the most validated marker of vascular aging.2,5,7,12 cfPWV increases with age, and an excessive increase in PWV at any age was associated with adverse outcome.9,10,12,13 This has been demonstrated in hospital-based hypertensive cohorts and in the general population in many different settings and locations. Thus, a value of cfPWV above a certain threshold at a given age has appeared as a true definition of EVA. However, the ability to define a threshold above which the risk increases for a given age strongly depends on the structure of the cohort database. Observational cohorts are affected by many biases. In addition, the relation of cfPWV with age is not linear but rather quadratic (ie, accelerated increase), whereas its relation with BP is linear.8 Thus, it is difficult to propose a single definition of EVA, and attempts that do not take age or BP into account have been oversimplistic38,41 since fixed thresholds have been used (7.6 or 10 m/s, respectively). Having a cfPWV value <10 m/s may correspond to EVA in a young subject, whereas in older people, it may correspond to SUPERNOVA.Thus, EVA can be more appropriately diagnosed in subjects who present an abnormally high cfPWV for their age and sex. For instance, in the population of the Guimaraes study, Cunha et al4 used a variable threshold according to age categories and used different levels expressed as percentiles (75th, 90th, and 97.5th) of the cfPWV distribution in the Reference Values Collaboration study.8 Although this definition takes into account age, it does also account for other risk factors, especially BP. Furthermore, because the relation with age is very strong, age has a remaining effect within age categories. Multivariate models taking into account nonlinearity and the continuity of risk factors have not been published to date.In the MARE study (Metabolic Syndrome, Arteries Research), Nilsson et al39 defined HVA as a cfPWV value below the age quintile–specific 10th percentile of the studied population and EVA as a PWV value above the age quintile–specific 90th percentile. However, these definitions were arbitrary and only used for research purpose. As discussed above, the wording average vascular aging can be used to qualify the median field between the 2 extremes of the PWV distribution (Figure 2), but no upper and lower limits of this median field have yet been defined, above which some subjects have EVA and below which others have HVA, respectively. No consensus has been reached, and we lack epidemiological studies comparing the predictive values of EVA and HVA according to various lower and upper thresholds, respectively, along the cfPWV distribution. This is why it is preferable to consider for the present time gray zones (Figure 2) for the boundaries between EVA, average vascular aging, and HVA along the distribution of cfPWV in populations for a given age. Indeed, the present article is addressing mainly the concept of vascular aging and aims at building a framework for research, rather than establishing rigid boundaries in a field of continuous variables.Download figureDownload PowerPointFigure 2. Schematic distribution of carotid-to-femoral pulse wave velocity (cfPWV; ie, arterial stiffness) within a given population. cfPWV values are presented for a given age and sex and according to an ideal gaussian distribution. Average vascular aging describes the median field between the 2 extremes of the pulse wave velocity distribution. The boundaries between early vascular aging (EVA), average vascular aging, healthy vascular aging (HVA), and supernormal vascular aging (SUPERNOVA) are presented as gray zones (Text).Finally, although the definition of EVA and HVA based on percentiles is scientifically valid, this information is not easy to translate to the patients. To better explain it to the patient, some investigators have proposed the notion of vascular age. Although apparently intuitive, this is complex. Basically, this is based on the comparison of the actual risk of CVD of the subject, compared with the risk of CVD of a healthy age-matched person unexposed to cardiovascular risk factors. Thus, vascular age can only be older than chronological age because of cardiovascular risk factors. Several calculators are available on the internet for calculating vascular age. Even promoters42 of the concept acknowledge that vascular age can be better approached through cfPWV, although this is limited to centers at which cfPWV measurements are available. Vascular aging calculated on risk factors and measured through cfPWV is not similar. Calculated vascular aging (ie, more accessible) evaluates the theoretical consequences of elevated (identified) risk factors, whereas the cfPWV (needing measurement) represents the actual consequences of risk factors on the cardiovascular system. As developed above, patients are unequal against the consequences of risk factors on the cardiovascular system. Thus, vascular aging based on cardiovascular risk factor may fail at identifying people excessively sensitive (EVA) to or protected against (HVA) risk factors, whereas PWV represents the cumulative damage of all cardiovascular risk factors on the arterial wall.Finally, we may ask whether PWV could be only calculated and not measured. Greve et al43 showed that cfPWV calculated from age and BP conferred similar predictive values for major cardiovascular events than measured PWV. However, it does not mean that the incremental value of measuring cfPWV is null, especially because the influence of age is strong in epidemiological cohorts and vascular age aims at proposing information beyond chronological age.From HVA to SUPERNOVAValues of arterial stiffness are scattered at any given age for any level or category of risk factors. If attention has been focused on subjects with high cfPWV (EVA), low and very low values have drawn little attention. We propose that very low values of cfPWV, whatever the level of risk factors, define a protective phenotype, and we propose to call this phenotype SUPERNOVA. The cfPWV values of SUPERNOVA subjects are by definition lower than the cfPWV values of HVA subjects,39 for instance, in the lower 2.5th percentile of the distribution. However, we currently prefer to consider, as discussed above, that the boundary between HVA and SUPERNOVA is rather a gray zone, as discussed above (Figure 2).SUPERNOVA subjects are protected against the influence of cardiovascular risk factors, despite being exposed to them. The difference with EVA is that cardiovascular risk factors are not translated into subclinical organ damage and cardiovascular complications. This can be confirmed in clinical practice through simple investigations. SUPERNOVA subjects do not represent a clinical problem and should not necessarily be followed with the same intensity as EVA patients or even subjects with average vascular aging. They represent, on the contrary, a challenge for academic research to better understand pathways for vascular protection, and if any identified, consider it as a potential therapeutic target in the future.One open analytical question is whether adjustments on cardiovascular risk factors should be extensive or not, to best select SUPERNOVA subjects among a large population. Some are easily accessible and must be taken into account (age, sex, BP, smoking), some are well known but more difficult to quantify (family history, perceived stress, socioeconomic factors for instance), and many remain unknown. In all cases (but age and genetics), the duration of exposure is difficult to quantify. The reference values that have been established for arterial stiffness according to age and BP8 are of crucial importance because they provide the equations linking risk factors to arterial stiffness for calculating vascular age. The programming of such equations depends on coefficients obtained from reference value cohorts and must integrate nonlinearity and interaction terms, as it was done previously for calculation of cardiovascular risk through Systematic COronary Risk Evaluation equation.44In conclusion, we consider that arterial stiffness (namely cfPWV) is the best proxy for the cumulative effect of known and unknown risk factors damaging the arterial wall, and we believe that it is preferable to express EVA and SUPERNOVA in term of very high or very low arterial stiffness.Epidemiology of HVA and SUPERNOVAIn arterial research, recent observational studies have documented that some people seem to be protected from the age-associated increase in cfPWV and also from complications. This was described in the Framingham cohort, defined by low cfPWV for a given age and absence of hypertension, and called HVA.38 It is believed that normal risk factor levels are essential to experience HVA, that is, regarding BP, glycemia, lipids, and lack of smoking, but most likely a favorable genetic profile and healthy lifestyle will contribute to this. The genetic influence could partly explain why a strong family history of cardiometabolic disease is associated with higher cfPWV in offspring, whereas the lack of such a family history seems to be protective.45,46 Although genetic studies to map the genetic background of EVA are ongoing since at least 10 years,15,47,48 no study on the genetic background of HVA is available based on Genome-Wide Association Study. In the Framingham Heart Study, a genetic risk score for coronary artery disease was not associated with HVA in that population.38A more advanced concept than HVA could be SUPERNOVA proposed here, as tentatively defined by an exceptionally low cfPWV for age and sex. Investigating the epidemiology and characteristics of SUPERNOVA in a number of cohorts would help to identify genetic and epigenetic factors related to protective mechanism acting on the vasculature. This approach could have the potential to find new treatments, thus drug targets, for cardiovascular protection. This ambition is a reflection of the proposed strategy to modify vascular aging by lifestyle and treatment of risk factors, especially hypertension, as proposed by the Lancet Hypertension Commission 2016.27The epidemiology of SUPERNOVA may benefit from observation of the few societies in the world that do not show any rise of BP with increasing age. Studies have shown that this was the case in subsistence-level populations, such as the Yanomamo Indians of Brazil, Papua New Guinean highlanders, and rural Kenyans, characterized by good physical aerobic fitness conditions, low body fat and low prevalence of obesity, low cholesterol level, low-salt diet, and high fruit and vegetable consumption.49,50 These were indirect observations, and it was only recently that arterial stiffness was measured in representatives of these populations. Particularly, Lemogoum et al51 measured cfPWV in Cameroon traditional pygmies on hunter-gatherer subsistence mode, contemporary pygmies who migrated to semi-urban area. They showed that hunter-gatherer lifestyle was associated with unchanged aortic stiffness with aging and attributed this findin" @default.
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• W2963655570 date "2019-08-01" @default.
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• W2963655570 title "Concept of Extremes in Vascular Aging" @default.
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