Aging and cardiovascular diseases: The role of gene–diet interactions

Aging and cardiovascular diseases: The role of gene–diet interactions

Ageing Research Reviews 18 (2014) 53–73 Contents lists available at ScienceDirect Ageing Research Reviews journal homepage: www.elsevier.com/locate/...

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Ageing Research Reviews 18 (2014) 53–73

Contents lists available at ScienceDirect

Ageing Research Reviews journal homepage: www.elsevier.com/locate/arr

Review

Aging and cardiovascular diseases: The role of gene–diet interactions Dolores Corella a,b , José M. Ordovás c,d,e,∗ a

Department of Preventive Medicine and Public Health, School of Medicine, University of Valencia, Valencia, Spain CIBER Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain c Department of Cardiovascular Epidemiology and Population Genetics, Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain d IMDEA Alimentación, Madrid, Spain e Nutrition and Genomics Laboratory, JM-USDA Human Nutrition Research Center on Aging at Tufts University, Boston, MA, USA b

a r t i c l e

i n f o

Article history: Received 1 May 2014 Received in revised form 15 August 2014 Accepted 18 August 2014 Available online 24 August 2014 Keywords: Aging Cardiovascular diseases Genome Epigenome Diet Nutrigenetics

a b s t r a c t In the study of longevity, increasing importance is being placed on the concept of healthy aging rather than considering the total number of years lived. Although the concept of healthy lifespan needs to be defined better, we know that cardiovascular diseases (CVDs) are the main age-related diseases. Thus, controlling risk factors will contribute to reducing their incidence, leading to healthy lifespan. CVDs are complex diseases influenced by numerous genetic and environmental factors. Numerous gene variants that are associated with a greater or lesser risk of the different types of CVD and of intermediate phenotypes (i.e., hypercholesterolemia, hypertension, diabetes) have been successfully identified. However, despite the close link between aging and CVD, studies analyzing the genes related to human longevity have not obtained consistent results and there has been little coincidence in the genes identified in both fields. The APOE gene stands out as an exception, given that it has been identified as being relevant in CVD and longevity. This review analyzes the genomic and epigenomic factors that may contribute to this, ranging from identifying longevity genes in model organisms to the importance of gene–diet interactions (outstanding among which is the case of the TCF7L2 gene). © 2014 Elsevier B.V. All rights reserved.

Contents 1. 2.

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Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Concepts and statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1. Aging, healthy aging, healthy life expectancy, and frailty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2. Statistics on cardiovascular diseases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Genes and genetic variants associated with longevity and aging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1. Key genes related to aging previously identified in non-human models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.1. Insulin-like signaling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.2. Sirtuin pathway . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.3. Target of rapamycin (Tor) signaling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.4. AMP-activated protein kinase (AMPK) signaling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.5. Other genetic pathways related to longevity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2. Genes and genetic variants identified as relevant in human aging and longevity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Calorie restriction, a relevant environmental factor in aging and the first gene–diet interaction in determining longevity . . . . . . . . . . . . . . . . . . . . . . Genes and relevant genetic variants in cardiovascular disease in humans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1. Association of the APOE gene with cardiovascular diseases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2. Factors that may have an influence on the low coincidence level between the main genes implicated in cardiovascular diseases and longevity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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∗ Corresponding author at: Nutrition and Genomics Laboratory, JM-USDA Human Nutrition Research Center on Aging at Tufts University, 711 Washington St., Boston, MA 02111, USA. Tel.: +1 617 556 3102; fax: +1 617 556 3344. E-mail address: [email protected] (J.M. Ordovás). http://dx.doi.org/10.1016/j.arr.2014.08.002 1568-1637/© 2014 Elsevier B.V. All rights reserved.

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Gene–diet interactions in determining aging and cardiovascular diseases in humans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1. Gene–diet interaction between the TCF7L2 polymorphism and the Mediterranean diet in determining cardiovascular risk factors and disease . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2. Clinical application of the gene–diet interactions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Beyond variations in the genome . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1. Transcriptomics, proteomics, and metabolomics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2. Epigenomics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2.1. DNA methylation, aging, and cardiovascular diseases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2.2. Histone modification, aging, and cardiovascular diseases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2.3. Non-coding RNA, aging, and cardiovascular diseases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conflict of interests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1. Introduction Over the years, life expectancy has increased throughout the world (Wang et al., 2012). This increase has led to a growing interest in so-called aging-associated diseases, which are diseases that are most often related with senescence. Age is known to be the number one risk factor of cardiovascular diseases (Niccoli and Partridge, 2012); therefore, cardiovascular diseases (CVDs) are not only the first cause of death worldwide, per age group, they are the leading cause of death in people over the age of 65 (Lozano et al., 2012). Cancer is also considered to be an age-related disease, as its risk progressively increases with age (Niccoli and Partridge, 2012; Serrano and Blasco, 2007). Yet, despite its increase as a cause of death worldwide, cardiovascular diseases continue to hold the top spot in the rankings (Lozano et al., 2012). Moreover, it has been estimated that the life expectancy of subjects having main cardiovascular risk factors, such as type 2 diabetes, hypertension, hypercholesterolemia, etc., is lower than that of the general population (Clarke et al., 2009). It is also interesting to note that cardiovascular diseases represent the principal cause of mortality in subjects with Hutchinson-Gilford Progeria Syndrome (HGPS), the best characterized human progeroid disease with clinical features mimicking physiological aging at an early age (Coppedè, 2012). In HGPS individuals, death results from myocardial infarction, stroke, or congestive cardiac failure in 75% of all cases (Capell et al., 2007; Coppedè, 2013). The relevance of the aging-cardiovascular disease binomial should be taken into account not only because it is the leading cause of mortality, but fundamentally because of the high incidence of these diseases and the effects that they can have in terms of reducing quality of life and increasing health costs (Heidenreich et al., 2011; Pandya et al., 2013). Moreover, before their appearance as clinical phenotypes, cardiovascular diseases are preceded by intermediate phenotypes, among which are high concentrations of plasma lipids, such as LDL-cholesterol and triglycerides, hypertension, high fasting glucose or type 2 diabetes, obesity, etc. (Payne, 2012). For these conditions, increasing age is an important risk factor as are genetic predisposition and non-genetic and environmental risk factors, such as an unhealthy diet, sedentary lifestyle, tobacco smoking, etc. (Huxley and Woodward, 2011; Mozaffarian et al., 2012). Consequently, elderly people often consume various types of drugs over many years in order to minimize the impact of these risk factors on manifestations of the disease (Maraldi et al., 2009; Peron et al., 2011). Moreover, the economic and social environment in which the individual has traditionally lived often deteriorates with old age, social support is often reduced together with a loss of purchasing power, alterations in sleeping patterns often occur, and depression and other mental problems often develop; in turn, these also constitute important cardiovascular risk factors (Almeida, 2012; Crowley, 2011; Everson-Rose

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and Lewis, 2013; Gellis and Kang-Yi, 2012; Marengoni et al., 2011; Nemeroff and Goldschmidt-Clermont, 2012). Furthermore, as one grows older, the heart and the different tissues involved in cardiovascular diseases also suffer a series of notable changes, including a decrease in elasticity of the heart walls and a decreased flexibility to respond to changes in the pressure of the arterial system (Stern et al., 2003). In addition, heart valves may thicken or leak and changes in heart rate, as well as a deterioration in the cells of the heart muscle and the ability of the heart to efficiently pump blood, may occur (Shah et al., 2013; Stern et al., 2003). These changes also affect the endothelia, the blood vessels, the blood characteristics, and the volume of blood that can efficiently circulate through those vessels (Shah et al., 2013; Simmonds et al., 2013; Thorin and Thorin-Trescases, 2009). Hence, at the onset of old age, early symptoms of cardiovascular disease, such as atherosclerosis (hardening of the arteries caused by a plaque build-up in the arteries), atrial fibrillation (the heart rate may increase and be irregular), angina (chest pain, pressure or squeezing in the chest caused by a temporary reduction of blood flow to the heart), orthostatic hypotension (a drop in blood pressure when shifting from a sitting to a standing position), etc., often appear (Stern et al., 2003; Shah et al., 2013; Thorin and Thorin-Trescases, 2009). All of this can lead to final phenotypes of cardiovascular disease, outstanding among which are myocardial infarction, stroke, etc. (North and Sinclair, 2012; Stern et al., 2003). However, despite the fact that all these manifestations are typical of aging, some individuals reach an advanced age without any of those symptoms while, in contrast, others present these manifestations at a very early age (Avogaro et al., 2013; Coppedè, 2012; Niccoli and Partridge, 2012). Therefore, it has been thought that individuals that are free of these symptoms in old age have been exposed to fewer genetic and environmental risk factors, whereas individuals who present symptoms earlier have been exposed to more genetic and environmental factors (Niccoli and Partridge, 2012; Vijg and Campisi, 2008). For many years, the environmental risk factors of cardiovascular diseases and aging have been investigated and healthy lifestyles have been identified as protective factors (Allen and Morelli, 2011; Haveman-Nies et al., 2003; Södergren, 2013). Over the past several decades, dozens of studies have also been conducted on the genetic factors that are implicated in aging and in cardiovascular diseases (Deelen et al., 2013a; Lieb and Vasan, 2013). Nevertheless, in spite of the fact that both processes constitute an important binomial, studies have been mainly undertaken from separate fields of knowledge: cardiovascular diseases and gerontology and aging (North and Sinclair, 2012). Consequently, in most studies carried out to identify the genes related to longevity or aging, interactions with environmental factors have not been taken into account, and the results, as far as the genetic variants identified in humans are concerned, have been less successful and reproducible (Brooks-Wilson, 2013). On the

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other hand, studies specifically undertaken to investigate the genes related to cardiovascular diseases or their intermediate phenotypes have been more successful, and a number of polymorphisms has been consistently identified in candidate genes (Asselbergs et al., 2012; Dichgans et al., 2014; Do et al., 2013; Ellinor et al., 2012; Kathiresan and Srivastava, 2012; Lieb et al., 2013; Thanassoulis et al., 2013; Teslovich et al., 2010; Willer et al., 2013). In addition, in the cardiovascular field numerous studies on humans have investigated gene–environment interactions, outstanding among which are studies conducted on diet (Corella and Ordovas, 2009; Phillips, 2013). Nevertheless, these studies have not paid sufficient attention to the age of the population studied, often jointly analyzing individuals of a wide age range (from 18 to 80 years in many studies), so that we are left with an average estimate of the effects found without knowing specifically at what age the effects of a gene variant are more important, or when the gene–environment interaction takes on its particular relevance. In spite of this apparent parting of the ways in research, the two approaches have coincided in highlighting the apolipoprotein E (APOE) as a crucial gene, both in terms of longevity-aging (Beekman et al., 2013; Davies et al., 2014) and cardiovascularrelated diseases (i.e., hypercholesterolemia, myocardial infarction, and stroke) (Khan et al., 2013; Schilling et al., 2013). Moreover, the APOE genotype has been strongly associated with neurodegenerative diseases such as Alzheimer’s disease and other vascular dementia (Corder et al., 1993; Seshadri et al., 2010). As such, this review will analyze the current understanding of this gene and consider in greater depth other candidate genes that are being profiled as very important by either of the approaches (cardiovascular and aging fields), mainly focusing on gene–diet interactions, as the effect of gene variants are not deterministic but modulated by the environment, which is something frequently ignored in many GWAS and which gives rise to great heterogeneity in the results. Here, we would do well to bear in mind the dynamic version of gene–environment proposed by Kulminski (2013) in which the concept of dynamic environment is insisted upon. According to this, the changing environment throughout life can activate different genes at different periods and modulate gene actions over the course of a lifetime and even generations. Thus, rather than following a deterministic vision, the effects of genes on aging-related traits should be taken more into consideration, as these must inevitably be shaped by aging-related processes within a dynamic environment. Moreover, this review will analyze the importance of epigenetics in aging and cardiovascular diseases, focusing on the intriguing contributions of microRNAs.

2. Concepts and statistics 2.1. Aging, healthy aging, healthy life expectancy, and frailty When talking about aging, a series of sometimes loosely defined concepts is used that requires better precision. In general, aging is a multi-factorial process characterized by a progressive loss of physiological integrity, leading to impaired function and increased vulnerability to death (López-Otín et al., 2013). In this process, cronological age is the main phenotype that has been analyzed, and it is usually expressed as lifespan (age at death) or longevity (being a specific advanced age or older at the time of study). However, taking into account the potential limitations of these chronological definitions, given that, at the same chronological age, the state of aging may be very different, several initiatives have been developed to redefine the phenotype studied focusing on healthy aging (a combination of old age and health, but of difficult definition). Therefore, most studies focus on chronological age. As far as chronological age is concerned, the terms young-old adults (60–74 years), old-old

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adults (75–89 years), and oldest-old adults (90+ years) are used in most studies, depending on the age group into which the individual falls (Cherry et al., 2013). Within the oldest-old group, it is common to place the 100+ year olds in another separate category in order to study the characteristics of their extreme longevity (Caruso et al., 2012). Nevertheless, increasing importance is placed not only on longevity, but also on so-called healthy longevity or healthy lifespan, which consists of evaluating the number of years lived, as well as the quality of life and the absence of disability of those years (Manolio, 2007; Dato et al., 2013; Avery et al., 2013). Arising out of this concept is a series of related terms, such as healthy aging (Swindell et al., 2010) or successful aging (Rowe and Kahn, 1987), which characterize a complex phenotype in which individuals maintain a high quality of life into the later stages of their lifespan, with few daily living impairments and a near absence of age-related diseases. On the other hand, an unhealthy aging course is associated with several comorbid diseases, diminished quality of life, and increased mortality risk (Södergren, 2013). These intricate factors are summarized using a simplified estimate called Healthy Life Expectancy (HALE). HALE is the number of years that a person at a given age can expect to live in good health, taking into account age-specific mortality, morbidity, and functional health status (Mathers et al., 2001). Salomon et al. (2012) assessed HALE for 187 countries between 1990 and 2010 using data from the Global Burden of Disease (GBD) 2010 study. They estimated that in 2010, global HALE at birth was 58.3 years for males and 61.8 years for females. Japan was the country with the highest HALE (68.8 years and 71.7 years in males and females, respectively). Interestingly, over the past 20 years HALE increased more slowly than life expectancy. Thus, for each one-year increase in life expectancy at birth since 1990, countries have gained only 10 months in HALE, on average. This decreased with age, and at the age of 50, each year of gain in life expectancy corresponded only to a 9-month gain in HALE. Frailty is another interesting concept in relation to aging and cardiovascular diseases. Frailty is a geriatric syndrome used to define older adults with impaired resistance to stressors due to a decline in physiologic reserve (Bergman et al., 2007). Although work is still being carried out to develop the scales and markers that validate frailty (Sourial et al., 2013), the term is already in frequent use. It has been estimated that, in elderly patients with documented severe coronary artery disease or heart failure, the prevalence of frailty was more than 50%, and this has been associated with an odds ratio (OR) of 1.6–4.0 for all-cause mortality (Afilalo et al., 2009). However, a relationship exists between frailty and cardiovascular disease; frailty may lead to cardiovascular disease and cardiovascular diseases may lead to frailty. Frailty has become a pressing issue in cardiovascular medicine as a result of the aging population (Afilalo et al., 2009). 2.2. Statistics on cardiovascular diseases To illustrate the magnitude of the problem of the agingcardiovascular disease binomial, we only have to point to some of the data from the United States. In 2008, heart disease and stroke were responsible for 30.4% of all deaths in that country, killing approximately 800,000 individuals (Gillespie et al., 2013). Per causes, coronary heart disease was ranked first, resulting in more than two-thirds of all cardiovascular deaths (Gillespie et al., 2013). In its updated statistics on the incidence of cardiovascular deaths, the American Heart Association detailed data per cause and age group for 2013 (Go et al., 2014). According to the data, the estimated average age for a first heart attack is 64.7 years for men and 72.2 years for women, and about 80% of people who die of coronary heart disease are age 65 or older. The incidence of cardiovascular

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diseases increased considerably in the 60–79 age group and mainly in the 80+ age group (Go et al., 2014). These figures have a great impact on the quality of life and disability of that population, as well as an enormous health cost, so preventive measures must be intensified and healthy aging must be promoted because a great percentage of cardiovascular diseases are considered to be avoidable (Kahn et al., 2008; CDC, 2013). Being aware that there is great heterogeneity in the phenotypes that elderly people present in relation to aging and disease, research into the factors that contribute to inter-individual variability in aging has recently intensified (Vijg and Campisi, 2008; Gladyshev, 2012; Lowsky et al., 2013). Perhaps because the first studies on aging were carried out on yeasts, worms, and flies (Martin, 2011; Tissenbaum, 2012), this field has focused on genetic factors, whereas the more complex gene–environment interactions that characterize aging in human beings have been less fully explored.

Thus, the great limitation that we find is that the published studies that have analyzed the genetic influence on human aging do not pay sufficient attention to the more complex gene–environment interactions (including socio-economic factors, psychological stressors, etc.) that daily occur in humans, all of which further complicates the isolated study of genetic or epigenetic factors related to aging and cardiovascular diseases. This is exemplified by the complexity of human dietary patterns compared to the simplicity and monotony of the dietary patterns used in experimental organisms. Therefore, the studies undertaken with experimental models use a simplified approach that needs to be modified if we want to reflect the human situation and draw conclusions that may contribute to furthering our understanding about the genetic and environmental factors that contribute to human aging and age-related diseases.

3. Genes and genetic variants associated with longevity and aging

A short lifespan and the ability to control the experimental conditions in the laboratory have made yeast, nematodes and flies highly convenient for studying genes that are associated with longevity. Thus, genetic approaches in model organisms have been a gold trove in revealing the main genes related to longevity in these species (Christensen et al., 2006). Thus, important discoveries in Saccharomyces cerevisiae, Caenorhabditis elegans and D. melanogaster over the past decades, have shown that single genes can regulate aging, and that, in these particular organisms, aging can be genetically manipulated (Finch and Ruvkun, 2001; Kenyon, 2010). Nevertheless, its translation to humans in general and to cardiovascular diseases in particular, is not an easy task. The great majority of lifespan-augmenting mutations were discovered in the nematode C. elegans (Klass, 1983) and further replicated in other model organisms from yeast to mice (Gems and Partridge, 2013). Conversely, when most of those findings have been investigated in humans only small associations have been found and more work is needed (Slagboom et al., 2011). Taking into account the relevance of the genetic discoveries in animal models we will briefly summarize the main findings, focusing on the so-called nutrient-sensing pathways.

Although identifying genes that influence longevity in humans has been a recent target of aging-related research, the findings of these studies have been rather limited (Atzmon et al., 2006; Beekman et al., 2013; Gentschew et al., 2013; Iannitti and Palmieri, 2011; Jacobsen et al., 2010; Kulminski et al., 2013a,b; Kulminski et al., 2014; Morris et al., 2013; Murabito et al., 2012; Rosvall et al., 2009; Salvioli et al., 2006). Various factors have contributed to that outcome. First, the complexity of the longevity phenotype (Balistreri et al., 2012; Brooks-Wilson, 2013) is compounded by the reliance on chronological age rather than the more germane biological age. Second, initial discoveries relied on simpler models, such as yeast, nematodes, and fruit flies (Arias, 2008; Kaeberlein, 2010; Schaffitzel and Hertweck, 2006), which may have over-simplified the interpretation of the research on human aging by assimilating humans to those organisms. Although these experimental model organisms are considered to be excellent models for studying the basic molecular mechanisms of aging, mainly those broadly shared across eukaryotic species (Kaeberlein, 2013), in these short-lived organisms, including mice, the influence of the gene–environment interactions is low compared to the framework of the relationships that humans have with their surroundings. Thus, their adequacy as models for intricate human aging has also been debated (Gershon and Gershon, 2001, 2002; Gruber et al., 2009; Johnson, 2003), and some authors have outlined the fact that although disrupting conserved pro-aging pathways identified in model organisms seems to be a pragmatic starting point for human lifespan extension, we must first determine whether these pathways really modulate human aging (Gershon and Gershon, 2001; Vijg and Campisi, 2008). Moreover, this limitation seems more important when studying the relationship between aging and cardiovascular diseases in humans. Although Drosophila melanogaster has a rudimentary heart and circulatory system and it is considered to be a good model for identifying evolutionarily conserved molecular signals that drive cardiovascular disease in humans (Wolf and Rockman, 2011), the translation of fly cardiac physiology to mammals is difficult because of relevant differences (i.e., a single-chamber open circulatory system, a fly does not have coronary circulation, etc.). In addition, although the influence of energy restriction as an environmental factor that impacts longevity has been widely studied in the different model organisms and notable influences on the aging process have been found (Lee and Min, 2013), other environmental factors relevant in humans, such as physical activity, smoking, alcohol consumption, drugs, chemical contaminants, etc., have not been studied in sufficient depth.

3.1. Key genes related to aging previously identified in non-human models

3.1.1. Insulin-like signaling The first pathway shown to influence aging in model organisms was the insulin/insulin-like growth factor (IGF-1) pathway (Kenyon, 2005). In particular, in C. elegans, genetic disruption of insulin-like signaling extended longevity 1.5- to 3-fold (Friedman and Johnson, 1988; Kenyon et al., 1993). Thus, mutations that decrease the activity of daf-2, which encodes a hormone receptor similar to the insulin and IGF-1 receptors, increased more than double the lifespan of nematodes (Kimura et al., 1997). Likewise, mutations in age-1, the catalytic subunit of the downstream phosphatidylinositol 3-kinase (PI3K), extend lifespan as well (Morris et al., 1996; Kimura et al., 1997). These phenotypes are also dependent on the downstream forkhead transcription factor DAF-16 (Jensen et al., 2006; Kenyon et al., 1993). These discoveries have also been replicated, but to a lesser magnitude in other organisms including fruit flies and mice (Tatar et al., 2001; Clancy et al., 2001; Carter et al., 2002). The intracellular signaling pathway of IGF-1 is the same as that elicited by insulin, which informs cells of the presence of glucose. Thus, IGF-1 and insulin signaling are known as the ‘insulin and IGF-1 signaling’ (IIS) pathway. In D melanogaster, mutations in its single insulin-like receptor (dInR), or in insulin receptor substrate protein chico, similarly extend lifespan (Tatar et al., 2001; Clancy et al., 2001). Mammals encode several daf-2 homologs [IGF-1 receptor (IGF-1R), insulin receptor (IR)-A and IR-B] displaying greater complexity (Benyoucef et al.,

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2007; Sadagurski and White, 2013). However, it has been suggested that despite these differences and complexity, a reduced IIS signaling may extend lifespan in mice (Bartke, 2008; Junnila et al., 2013; Richardson et al., 2004), although it remains controversial in humans (Barzilai et al., 2012; Junnila et al., 2013). In mice, it has been observed that heterozygosity for IGF-1R increases lifespan. However, knockdown of the IR in adipose tissue, or knockdown of insulin receptor substrate-2 (IRS2) in the brain, results in a substantial increase in lifespan (Blüher et al., 2003; Holzenberger et al., 2003; Taguchi et al., 2007). Nevertheless in humans, paradoxical observations regarding IGF-1 concentrations and age-associated diseases are found (Junnila et al., 2013; Di Somma et al., 2011). Circulating IGF-1 is generated by the liver under the control of growth hormone (GH). Secretion of GH, and therefore IGF-1, declines over time, aging being associated with decay in the somatotroph axis so-called somatopause (Di Somma et al., 2011; Sattler, 2013). In addition, there is increasing evidence that IGF-1 exerts pleiotropic protective effects on the vasculature, cardiovascular risk factors and stroke (Higashi et al., 2012; Kooijman et al., 2009; Perticone et al., 2008). Moreover, the longevity effects of reducing insulin-like signaling are caused by activation of the transcription factor Foxo (Forkhead box protein O), which is negatively regulated downstream of Akt by ISS (reviewed by Schaible and Sussman, 2013). Identifying the processes regulated by FOXO that control aging is intricate (Gems and Partridge, 2013). FOXO (DAF16 in C. elegans) proteins are multifaceted transcription factors that are responsible for fine-tuning the expression of a broad range of genes both during development and in adult tissues (Gems and Partridge, 2013; Watroba et al., 2012). In humans, the function of these proteins is complex and many members have been characterized (FOXO1, FOXO3, FOXO4, and FOXO6) (Ponugoti et al., 2012). Although currently they are emerging as key regulators of cardiac and vascular development in mice (Furuyama et al., 2004; Papanicolaou et al., 2008) and there are some results supporting a similar role in humans (Ronnebaum and Patterson, 2010; Oellerich and Potente, 2012), their implication in human longevity and agerelated diseases still requires more research in order to obtain more consistent results. Although in various epidemiological studies on humans, a significant association with longevity has indeed been found between variations in the FOXO3 gene (Anselmi et al., 2009; Flachsbart et al., 2009; Willcox et al., 2008), other studies that have analyzed this gene or others of the family and not found any association in other populations (Flachsbart et al., 2009; Morris et al., 2013). 3.1.2. Sirtuin pathway Apart from the insulin-like signaling pathway, other nutrientsensing pathways associated with longevity have also been characterized in these model organisms. Among them, sirtuins, which sense low energy states. Sirtuins are a family of nicotinamide adenine dinucleotide (NAD)-dependent protein deacetylases, which modulate a wide range of biological processes, spanning from DNA repair and oxidative stress responses to energy metabolism (Verdin, 2014). They were first implicated in aging when it was observed that overexpression of the sirtuin member Silent Information Regulator 2 (SIR2) extended lifespan in yeast (Kaeberlein et al., 1999). Subsequent studies in C. elegans and in D. melanogaster confirmed that overexpression of SIR2 orthologues can also promote longevity (Rogina and Helfand, 2004; Tissenbaum and Guarente, 2001). However, these findings have been re-examined and questioned (Burnett et al., 2011). In mammals more complexity exits, seven sirtuins are present (SIRT1–SIRT7) and their functions do not appear to be redundant: three are primarily nuclear (SIRT1, -6, and -7), three are mitochondrial (SIRT3, -4, and -5), and one (SIRT2) is cytoplasmic (Guarente,

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2013). The deacylase activity of sirtuins is dependent on NAD(+) (oxidized form of NAD), and it has been shown that NAD(+) levels are reduced in aged C. elegans and even mice (Mouchiroud et al., 2013), suggesting that of NAD utilization or production can increase lifespan in a sirtuin-dependent manner (Cantó et al., 2012; Mouchiroud et al., 2013). Also in mice it has been reported that overexpression of SIRT1, the closest SIR2 orthologue, protected against age-associated diseases including cancer and diabetes and conferred an improved healthspan. However, these effects were not sufficiently potent to increase lifespan (Herranz et al., 2010). In terms of cardiovascular diseases, current evidence suggests a link between SIRT6 and heart disease in mice and humans (Webster, 2012; Sundaresan et al., 2012). Nevertheless, the general role of sirtuins in human longevity and cardiovascular diseases is far from being well established. Although there are a few studies showing associations between SIRT1 polymorphisms and increased longevity in humans (Figarska et al., 2013; Zhang et al., 2010), no significant associations have been reported by others (Flachsbart et al., 2006; Kuningas et al., 2007). A similar lack of consistency is found regarding the link between SIRT1 polymorphisms and cardiovascular risk factors and disease (Cui et al., 2012; Dong et al., 2011; Shimoyama et al., 2012). 3.1.3. Target of rapamycin (Tor) signaling Tor is a serine-threonine protein kinase conserved from yeast to humans that is inhibited by rapamycin and constitute another nutrient sensing pathway (sensing of amino acids), especially relevant in aging (Johnson et al., 2013). It has been reported that inhibition of TOR activity induces longevity in yeast (Kaeberlein et al., 2005), C. elegans (Vellai et al., 2003), and D. melanogaster (Kapahi et al., 2004). mTOR (mammalian TOR) interacts with several proteins to form two complexes named mTOR complex 1 (mTORC1) and 2 (mTORC2) (Laplante and Sabatini, 2012). mTOR integrates signaling from intracellular amino acids, insulin, growth factors, energy status, stress, hypoxia etc. to control many major processes, including protein and lipid synthesis and autophagy (McCormick et al., 2011;). Although it has been shown that inhibition of mTOR with rapamycin expands lifespan in mice (Harrison et al., 2009; Miller et al., 2011), side effects such as immunosuppression or diabetes may limit the use of rapamycin as a potential longevity drug. Moreover, in humans the effects of rapamycin on mTOR signaling are much more complex than originally expected and our understanding of its role is still evolving. Paradoxically, rapamycin treatment in type 2 diabetic mice significantly reduced body weight, heart weight, fasting plasma glucose, triglyceride and insulin concentrations (Das et al., 2013). Thus, in view of the implication of mTOR in type 2 diabetes, adipogenesis, lipogenesis and cardiac hyperthropy (North and Sinclair, 2012; Hu and Liu, 2013), it has been postulated that this pathway may be a vital mediator of aging and the cardiovascular system (North and Sinclair, 2012). Thus, based on the results in mice, mTOR inhibition may represent a pharmacological strategy to prevent or treat cardiovascular disease in humans (Li et al., 2013; Wu et al., 2013). Currently, rapamycin is at the center of a debate over whether it is useful against aging in humans (McCormick et al., 2011). Preliminary results of a clinical trial with rapamycin on five men in their late 80s and 90s showed several benefits on walking ability (Leslie, 2013). However, although in this study only diarrhea was reported as a side effect, its potential serious side effects in the longterm administration are a serious limitation to conduct additional studies in humans to demonstrate whether this drug is useful in geriatric patients. 3.1.4. AMP-activated protein kinase (AMPK) signaling AMPK is a heterotrimeric protein with a catalytic ␣ subunit and two regulatory ␤ and ␥ subunits encoded by multiple genes. It was

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initially identified as a kinase that negatively regulates several key enzymes of the lipid anabolism (Hardie, 2007). Currently, AMPK is regarded as the major energy-sensing kinase. It operates as an evolutionarily conserved cellular energy sensor mediating the cellular response to energetic stress by activating energy-producing activities, while inhibiting energy-consuming ones (Swick et al., 2013). Thus, AMPK regulates a wide array of functions including cellular growth and proliferation, mitochondrial functioning, autophagy, inflammation, oxidative stress, among others (Ruderman et al., 2013; Zaha and Young, 2012). Several studies with model organisms have revealed that increased AMPK expression and activity can extend the lifespan (Apfeld et al., 2004; Funakoshi et al., 2011). On this regard, the identification of AMPK as an indirect target of established anti-diabetic drugs, such as metformin (Shaw et al., 2005; Zhang et al., 2009), generated increasing interest in the development of novel and more specific AMPK activators. Moreover, it has been demonstrated that AMPK activation may mediate lifespan-extension following metformin administration to C. elegans and mice (Anisimov et al., 2011; Onken and Driscoll, 2010). Currently, several studies in rodents and humans suggest a close link between dysregulation of AMPK and insulin resistance and cardiovascular risk factors and diseases (reviewed in Ruderman et al., 2013). Although activation of AMPK shows promise as a therapeutic strategy in the treatment of metabolic and heart-related diseases (Zaha and Young, 2012), the potential applications of AMPK activators in human aging, is still in its infancy and much work needs to be performed. Despite the separate description of the main nutrient sensing pathways (Insulin-like signaling, sirtuins, Tor and AMPK signaling), these pathways are highly interconnected (Alers et al., 2012; Barzilai et al., 2012; Salminen and Kaarniranta, 2012; Hu and Liu, 2013), being jointly regulated and adding more complexity to the mechanisms involved, that may integrate multiple gene-gene interactions in humans. One of the best described is that of the AMPK signaling that controls the aging process via an integrated signaling network (Salminen and Kaarniranta, 2012). 3.1.5. Other genetic pathways related to longevity In addition to the genes identified as master regulators in their specific pathways related to longevity in the different model organisms, another series of processes has been identified as hallmarks of aging in a comprehensive review (López-Otín et al., 2013), whose good or bad functioning may alter the aging process. Among them are: genomic instability, epigenetic alterations, loss of proteostasis, mitochondrial dysfunction, telomere attrition in mammals, etc. Although a detailed review of all the hallmarks it is beyond the scope of this work, we have to bear in mind that all the relevant genes in these processes could, in turn, be considered as candidate genes in modulating longevity in humans. 3.2. Genes and genetic variants identified as relevant in human aging and longevity Recently, dozens of studies have been carried out in humans to identify the genes related to aging and longevity (Anselmi et al., 2009; Barzilai et al., 2003; Beekman et al., 2013; Conneely et al., 2012; Deelen et al., 2011, 2013b; Däumer et al., 2014; Flachsbart et al., 2006, 2009; Figarska et al., 2013; Frisoni et al., 2001; Garagnani et al., 2013; Garatachea et al., 2014; Gentschew et al., 2013; Kim et al., 2012; Koropatnick et al., 2008; Kuningas et al., 2007; Lunetta et al., 2007; McKay et al., 2011; Morris et al., 2013, 2014; Nebel et al., 2011; Newman et al., 2010; Novelli et al., 2008; Nygaard et al., 2013; Sebastiani et al., 2012, 2013; Schächter et al., 1994; Schupf et al., 2013; Soerensen et al., 2013; Walter et al., 2011; Wang et al., 2014). Human aging studies include both genomewide association studies (GWAS) (Table 1) and candidate genes

(Table 2), mainly based on the relevant genes previously identified in animal models as well as on previous GWAS. Nevertheless, as we have previously mentioned, the consistency of the findings from these studies has been low and there are inconsistencies in the variants identified among the studies. This suggests different bias, whether in the definition of phenotype, in the statistical algorithms used for selecting the genes, or as a result of the existence of important gene–environment interactions in the diverse populations analyzed, which makes one gene or another appear to be more important depending on the most prevalent environmental factors in each population. In addition, association studies of survival to very old age tend to be underpowered, because sample sizes are small due to the limitations of gathering centenarians or nonagenarians, so random errors are likely to be present in these studies (Brooks-Wilson, 2013). Some of the studies were carried out on centenarians, whereas others included both centenarians and nonagenarians (or even individuals in other age groups). Chronological age is the main phenotype that has been analyzed, and it is usually expressed as lifespan (age at death) or longevity (being a specific advanced age or older at the time of study). However, taking into account the potential bias and limitations of these chronological definitions, several initiatives have been developed to refine the phenotype studied focusing on healthy aging (a combination of old age and health, but with a difficult definition). Among these initiatives is the European Union-integrated project GEHA (GEnetics of Healthy Aging). This project aimed to identify the genes involved in healthy aging and longevity, which allow individuals to survive to advanced old age in good cognitive and physical function and in the absence of major age-related diseases (Franceschi et al., 2007). Likewise, the relevance of the definition and standardization of a healthy aging phenotype has also been outlined in the projects supported by the National Institutes of Health (NIH) (Manolio, 2007). Currently, we are still waiting the main results of the most relevant on-going studies focusing on the genetics of the standardized healthy aging phenotype (Table 3). To date, studies on aging and longevity have in general shown few consistent results and many inconsistent results. Only one gene, the APOE, highly relevant in cardiovascular diseases and dementia, has emerged as being consistently associated with the aging phenotypes both in the GWAS (Beekman et al., 2013; Deelen et al., 2011; Nebel et al., 2011; Sebastiani et al., 2012) and in the candidate gene studies (Frisoni et al., 2001; Garatachea et al., 2014; Lindahl-Jacobsen et al., 2013; McKay et al., 2011; Novelli et al., 2008; Schächter et al., 1994; Schupf et al., 2013; Soerensen et al., 2013). Although another relevant gene, the FOXO3A, has been replicated in several candidate gene studies (Anselmi et al., 2009; Däumer et al., 2014; Flachsbart et al., 2009; Willcox et al., 2008), no consistency for it has been obtained in the GWAS. However, taking into account that many of the GWAS on aging are unpowered, some researchers have also focused on interesting genes that have signals that hover just below the statistical significance found in the GWAS, but that reach significance as their pathways were analyzed (Sebastiani et al., 2012). To provide more detailed information regarding genes related to aging, de Magalhães et al. (2005) created the Human Ageing Genomic Resources (HAGR) as a collection of online resources for studying the biology of human aging. HAGR contains two main databases: GenAge and AnAge. GenAge is a curated database of genes related to human aging, whereas AnAge is an integrative database describing the aging process in several organisms. Recently, these databases have been updated and extended (Tacutu et al., 2013) as a freely available online collection of research databases and tools for the genetics and biology of aging (HAGR, http://genomics.senescence.info); this resource also includes a Longevity Map (Budovsky et al., 2013).

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Table 1 Relevant genome-wide association studies in human aging and longevity. Reference

Population analyzed

Phenotype analyzed

Study characteristics

Main results

Lunetta et al. (2007)

1345 Framingham Study participants

Several longevity-related traits including age at death and morbidity-free survival at age 65

A GWAs in a prospective cohort

Newman et al. (2010)

N = 1836 longevity subjects and n = 1955 control subjects from the Age, Gene/Environment Susceptibility-Reykjavik Study, the Cardiovascular Health Study, the Framingham Heart Study, and the Rotterdam Study 403 nonagenarians included in the Leiden Longevity Study (LLS) and 1670 younger population controls and additional replication populations

Longevity (defined as survival to age 90 years or older)

A meta-analysis of GWAS in Caucasians from four prospective cohort studies and two replication cohorts (Leiden Longevity Study cohort and the Danish 1905 cohort)

The most significant SNPs for age at death were: rs1528753, rs2371208, rs10496799-NXPH2, rs10489006, and rs3757354-MYLIP. For morbidity-free survival the most significant SNPs were: rs1412337-DPT, rs32566, rs10484246, rs4831837 and rs2639889. Only one SNP, the rs9664222 in a region near MINPP1, reached significant association for the combined meta-analysis of the four initial studies and the two replications cohorts. Significant associations with SNPs in genes LASS3 and PAPPA2 were not replicated in the stage 2 cohorts.

Nonagenarians vs controls

A GWAs in the LLS and replication in a meta-analysis of cases from the Rotterdam Study, Leiden 85-plus study, and Danish 1905 cohort

Walter et al. (2011)

25,007 participants of European origin (aged ≥55 years from nine longitudinal cohort studies participating in the CHARGE Consortium)

All-cause mortality, and survival free of major disease or death

A meta-analysis of GWAs from nine studies

Nebel et al. (2011)

763 long-lived individuals and 1085 controls from Germany

Long-lived individuals vs controls

A GWAs case–control study and a replication sample

Sebastiani et al. (2012)

801 centenarians in the the New England Centenarian Study and 914 matched healthy controls 2715 cases and 2725 controls from the USA, Europe and Japan

Exceptional longevity

A GWAs and replication in an independent sample

Centenarians and long-lived individuals

A meta-analyses of five case–control studies

Longevity

A genome-wide linkage study and a GWAS in a subgroup of 1228 nonagenarian and 1907 controls

Deelen et al. (2011)

Sebastiani et al. (2013)

Beekman et al. (2013)

2118 nonagenarian Caucasian sibling pairs of 11 European countries in the Genetics of Healthy Aging (GEHA) project

Apart from these studies in which the identification of genes has been undertaken by means of SNP analysis, currently next-generation sequencing (NGS) applied to the study of the complete genome or exome is beginning to provide firsts results by allowing us to undertake an analysis of all kinds of genetic variants in an exhaustive manner. In this way, the whole genome of long-living individuals is being compared with younger individuals. Among these, we should mention the study conducted by Ye et al. (2013), which was carried out on two pairs of monozygotic twins (one pair of 40-year-old twins and one pair of 100-year-old twins), by two independent next-generation sequencing (NGS) platforms. However, a lower than anticipated number of somatic variants was detected by using two NGS platforms. Nevertheless, despite the fact that the cost of using NGS is still high for populations with a large sample size, their use is going to gradually increase and will no doubt provide interesting data both on the genetics of aging and cardiovascular genetics.

The rs2075650 on TOMM40, next to the APOE was the only SNP that was associated at the genome-wide significance level. In the meta-analysis, the rs429358-APOE (Cys112Arg; ␧4), in moderate LD with rs2075650, was the most associated with longevity. No associations were found with the FOXO3A SNPs. They found 14 independent SNPs that predicted risk of death, and 8 SNPs that predicted event-free survival. Most of these genes were in or near genes involved in brain-neural functions (HECW2, HIP1, BIN2, GRIA1, KCNQ4, LMO4, GRIA1, NETO1) and autophagy (ATG4C). The most significant association was found with SNP rs4420638, located downstream of the APOE locus and in high LD with the rs429358-APOE (␧4) SNP. The SNP rs2075650-APOE/TOMM40 reached genome wide significance

Six SNPs reached Bonferroni corrected significance including all populations: rs4729049-CDK6, rs11954180-SLC6A7, rs2596230-RYR3, rs1800392-WRN and two SNP with no gene identified (rs1525501 and rs1456669). In Caucasians, the rs2075650 TOMM40/APOE SNP was the most significantly associated. The rs4420638 SNP at the TOMM40/APOE locus showed significant association with longevity. The association of longevity with APOE␧4 and APOE␧2 alleles explained the linkage at 19q13.11-q13.32.

In addition to these findings at the genomic level, for the most relevant candidate genes related to aging, the next step is to characterize the most important gene–environment interactions with a higher level of scientific evidence.

4. Calorie restriction, a relevant environmental factor in aging and the first gene–diet interaction in determining longevity Calorie restriction without malnutrition has been the main environmental factor analyzed in the study of longevity in nonhuman models (reviewed by Chung et al., 2013; Cypser et al., 2013; Dilova et al., 2007). Although energy restriction has been shown to increase lifespan in a wide range of species including S. cerevisiae (Smith et al., 2007), C. elegans (Park et al., 2010), D. melanogaster (Min et al., 2008), and rodents (Wang et al., 2004), its influence on

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Table 2 Relevant candidate gene association studies in human aging. Reference

Population analyzed

Phenotype analyzed

Study characteristics and genes analyzed

Main results

Schächter et al. (1994)

Centenarians (n = 338) and control adults aged 20–70 years.

Survival and related phenotypes

Case–control study APOE and ACE SNPs

Frisoni et al. (2001)

Centenarians living in Finland (n = 179)

Lifespan

Cross-sectional APOE SNPs

Barzilai et al. (2003)

213 Ashkenazi Jewish probands with exceptional longevity, their offspring (n = 216) and a control group

Exceptional longevity, plasma lipids, lipoprotein and its particle sizes

Case–control design CETP SNPs

Flachsbart et al. (2006)

1573 long-lived individuals (centenarians and nonagenarians) and matched younger controls 1245 participants of the population-based Leiden 85-plus Study

Longevity

Case–control Five SIRT1 SNPs

The APOE-epsilon 4 allele was less frequent in centenarians than in controls, while the epsilon 2 allele was significantly increased. The ACE SNPs was more frequent in centenarians Carriers of the epsilon2 allele of APOE were more prevalent in the group of extremely old age. Subjects with exceptional longevity and their offspring had increased homozygosity for the I405V variant in CETP. Significantly larger HDL and LDL particle sizes and a lower prevalence of cardiovascular disease was also found. No association was detected between any of SIRT1 SNPs and the longevity phenotype

Mortality, age-related diseases, and cognitive functioning

Cohort SIRT1 SNPs

Total mortality, cause-specific mortality, and healthy survival Longevity

Cohort study (8 years follow-up) CETP SNPs

Kuningas et al. (2007)

Koropatnick et al. (2008)

Japanese-American men (n = 3562)

Novelli et al. (2008)

Two groups of long-lived individuals and younger controls from United States. Group 1 (n = 381) and group 2 (n = 368) subjects. 3741 Japanese American men aged 71–93 and 402 controls

Willcox et al. (2008)

Longevity, insulin sensitivity-related phenotypes and cardiovascular diseases

Case–control Several candidate genes: APOE, KL, CETP, PON1, APOC3, and MTP A nested-case–control study 5 candidate longevity genes: ADIPOQ, FOXO1A, FOXO3A, SIRT1, and COQ7. Case–control study 16 FOXO3A SNPs

Flachsbart et al. (2009)

1762 German centenarians/nonagenarians and younger controls (n = 731). Replication in a French centenarian sample (n = 535) and 553 younger controls

Longevity

Anselmi et al. (2009)

Males from the Southern Italian Centenarian Study

Extreme longevity

McKay et al. (2011)

10,623 participants from 15 case–control and cohort studies in populations of European ancestry

Longevity

Kim et al. (2012)

224 Caucasian nonagenarian cases and 293 young controls. In addition a replication sample (n = 390) was analyzed Offspring from long-lived families (n = 2307) and spouse controls (n = 764) from United States and Denmark

Longevityand leukocyte telomere length

Case–control study SNPs in SIRT1 and XRCC6 genes

Longevity

Case–control study SNPs in APOE and rs2075650 in TOMM40

US Caucasian long-lived individuals (n = 873) and younger controls (n = 443). Further replication and meta-analysis in other four independent samples 1089 oldest-old (ages 92–93) and 736 middle-aged Danes. Also a replication sample consisting of 1613 oldest-old (ages 95–110) and 1104 middle-aged Germans was analyzed. 1349 individuals from Northern/Central Italy, including 562 diabetic patients, 558 matched controls and 229 centenarians

Longevity

Meta-analysis including 5 studies 16 SNPs in the LMNA gene

Longevity

Case–control study 102 SNPs in 16 genes: APOE, ACE, CETP, HFE, IL6, IL6R, MTHFR, TGFB1, APOA4, APOC3, SIRTs 1, 3, 6 and HSPAs 1A, 1L and 14. Case–control designs 31 SNPs associated with diabetes, and SNPs related to telomere stability and age-related diseases

Schupf et al. (2013)

Conneely et al. (2012)

Soerensen et al. (2013)

Garagnani et al. (2013)

Lifespan/diabetes

Case–control and meta-analysis with the Japanese American men (Willcox et al., 2008) FOXO3A SNPs Pooled analysis APOE genotype

No significant associations of SIRT1 SNPs with mortality were found. However, a significant result was obtained with cognitive functioning. The Int 14A variant of the CETP gene increased odds for healthy aging. Low HDL-C was associated with higher cardiovascular mortality. Only the APOE gene was significantly associated with longevity.

A FOXO3A3 SNP (rs 2802292) was strongly associated with longevity. Also, FOXO3 was associated with insulin sensitivity and less prevalence of cardiovascular diseases In the whole sample of Germans, FOXO3 SNPs were nominally associates with longevity and three top-ranking FOXO3A SNPs (rs3800231, rs9400239, and rs479744) remained significant after correction for multiple comparisons in the sub-group of centenarians. However no significant replication was observed in French SNPs rs2802292 in the FOXO3A was highly associated with extreme longevity

The frequency of the ␧4 allele decreased from 17.6% to 8.3% with increasing age, supporting a strong association between the APOE gene and longevity SIRT1-rs7896005 was associated with longevity. Associations with telomere length were also found. Significant associations with the APOE locus. The reduction in the frequency of the ␧4 allele and increase in the frequency of the ␧2 allele contribute to longevity. In the meta-analysis combining all five samples, a LMNA haplotype remained significantly associated with longevity.

Significant associations of SNPs in the APOE, CETP, and IL6 with longevity were found.

The rs7903146-TCF7L2 SNP showed relevant associations with a constant increase in the frequencies of risk genotype (TT) from centenarians to diabetic subjects

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Table 2 (Continued) Reference

Population analyzed

Phenotype analyzed

Study characteristics and genes analyzed

Main results

Garagnani et al. (2013)

1612 long-lived individuals (centenarians and nonagenarians) and 1104 younger controls 1651 members of the Danish 1905 birth cohort

Lifespan

Case–control study. 19 SNP in three SOD genes (SOD1, SOD2, SOD3) Cohort study. APOE SNPs

No significant associations were observed

LindahlJacobsen et al. (2013) Deelen et al. (2013b)

Morris et al. (2013)

403 unrelated nonagenarians from the Leiden Longevity Study and 1670 younger population controls

American men of Japanese ancestry Longevity cases were defined as participants who had survived a minimum of 95 years (n = 213) Controls (n = 402). Consisted of individuals who had died near the cohort mean age.

Mortality and cognitive function in the oldest old Long-lived individuals vs controls

Case–control study 1021 SNPs in the insulin/insulin-like growth factor signaling (IIS) pathway genes and 88 SNPs in the telomere maintenance (TM) pathway genes A nested case–control study Several SNPs in the insulin/IGF-1 signaling pathway or related gene networks that may be influenced by FOXO3, namely, ATF4, CBL, CDKN2, EXO1, and JUN. Case–control and longitudinal cohort 7 SNPs in the AKT1 gene

Significant associations were found for some SNP in genes of the IIS pathway (AKT1, AKT3, FOXO4, IGF2, INS, PIK3CA, SGK1, SGK2, and YWHAG), while only one gene was significant (POT1) for the TM pathway.

All-cause mortality

Prospective cohort study 25 SIRT1 SNPs

The rs12778366-SIRT1 was significantly associated with mortality.

Longevity and other aging-related phenotypes

Nested case–control study in a prospective cohort. 6 SNPs for MTOR, 61 for RPTOR, 7 for RICTOR, or 5 for RPS6KA1 Case–control study

No significant associations were found

Long-lived cases vs controls

2996 long-lived individuals (nonagenarians and centenarians) and 1840 younger controls of Danish and German ancestry Figarska et al. (2013)1390 subjects from a general population-based cohort of white individuals of Dutch descent. Morris et al. 440 subjects aged 95 years and older (2014) and 374 controls of Japanese and American ancestry.

Long-lived cases vs controls

Garatachea et al. (2014)

Longevity

Nygaard et al. (2013)

Däumer et al. (2014)

Centenarians (n = 163, n = 79 and n = 729 from Spain, Italy and Japan) and controls (n = 1039, n = 597, and n = 498 from Spain, Italy and Japan). Old-aged individuals and controls (1613 German centenarians/nonagenarians and 1104 controls; 1088 Danish nonagenarians and 736 controls)

A 22% increased mortality risk for APOE ␧4 carriers was found.

Longevity

increased longevity in humans is unclear (Anton et al., 2013; Cava and Fontana, 2013; Willcox and Willcox, 2014). Even in rodents, where it has been shown that energy restriction can delay the onset and reduce the severity of many diseases, including cardiovascular disease, metabolic syndrome, and diabetes (Cui et al., 2013; Han et al., 2012; Takatsu et al., 2013), little is still known about the mechanisms through which it may act (AlGhatrif et al., 2013; Nakagawa et al., 2012; Swindell, 2012). However, the importance of calorie restriction in aging lies not only in its effects on the lifespan of the model organisms but also in the fact that it provides important pioneering examples of the so-called gene–diet interactions (Swindell, 2012). Thus, the effect of the genes mentioned above as important to lifespan in non-human organisms often depends on whether or not they have been subjected to energy restriction, and vice versa (Hansen et al., 2007; Kaeberlein et al., 2005; Kapahi et al., 2004; Liao et al., 2010; Schleit et al., 2012, 2013). As previously mentioned, in model organisms, IIS represents a key nutrient signaling pathway for which reduced IIS (by directed mutations) is a good mechanism for extending life span. However, in the fruit fly, mutations in chico (which encodes the insulin receptor substrate) extended lifespan as expected only when nutrient intake was high. However under energy restriction, the flies having mutations in chico lived a shorter amount of time than the controls (Clancy et al., 2001). Another example of gene–diet interaction involving energy restriction was reported for S. cerevisiae (Kaeberlein et al., 2005). From

Case–control study 21 FOXO3A SNPs in Germans and 15 FOXO3A SNPs the Danish sample

Only SNP rs6589722 in CBL was significant associated, but disappeared after correction for multiple comparisons. Thus, no association of genetic variation in ATF4, CBL, CDKN2, EXO1, and JUN with longevity in American men of Japanese ancestry was found.

None of SNPs tested in the AKT1 gene were significantly associated with longevity in either a case–control or a longitudinal setting

The APOE-epsilon 4 allele was negatively associated with longevity in the three populations. FOXO3A variation was significantly associated with longevity

a large-scale analysis of 564 single-gene-deletion strains of yeast, the authors identified 10 gene deletions that increased replicative lifespan. However, among these deletions, those corresponding to the genes encoding the mTOR and S6K homologues failed to extend lifespan when combined with energy restriction. A similar relationship was detected when RNAi knockdown of mTOR was combined with dietary restriction in C. elegans (Hansen et al., 2007). In agreement with these results, in their study of Drosophila Kapahi et al. (2004) also observed that dominant-negative alleles of mTOR and S6K extend lifespan in a nutrient-dependent manner. These effects, which were initially observed by chance, have been more systematically analyzed in later studies. Liao et al. (2010) analyzed the influence of the genotype in response to calorie restriction on the mean lifespan of males and females in 41 recombinant inbred strains of mice. The authors observed remarkable variations in lifespan depending on the strain, with differences ranging from an increase in life span exceeding 400% to a decrease of more than 90%. These results demonstrate that the lifespan response to the same level of calorie restriction exhibits a wide variation depending on genetic background. Schleit et al. (2013) investigated the possible molecular mechanisms that underlie genotype-specific responses to calorie restriction in S. cerivisiae. They examined the influence of 166 single-gene-deletion strains on the effect of calorie restriction, observing an important variation in the effects on lifespan depending on the genetic background, with several results that vary

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Table 3 Relevant genes and genetic variants associated with intermediate cardiovascular phenotypes and cardiovascular diseases. References

Gene

Genetic variant

Intermediate phenotype

Cardiovascular disease

Khan et al. (2013)

APOE

Ridker et al. (2009)

CETP

Wang et al. (2011)

LPL

Common E2, E3 and E4 polymorphism (rs4420638 and rs7412) Several common SNPs in partial LD: rs708272, rs7202364 and rs4329913 Several common SNPs in partial LD: rs328 and rs230

The APOE-E4 allele associated with higher risk of cardiovascular diseases (stroke and myocardial infarction) Lower myocardial infarction risk in carriers of the minor allele, but some controversial results Lower stroke risk in carriers of the minor allele

Zhang et al. (2011)

APOA5

Teslovich et al. (2010) Teslovich et al. (2010) Teslovich et al. (2010) Teslovich et al. (2010) Kathiresan and Srivastava (2012) Kathiresan and Srivastava (2012) Do et al. (2013)

LDL-R

APOA1

Common SNP: −1131T>C (rs662799) and S19W (rs3135506) SNP rs6511720 SNP rs10401969 SNP rs629301 SNP rs4731702 Several common SNPs in partial LD: rs2954029, rs2954022 and rs2980885 A common missense variant is associated with lower LDL-C SNP rs10790162

Higher LDL-C and total cholesterol in APOE-E4 allele carriers in comparison with E3/E3 subjects Higher HDL-C and APOA1 concentrations in carriers of the minor allele Higher HDL-C and lower triglyceride concentrations in carriers of the minor allele Higher triglyceride concentrations in carriers of the minor alleles

Do et al. (2013)

APOB

SNP rs1367117

Dichgans et al. (2014)

SH2B3, ABO, HDAC9, RAI1-PEMT-RASD1, EDNRA, CYP17A1CNNM2-NT5C2 PITX2 and ZFHX3

Several SNPs

Traylor et al. (2012) Tragante et al. (2014)

Holmes et al. (2014) Willer et al. (2013) Williams et al. (2014)

CILP2 SORT1 KLF14 TRIB1

PCSK9

PDE1A, HLA-DQB1, CDK6, PRKAG2, VCL, H19, NUCB2, RELA, HOXC@ complex, FBN1, and NFAT5 A score of several genes

SNPs rs6843082 and rs879324 Several SNPs

67 SNPs

RBM5 and CMTM6

SNPs: rs2013208 and rs7640978, respectively

ALDH1L1

SNP rs2364368

Higher risk of coronary artery disease in carriers of the minor alleles

Lower LDL-C and total cholesterol in carriers of the minor allele Lower LDL-C and total cholesterol in carriers of the variant allele Lower LDL-C and total cholesterol in carriers of the minor allele Higher HDL-C in carriers of the variant allele Variant allele is associated with lower triglycerides, lower LDL-C cholesterol, and higher HDL-C Variant allele is associated with lower LDL-C

Lower risk of coronary artery disease in carriers of the minor allele Lower risk of coronary artery disease in carriers of the variant allele Lower risk of coronary artery disease in carriers of the minor allele Lower risk of coronary artery disease in carriers of the variant allele Carriers of the variant allele have lower risk of coronary heart disease

Variant allele is associated with higher triglycerides and LDL-C Variant allele is associated with higher triglycerides and LDL-C Variant alleles associated with different intermediate phenotypes including blood pressure, plasma lipids and platelet count. Association with atrial fibrilation

Carriers of the variant allele have higher risk of coronary artery disease Carriers of the variant allele have higher risk of coronary artery disease Variant alleles associated both with coronary artery disease and ischemic stroke

Variant alleles associated with blood pressure

Some loci associated with cardiovascular disease

Score associated with higher triglycerides SNP in RBM5 was associated with HDL-C and SNP in CMTM6 was associated with LDL-C Hyperhomocysteinemia

Higher score associated with higher risk of coronary heart disease Both SNPs associated with coronary artery disease risk

Carriers of the variant allele have lower risk of coronary heart disease

Association with cardioembolic stroke

Higher incidence of ischemic stroke

between a 79% reduction to a 103% increase. Moreover, after investigating the possible mechanisms of action they concluded that, in addition to the magnitude and direction of the effect, the mechanisms by which calorie restriction influences longevity can also change depending on the genetic context. This provides us with a sound experimental basis to start from, using gene–diet, and by extension gene–environment interactions, as crucial modulators in the aging process.

definition and are easier to measure than the complex phenotypes related to aging. For this reason, we must improve their definition and validate the aging biomarkers, telomere length, etc. It is interesting to note that a recent GWAS (Codd et al., 2013) identified several polymorphisms in the TERC, TERT, NAF1, OBFC1, and RTEL1 genes associated with telomere length. Moreover, the alleles related to shorter telomere length were associated with increased risk of coronary artery disease.

5. Genes and relevant genetic variants in cardiovascular disease in humans

5.1. Association of the APOE gene with cardiovascular diseases

Studies that have focused on identifying the genes and genetic variants associated with the different types of intermediate and cardiovascular disease phenotypes in humans have been more consistent and successful than studies undertaken to identify genetic variants associated with longevity. What may have contributed to this greater success is that the cardiovascular phenotypes studied, both intermediate phenotypes (plasma lipid concentrations, blood pressure, etc.) and final phenotypes (myocardial infarction, stroke, and other cardiovascular diseases), have a more standardized

APOE is one of the few genes where the associations between cardiovascular diseases, aging, and longevity in humans coincide (Deelen et al., 2011; Garatachea et al., 2014; Khan et al., 2013; Kathiresan and Srivastava, 2012; Lindahl-Jacobsen et al., 2013; McKay et al., 2011; Novelli et al., 2008; Schächter et al., 1994). The common polymorphism defined by the alleles E2/E3/E4 has been most often studied. It is defined by two base changes in the DNA that give rise, respectively, to two amino acid changes with the following characteristics: rs429358 (Cys112Arg; E4) and rs7412 (Arg136Cys; E2). Soon after, the E4 allele was consistently

D. Corella, J.M. Ordovás / Ageing Research Reviews 18 (2014) 53–73

associated with higher LDL-C concentrations and total cholesterol (Sing and Davignon, 1985). Although the E4 allele was also associated with greater cardiovascular disease risk (Bennet et al., 2007; Stengård et al., 1996; Song et al., 2004), the evidence of this association was lower than that observed for LDL-C concentrations (Bennet et al., 2007; Khan et al., 2013; Yin et al., 2013). At the same time, evidence began to accumulate on the association between the E4 allele and the greater risk of dementia, in particular Alzheimer’s disease (Poirier et al., 1993; Liddell et al., 1994). With the advent of GWAS, these studies soon confirmed the consistent association between the APOE genotype and LDL-C concentrations (Teslovich et al., 2010; Willer et al., 2013), as well as their association with Alzheimer’s disease (Coon et al., 2007). Meta-analyses of GWAS have also shown the association between the APOE genotype and cardiovascular diseases (Willer et al., 2013), although it is weaker than the previously mentioned associations. With regard to aging, studies of candidate genes have consistently shown the association of the E4 allele with lesser longevity (Garatachea et al., 2014; McKay et al., 2011; Novelli et al., 2008; Schupf et al., 2013), so reflecting the greater risk of cardiovascular disease and neurogenerative diseases, whereas GWAs usually detect an association signal with the polymorphisms rs2075650, in the TOMM40 gene (Beekman et al., 2013; Deelen et al., 2011; Sebastiani et al., 2012, 2013), close to APOE, which possibly reflects the linkage disequilibrium of this SNP with the rs429358 (allele E4). This is important as, for technical reasons, the common APOE polymorphism is not included for determining in GWAs. In addition, several gene–environment interactions between the APOE polymorphism and environmental factors, including tobacco smoking and physical activity, have been reported to play a role in determining intermediate and disease phenotypes (Bernstein et al., 2002; Corella et al., 2001a,b; Grammer et al., 2013; Gustavsson et al., 2012; Humphries et al., 2001; Pezzini et al., 2004; Talmud et al., 2005). Among these studies, it is interesting to note that Grammer et al. (2013) analyzed the association between the APOE polymorphism, smoking, angiographic coronary artery disease, and mortality in participants of the Ludwigshafen Risk and Cardiovascular Health study. They found that the presence of the ␧4 allele in current smokers increased cardiovascular disease mortality and all-cause mortality. No interaction between smoking and the E4 allele was seen in relation to non-cardiovascular mortality. Although the APOE-smoking interaction in cardiovascular diseases has been previously reported in other studies (Humphries et al., 2001; Pezzini et al., 2004; Talmud et al., 2005), more evidence is still needed. 5.2. Factors that may have an influence on the low coincidence level between the main genes implicated in cardiovascular diseases and longevity Despite the greater success in identifying genes related to cardiovascular disease, it is intriguing to note that these do not appear to coincide with the most relevant variants in aging. With the exception of the paradigmatic evidence of the APOE and a few other genes, it is surprising that, with cardiovascular diseases being the main cause of disease and death in the elderly, the genes contributing to these diseases are not identified as the main genes related to aging. This may be due to the intrinsic limitations of extrapolating the results obtained from animal models to humans; however, as mentioned earlier, the most important limitation in humans is the definition of the phenotypes of aging. Moreover, there are weaknesses related to the epidemiological design including the cross-sectional analysis that creates a bias due to population selection at old age, the recruitment bias, and the population stratification bias (Brooks-Wilson, 2013). Additional factors, including cancer mortality and other still unknown factors, could be at work,

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which would explain the apparent disconnect between aging and cardiovascular disease genetics. On the other hand, we should not overlook the gender differences in longevity observed in most industrialized societies, with women living longer than men (Gems, 2014). These differences have not been extensively studied in genetic models of longevity; moreover, cardiovascular genetic studies have not paid particular attention to gender differences. Therefore, future analyses and studies should more deeply investigate the influence of gender in these processes. Another important factor that we must bear in mind is that most of the studies that have analyzed the genetic influence on intermediate and final phenotypes related with cardiovascular diseases have pooled participants representing ages ranging between 18 and 90. This wide age range may have diluted or concealed specific age-related genetic influences (Kulminski et al., 2011, 2013a). Therefore, we need to conduct more specific analyses of the effects of gene variants on intermediate and final phenotypes of cardiovascular diseases in different age groups. Moreover, we must not overlook the relevance of the gene–environment interactions that take place throughout life, thereby increasing or reducing the risk of biological aging as well as cardiovascular disease risk.

6. Gene–diet interactions in determining aging and cardiovascular diseases in humans A priori multiple gene–environment interactions may exist in determining aging and cardiovascular diseases in humans, including factors such as smoking, physical activity, drugs, diet, social context. etc. In this regard, an interesting initiative is the consortium called Interplay of Genes and Environment across Multiple Studies (IGEMS), which involves eight longitudinal twin studies established to explore the nature of social context effects and gene–environment interplay in late-life functioning (Pedersen et al., 2013). However, the results are not yet available. Another outstanding initiative is the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium that was formed to facilitate genome-wide association studies meta-analyses and replication opportunities among multiple large population-based cohort studies (Psaty et al., 2009). In addition to cardiovascular genetics, the CHARGE Consortium is already providing some data about the genomics of aging (Newman et al., 2010), but results regarding gene–environment interactions are still scarce (Hruby et al., 2013). Given the complexity of the holistic study of gene–environment interactions in aging and cardiovascular diseases, this review will focus on gene–diet interactions because diet is one of the most important environmental factors that humans are exposed to every day, while not overlooking other environmental factors that may also modulate the relationship (Ordovas and Corella, 2004). Specifically, in the aging field very few studies have analyzed the influence of the gene–diet interaction on aging phenotypes (McGue et al., 2014; Choi et al., 2013). However, in the field of cardiovascular diseases, gene–diet interactions have been analyzed for many years and a significant number of those study results have demonstrated that those interactions have an impact on determining the intermediate and final phenotypes of cardiovascular diseases (Allayee et al., 2008; Campos et al., 2001; Corella et al., 2001a,b, 2006, 2009, 2011, 2013; Cornelis et al., 2006, 2007; D’Angelo et al., 2000; Dedoussis et al., 2004; Do et al., 2011; Dwyer et al., 2004; Fumeron et al., 1995; Garcia-Rios et al., 2011; Hartiala et al., 2012; Hines et al., 2001; Jansen et al., 1997; Jensen et al., 2008; Markus et al., 1997; Nettleton et al., 2009; Norat et al., 2008; Ordovas et al., 2002; Pérez-Martínez et al., 2008; Ortega-Azorín et al., 2014; Richardson et al., 2011; Ruiz-Narváez et al., 2007; Tai et al., 2005; Vinukonda et al., 2009; Yang et al., 2007; Younis et al.,

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Table 4 Selected studies showing statistically significant gene–diet interactions in determining intermediate cardiovascular phenotypes. Reference

Phenotype

Description of the gene–diet interaction

Jansen et al. (1997) a

Postprandial LDL-C

D’Angelo et al. (2000) a

Plasma homocysteine

Campos et al. (2001)

VLDL and HDL-C

Corella et al. (2001a,b)

Fasting plasma LDL-C concentrations

Ordovas et al. (2002)

HDL-C concentrations and HDL particle size

Dwyer et al. (2004)

Carotid-artery intima-media thickness, and markers of inflammation Plasma Homocysteine

Postprandial LDL-C response to dietary fat is influenced by the 347Ser mutation of APOA4. Carriers of the 347Ser allele presented a greater decrease in LDL-C when they were switched from the SFA to the NCEP type 1 diet than homozygous the 347Thr allele. The C677T SNP in the MTHFR gene interacted with folate and vitamin B12 levels in determining plasma homocysteine concentrations The APOE genotype interacted with saturated fat in determining VLDL and HDL-C concentrations (higher VLDL and lower HDL-C in E2 carriers with a high fat) Alcohol intake interacted with the APOE SNP in determining LDL-C in men. In E2 subjects, LDL-C was significantly lower in drinkers than in nondrinkers but was significantly higher in drinkers than in nondrinkers in E4 subjects. The −514C>T LIPC polymorphism interacted with dietary fat in determining HDL-related measures. T allele was associated with significantly greater HDL-C concentrations and large HDL size only in subjects consuming <30% of energy from fat. Increased dietary arachidonic acid significantly enhanced the apparent atherogenic effect of the 5-lipoxygenase genotype, whereas increased dietary intake of n-3 fatty acids blunted the effect, suggesting that omega-6 promote, whereas marine omega-3 inhibit, leukotriene-mediated inflammation

Dedoussis et al. (2004)

Tai et al. (2005)

Zhang et al. (2006)

Fasting triglycerides and apolipoprotein C-III (apoC-III) Blood pressure levels and hypertension.

Corella et al. (2006)

Insulin resistance

Pérez-Martínez et al. (2008) a

Norat et al. (2008)

Plasminogen Activator Inhibitor Type 1 concentrations Blood pressure

Nettleton et al. (2009)

Plasma HDL-C

Corella et al. (2009)

Body mass index

Delgado-Lista et al. (2010) a

Postprandial lipids

Garcia-Rios et al. (2011)

Fasting triglycerides, triglyceride rich lipoproteinstriglycerides and free fatty acids Anthropometric measures and lipids

Richardson et al. (2011)

Zhang et al. (2012a) a

Blood pressure phenotypes

Zhang et al. (2012a) a

Plasma lipids

Zheng et al. (2013)

Insulin resistance and metabolic syndrome

Richardson et al. (2013)

Plasma lipids

Zheng et al. (2014)

Insulin resistance

A gene–diet interaction between the C677T SNP in the MTHFR and Mediterranean diet was found. Higher adherence to the Mediterranean diet was associated with reduced homocysteine concentrations in carriers of the T allele but not in those with the CC genotype. The L162V polymorphism at the PPARA gene interacted with dietary PUFA intake in determining fasting triglycerides and apoC-III concentrations. The 162V allele was associated with greater TG and apoC-III concentrations only in subjects consuming a low-PUFA diet The angiotensin I-converting enzyme insertion-deletion polymorphism (ACE I/D) interacted with dietary salt intake. In the ID + II genotype, hypertension was increased by high salt intake, while in the DD genotype it was not. The interaction was more prominent in the overweight group. The PLIN 11482G>A/14995A>T polymorphisms (in high linkage disequilibrium) interacted with saturated fat and carbohydrates in determining HOMA-IR in Asian women. These interactions were in opposite directions. Women in the highest SFA tertile had higher HOMA-IR than women in the lowest only if they were homozygotes for the PLIN minor allele. The plasminogen Activator Inhibitor Type 1 (PAI-1) −675 4G/5G polymorphism interacted with dietary saturated fat in determining PAI-1 concentrations. Carriers of the 4G allele showed a decrease in PAI-1 concentrations after the MUFA diet, compared with the SFA-rich and carbohydrate-rich diets The M235T polymorphism in the AGT gene interacted with dietary salt intake in determining blood pressure. The regression coefficient for systolic blood pressure associated with each unit of sodium for each of the MT and TT genotypes was approximately double that for the MM homozygotes. A common SNP in the angiopoietin-like four gene (ANGPTL4[E40K]) interacted with carbohydrates in determining plasma HDL-C concentrations. In men, the inverse association between carbohydrate and HDL-C was stronger in A allele carriers than non-carriers A polymorphism in the APOA2 −265T>C gene promoter interacted with and saturated fat on BMI. This interaction was replicated in three American populations. The CC genotype was significantly associated with higher obesity prevalence only in the high-saturated fat stratum. ABCA1 variants (rs2575875 and rs4149272) modified the human postprandial lipid metabolism after a fatty meal. Postprandial values of apolipoprotein A1 were higher and lipemia was much lower in homozygotes for the major alleles. Two genetic variations at the LPL gene (rs328 and rs1059611) interacted with plasma n-6 PUFA to modulate Fasting triglycerides, triglyceride rich lipoproteins-triglycerides and free fatty acids.

The rs8887 minor allele in the PLIN4 3 UTR gene interacted with PUFA intake modulating anthropometric and lipid phenotypes. This allele predicted a seed site for the human microRNA-522 (miR-522), suggesting a functional epigenetic mechanism. The rs16147 polymorphism in NPY gene promoter interacted with a 2-year dietary intervention in multiple BP measures in The risk allele (C allele) was associated with a greater reduction of blood pressuere phenotypes in response to low-fat diet, whereas an opposite genetic effect was observed in response to high-fat diet. The APOA5 rs964184 polymorphism interacted with and dietary fat intake in the determination of changes in total cholesterol, LDL-C and HDL-C in an intervention trial. In the low-fat intake group, carriers of the risk allele (G allele) exhibited greater reductions in total cholesterol and LDL-C cholesterol than did noncarriers. The rs7578326 and rs2943641 polymorphisms in the IRS1 gene interacted with dietary intake in determining IR and metabolic syndrome. G-T carriers had a significantly lower risk of IR and metabolic syndrome when the dietary saturated fatty acid-to-carbohydrate ratio was low. The rs13702 minor allele in the LPL gene that disrupts a microRNA recognition element seed site for the miR-410, interacted with PUFA in determining triglycerides. Replicated in a meta-analysis including nine populations. The rs2943641 polymorphism in the IRS1 gene interacted with circulating 25(OH)D on HOMA-IR in women. This interaction was replicated in four populations.

a Intervention study in which short- or long-term diets or specific components have been evaluated. In the other studies habitual dietary intakes were analyzed in observational designs.

D. Corella, J.M. Ordovás / Ageing Research Reviews 18 (2014) 53–73

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Table 5 Selected studies showing statistically significant gene–diet interactions in determining final cardiovascular disease phenotyes. Reference

Phenotype

Description of the gene–diet interaction

Fumeron et al. (1995)

Myocardial infarction

Markus et al. (1997) Hines et al. (2001)

Ischemic cerebrovascular disease Myocardial infarction

Younis et al. (2005)

Coronary heart disease

Cornelis et al. (2006)

Myocardial infarction

Yang et al. (2007)

Myocardial infarction

Ruiz-Narváez et al. (2007)

Myocardial infarction

Cornelis et al. (2007)

Myocardial infarction

Allayee et al. (2008)

Myocardial infarction

Jensen et al. (2008)

Myocardial infarction

Vinukonda et al. (2009) Corella et al. (2011)

Coronary artery disease Coronary heart disease

Do et al. (2011)

Cardiovascular diseases and myocardial infarction Myocardial infarction

Alcohol consumption interacted with the CETP-TaqIB SNP in determining myocardial infarction. The odds-ratio (OR) for myocardial infarction of B2B2 (homozygous subjects for the variant allele) decreased from 1.0 in nondrinkers to 0.34 in those drinking 75 g/d or more. The C677T SNP in the MTHFR was associated with homocysteine concentrations and there was a significant interaction between MTHFR genotype, serum homocysteine, and serum folate. TT frequency was 10.7% in case subjects and 13.7% in control subjects. However, when serum folate concentrations were low, the TT genotype was at higher risk. Alcohol consumption interacted with the ADH3 polymorphism in determining myocardial infarction. Among men who were homozygous for the gamma1 allele, those who consumed at least one drink per day had a relative risk (RR) of myocardial infarction of 0.62 (95%CI: 0.34–1.13), as compared with the risk among men who consumed less than one drink per week. However, carriers of the gamma2 allele had the greatest reduction in risk (RR: 0.14; 95%CI: 0.04–0.45) A significant alcohol-ADH3 genotype interaction on coronary heart disease risk was observed, with gamma2 homozygotes, who were modest drinkers, having a risk reduction compared to gamma1. gamma2 (78% cardiovascular risk reduction compared to gamma1 homozygotes; HR = 0.22, 95% CI 0.05–0.94). A cytochrome P450 1A2 (CYP1A2) polymorphism interacted with coffee intake on myocardial infarction. Coffee was associated with an increased risk only among individuals with slow caffeine metabolism. For individuals younger than the median age of 59 years, the ORs (95% CIs) associated with consuming less than 1, 1, 2–3, or 4 or more cups of coffee per day were 1.00, 1.24 (0.71–2.18), 1.67 (1.08–2.60), and 2.33 (1.39–3.89), respectively, among carriers of the *1F allele. The corresponding ORs (95% CIs) for those with the *1A/*1A genotype were 1.00, 0.48 (0.26–0.87), 0.57 (0.35–0.95), and 0.83 (0.46–1.51). The APOE SNP interacted with saturated fat intake in determining myocardial infarction. E2 and E4 gene variants increase susceptibility to myocardial infarction in the presence of high saturated fat diet. High saturated fat intake was associated with a 2.2-fold increased risk of MI among carriers of APOE2 (OR = 3.17; 95% CI, 1.58–6.36) and with a 1.6-fold increase among carriers of the −491T and APOE4 variants together (OR = 2.59; 95% CI, 1.38–4.87). The Pro12Ala PPARG polymorphism interacted with PUFA intake on myocardial infarction. The protective effect of PUFA intake was attenuated in carriers of the Ala12 allele. OR (95% CI) for myocardial infarction per each 5% increase in energy from polyunsaturated fat were 0.66 (0.53, 0.82) in Pro12/Pro12 subjects and 0.93 (0.61, 1.42) in carriers of the Ala12 allele. The GST genotypes modified the association between cruciferous vegetable intake and myocardial infarction. The Highest tertile was associated with a lower risk of diseae only among persons with the functional GSTT1*1 allele. Compared with the lowest tertile of cruciferous vegetable intake, the highest tertile was associated with a lower risk of MI among persons with the functional GSTT1*1 allele (OR: 0.70; 95% CI: 0.58, 0.84) but not among those with the GSTT1*0*0 genotype (OR: 1.23; 95% CI: 0.83, 1.82) (P = 0.006 for interaction). A 5-LO SNP interacted with dietary arachidonic acid (AA) in determining myocardial infarction. Relative to the common five repeat allele, the three and four alleles were associated with a higher myocardial infarction risk in the high (> or = 0.25 g/d) dietary AA group (odds ratio: 1.31; 95% CI: 1.07, 1.61) and with a lower risk in the low (<0.25 g/d) AA group (0.77; 0.63, 0.94) (P for interaction = 0.015). The CETP-TaqIB SNP interacted with alcohol consumption in determining myocardial infarction. Alcohol consumption was associated with lower risk in carriers of the B2 allele. Among non-carriers the OR for cardiovascular disease among women with an intake of 5–14 g/day was 1.4 (95% CI: 0.6–3.7) compared with non-drinkers, whereas among B2 carriers the OR was 0.4 (0.2–0.8). Alcohol intake in subjects with MTR 2756G allele was found to increase the risk for coronary artery disease [OR: 4.15, 95% CI: 1.35–12.69]. Saturated fat and alcohol intake modified the effect of the APOE polymorphism in determining coronary heart disease risk. The APOE polymorphism was only associated with the disease when saturated fat intake was high. When saturated fat intake was low (<10% of energy), no statistically significant association between the APOE polymorphism and coronary heart disease risk was observed (P = .682). However, with higher intake (≥10%), the polymorphism was significant (P = .005), and the differences between E2 and E4 carriers were magnified (OR for E4 vs E2, 3.33; 95% CI, 1.61–6.90) The rs2383206 SNP in the 9p21 region interacted with a factor-analysis-derived “prudent” diet pattern. The combination of the least prudent diet and the risk allele was associated with an increased risk of these diseases (the least prudent diet and two copies of the risk allele was associated with a twofold increase in risk for myocardial infarction (OR = 1.98; P < 0.001)). The association between the PLA2G4A-rs12746200 polymorphism with myocardial infarction was modulated by PUFA. A higher n-6 PUFA intake increased the protective association of the variant allele. The reduced risk of myocardial infarction was observed primarily in AG/GG subjects who were above the median for intake of dietary omega-6 (n-6) PUFAs (OR: 0.71; 95% CI: 0.59, 0.87; P-interaction = 0.005). An intervention with Mediterranean diet modified the association between the TCF7L2-rs7903146 polymorphism and stroke. TT subjects had a higher stroke incidence in the control group (adjusted HR 2.91 [95% CI 1.36–6.19]; P = 0.006 compared with CC), whereas dietary intervention with Mediterranean diet reduced stroke incidence in TT homozygotes (adjusted HR 0.96 [95% CI 0.49–1.87]; P = 0.892 for TT compared with CC). An intervention with Mediterranean diet modified the association between the MLXIPL-rs3812316 polymorphism and stroke. In the Mediterranean diet, but not in the control group, we observed lower myocardial infarction incidence in G-carriers vs CC (hazard ratios, 0.34; 95% CI, 0.12–0.93; P = 0.036 and 0.90; 95% CI, 0.35–2.33; P = 0.830, respectively).

Hartiala et al. (2012)

Corella et al. (2013) a

Stroke

Ortega-Azorín et al. (2014) a

Myocardial infarction

a

Intervention study. In the other studies habitual dietary intakes were analyzed in observational designs.

2005; Zhang et al., 2006, 2012a,b; Yoo and Park, 2000; Zheng et al., 2013, 2014). Tables 4 and 5 show, by way of example, several of the main statistical gene–diet interactions in the intermediate and final phenotypes of cardiovascular diseases, respectively. One limitation in the study of gene–diet interactions is that most of the studies are observational, so they provide a lower level of evidence than experimental studies. In addition, until very recently, those experimental studies have had an inadequate sample size.

Therefore, even in research, it is necessary to increase the level of evidence in support of the gene–diet interactions in cardiovascular diseases. Given the complexity of the subject matter and the lack of gene–diet interaction studies on the incidence of the final phenotypes of cardiovascular diseases, very few studies have investigated this association, which is another limitation of the research (Corella et al., 2013; Ortega-Azorín et al., 2014). Among the existing studies, we would like to mention one published by our own group. In that

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study we found that a gene–diet interaction between a variation in the TCF7L2 gene and an intervention using the Mediterranean diet played a role in determining stroke risk (Corella et al., 2013). This interaction could be relevant not only in determining cardiovascular disease, but also in aging, as the TCF7L2 gene is implicated in the ISS pathway that also involves FOXO (Jin, 2008). TCF7L2 is a component of the Wnt signaling pathway. This pathway controls almost every aspect of embryonic development and mediates homeostatic regeneration in adult tissues (Clevers and Nusse, 2012). The key effector of the canonical Wnt pathway is the bipartite transcription factor ␤-cat (␤-catenin)/TCF (Transcription Factor), composed of ␤-cat and a member of the TCF family (TCF7, LEF-1, TCF7L1, or TCF7L2). An evolutionarily conserved interaction between the Wnt pathway effector ␤-cat and FOXOs was discovered in 2005 (Essers et al., 2005). It is known that nuclear FOXOs increase during aging (Jin, 2008) and that FOXOs compete with TCF proteins, including TCF7L2, for the limited pool of ␤-cat. This may lead to reduced Wnt activity, which is important for lipid and glucose metabolism and offers a new perspective on the pathogenesis of type 2 diabetes, cardiovascular diseases, and other age-related diseases. Moreover, our recent finding that gene–diet interactions involving the TCF7L2 gene play a role in determining fasting glucose, lipids, and stroke risk (Corella et al., 2013) may help expand this perspective.

6.1. Gene–diet interaction between the TCF7L2 polymorphism and the Mediterranean diet in determining cardiovascular risk factors and disease In line with the interaction between TCF7L2 and FOXO are the recent results of an Italian study (Garagnani et al., 2013) highlighting that the rs7903146 polymorphism in the TCF7L2 gene is as an important genetic marker related to longevity. The authors found a lower prevalence of TT individuals among centenarians. This genotype has been very consistently associated with a higher risk of type 2 diabetes in numerous studies (Guinan, 2012; Peng et al., 2013). In addition to this greater risk of type 2 diabetes, it has been reported that T-allele carriers have a higher risk of cardiovascular diseases (Bielinski et al., 2008; Kucharska-Newton et al., 2010; Muendlein et al., 2011; Sousa et al., 2009). Recently, we have described the important effect that gene–diet interaction between the rs7903146-TCF7L2 polymorphism and the Mediterranean diet has on the incidence of stroke (Corella et al., 2013); we found that TT individuals that consumed the control diet had a higher risk of stroke than individuals that consumed a Mediterranean intervention diet, indicating that the latter is capable of counteracting the genetic risk of stroke. Fig. 1 shows cumulative free (stroke) survival curves in subjects in the control group (A) and in the Mediterranean diet intervention groups (B). Moreover, we observed that this interaction with a Mediterranean diet could also be observed in intermediate phenotypes of cardiovascular risk. Thus, TT individuals who had low adherence to the Mediterranean diet presented higher fasting glucose concentrations, total cholesterol, LDL-C, and triglycerides than C-carriers. Nevertheless, with high adherence to Mediterranean diet, these parameters became normal and no statistically significant differences between the genotypes were detected. Our results, together with those obtained in the longevity study conducted by the Italian group, underscore the importance of the TCF7L2 gene and of the Wnt signaling pathway as an important hub for genetic-related cardiovascular diseases and longevity in which relevant gene–diet interactions also have an influence. Therefore, new research is needed to investigate the effects of the interaction between the TCF7L2 gene and the Mediterranean diet on longevity and healthy aging.

Fig. 1. Gene–diet interaction between TCF7L2-rs7903146 polymorphism and dietary intervention in determining stroke risk in the PREDIMED randomized controlled trial. Panel A shows cumulative stroke free-survival by TCF7L2-rs7903146 genotypes (CC, CT and TT) in subjects in the control group is shown (n = 2291), and panel B show cumulative stroke free-survival en subjects allocated in the Mediterranean diet intervention groups (n = 4827). Cox regression models were adjusted by sex, age, center, type 2 diabetes, body mass index, intervention group, alcohol, smoking, total energy intake and adherence to the Medieterranean diet at baseline (Corella et al., 2013). *Statistically significant differences between TT and CC subjects.

6.2. Clinical application of the gene–diet interactions Research into gene–diet interactions is crucial in order to obtain information that will allow us to undertake clinical applications of early genetic diagnosis. If a risk variant associated with certain intermediate cardiovascular phenotypes is detected (for example greater risk of hypertension, dyslipemias, diabetes, etc.) and it is known that a certain diet can counteract that genetic risk, the disease will be preventable through a more personalized diet. To date, most studies have focused on primary prevention, but great interest has also been shown in discovering the gene–diet interactions in secondary cardiovascular prevention so as to be able to recommend the best diet for people who have already had a non-fatal cardiovascular disease and so avoid a second attack. Undoubtedly, a more accurate genetic diagnosis through next generation sequencing will contribute to improving the study of gene–diet interactions

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in particular and gene–environment interactions in general. The results of these interactions will provide us with new knowledge to be applied in achieving healthier aging. 7. Beyond variations in the genome When it comes to identifying the genes associated with aging, we not only have techniques for genetic analyses of SNPs or any other type of DNA polymorphisms, such as CNVs, etc., but other omics are also gradually being applied that are providing interesting results (Tsai et al., 2012; Valdes et al., 2013). The applications of several of these omics have also been extended to the study of the epigenome, which is becoming increasingly more relevant (Tsai et al., 2012; Vasto et al., 2010). However, due to space limitations in this review, we can only provide a brief outline of the main findings and applications of those studies. 7.1. Transcriptomics, proteomics, and metabolomics Transcriptomics, proteomics, and metabolomics are gradually being incorporated into cardiovascular and aging studies to help us better understand the mechanisms of the observed effects and the genomic or phenotypic level (Valdes et al., 2013). Transcriptomic studies, for both protein coding and non-coding RNAs, may help reveal some of these action mechanisms, as they allow us to know which genes are differentially expressed in young subjects and in elderly subjects or in healthy participants and in diseased participants (Passtoors et al., 2013; van den Akker et al., 2014; Joehanes et al., 2013). Thus, interesting results were obtained in the Leiden Longevity study (Passtoors et al., 2013) when the transcriptional role of the mTOR pathway was investigated in blood cells. By comparing mRNA levels of nonagenarians and middle-aged controls, the researchers found significant differences in the mTOR signaling gene set. Single gene analysis showed that RPTOR was one of the most relevant genes associated with familial longevity. Likewise, there are interesting results regarding coding RNA in transcriptomic analysis related to cardiovascular diseases (Joehanes et al., 2013; Pedrotty et al., 2012). Thus, in the Framingham study a total of 35 genes were differentially expressed in cases with cardiovascular disease vs controls; of those, GZMB, TMEM56, and GUK1 were found to be the most relevant (Joehanes et al., 2013). Moreover, transcriptomic studies allow us to know which genes are regulated following the consumption of certain foods or other types of environmental ˜ et al., 2013). However, exposure (Kawakami et al., 2013; Castaner although recent research has highlighted the role of transcriptomic analyses as important tools to investigate the mechanistic bases of gene–diet interactions in determining cardiovascular diseases, more integrated research is needed to understand how the complex interactions among the different environmental and genetic factors affect gene expression in different tissues and how these expression levels relate to healthy or unhealthy aging. Through the comprehensive study of proteins and metabolites, proteomics and metabolomics are also beginning to be applied, providing promising results for both the cardiovascular and aging fields (Geier et al., 2013; Menni et al., 2013; Rizza et al., 2014). Rizza et al. (2014) evaluated aging mitochondrial dysfunction by metabolomic profiling in a group of very old participants. This metabolomic signature improved the prediction of cardiovascular events in the elderly subjects. Again, a more holistic approach is needed in future work in these areas to integrate cardiovascular disease and aging. 7.2. Epigenomics The epigenome is likely to play a critical role in the maintenance of cardiovascular health and function throughout a person’s entire

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lifespan. Therefore, its study is critical in aging. The term epigenomics is used to describe a variety of modifications to the genome that do not involve changes in DNA sequence but that can result in alteration of gene expression permitting differential expression of common genetic information (Tammen et al., 2013). The epigenetic marks are reversible and they allow a rapid adaptation to the environment. There are three main categories of epigenetic alterations (Klironomos et al., 2013; Varley et al., 2013): DNA methylation, histone modification, and non-coding RNA (although it is still debated whether or not non-coding RNA can be included). 7.2.1. DNA methylation, aging, and cardiovascular diseases Significant genome-wide DNA methylation changes occur as part of the aging process (Bell et al., 2012). This age-associated epigenetic drift happens preferentially in genes that occupy peripheral network positions, and these genes synergize topologically with disease and longevity genes, forming large network communities (West et al., 2013). Although changes to the epigenomic landscape are now known to be associated with aging, the causal links to longevity are only beginning to be revealed (Boyd-Kirkup et al., 2013). Likewise, in recent years, interest in the role that DNA methylation plays in cardiovascular diseases has increased (Ordovás and Smith, 2010; Zaina, 2014). Although several works have revealed new knowledge (Duygu et al., 2013; Fiorito et al., 2013; Haas et al., 2013; Sun et al., 2013), understanding the global change in DNA methylation during atherosclerosis remains a challenge. Furthermore, it has been demonstrated that diet affects DNA methylation (Schwenk et al., 2013; Glier et al., 2014), contributing to the idea that nutrition may affect the aging-disease process through epigenetic mechanisms. However, much work is needed to characterize valid epigenetic biomarkers involving these diseases. 7.2.2. Histone modification, aging, and cardiovascular diseases Changes in the structure of chromatin led to functional changes that are important determinants of gene regulation. These structural changes are regulated by modifications of histones and DNA, both of which are components of nucleosomes (Horikoshi, 2013). Several post-translational modifications may occur in histones, including acetylation, phosphorylation, methylation, etc. Several works have described histone modification both in aging (Greer and Shi, 2012; Wang et al., 2013) and in cardiovascular diseases (Mahmoud and Poizat, 2013). In addition, diet can affect histone modification via different mechanisms (Pham and Lee, 2012; Tammen et al., 2013). Thus, a better integration of the different results may help us better understand the link between the different mechanisms. 7.2.3. Non-coding RNA, aging, and cardiovascular diseases Non-coding RNAs, such as microRNAs, small nucleolar RNAs (snoRNAs), and long non-coding RNAs, represent the next major step in understanding the intricacy of gene regulation and expression (Sepramaniam et al., 2013). Although miRNAs are currently the most investigated and crucial discoveries that have been made related to cardiovascular diseases and aging (Menghini et al., 2014; Rippo et al., 2014; Stellos and Dimmeler, 2014; Noren Hooten et al., 2013), the biological importance of other classes of non-coding RNAs is also rapidly increasing and other important works are focusing on long non-coding RNAs (Gupta et al., 2014; Falaleeva et al., 2013). The differences in the microRNA expression profiles between centenarians and octogenarians have been demonstrated (Serna et al., 2012). However, when analyzing high-throughput microRNA expression, as well as other types of RNA expression, methodological uses regarding sample size, false positive results, and other bias are crucial and must be correctly addressed (Lau et al., 2014). Several specific mechanisms involved in miRNA targeting and action have been suggested (Menghini et al., 2014) and

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interesting results have been obtained by analyzing target site SNPs and cardiovascular-related phenotypes (Martin et al., 2007; Miller et al., 2014). Moreover, gene–diet interactions involving miRNA target sites have also been demonstrated (Richardson et al., 2011, 2013). Thus, for example, the minor allele of the SNP rs13702 in the lipoprotein lipase (LPL) 3 UTR gene disrupts an miRNA recognition element seed site for the human miRNA-410, resulting in a gainof-function and lower plasma triglycerides and interactions with PUFA intake in determining plasma triglycerides (Richardson et al., 2013). On the other hand, non-coding RNA, mainly microRNA, can be released into the extracellular environment and, thereby, travel between tissues within an organism (Avraham and Yarden, 2012). Thus, there is great interest in the possibility that circulating microRNAs and other non-coding RNAs can serve as markers of specific disease states. Currently, there is strong evidence that microRNAs that are circulating in the bloodstream can be taken up by cells and alter gene expression, playing a crucial role in a newly discovered system of signaling (Hergenreider et al., 2012; Vickers et al., 2011). Although significant differences in circulating microRNAs have been demonstrated in several types of cancer (Schwarzenbach et al., 2014) and cardiovascular diseases (Cheng et al., 2014), the understanding of the functions of circulating microRNA in relation to aging is currently at a very early stage (Dhahbi, 2014). The first observation regarding the association between altered levels of circulating microRNA and aging focused on the increase of miR-34a (a target of SIRT1) in the plasma of old mice (Li et al., 2011). Later in humans, miRNA plasma profiling was investigated in healthy young and older subjects (Olivieri et al., 2012).

8. Conclusions Cardiovascular diseases remain the main cause of death in most industrialized countries; however, the findings of genetic studies related with aging rarely agree with those obtained for cardiovascular diseases. This discordance may reflect one of the problems associated with aging-related research, namely, the definition of the “ageing” phenotype that is often equated with mortality, longevity, or healthy lifespan. In contrast, cardiovascular phenotypes are more precisely defined and this has resulted in a greater success in identifying related genes. Moreover, it is important to keep in mind that common age-related traits and diseases are not genetically determined; rather, their expression depends on complex interactions between relevant genes and environmental factors. Therefore, emphasis should be placed on investigating gene–environment interactions, especially when it comes to agingrelated research. Outstanding among these gene–environment interactions are interactions with diet. The current paradigm for healthy aging and longevity is based on calorie restriction and some gene–diet interactions have been shown to play a significant role. However, previous findings in experimental models must be validated in humans. Likewise, multiple gene–diet interactions have been reported in relation to cardiovascular diseases, both in intermediate and final phenotypes; however, they have not been carefully examined in the context of aging. Moreover, the relevance of epigenetics has been demonstrated both in terms of aging and cardiovascular diseases, and their contribution will become more apparent as more solid research is produced in the coming years. In addition, another important aspect that has not been emphasized in this review relates to the gender differences present in the aging process. In most developed countries, women live longer than men; as such, we need to better understand the genetic and epigenetic aspects involved and the environmental factors that contribute to greater female longevity. Hence, new research has to be aimed

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