Genetics of Human Longevity Within an Eco-Evolutionary Nature-Nurture Framework
Abstract
Human longevity is a complex trait, and to disentangle its basis has a great theoretical and practical consequences for biomedicine. The genetics of human longevity is still poorly understood despite several investigations that used different strategies and protocols. Here, we argue that such rather disappointing harvest is largely because of the extraordinary complexity of the longevity phenotype in humans. The capability to reach the extreme decades of human lifespan seems to be the result of an intriguing mixture of gene-environment interactions. Accordingly, the genetics of human longevity is here described as a highly context-dependent phenomenon, within a new integrated, ecological, and evolutionary perspective, and is presented as a dynamic process, both historically and individually. The available literature has been scrutinized within this perspective, paying particular attention to factors (sex, individual biography, family, population ancestry, social structure, economic status, and education, among others) that have been relatively neglected. The strength and limitations of the most powerful and used tools, such as genome-wide association study and whole-genome sequencing, have been discussed, focusing on prominently emerged genes and regions, such as apolipoprotein E, Forkhead box O3, interleukin 6, insulin-like growth factor-1, chromosome 9p21, 5q33.3, and somatic mutations among others. The major results of this approach suggest that (1) the genetics of longevity is highly population specific; (2) small-effect alleles, pleiotropy, and the complex allele timing likely play a major role; (3) genetic risk factors are age specific and need to be integrated in the light of the geroscience perspective; (4) a close relationship between genetics of longevity and genetics of age-related diseases (especially cardiovascular diseases) do exist. Finally, the urgent need of a global approach to the largely unexplored interactions between the 3 genetics of human body, that is, nuclear, mitochondrial, and microbiomes, is stressed. We surmise that the comprehensive approach here presented will help in increasing the above-mentioned harvest.
It is recognized that long-lived mammalian species (eg, naked mole rat, blind mole rat, and elephant, among others) evolved unique traits, highly related to their specific environmental niche, to attain longevity.1 Many studies have been made on animals models, but in humans, the capability to reach the extreme decades of lifespan seems to be the result of an intriguing mixture of gene-environment (G×E) interactions, and in this review despite the interesting findings in animals models, we will focus only on the genetics of longevity in humans. Homo sapiens is a long-lived mammal and likely evolved peculiar longevity mechanisms. Modern humans are characterized by a higher level of complexity because of their biological and cultural capabilities of adapting to all areas of the planet and changing their environments, by developing an extraordinary variety of cultural adaptive strategies. This specific characteristic had and still has a strong impact on the molecular and cellular mechanisms involved in aging and longevity. Accordingly, the genetics of human longevity is here described as a highly context-dependent phenomenon, within a new integrated, ecological, and evolutionary perspective, and it is presented as a dynamic process, both historically and individually. During the entire lifespan, new G×E interactions emerge as a consequence of the continuous remodeling process that the body set up to adapt to the deteriorative changes occurring over time,2 which on the other hand occur concomitantly with the changes of the environment. In this complex scenario, many factors (population genetics, demography, sex, family, immunobiography, physical/geographical and cultural/anthropological environment, social networks, socioeconomic status, and education) need to be carefully considered and integrated to understand the contribution of genetics in attaining healthy aging and extreme longevity. The high throughput technologies used for the analysis of genetic data—as well as the data on the most relevant genes—highlighted the close relationship between the genetics of longevity and population-specific dynamics, as well as the importance of small effect alleles, pleiotropy, and the complex allele timing, supporting the assumption that genetic factors involved in human longevity need to be defined according to a large range of biological and nonbiological variables. Moreover, the genetics of longevity is linked with many age-related diseases (especially cardiovascular diseases [CVD]), coherently with the new geroscience perspective.3 Geroscience suggests that the mechanisms underpinning aging are deeply involved in the pathogenesis of age-related noncommunicable chronic diseases and has identified 7 common pillars (adaptation to stress, epigenetics, inflammation, macromolecular damage, metabolism, proteostasis, and stem cells and regeneration). Geroscience emphasizes that aging promotes diseases, being the most important risk factor, and that in turn diseases and their treatments accelerate the aging process,4 and at the same time represents the conceptual framework to understand the role of genetic (protective) factors that allow few individuals to reach the extreme limit of human life. This review highlights also the plastic and deep interactions between autosomal DNA, mitochondrial DNA (mtDNA), and microbiomes which, together with somatic mutations occurring lifelong, continuously create new and unique interactions among these genomes in each individual.
Complexity of Human Longevity
Definition of Longevity
The definition of longevity is highly debated, and the lack of a universally recognized definition increases the possibility of misinterpretation and biases in comparing different studies aimed at establishing the genetic contribution on this trait. Moreover, in the literature, the terms lifespan, oldest old, aging, and longevity are often use interchangeably. Longevity can be defined as the result of mortality selection overall age classes, where cumulative mortality is historically and context dependent. The birth cohort and the percentile of survival are thus 2 parameters crucial for the definition of longevity rather than the chronological age itself. Demographic studies showed that there are differences between members of a birth cohort who live 90, 100, 105, and 110+ years, and thus they may reflect important genetic differences.5 The study of Sebastiani et al5 showed that the siblings’ relative probability to survive to older ages increase with age, suggesting that the genetic influence on survival continuously increases reaching the highest levels around the extreme limits of lifespan.
This observation leads to the important consideration that the power to detect genetic association with longevity is greater for centenarians versus nonagenarians subjects of the same birth cohort.6 Thus, a recent article—to avoid problems of inconsistency in the definition—suggests that longevity has to be defined according to percentile survival based on the reference birth cohort for each population, indicating that the environmental context play a critical role on this specific phenotype. One percentile survival is the threshold suggested to maximize the probability to identify genetic association.7 Moreover, familial longevity is also a criteria used in the study of the genetics of human longevity: the GEHA (Genetics of Healthy Ageing) Study includes nonagenarian sibling pairs to select families enriched for genetic influences on longevity.8
Heritability and Missing Heritability in Longevity
The definition of longevity opens new consideration on the estimation of heritability that is often considered ≈25%±5% on the basis of twin studies that, however, includes a limited (or null) number of 90+ couples. In fact, these studies likely reflect the heritability for age at death in modern society rather than the variation of the trait (longevity) explained by genetic determinants.
An example of the problems and bias inherent in the definition of longevity in human is exemplified by the study from Kaplanis et al,9 where the authors estimated heritability of longevity ≈16%. The results suggested a robust additive genetic component, a small impact of dominance, and no detectable epistasis on longevity heritability. The study is interesting for the digital approach to genealogical data and for the high number of individuals considered (≈3 million from online data set of genealogy profiles of worldwide population). However, the estimation of heritability is built around a high range of birth cohorts (1600–1910), from different populations, and no long-living individuals are present (individuals >100 years old were filtered out).9 These contrasting results take us to the emerging concept of missing heritability in the study of complex traits, including longevity. To date, genomics identified only a small proportion of the expected heritability of longevity. In a recent study, it was proposed that the missing heritability is strictly related to the methods used for the heritability calculation itself.10 In particular, the evaluation of heritability is based on an additive model for complex traits that does not consider other inheritance patterns, such as epistasis among others.10 In literature, example of epistatic interactions in longevity exists.11–13 Tan et al14 analyzing centenarians identified a significant association between the interaction REN gene allele/mitochondrial haplotype H and longevity while the 2 genes analyzed separately showed no significant association.
Moreover, heritability may be biased by unaccounted environmental effects whose relationship with the genetics of human longevity will be extensively described. Variation between phenotypes in a population arises because of the average differences between the genotypes or because each genotype exhibits phenotypic variance because of environmental variation. A detailed analysis of variants affecting behavior and mating will be relevant to understand another potential bias in heritability, that is, the covariance between genes and culture. The polygenic nature of longevity promoted the idea that larger cohorts are needed to identify the variants, but this approach inevitably causes an increase of the heterogeneity of the cohorts and of environmental confounding factors at the expense of the biology underpinning the trait. Last, some authors suggested that part of the missing heritability should be searched in non-DNA factors that may be transmitted between generations15 as important data that connect epigenetic inheritance and longevity from nonhuman models are emerging.16–19 Interestingly, analysis and integration of DNA methylation data and RNA sequencing will be useful tool,20 but a big effort is still needed to collect data on the same pool of samples.21
Phenotype of Human Extreme Longevity
From the previously mentioned studies, it is emerged that the genetic contribution to longevity increases with age, thus centenarians (100 years of age), semisuper centenarians (≥105 years of age), and super centenarians (≥110 years of age) are the more informative subjects for investigating the genetics of human longevity. One of the emerging problems in the genetic studies is the characterization of the phenotype to include in the analysis. Phenomics is a term introduced by the evolutionary biologist Michael Soulè, and it includes all the possible way to characterize populations and individuals.22 Centenarians and semisupercentenarians have been deeply characterized, and the main results are briefly summarized (and reported in Figure 1):
1.
2.
Prevalence of age-related diseases: centenarians markedly compress the period of their lives spent with disability,25 and they avoided or largely postponed all major age-related disease.4,26–30 Consistent with the compression of morbidity scenario, people surviving beyond age 105 years are also phenotypically much more similar to one another than younger individuals dying in their 80s and 90s.5
3.
Epigenetics: according to the epigenetic clock, centenarians showed a decreased in DNA methylation ages,31 indicating that they are biologically younger than their chronological age.
4.
Metabolomics: an analysis conducted on plasma and urine of Italian centenarians and their offspring has identified the first metabolomic signature of healthy aging. Specific glycerophospholipids and sphingolipids and a decrease in tryptophan concentration have been described in centenarians from North Italy.32 This centenarian metabolomic signature showed peculiar changes in pro- and anti-inflammatory compounds and detoxification through specific modulation of the arachidonic acid metabolic cascade and enhanced cytochrome P450 (CYP) enzyme activity.
5.
Biology of circadian rhythms: this is an emerging aspect linked to familial longevity, likely influenced by genetic background and by social and environmental dynamics.33 Circadian rhythms, including central (located in the suprachiasmatic nucleus) and peripheral clocks, deteriorate during aging,34 but they seem to be well preserved in centenarians, including sleep,35 even if the data are still scarce and often related to a limited number of subjects.23 Studies on offspring of long lived subjects showed preserved rhythmicity of serum non–high-density lipoprotein cholesterol concentration when compared with general population, and centenarians present good sleep quality. Genetics of circadian rhythms is highly population specific in humans.36,37 Study in mice showed that SIRT1 is the gene at the intersection between longevity and circadian rhythms, and increasing SIRT1 levels in the brain of genetically altered mice seems to reduce the age-related decay of circadian functions.38
Definition of Controls in the Genetics of Longevity
The definition of controls in the study of the genetics of longevity has constituted a major challenging issue. Noteworthy, the studies can be divided into 2 main groups: (1) studies including younger healthy controls from the same family or from the general population; (2) studies including patients affected by age-related diseases assuming that such diseases constitute a condition of accelerated aging, as recently argued.4 In addition, a new definition of controls is emerging thanks to the new era of molecular biomarkers of aging that introduce the concept of biological age (described in the section Perspectives).
Controls as Healthy Individuals Younger Than Cases (Eg, Centenarians)
For the study of sporadic longevity, the choice of controls constituted a challenging and debated issue. Once the threshold for longevity is defined, many studies randomly selected unrelated subjects from the general population as controls, assuming that the prevalence of possible centenarians in the control group is small and negligible owing to the rarity of the trait.39 A second strategy is to select as a group of control individuals who did not survive over the average life expectancy of their demographic cohort, thus reducing in the control group the possible enrichment of longevity variants and increasing the power of a study. Both strategies have pros and cons, as illustrated in a recent paper,7 but in both cases, the results may be biased by the different birth cohorts of centenarians and controls.
Controls as Individuals Affected by Age-Related Diseases
Recently, the idea emerged that patients affected by age-related diseases could be an effective control group for the study of the genetics of longevity,35,40,41 for 2 main reasons: (1) long-living subjects, such as centenarians and patients affected by age-related diseases, constitute extreme phenotypes, and the comparison between most divergent phenotypes can increase the probability to identify disease-specific genetic protective factors linked to longevity; (2) a group of individuals with a specific age-related disease help to disentangle the genetic heterogeneity of the control group that inevitably is composed of individuals most of whom will likely develop ≥1 age-related diseases that eventually reduce their chances to reach longevity (Figure 2). The concept that aging and chronic pathologies share common mechanisms and constitute a continuum where centenarians and patients affected by age-related diseases represent the opposite side of the spectrum is at the basis of the inclusion of age-related patients in studies on the genetics of human longevity.41
As far as we know, the first study within this framework was performed in 2013 by Garagnani et al35 on the most important genetic risk factor for type 2 diabetes (T2D) mellitus, that is, the TCF7L2 rs7903146 genetic variant. The authors investigated the TCF7L2 risk allele distribution in a phenotypic continuum spanning from diabetic patients with micro- and macrovascular complications (ie, the most severe age-related phenotype) to centenarians (that never had T2D) on a total of 1349 individuals. The frequency of the risk allele genotypes (TT) and the odds ratio value increased gradually according to the decreasing of health/longevity and the increasing of T2D severity.
A second study40 applied a cross-diseases analysis for the study of longevity. The score (weight) was able to distinguish between disease-specific single-nucleotide polymorphisms (SNPs) and aging SNPs that are involved in multiple diseases. Then, these weights were used for the P values calculated from longevity association analysis to integrate in the longevity assessment the age-related disease genetic knowledge. Eight genes involved in extreme longevity likely protecting against specific age-related diseases, such as apolipoprotein E (APOE)/TOMM40 (associated with Alzheimer disease [AD]), CDKN2B/ANRIL (CVD), ABO (tags the O blood group), SH2B3/ATXN2 (neurological disease), HLA, LPA, FADS1, and KCNT2, were identified. The latter study we consider compared 53 healthy centenarians and 45 patients with AD both of Ashkenazi Jewish ancestry.42 The authors found that a burden for rare protein-truncating mutations is absent in centenarians while they characterized patients with AD. This extreme phenotype approach is twice powerful because it provides information both on risk and protective factors for major age-related diseases.43–45
Figure 2 summarized the genetic heterogeneity of the groups considered for the genetic study of human longevity.
Genetics of Longevity: Insight From Ecology
It is well established that longevity is influenced by a complex relationship between environment, genetics, and stochastic factors. Such complexity is appropriately taken into account by ecological models rarely considered in the study of human longevity.46 The evolutionary concept of niche construction47–50 suggests that individuals’ genetics interacts with the external world and the environment that each species creates. Accordingly, we will assume the relationship with the environment as the fil rouge of the analysis of the genetics of longevity. To understand this complex ecosystem, we assume that all possible interactions may vary according to the specific characteristics of each individual. Thus, we start first from the description of the biological identity of each individual that is determined by innate characteristics (such as sex and rare variants) and by features acquired during lifetime (such as somatic mutations and immunologic stimuli that constitute immunobiography). Then, we describe 3 dimensions capable of encompassing the different timescale of interactions between genes, culture, and ecology as reported in Figure 3 (ecological space):
1.
the genetics of families (assortative mating or indirect effect of parents’ genotypes);the genetics of human populations (population genetics and population-specific G×E interactions);
2.
the genetics of longevity and socioeconomic factors.
Genetics of Longevity: Individual Variability
Biological Biography: the Example of Immunobiography
The ecological perspective indicates that longevity is the result of dynamic interactions between genetics and environment. In particular, G×E interactions in centenarians are peculiar of each individual because the combination of stimuli experienced during life interacting with the unique genetic background results in a unique signature. This concept has been conceptualized from an immunologic perspective and dubbed immunologic biography.51,52 Owing to its memory and plasticity, the immune system, including both the innate and the adaptive ones, is capable of recording all the immunologic experiences and stimuli it was exposed to. Immune responses are influenced and shaped by, at least, 2 dimensions: (1) space, composed by the relationships between human being and their specific peculiar environment, according to the geography and history of the population to which she/he belongs to (pathogens, nutrition, climate, lifestyle, among others); (2) time because each individual is the result of time-dependent adaptive processes (such as immunosenescence and inflammaging).
These considerations can be applied to other organs and systems of the body (such as the brain, the endocrine systems, the gut microbota among others) which also have the capability to make records of previous lifelong stimuli, contributing to create the individual biological biography that impacts on the aging process and trajectory, starting from the first stage of life.
Individual Genetic Identity: Germline (Rare Variants) and Acquired (Somatic) Mutations
The genetic identity of each individual is a dynamic process because each person has a unique genetic composition with a peculiar combination of common and rare or even private de novo variants (germline variations), but at the same time, accumulation of somatic mutations is observed during aging. The combinations of these genetic variants constitute the individual genetic identity that continuously evolves with age.
The study of rare variants in aging and longevity is challenging because longevity is by definition a rare phenotype. To this regard, 2 main approaches can be envisaged: (1) to study the effect of these variants combining large-scale DNA sequencing and their functional testing in targeted molecular experiments53,54; (2) to study population isolates. This approach has been successful in detecting rare variants associated with complex phenotypes55 because these populations are characterized by genetic homogeneity and rare variants may have drifted up in frequency, in fact the effect of random genetic drift on increasing allele frequencies is higher for rare compared with common variants.56,57
Another element contributing to genetic identity is somatic mosaicism that occurs lifelong in all human tissues: from the fertilization until death, individuals accumulate somatic mutations. This accumulation is the result of exposure to external agents (such as ultraviolet and pollutants) but also a consequence of errors in DNA duplication process, DNA double-strand breaks, inefficient DNA repair, and unbalanced chromosomal segregation.58,59 The accumulation of unrepaired DNA damages, the decline in DNA repair efficiency, and replicative senescence are well-established characteristics of aging,60–63 as confirmed in a study on Italian centenarians.64 Somatic mutations in autosomal DNA and in particular clonal hematopoiesis are an emerging link between longevity and CVD, and a detail description is reported in Somatic Mutations and Clonal Hematopoiesis.
Somatic mosaicism of mtDNA, referred as heteroplasmy, was reported in many aging studies showing an accumulation of mtDNA mutations during aging in healthy individuals. Moreover, a study that applied an ultradeep DNA sequencing approach, capable of detecting low frequency alleles, on female centenarians and their offspring65 showed a heteroplasmy profile peculiar for each individual (private component) but also the presence of heteroplasmies shared between centenarians mother and daughter within each family.
Genetics of Human Longevity and Sexual Dimorphism
It is well-known that human longevity—with some exception—is strongly correlated with sex and that females generally live longer than male, likely because of the combination of biological factors and social factors66–68 as showed in Figure 4.
Sex influences also the inheritance of exceptional longevity.69 The analysis of parental age of a cohort of Ashkenazi Jews with exceptional longevity revealed that in males both maternal and paternal inheritances are likely contributors to exceptional longevity, whereas among females, maternal inheritance seems to be more influential.69 Evolutionary studies provide clues for the interpretation of these sex-specific influences.70
First, sex hormones, in particular the removal of testosterone at young ages, has been demonstrated to increase lifespan in mammals71 and in humans, as supported by the Korean population of eunuchs.72 In male, there is a trade-off between immune investment and traits that improve competitive success, which has driven genetic variation in genes involved in testosterone levels during human evolution. Testosterone plays a major role in male to increase muscle growth but at the same time reduces immune responses (testosterone is considered an immunosuppressant)73,74 according to the resources allocation theory.
Second, cultural factors contribute to sex differences. The grandmother hypothesis spread ≈1980 when researchers found that among Hadza hunter-gatherers (Tanzania) mothers faced a trade-off between foraging for food for themselves and caring for new babies. This cultural process—observed only in humans—may have shaped genetic variants conferring sex-specific advantage for survival after menopause because women who remained healthy beyond their fertile years may have enhanced the reproductive success of the offspring, providing care for their grandchildren.75
Third, mtDNA is maternally inherited, and this genome can only make a direct and adaptive response to selection through females as experimentally demonstrated in Drosophila.76,77 This means that mutations with a negative effect in males can accumulate in the population if they are neutral or beneficial for females (phenomenon called Mother Curse) as reported in Figure 5. This is because females can have a limited number of offspring for physiological reasons (number of gametes and energetic costs of each pregnancy) while males tend to be limited by the number of matings. This leads to a different selection dynamics in female that it is not observed in male, as supported by a recent study that showed an accumulation of deleterious mutations in genes exclusively expressed in male.78 The study of Camus et al77 suggested that mitochondrial genome is a hotspot for mutations that affect sex-specific patterns of aging, and this phenomenon contributes to sexual dimorphism in aging.79 Moreover, the interaction between nuclear DNA and mitochondrial genome (hereafter mitonuclear interactions) plays a major role in explaining variance in penetrance and expression of mitochondrial diseases with different trend in different sexes.80 The accumulation of mtDNA mutations, which impact on the oxidative phosphorylation functionality, constitutes an intense selective pressure on the nuclear genome for counteradaptations that restore the compromised function.
Data in Drosophila melanogaster suggested that this process is strongly influenced by sex. Experiments on isonuclear fly lines whereby distinct mitochondrial haplotypes have been placed into a standardized foreign nuclear background indicate that a disruption of the coevolved mitonuclear genotype leads to males, but not females, sterility.76,81
The dimorphism in longevity has been only recently introduced in the study of the genetics of human longevity. In the GEHA study, a linkage analysis was performed in 90+ male (N pairs=263) and female (N pairs 1145) sibpairs, and 3 loci that linked to longevity in a sex-specific manner were identified: 8p11.21-q13.1 (men), 15q12-q14 (women), and 19q13.33-q13.41 (women).8 A recent genome-wide association study (GWAS) study82 investigated the association with longevity separately in men and women in a cohort of >2200 Chinese centenarians. The authors did not find any locus associated with longevity above the formal 10–8 GWAS threshold, but a series of interesting sex-specific longevity alleles were identified. In the study, the polygenic risk score indicates that different pathways contribute to longevity in men and women. In particular, paths involved in inflammation and immunity emerged as male-specific while those involved in PGC-1α (PPARγ coactivator-1α) function and tryptophan metabolism emerged as female specific. It is worth noting that the GWAS approach likely limited the sex-specific analysis of longevity because of the obvious reduction of the dimension of the cohorts when splitted into females and males.
Familial Ecology and the Genetics of Longevity
Longevity runs in families is a statement that introduces the vast majority of papers about the genetics of human longevity and families constitute a niche in ecological term. One of the first evidences came from studies in the Sardinian population (Italy), based on a geographical-genealogical approach. These studies showed that (1) a nonrandom distribution of centenarians by place of birth and peculiar area (in the province of Nuoro) of exceptional male longevity is identified and called blue zone83; (2) longevity clusters in families and occurs among the ascendants of a particular branch of the family.84 A peculiar characteristic of Sardinian familial longevity is that children born from mothers who later became centenarians had significant lower infant mortality when compared with children born to those women belonging to the same cohorts but who did not became centenarians.85 Thus, history of familial longevity contributes to increasing the odds for an individual’s longevity, as reported in a study of 1700 sibships from families of centenarians in the New England Centenarian Study.86 Moreover, centenarians offspring are characterized by a better health status when compared with age-matched controls born from parents who died before reaching the expected lifespan for their cohort.87
However, not only genes but also cultural and ecological dynamics cluster in families, especially in small populations that maintain local tradition and where marriages occurs within the same (or in any case close) small communities (such as in Nuoro).84 One clear example of the effect of the social and family structure on the genetics of longevity comes from 2 studies in different populations, such as Dutch and Calabria (South Italy).88,89 The first found that spouses of long-lived partner, even sharing most of their adult life with their partner, did not present any advantage in term of survival, suggesting that the effect observed was mainly linked to genetic factors. On the contrary, in South of Italy (Calabria), female spouses of long-lived siblings also live longer than members of the corresponding birth cohort, indicating that females may benefit more than males of a favorable environment to become centenarian.
These observations constitute the strength of the study as they clarified to what extent the study of genetics of longevity may depend on familial, social structure, and anthropological habits. Also in the LLFS (Long Life Family Study)— a longitudinal family-based study of longevity and healthy aging—spouses of members of long-living families tend to be healthier than a sex- and age-matched members of the general population.90 The underlying mechanism at the basis of these observations could be also genetic. Assortative mating based on characteristics, such as anthropometric measures, behavior, educational level, cognitive abilities, which all have substantial genetic components may be a mechanism. A high degree of genetic similarities of spouse pairs, particularly for older generations, has been identified and provide evidences for ancestry-related assortative mating also in families selected for longevity.91 The first studies and hypothesis of assortative mating in human longevity date back to 1914 and are based on the fact that familial longevity was a trait that families could be proud of. Assortative mating, differently from high level of inbreeding that affects the frequency of all loci independently, is a phenotype-based assortative mating (driven by different factors, such as educational level, socioeconomic status, language, behavior, culture etc) and can change linkage disequilibrium and the distribution of the genotype frequencies of the loci involved in the assortment to an excess of homozygosity.92 Accordingly, a recent study showed that genetic variants linked to education predict longevity, suggesting that individuals with more education-linked genetic variants had longer-living parents.93 In addition, also the parental genomes (usually ignored in genetic studies) seem to play a role as nontransmitted alleles can affect a child through their impacts on the parents and other relatives, a phenomenon recently called genetic nurture.94
Genetics of Longevity: a Population Approach
Longevity is highly context dependent as demonstrated by many studies. What is new is that also genes and pathways that impact on longevity are also population specific. The study about longevity in the Chinese population identified peculiar pathways linked to longevity that are context dependent.95 It is likely that longevity could be achieved in a population-specific way, involving both public and private mechanisms65,96 and could be conceptualized as a sort of convergent phenotype,41 reached by context-specific mechanisms (genetic and nongenetic) that are in part public (at least for biological functions) and in part private of each population.
Within this perspective:
1.
G×E interactions can be strongly influenced by the fast changing and population-specific environmental conditions, and thus the same alleles can exert different effects in different populations;
2.
Allele frequency can reach high frequency in certain populations because of evolutionary dynamics, such as demographic history, migration, bottleneck, drift or positive selection that acted in the past.
An example of the first scenario is TCF7L2 rs7903146-TT, the most significant genetic marker of T2D mellitus associated with cardiovascular events. A study of Corella et al97 showed that the Mediterranean diet is able to counteract the effect (in term of stroke) of the risk genotype, suggesting that a genetic association can be influenced by population-specific environmental factors.
An example of the second scenario come from a genetic study on the Italian population where the frequency of variants involved in different diseases showed different pattern according to past environmental selective pressures. The study published by Sazzini et al98 considered >500 000 SNPs in 780 individuals recruited in the Italian population and demonstrated that past local adaptations and different admixture events with continental and Mediterranean populations shape the frequency of risk variants for complex pathologies—T2D mellitus and CVD among others—considering North to South cline. Variability between populations is not the only source of variation to consider in the study on the genetics of longevity. Subtle but concordant changes in allele frequencies across population that share geographic area but which differ for ecoregion, dietary components, and mode of subsistence have been reported.99 Thus, the same adaptive allele may increase in frequency in certain populations in different geographic areas that share the same environment. The case of APOE is paradigmatic and described below in a dedicated paragraph. The same considerations apply to mtDNA and Y chromosome, and also coevolutionary trajectories of mitonuclear coadaptation are predicted to be population specific.80
Genetics of Longevity in Different Socioeconomic Settings
The social and ecological dimension is a further niche constructed by humans that plays a major role in the genetics of complex traits. It is well-known that socioeconomic factors may impact on human longevity and on the aging rate. One of the most striking examples for longevity come from the city of Glasgow where higher level of mortality has been registered in persistently deprived areas.100 Another example is the case of Korea: South Korean population experienced a rapid increase in economic condition becoming one of the most developed nations. On the contrary, the North Korean population remains one of the poorest countries. North Korean had 0.7 centenarians/1 million people in 1925 and 2.7 centenarians/1 million people in 2010 and South Korean population showed 2.7 centenarians/1 million people in 1925 and 38.2/1 million people in 2010.101 These demographic data open new insight for the study of the genetics of human longevity as genetic influence in the same population may change according to social pressures as demonstrated in a study by Rimfeld et al.102 They considered 12 500 individuals from Estonia and demonstrated that during the Soviet era, 2% of the variance seen in educational and success was because of differences in genetic factors and that this number increased to 6% after independence.102 Two recent studies investigated the effect that the socioeconomic status at birth exerts in whole life mortality and showed that illegitimacy of the birth and parental occupation (in particular paternal one) were associated with an increase in mortality at all ages.103,104 The most obvious explanation of these differences is that socioeconomic status impacts on nutritional profile, stress, and exposure to infections, but recent data showed that epigenetic modifications (and in particular DNA methylation) may recode information of the father’s environmental exposure and then be transmitted to the offspring,105–107 supporting the crucial role of evaluating these factors for the missing heritability. Moreover, it has been suggested that—even if social interactions tend to reduce during aging—the maintenance of social connections help to live longer108 and is associated with lower mortality. In fact, individuals with social relationships have an higher probability of survival (50% greater) compared with those individuals characterized by few and poor social interactions, as demonstrated by a meta-analysis of 308 849 individuals, followed for an average of 7.5 years.109 On the contrary, the absence of interactions with the families and in particular with grandchildren can be associated with a higher susceptibility for depression in the elderly.110 Data on the role played by the behavioral genetics in determining these social interactions have not been analyzed even if a role may be hypothesized. In fact, genes may evolve through positive selection according to their effects on social behavior (called also indirect effect because they confer a characteristic crucial to boost social interactions). The most striking example in the study of human evolution is the language gene FOXP2 that indirectly increases the possibility of social interactions between individuals and likely its frequency increased owing to the advantage in social behavior. Likewise, genes involved in testosterone profoundly changed during evolution under social selective pressure to allow social interactions after the shift from nomadic to settled societies (more numerous). In conclusion, (1) the genetics of longevity is highly dependent from the socioeconomic status because the process of remodeling and the genes favoring longevity (or predisposing to major chronic health conditions) seem to be different in different socioeconomic backgrounds as reported by the results of the Health and Retirement Survey in United States111; (2) new socioeconomic conditions have emerged recently, such as the obesogenic environment. Thus, genes that favored longevity a century ago (eg, during the First World War or when antibiotics did not exist) are likely different from the genes that favor healthy aging today.
Genetics of Human Longevity: the Main Findings
From Whole-Genome Sequencing
To date, whole-genome sequencing is the most informative approach for genetic analysis. Recent advances in sequencing technologies and the decline in sequencing costs provide researchers with new possibilities for genetic studies.
Although the first studies on the genetics on human longevity were based on candidate gene analysis, to date the approach is based on the use of genome-wide techniques to identify the most informative loci, and functional studies both in vitro and in vivo constitute the last step of the analysis. The main advantage of whole-genome sequencing is that it allows the study of the existing genomic variability of each individual (both in coding and noncoding regions) without using imputation process, that may include some bias because of the reference population. To date, the only few whole-genome sequencing studies on the genetics of human longevity are available: (1) the first studies were performed by Sebastiani et al112 and by Ye et al113 and analyzed few individuals reaching the extreme decades of life. The Sebastiani’s study published in 2011 sequenced 2 supercentenarians (>110 years old), but the low number of subjects does not allow any statistics. However, an interesting data emerged: the 2 subjects likely reflected 2 different strategies to reach the last decades of life. The female was characterized by significant stretches of homozygosity across many chromosomes, consistent with inbreeding among her ancestors, and she carried only 5 of 16 common variants selected for their role in metabolism that resulted associated with longevity in GWAS studies. The same analysis in male showed no similar stretches of homozygosity, and 11 of the 16 common variants associated with longevity. Overall, these results suggested that the woman was enriched for private mutations that promote exceptional longevity (because of her familial genetic background).
(2) The second study113 considered a monozygotic twin pair reaching 100 years, and owing to the peculiarity of the samples, the study also investigated the prevalence of somatic mutations. (3) The third study published in 2014 by Gierman et al114 considered 17 supercentenarians (≥110 years) of European ancestry, but the use of public available genomic data as control reduces the efficacy of the analysis (reducing the number of variants to analyze). The main findings can be summarized as follows: (1) no significant evidence of enrichment for a single rare protein-altering variant or for genes harboring rare protein-altering variants in supercentenarian compared with controls was found; (2) the gene most enriched for rare protein-altering variants in this cohort of supercentenarians was TSHZ3, but the replication on a second cohort of 99 long-lived individuals failed. (3) The most recent paper—and also the largest in term of sample size—was published in 2016 by Erikson et al,115 but the authors studied an healthy ageing phenotype, based on self-reported data (called by the author Wellderly, in subjects aged 84.2±9.3 years). The age range of the study indicates that limited information on longevity have been investigated.116 The main result of this study is the association with healthy aging in loci in the COL25A1 gene encoding for a protein secreted in the brain and associated with amyloid plaques.
Thus, sequencing study on longevity is emerging, but available data did not allow a real discovery phase in comparison with preexisting data. Few samples and the lack of controls impair the interpretation of the sequencing data.
From GWAS
The literature on exome sequencing in human longevity is not so rich, and only few studies used this type of protocol.117–119 The vast majority of data on GWAS are based on high-dense microarrays analysis (usually Affymetrix or Illumina) that thanks to imputation process can be compared between different cohorts and populations for millions of SNPs. Even if the purpose of this review is not to elucidate the limitation of imputations process, a detailed description on this topic can be found in Refs 120,121. GWAS can be ideally divided into 2 major types according to the considered phenotype: the first is focused on aging, and longevity evaluates the genotype of each individual correlated with the phenotype of the individual itself; the second includes parental longevity and evaluates the genotype of each individual correlated with the age phenotype of the parents. In Online Table I, an overview of all (as far as we know) the GWAS studies is reported. The purpose of this section is not to purely describe the genes emerged from each study as other excellent reviews have been produced on this topic but to let clarify the main conceptual assumptions, results, and methodological strength/limitation of the available studies which in a sense have revolutionized the field.
Link Between Longevity Variants and Age-Related Diseases Emerges From GWAS Studies
The relationship between aging and chronic age-related diseases is based on epidemiological evidence and experimental data that aging is the major risk factor for such pathologies and assumes that aging and chronic age-related diseases/geriatric syndromes share a common set of basic biological mechanisms. Age-related diseases can be conceptualized as accelerated aging and that aging and age-related diseases are part of a continuum, the 2 extremes being represented by centenarians who largely avoided or postponed most age-related diseases, and patients who experienced ≥1 severe age-related disease in their 60s, 70s, and 80s.4
Longevity GWAS provided important evidences supporting the tight interconnection between aging and the age-related diseases. Many longevity variants have been identified, but function analysis showed that most of them are involved in age-related diseases. APOE/TOMM40—that emerged from the vast majority of studies8,39,122–127—is a remarkable example as it is involved in AD onset, in CVD, and cancer (see Table 1). Deelen et al124 in their GWAS study on European longevity in the framework of the GEHA project identified a novel locus on chromosome 5q33.3 associated with survival beyond 90 years and replicated the locus TOMM40/APOE/APOC1. In particular, the SNP, rs2149954-T, in chromosome 5q33.3 is associated with low blood pressure, and they showed that minor allele (T) carriers present lower mortality risk for CVD, eventually linking longevity with a lower risk for stroke. The authors identified for the first time a real longevity locus as the association observed is positive, meaning that centenarians are characterized by an high frequency of the alleles associated with low blood pressures.129 The study of Fortney et al40 described for the first time the overlap at genome-wide level between longevity and age-related diseases. The approach developed by Fortney et al40 provides to each SNP an age-related risk weight (the approach was called informed GWAS). In particular, it revealed that in longevity, there is a reduction of the genetic risk for artery disease, ADs, diastolic and systolic blood pressures, 3 blood lipid traits (triglycerides, total cholesterol, low-density lipoprotein), chronic kidney disease, and bone mineral density. Beekman et al130 and Sebastiani et al39 used a different approach to investigate disease risk alleles in longevity and used sets of disease SNPs to construct genetic risk scores (or polygenic risk scores111). They found no significant differences between centenarians and controls, concluding that disease risk alleles do not compromise human longevity. These contrasting results are likely related to the different statistical tools applied on the same question. However, to move a step forward on this hot topic, it is necessary to carefully study each genetic risk alleles in centenarians and patients matched for geographical origin.
| Lessons From the Past | Guidelines for the Future |
|---|---|
| The proper definition of phenotypes to include in genetics studies | |
| 1. Longevity is defined according to demographic criteria (1 percentile survival) | 1. Avoid definition of longevity on the basis of self-reported data |
| 2. Controls can include healthy individuals, familial members, and also individuals with age-related diseases as a paradigm of accelerated aging | 2. Biological age need to be included in the definition of controls |
| 3. Longevity is different between males and females | 3. Genetics analysis need to performed separately for male and females when sample size is high enough |
| The ecological perspective | |
| 1. (Immuno)biography and somatic mutations are dynamic processes that change during aging | 1. The genetics of longevity is not a static concept, and a protective/risk allele needs to be integrated with other data that characterized the individual in a given time |
| 2. The familial history shapes (directly or indirectly) different gene-environment interactions | 2. Identify statistical model to evaluate the direct effect of inherited variants but also the indirect effect of variants carried by parents |
| 3. Geography, cultural aspect, birth cohort, and socioeconomic dynamics change gene-environment interactions | 3. Define risk/protective allele according to the birth cohort, geography, and socioeconomic status |
| 4. Evolutionary dynamics (migration and natural selection) shape the genetic background of different populations (such as APOE), and the genetics of longevity is population specific | 4. An evolutionary medicine and the study of genetic structure of each population will help in identifying population-specific genes and pathways correlated with longevity |
| State of the art | |
| 1. Variants involved in age-related diseases were identified in many GWAS/gene candidate studies of longevity (such as APOE, chr9p21) | 1. Patients with age-related diseases have to be included in the study of human longevity (following the geroscience perspective), and centenarians need to be included in the study of age-related diseases as a group of supercontrol |
| 2. Genetic risk factors are age specific and play a major role in the process of remodeling | 2. Complex allele timing and antagonistic pleiotropy dynamics help in identifying the role of the genetic variants in different environments |
| 3. GWAS highlighted that common and population-specific genes involved in longevity | 3. To perform study where centenarians and controls are homogeneous in term of population genetic structure |
| 4. Longevity depends on small-effect alleles (and association signals tend to be spread across most of the genome). The GWAS threshold of 10–8 may not be valid for longevity | 4. Include new methods of analysis, such as pathway analysis or as suggested by Boyle et al128 using an omnigenic approach mapping cell-specific regulatory networks |
| 5. Gene candidate studies showed the role many genes involved in longevity: APOE/TOMM40, chr9p21, chr15q33.3 FOXO, IGF-1, IL6, sirtuins among others | 5. Evaluate the effect of these genes by genome editing |
| Human is a metaorganism | |
| 1. Host genome interacts and coevolved with mtDNA | 1. Characterize mtDNA variability and heteroplasmy and the nuclear-mitochondria interactions |
| 2. Host genome interacts and coevolved with gut microbiota | 2. Focus on the interaction between the 3 genetics (host, mtDNA, microbiome) usually considered separately |
The first column reports the data obtained from the published studies, and the second column describes possible guidelines for future researches. APOE indicates apolipoprotein E; GWAS, genome-wide association study; and mtDNA, mitochondrial DNA.
In any case, as expected, from available data, the enrichment of age-related disease variants has never been observed in centenarians, in accord the prediction of geroscience that aging is the greatest risk factor for a majority of chronic diseases, driving both morbidity and mortality. Thus, we surmise that the geroscience architecture3 has missed the eighth pillar: genetics. This consideration is supported by a recent review on progeroid models that highlighted the notion that genetics has profound effects in determining the aging process: in particular, looking at lamin-linked progeroid disorders, it is clear that determinants of aging may originate from an altered genetic background.131 Accordingly, a GWAS study132 identified a genetic correlation with longevity in the WRN gene, responsible for the Werner syndrome, a progeroid disorder characterized by premature aging.133,134
Longevity Depends on Small-Effect Alleles
The GWAS study of Yashin et al135 revealed the importance of including in the analysis also signals with P≤10–6 in the study of genetics of longevity, suggesting that the traditional P≤5×10–8 is likely not appropriate for this trait. This cutoff widely used in GWAS data to reduce the number of false-positive may be difficult to apply to studies on longevity that are characterized by a small sample size owing to the fact that the recruitment of high number of centenarians, particularly from the same population, is often difficult and challenging. In our opinion, the use of statistics needs to be contextualize into the biological problem (in this case, longevity) to identify the right trade-off between the risk of identifying false-positive signals and the possibility to filter out biologically relevant signals that fall under this cutoff. To assess this problem, Yashin et al135 tested the hypothesis that longevity depends on several small-effect alleles using 550 k SNPs data in 1173 genotyped participants of the FHS (Framingham Heart Study). They demonstrated that the joint influence of small-effect alleles on life span is both significant and substantial, explaining in part the numerous GWAS that did not find any P≤5×10–8 result.122,126,132,136 This study let emerge the importance to evaluate nonadditive (nonlinear) joint genetic influence (epistasis) to investigate the genetic dose-phenotypic response relationship in longevity. Moreover, this result impacts on genetic calculations, such as heritability, whose main assumption is the additive nature of genetic component of phenotypic variations.
New mathematical model to calculate epistasis and effective tools to reduce the high dimensionality of the data and the use of both additive e nonadditive interactions are needed (Figure 6).
A new method for the analysis of small-effect alleles is discussed in the article of Boyle et al.128 They proposed an omnigenic model based on the definition of core and peripheral genes. According to the authors, the first constitutes a network of genes whose mutation lead to the strongest effects on the trait under study while the second class of genes includes all the genes with no apparently relevant effects on the trait. They observed that a large proportion of the genetic contribution to a complex trait comes from peripheral genes that are only a few steps (in term of distance between the nodes of the network) from the nearest core gene and thus may have an effect on the trait in specific tissues. Boyle et al128 propose that complex traits, such as longevity and age-related diseases, are the results of many processes that involve different cell types and tissues, and the role of the genetic variants has to be contextualized in such locations.
GWAS Analysis Showed Monotonic as Well as Nonmonotonic Age Patterns of Allele Frequencies
The study of different allele/genotype frequencies in distinct age classes has been used as evidence of the presence of genetic influence on survival.137 With this method, called gene frequency methods, it was possible to divide variants in frail, neutral, or robust according to the trends showed with aging (as reported in Figure 7). Gene frequency method is based on 2 main assumptions: (1) the allele frequencies in all the considered birth cohorts are the same; (2) mortality for genotypes does not change between birth cohorts. This approach was adopted in 2 studies.138,139 The change of allele frequency with age may follow linear trends (monotonic) or nonmonotonic patterns (usually U-shaped patterns or constant until a certain age and then linear) in which allele frequency decreases to a given age but then increases, reflecting trade-offs in their effect at young and old ages.
Nonmonotonic trends have been suggested to correspond to genetic variants whose effect on mortality risk change with increasing age, from detrimental to beneficial (or vice versa).138
Such trajectories increase the complexity of the study of genetics of longevity because it indicate that the effect of a given allele varies according to the changes of the internal environment that occur from young and adults to the oldest old.140,141 This feature, called complex allele timing by De Benedictis and Franceschi,140 points out the fact that genetic risks or protective factors are likely age specific. The concept of allele timing should not to be confused with that of antagonistic pleiotropy. According to the antagonistic pleiotropy theory,142 the same allele can act not only on the probability of survival at young/adult age but also on antagonistic traits (eg, fertility or traits link to disease) later on, in a different period of life. For example, an allele may confer high fertility (or protection from infections) early in life, and the same allele might be deleterious for survival late in life. The allele timing is instead linked to the adaptive process of remodeling of cellular and molecular mechanisms that occurs lifelong. According to the remodeling theory of aging,2,143 the same allele can exert different effects on survival because its function change (eg, its gene expression) according to the internal microenvironment that in turn changes with age). In this case, there is no pleiotropy as the effect is on the same traits.
An example of genes whose variants follow a U-shape trend is KLOTHO (an aging gene associated with low high-density lipoprotein and reduced CVD risk) and LPA (lipoprotein A) genes.144 Other studies highlighted the importance of identifying allele frequency changes during aging as a method of distinguishing between longevity and buffered-deleterious genotypes.145 Some risk alleles with age-related U-shaped frequency may be buffered by other prolongevity variants, as in the case of CETP (cholesteryl ester transfer protein; favorable) genotype that was demonstrated to neutralize the deleterious effects of the LPA gene.144,145 To this regard, it is interesting to note that the product of LPA gene, that is, lipoprotein(a), a widely accepted risk factors for atherosclerotic vascular diseases, did not change with age until the extreme ages, and several atherosclerosis-free centenarians had high lipoprotein(a) serum levels, together with low HDL (high-density lipoprotein) and relatively high TG (triglycerides).146 These data support the hypothesis that a continuous and complex reshaping of lipid metabolism occurs in physiological aging, likely contributing to longevity.
GWAS Remarks the Population Specificity of Longevity
Other GWAS studies95,122,147 let clearly emerge the importance to take into account population stratification in genetic studies on longevity, in accord with the above-described ecological perspective. The study of Zeng et al95 on Chinese centenarians is particularly interesting as the demographic history of the country reduces, at least in part, population stratification and concomitantly allowed the inclusion of an unprecedented number of centenarians. The authors identified both shared and population-specific genes and pathways involved in longevity. They confirmed the loci in APOE and chr5q33.3 and identified new loci in IL6, the well-known gene of gerontologist, and in ANKRD20A9P. It is to note that the P values in the cohort of Northern Chinese (1115 centenarians) and in Southern Chinese (1063 centenarians) did not reach 10–8 while the combined analysis of the 2 reached the GWAS threshold. The relative homogeneity of the population and the extreme phenotype (all cases are within the one percentile of survival) support—as mentioned above—that suggestive, nominally significant signals are fundamental in the study of extreme longevity. The study highlighted also longevity pathways peculiar of the Chinese context (xenobiotic, starch, and sucrose metabolism), as well as diet-mediated mechanisms, which can differ according to culture and ethnicity. A second study of Malovini et al147 considered a population of South Italy and identified the association between longevity (defined here with an age range between 90 and 109 years) and a gene never described by any other study, that is, CAMKIV gene. They also demonstrated by a functional study that CAMKIV is important in the activation of proteins involved in survival, such as AKT, SIRT1, and FOXO3 (Forkhead box O3) A. In general, these examples show that longevity is the result of phenotypic convergence41 reached by context-specific mechanisms (genetic and nongenetic) that in part are public and shared among individuals and in part are private in terms of population, family, and individual specificity. In a 2006 article, De Benedictis and Franceschi140 suggested to conceptualize the Italian and European centenarians as phenocopies, according to the classical definition of subjects who share the same phenotype (ie, longevity) reached by different gene/environment combinations.148
Results From Genome-Wide Linkage Analysis (GWLS)
GWLS searches for chromosomal segments that cosegregate with the phenotype through families. The characteristic of linkage studies is that this approach is highly informative when highly penetrant variants cause the disease/phenotype as they rely on the presence of a single mutation of strong effect while genetic heterogeneity, incomplete penetrance, and environmental phenocopies reduce the power of this approach. GWAS and GWLS are complementary and not concurrent.149 Accordingly, even in the same population, loci identified through GWLS might not overlap with those identified by GWAS. In the field of longevity, one of the first studies based on linkage analysis was performed in 2001 by Puca et al149a in 137 long-lived sibships, and they genotyped 400 microsatellite markers detecting linkage at 4q25 (LOD=3.65), but subsequent studies failed to replicate this signal.150–152 The first GWLS on a large cohort of European ancestry was performed in the GEHA Study on 2118 European 90+ sibships (couples of brothers and sisters both >90 years of age).8 The study identified 4 regions linked to human longevity, and one of them was the APOE region. The authors suggested that the absence of APOE-e4 haplotype in nonagenarians is expected while the APOE-e2 haplotype had to be considered a longevity gene as the linkage they observed was mainly explained by its high frequency.
Parental Lifespan: a Different Approach in GWAS Studies
The availability of GWAS data in large population cohorts and the parents age of death data opened to the possibility to study the genetics of parental life expectancy phenotype.153–156 Such approach presents the following limitations: (1) these studies investigate the association between offspring genotype with parent phenotype (ie, parent’s lifespan), thus the genetic association is indirect and based on the heritability estimation of the life expectancy trait; (2) these studies address the life expectancy phenotype and draw conclusion on longevity that is a different phenotype as discussed in a previous paragraph.157 Moreover, this approach underestimates the relationship between genes and the environmental changes that occur generations after generations, so that genes that impact on longevity may be different in different birth cohort (Figure 8). Industrial progress, improvements in living conditions, changes in nutrition, and other transformations in the human environment (including the emerging obesogenic environment) may have different survival effects on individuals with similar or different genotypes, as previously hypothesized137 and recently supported.137,158 This scenario is further complicated by the fact that for certain genes, G×E interactions change with age.159
APOE and CHRNA3/5 (nicotinic acetylcholine receptor) are the genes identified and replicated in the highest number of study on parental lifespan. APOE will be thoroughly discussed in one of the following paragraph while CHRNA3/5 was never identified in other studies on human longevity, and it has been associated with traits linked to smoking behavior (nicotine dependence, lung cancer, chronic obstructive pulmonary disease).156 Moreover, some authors suggested that CHRNA3/5 exhibits sexually dimorphic effects on parental mortality, driven mostly by younger male deaths.160
Longevity Genes in CVD
In this section, a subset of the most studied and relevant genes identified in the study of the genetics of longevity are described focusing on the link with CVD. Then, a study using the novel approach of Mendelian randomization (MR) is reported to investigate the causal effect between lipid profile, genetic variation, and longevity. Common genes identified in longevity studies and in studies on CVD are reported in Figure 9
Apolipoproteins and APOE/TOMM40
Apolipoproteins are soluble molecules present in plasma, and many genetic studies showed their role in longevity and in age-related diseases.25 The idea that they are druggable explains the number of studies devoted to the different apolipoproteins. Some associations with longevity were described for APOB-VNTR in Italian centenarians,161,162 for APOC1 in the Bama populations,163 and for APOC3 (−641C allele) in Ashkenazi Jewish.164 These genetic variants likely influence lipoprotein profile, cardiovascular health, and insulin sensitivity. Among all these genes, the most replicated is the region of APOE/TOMM40,8,39,122–127 that is considered the master longevity gene.165 The data show a negative association with APOE-e4 allele, suggesting that it has to be considered frailty rather than a true longevity allele.166,167 On the contrary, an enrichment of APOE-e2 allele was observed in long-lived individuals,165,168,169 and this suggests a protective role of this allele supported by a positive association between APOE-e2 and healthy cognitive status.169,170
APOE is involved in lipid metabolism, by regulating production, conversion, and clearance of lipoproteins, and is expressed in different tissues (adipose tissue, nervous system, and liver) and cells (macrophages).171 APOE-e4 is associated with AD and CVD.123,124,126,172–177 TOMM40 encodes for a protein that forms a channel in the outer mitochondrial membrane and is crucial for mitochondrial health. Under this scenario, 3 main questions emerge:
(1) why APOE-e4 has not been replaced by natural selection with the health-beneficial APOE-e2 allele?
APOE-e4 is the ancestral allele typical of modern humans while APOE-e2 and APOE-e3 evolved ≈200 000 to 300 000 years ago.178 An hypothesis proposed by Finch and Stanford179,180 suggests that APOE-e4 confers resistance to diseases associated with meat eating. APOE-e4 is also associated with improved cognitive function in Amazonian forager-horticulturalists with a high parasite burden,181 thus conferring an advantage in a specific environment. This and other results led to the hypothesis that the high frequency of APOE-e4 was maintained because it confers protection in environments where infectious diseases are a relevant cause of mortality.182 APOE gene also influences fertility, and APOE-e4–positive women have significantly higher levels of mean luteal progesterone, suggesting increased fertility, that can contribute to explain the conservation among different populations of this deleterious allele.183
(2) Why the association of APOE-e4 with longevity seems to vary across different populations? APOE-e4 show a cline in Europe (from ≈20% in North Europe to ≈6/7% in Southern Europe) and a high level of frequency diversity among populations.184 Today, the frequency of the APOE-e4 allele in human populations follows a sort of U-shaped latitudinal trajectory, where the highest frequencies (up to ≈40%–50% of the population) are observed at equatorial and high latitudes and lower ones at middle latitudes.185 This evolutionary-determined allele frequency distribution makes difficult to assess longevity association in population where APOE-e4 is present at low frequency (≈6%–7%), such as in Southern Italy as shown in the study of Deelen et al.124 Moreover, APOE-e4 is a pleiotropic gene, showing many interactions with the environment and in particular with diet, thus creating population-specific and context-dependent G×E interactions (for a detailed review, see ref 186).
Cardiovascular Risk
APOE has been associated with cardiovascular health in a recent meta-analysis. The study identified an SNP (rs445925) in the APOC1/APOE region that was associated with clinical ideal cardiovascular health (defined according to American Heart Association 2020 goals187) at genome-wide level of significance (P<2.0×10–9).188 Moreover, APOE (rs7412) has been identified in a study on the genetic architecture of coronary artery disease.189 A possible mechanisms for the link was proposed by Li et al,189a who suggested that ApoE regulates the expression of anti-inflammatory microRNA (miR-146a) in macrophages and intravascular delivery of miR-146a mimetics can inhibit atherogenesis in mouse models.190 The data about APOE are summarized in Table 2.
| Macro and microevolutionary background |
| APOE-e4 is the ancestral allele typical of modern humans |
| APOE-e2 and APOE-e3 evolved ≈200 000–300 000 y ago |
| APOE-e4 is associated with improved cognitive function in Amazonian forager-horticulturalists with a high parasite burden |
| High frequency of APOE-e4 was maintained in the population because it confers a beneficial effect during infections and in the environment where pathogens were more prevalent and the first cause of mortality (data on a rural Ghanaian population) |
| Today geographic distribution |
| APOE-e4 showed a cline in Europe (from 20% in North Europe to 6/7% in Southern Europe) |
| e4 allele in human populations follows a sort of U-shaped latitudinal trajectory, high frequencies (up to ≈40%–50% of the population) in equatorial and high latitudes and low frequencies in middle latitudes |
| Involvement in physiological traits |
| Lipid metabolism, regulating production, conversion |
| APOE-e4 is associated with high cholesterol |
| Clearance of lipoproteins |
| It is expressed in different tissues, macrophage, adipose tissue, nervous system, and liver |
| APOE-e4 allele had significantly higher levels of mean luteal progesterone than women with genotypes without APOE-e4, which indicates higher potential fertility |
| In mice, APOE-e4 carriers are more vulnerable to a dietary deficiency in omega-3 fatty acids and cognitive decline |
| High levels of physical activity reduce disease risks in APOE-e4 carriers |
| Spatial memory of transgenic mice carrying human forms of these proteins and find that it is impaired in mice with apoE4 but not those with apoE3 |
| Involvement in pathological traits |
| APOE-e4 is associated with Alzheimer disease (AD) and cardiovascular diseases |
| People with APOE-e3 o APOE-e2 have later AD onset than APOE-e4 |
| Longevity |
| APOE-e4 is negatively associated with longevity in many studies and meta-analysis on human longevity |
| These APOE gene variants are part of a linkage disequilibrium (LD) block that includes APOE, TOMM40, and APOC1 genes |
APOE indicates apolipoprotein E.
IGF-1 and FOXO3
Genetic variance in the IGF-1 pathway has been associated with longevity in several studies. IGF-1 and related genes is one of the most conserved genetic determinants of longevity across a variety of model organisms.191 IGF-1 pathway is involved in a broad spectra of functions related to metabolic regulation and energy management.192 There is evidence that in centenarians IGF-1 circulating levels are significantly lower,193 and a positive association between higher circulating IGF-1 levels and all-cause mortality, but not CVD events, has been reported.194 In 2003, Bonafè et al195 in a study on Italian centenarians reported an association between an SNP in the IGF-1R gene and low plasma levels of IGF-1, and the allele responsible of the low IGF-1 levels was found over-represented in centenarians.196 Suh et al197 in 2008 sequenced the IGF-1R gene in a cohort of Ashkenazy Jewish centenarians, and they found that centenarians were enriched in genetic variants capable to reduce the efficiency of IGF-1R, generating a sort of IGF-1 resistance. According to a 2011 study on an Italian cohort of subjects >85 years of age, the IGF-1R rs2229765 polymorphism favors longevity in male carriers of the homozygous A allele.196
FOXO3 is a transcription factor that exerts a downregulation activity on IGF-1 pathway, and its genetic variability is one of the most consistent longevity heritable factors in humans. Sequencing studies on this gene showed that the alleles linked to longevity reside in the 3′ region.198–200
In a study by Flachsbart et al,201 the authors applied a combined candidate-gene sequencing—SNPs genotyping approach and identified variants linked with longevity in 3 North European populations, that is, France, Germany, and Denmark. In particular, 2 variants, rs4946935 and rs12206094, were identified, one of them (rs4946935) having been previously identified as longevity gene.198 A functional validation of these 2 noncoding variants was performed, and the results show that the longevity-associated allele promotes the expression of the gene. In a broad meta-analysis on 30 884 individuals of European ancestry,202 the authors studied the genetic association with circulating levels of IGF-1 and IGF-binding protein 3. This original approach allowed the authors to establish a significant association with the circulating levels of IGF-1 and the rs2153960 in FOXO3 gene. Several loci involved in growth hormone path also emerged, in particular showing significant associations for the IGF-1, IGFBP3, IGFALS, GHSRASXL2, and CESLSR2 genes, and many of the identified variants were also associated with elderly health traits and with longevity. Functional studies suggested the existence of an HSF1-FOXO3 axis in human cells that could be involved in stress response.203 FOXO3 became physically connected, through chromatin looping, with 46 other genes on chromosome 6, thus forming an aging hub.199
Cardiovascular Risk
IGF-1 is implicated in CVD, and evidences suggest that IGF-1 has a vascular protective role for the treatment of chronic heart failure, and its deficiency may cause CVD.204 Moreover, IGF-1 has a pleiotropic action in hearts, and cardiac activation of IGF-1 receptor protects from the detrimental effect of a high-fat diet and myocardial infarction.205 As far as we know, no evidence has been reported for FOXO3 and cardiovascular implications.
Interleukin 6
Interleukin 6 (IL6), known as the cytokine of gerontologists,206 is a multifunctional cytokine that presumably plays its major role as a mediator of the acute phase of inflammatory responses and becomes detectable in advanced age and in conditions characterized by the presence of chronic inflammation. Elevated serum IL6 levels are associated with significantly increased risk of morbidity and mortality in the elderly.207 The previously mentioned GWAS study on Chinese centenarians95 showed that the minor allele of rs2069837 located in IL6 (chromosome 7p15.3) is the variant (intronic) most significantly (negatively) associated with longevity, being significantly less frequent among centenarians than middle-age individuals in Han Chinese (odds ratio=0.61; P=1.80×10−9), and alone explaining ≈1% of the variance of the variance in surviving to ages 100+ from middle age. A study on 323 Italian centenarians identified a significant association between rs1800795-IL6 (−174 C/G at promoter locus) and longevity and showed that GG homozygotes decreased in centenarians males, suggesting a sex-specific detrimental effect.208,209 Other numerous studies showed the implication of SNPs located in IL6 in longevity.209–213
The case of IL6 is peculiar because it constitutes a link between genetic susceptibility to inflammation, age-related diseases, and longevity. However, it is to note that the above-mentioned SNPs are not closely linked to IL6 plasma levels as the SNPs that is better associated with circulating IL6 levels are located in IL6 receptor (IL6R rs7529229, rs8192284, rs2228145), as demonstrated by a MR analysis in a study that showed that IL6R signaling seems to have a causal role in the development of coronary heart disease.214,215
Cardiovascular Risk
Long-term increase of IL6 levels is associated with a high risk of coronary heart disease.216 A recent study identified a genetic variant (rs7529229) associated with increased IL6 levels circulating in the blood but decreased IL6R signaling. The authors investigated the effect of the variant comparing it with the effect of anti-IL6R drug (tocilizumab). The results showed that the variant was associated with the same biological changes as the inhibiting drug. The authors suggested that genetic studies in populations could be used more widely to help to validate and prioritize novel drug targets, and IL6R blockade could provide a novel therapeutic approach for coronary heart disease.215 Also the G allele of −174 C/G polymorphism was found to be associated with increased risk of CVD and other age-related diseases, such as Alzheimer dementia and T2D.217–219
Sirtuins
The link between sirtuins and longevity can be attributed to discoveries in animal models (in particular in Caenorhabditiselegans and in Drosophila), and the sirtuin story dominated the agenda of several meetings on aging. However, in 2011, some researchers claimed that there was a technical problem: the study published in Nature demonstrated the lack of effects of sirtuin overexpression on aging after proper standardization for genetic background in both Celegans and Drosophila.220
In humans, 7 sirtuin genes (SIRT 1–7) exist, but only few of them are involved in longevity, and studies on the effects of genetic variants in human sirtuins on lifespan are limited and can be summarized as follows: (1) SIRT1 did not showed any genetic association with human longevity,221,222 but it influenced survival in patients with T2D in interaction with dietary niacin and smoking223; (2) SIRT3 has been associated with adaptations to nutrient stresses, such as fasting and calorie restriction. A study by Rose et al224 found that in males the TT genotype (G477T) increased survival (P=0.0272) while the GT genotype decreases (P=0.0391) survival in the elderly. The second study investigated the frequency of VNTR polymorphism (72-bp repeat core) in intron 5 in 945 individuals (20–106 years) and found that the allele lacking enhancer activity was absent in males >90 years.225 The third study is a meta-analysis of SIRT3 SNPs among Italian, French, and German centenarians, and no robust positive association was found; (3) SIRT6 is a chromatin-associated protein promoting heterochromatin silencing at repetitive DNA sequences, repressing the activity of L1 retrotransposons (that become more active in somatic tissue during aging), thus increasing genome stability.226 A study performed on the Iowa population found association of the SIRT6 SNP rs107251 minor allele homozygotes (TT) with a decreased lifespan of 5.5 and 5.9 years,227 confirming a previous study by Soerensen et al228 about Danes longevity. Negative results emerged in a study on longevity in Han Chinese centenarians.229 Donlon et al230 tested 459 SNPs in 58 candidate genes, of which 47 genes were selected as the most differentially expressed in mice under calorie restriction, in a cohort of 440 subjects aged 95 years and over in comparison with controls. Among these genes, SIRT7 and SIRT5 showed for the first time a positive association with longevity.
Cardiovascular Risk
SIRT1 has a protective effect against CVD and through its deacetylase activity regulate exerts many functions, promotion of angiogenesis being one of them.231 Different polymorphisms in SIRT1 gene have been associated with susceptibility to CVD,232 blood pressure in Japanese,233 and carotid plaque.234 Moreover, a study in the Iranian population found that the SIRT1 rs3758391-CC genotype was a risk factor for CVD and that carriers of this genotype were at 3- or 3.7-fold increased risk of CVD than subjects with the TT genotype, respectively,235 but further studies are needed to validate the role of SIRT1 genetic variations in cardiovascular impairment.
Somatic Mutations and Clonal Hematopoiesis
The article of Jaiswal et al236 demonstrated that there are recurrent somatic mutations in genes involved in hematologic malignancies. This condition is called clonal hematopoiesis or clonal hematopoiesis of indeterminate potential (CHIP). CHIP is a functional phenomenon in which a hematopoietic stem cell has acquired a survival and proliferative advantage. By whole-exome sequencing, a common, age-related expansion of hematopoietic clones carrying recurrent somatic mutations, most frequently loss-of-function alleles in the genes DNMT3A, TET2, and ASXL1, was found. This condition has been shown to be associated with increased CVD risk and reduced overall survival among people with expanded clones, with an effect much larger than that which can be explained by hematologic cancers alone. CHIP seems to play a crucial role in aging, and such clones rarely accumulate in people <40 years of age but are present in >10% of people >70 years of age. It is interesting to note that in centenarians the prevalence of CHIP is low (≈2.5% in semisupercentenarians).237 These results show that centenarians seem spared from the age-related exponential increase of CHIP, and this might have contributed in protecting from CVD.
MR in the Study of the Genetics of Longevity
MR is an analytical method applied to observational study capable to infer and examine causal effects when confounding factor may cause indirect associations. However, this approach is based on the assumption that the gene(s) under study is (are) not pleiotropic, and thus it is likely not suitable for many genes, like APOE.238 A study applied MR to disentangle in a Han Chinese population whether the alleles (such as rs662799-C) associated with triglyceride levels are also associated with longevity because triglyceride’s levels are in turn associated with longevity.239 MR analysis revealed that genetically predicted triglycerides were not associated with probability of longevity while serum triglycerides were observationally strongly associated with longevity.239 A second study240 analyzed 3 Dutch cohorts of older subjects that did not show (or show inverse) correlation between cholesterol levels and mortality. In this study, MR was applied on a genetic risk score, including 51 SNPs associated with cholesterol levels (low-density lipoprotein). Interestingly, the authors showed that individuals with a genetic predisposition for longevity are characterized by a lower low-density lipoprotein genetic risk score.
Host Genome and Its Interactions
Longevity results from the interactions between genomes (mtDNA, microbiome, and nuclear DNA) residing in the human organism itself (referred as holobiont/metaorganism), environment, and somatic mutations occurring during aging in all the genomes (Figure 10). Thus, the phenotype emerges from multiple levels of selection that operate simultaneously on the metaorganisms,241 and the study of such complex interactions in longevity is still in its infancy. Somatic mutations occurring in the different genomes are inherited from cell to cell and their frequency change during development, following evolutionary dynamics similar to those applied to population genetics (migration, drift, selection, etc), so that the term somatic evolutionary genomics has been recently proposed for the study of these processes during lifetime.242 These mutations may cause variable genetic mosaicisms that could hide important implications for the study of all age-related modification (pathological and physiological).242,243
mtDNA in Longevity and Its Nuclear Genome Coevolution
Mitochondria are the result of an ancient symbiotic event where a bacterium specialized in energy transformation has been integrated in a eukaryotic cell. As remnants of such event, mitochondria have their own circular genome. In each mitochondria, several copies of a small (16 569 base pairs) genome (mtDNA) is present. mtDNA contains genes involved in mitochondria functions, but the vast majority of the genes necessary for mitochondria operations reside in autosomal chromosomes. This dislocation of information generates epistatic effects that are responsible for different mitochondrial and energy phenotypes likely critical for aging processes.244
mtDNA has been correlated to longevity in different studies. The first studies investigated haplogroups frequency in broad cohorts of European long-living people, and an association between haplogroup J and longevity in Europe, Near East, Northern Italy, Ireland, and Finland was found.245–247 Functional studies showed that haplogroup J produces less ATP and ROS (reactive oxygen species).248 In Japanese centenarians, the haplogroup D (characterized by variations in the complex I, such as haplogroup J) is at high frequency.249,250 However, these associations seem to be highly population-specific, considering the absence of association described in many studies.251–253 However, the studies on haplogroups are only part of the possible role of mtDNA in longevity. For this reason, mtDNA studies started to focus not only on the analysis of haplogroups but also on the whole mtDNA sequence. The above-mentioned GEHA study analyzed 2200 nonagenarians belonging to 90+ sibpairs and the same number of younger controls, and of these samples, 650 ultranonagenarians (and an equal number of controls) were completely sequenced. The study revealed that mutations in subunits of the oxidative phosphorylation complex I had a beneficial effect on longevity while the simultaneous presence of mutations in complexes I and III (which also occurs in J subhaplogroup involved in LHON [Leber’s hereditary optic neuropathy]) and in complexes I and V seemed to be detrimental. The authors also showed differences among the different populations and suggested that a larger sample size is needed to identify more significant associations.254
A study identified the rs2075650, located in TOMM40, to be associated with longevity.123,255 Such variant likely affects the relative configuration of the partners of TOMM complex, resulting in a more efficient clearance of damaged mitochondria further supporting a role for mitonuclear interactions in healthy aging.
Gut Microbiota in Longevity and Its Nuclear Genome Coevolution
In the past decades, the numbers of study on gut microbiota (GM) increased exponentially, and on the whole, the results showed that its composition plays an important role in age-related diseases, such as CVD.256
Microbiotas Within an Evolutionary Perspective
GM is a sophisticated ecosystem sensible to environmental stimuli, host genetics, and physiological state (eg, diet, age, among others), in which the different species are in a flux of continuous, ecological reshaping, and it is necessary to apply models derived from theoretical ecology to understand and predict its evolution.257,258 In a perspective by Foster et al,259 the authors proposed that the far-reaching holobiont concept (Figure 10) might be misleading if applied simplistically to the host-microbiota coevolution analysis because it bias the evolutionary thinking toward a conceptualization where host and microbiotas would act in view of common interests. On the contrary, host and each microbial species are independent evolutionary objects that react and evolve according to Darwinian dynamics, thus undergoing potentially divergent selective pressures. This perspective introduces evolutionary tension in the holobiont system that could explain the capability of the microbiota ecosystem to respond to a variety of stimuli. A major issue in the study of host-microbiotas coevolution in humans are the profound changes introduced in the past century with the sanitation of the anthropological environment. According to the hygiene hypothesis, the new clean and rather sterilized environment, together with the introduction of antibiotics, caesarean delivery, and changes in nutritional habits, resulted in a drastic reduction of microbiota’s diversity.260 Such changes are too recent to allow host genome adaptation. This mismatch likely represents a common source of dysbiosis, with consequences for aging, age-related diseases, and longevity. Among the microbiotas of the human body, GM plays a pivotal role in host metabolism homeostasis and immune system maturation, functioning, renewal and stability. Altered GM composition has been found in several pathological conditions, such as obesity, T2D, and autism, among others.261 In a milestone study from Xie et al,262 the authors analyzed the GM composition of 250 monozygotic and dizygotic twin pairs and showed that host genetic background actively influences GM ecosystem. The authors also reported that with age the GM concordance tend to decrease within couples. Overall, it emerged that a significant portion of the phylotypes associated with diseases are not under genetic control but are environmentally determined, indicating that clinical interventions to restore the physiological GM ecology are likely not impaired by host genetic background.
GM and Aging
GM biodiversity decrease during aging together with a concomitant increase of pathobionts that usually are present at low concentration in healthy individuals and 263 that exert proinflammatory activity. However, population-specific dynamics shape this process, and the study of Biagi et al263 demonstrated that the Bacteroidetes proportion is conserved in elderly Italians, whereas strikingly increase in Irish ones.264 Biagi et al265 provided for the first time the phylogenetic microbiota analysis of semisupercentenarians, in comparison to adults, elderly, and centenarians, thus reconstructing the longest available human microbiota trajectory along aging. In extreme longevity, GM is characterized by the loss of genes for short chain fatty acid production (considered anti-inflammatory GM metabolites). A continuous remodeling of GM lifelong emerged, and GM of centenarians and semisupercentenarians showed changes that accommodate opportunistic and allochthonous bacteria, as well as an enrichment and higher prevalence of health-associated groups (eg, Akkermansia, Bifidobacterium, and Christensenellaceae).265 It is interesting to note that Christensenellaceae are among the most heritable microbes.266 As reviewed by Goodrich et al,266 the heritability of GMs revealed a subset of microbes whose abundances are partly genetically determined by the host, but the identification of the responsible human genetic variants has proven challenging. However, the characterization of GM influencing allele is of utmost interest because GM is at the intersection between metabolic, immunologic, and neuronal processes as recently described.267,268 It has been demonstrated that the different classes of dietary lipids (experiments in mice fed lard and mice fed fish oil) affect the GM composition that, in turn, contributes to white adipose tissue inflammation, insulin sensitivity, and Toll-like receptor activation, independently from adiposity.269 The interaction between host genome and GM during ageing is supported by a study on C elegans daf-2 mutants that live longer also as an effect of bacterial colonization.270
Perspectives
Genome Editing in Aging Research
A major contribution to evaluate the effect of specific putative longevity genetic variants is provided by the recent development of genome editing techniques based on the molecular machineries constituted by zinc finger nuclease, transcription activator-like effector nuclease, and clustered regulatory interspaced short palindromic repeat/Cas9–based RNA-guided DNA endonuclease. Such techniques allow printing with high precision-specific mutations in animal and in vitro preclinical models to test the effect of genetic variants in iso-genic cells or organisms. The in-depth description of such protocols and techniques is out of the scope of the present article, and a broad and detailed description of both genome and epigenome editing opportunities can be found in Gaj et al,271 Keung et al,272 and Lau and Suh.273 Here, we like to stress some aspects: (1) such techniques will allow to assess the biological effect of rare mutations and of epistatic effects of combined genetic variants; (2) even more interesting could be the combined analysis of genetic and epigenetic genome editing. A combined approach of these 2 techniques could provide innovative information on (1) allele timing, (2) neutralizing or modifying effects of epigenetic modification, and (3) organ- and tissue-specific epigenetic aging rate effects on the phenotypic expression of candidate mutations.
Inclusion of Environmental, Social, and Cultural Variables in the Study of the Genetics of Longevity
Culture evolves more rapidly than genes, creating novel environments that expose genes to new selective pressures. Counteractive niche construction may help also to explain the difficulties of genetics studies to identify large portions of the genetic basis of common, complex traits in humans. Population genetics models and anthropologists demonstrated that cultural processes profoundly shape human evolution and that the genetic background records all the major cultural changes occurred during human evolution (ie, milk usage, domestication of plants, agriculture, changing climates, social intelligence, language, cooking etc). Cultural practices modified the environmental conditions that in turn led to changes in allele frequencies, as demonstrated by recent acceleration observed in mutation accumulation of certain regions, and some traits have an high level of correlation with cultural history rather than ecology. Thus, an ecological approach of the study of the genetics of human longevity needs to incorporate an integrated view of possible cultural selective pressure.274 This process is challenging because 2 main issues exist in the field: (1) there are still analytic challenges in scanning for selection on genetic data; (2) lack of the interdisciplinary expertise that makes possible to integrate cultural data in the genetic analysis. Especially, in observational study, it is almost impossible to collect accurate data on the past. The use of new omics such as epigenetics may represent a molecular mechanism to recode important information on environmental pressure of external and internal environments.
Use of Biological Age Phenotype in the Genetics of Longevity
Recently, biomarkers of biological versus chronological age have been identified,275–277 and an increasing number of investigations showed that such tools are effectively capable to catch the aging rate at the individual level, which in turn correlates with relevant health parameters in the elderly. These studies show that people who are or will be affected in coming years by age-related diseases tend to present a DNA methylation signature of accelerated aging, as well as in human model of accelerated aging, such as Down syndrome people.31,278 Conversely, when these markers were applied to centenarians and their offspring, a signature of decelerated aging emerged.279,280 Besides DNA methylation, among the most promising markers of biological age, we like to mention N-glycans profiling281–284 as well as markers derived from biochemical and anthropometric measurements, such as hand grip, chair stand, and lung capacity.285 Particularly promising for the assessment of biological age in individuals is the combination the above-mentioned markers, possibly with the integration of classical biochemical and hematological markers (high-density lipoprotein, low-density lipoprotein, TG, glucose, insulin, albumin, urea, number of leukocytes, presence of anemia, among others) and longitudinal medical data (drugs assumption, hospitalizations). Of particular interest is the idea to generate a proinflammatory biological age marker capable to assess the inflammaging status.286
Thus, biological age represents an innovative trait that should be integrated in the study of the genetics of longevity and of age-related diseases. In particular, biological age should complement chronological age data in the control cohorts, in which, with age, increase the health status heterogeneity. Biological age could integrate the aging rate phenotype that is known to be accelerated in age-related diseases and decelerated in longevity.
Interventions
Many studies on pharmacological interventions have been performed in animal models to extend and improve lifespan, but the possible role exerted by the genetic background has been relatively neglected. Until now, the only intervention that is spreading also in humans is calorie restriction. Data from animal models (invertebrates and rodents) demonstrated that both chronic dietary restriction and genetic mutations in nutrient and growth signaling pathways can extend longevity by 30% to 50% and reduce many age-related diseases, including CVD.287 A meeting held in 2013288 contributed by the major experts in the field supported the idea that a strategy to translate these results to humans is to intervene with drugs targeting nutrient-response pathways and mimicking the effects of dietary restriction, that are more practical, realistic, and safe than drastic dietary interventions. The most promising strategies taken into account were the following: pharmacological inhibition of the growth hormone/IGF-1 axis, protein restriction and Fasting Mimicking Diets, pharmacological inhibition of the TOR-S6K pathway, pharmacological regulation of certain sirtuin proteins and the use of spermidine and other epigenetic modulators, pharmacological inhibition of inflammation and chronic metformin use. More details are reviewed by Longo et al.288 We surmise that it is urgent to set up studies capable of assessing the role that the 3 genetics (nuclear, mitochondrial, and GM) can play regarding responsiveness to these and other intervention strategies at the individual level.
Conclusions
An innovative road map in the field of the genetics of longevity has been described in this review through the lens of ecological and evolutionary dynamics to better understand the individual aging trajectories assumed as highly context dependent. First of all, a reappraisal of the definition of longevity, based on stringent demographic criteria and potentially applicable to all populations, and of the definition of the control group was pursued. The genetics of human longevity has been presented on the basis of strong evidence that both human body and environment are not static but underwent in the past and still undergo a complex reciprocal remodeling.2 Such dynamics has a strong impact on aging rate and quality and length of life (longevity). The available literature on the genetics of human longevity has been critically scrutinized assuming that all factors contributing to the individual’s ecological space—such as sex, individual (immuno) biography, family, population ancestry, social structure, economic status, and education—need to be taken into account as major variables for disentangling the complex G×E interactions responsible for aging and longevity in humans. Within this framework, the strength and limitations of the most powerful tools used in the genetics of longevity, such as GWAS and whole-genome sequencing, have been discussed, paying particular attention to genes and regions emerged from such studies, such as APOE, FOXO3, IL6, IGF-1, chromosome 9p21, and their role in CVD. Moreover, available data on the interactions between the 3 genetics of human body (nuclear, mitochondrial, and GM) have been reviewed, taking into account the recent data on the fundamental role of the mitochondria and the emerging pervasive role of microbiotas and particularly of GM in the aging process. We surmise that such a global approach should also include somatic mutations that occur lifelong in the above-mentioned genomes, thus creating always new interactions. In conclusion, we support that the integration of the above-mentioned data is crucial and that evolutionary and ecological considerations should guide this process for a better understanding of the genetics of longevity, particularly in humans. This approach should also contribute to a better integration of the data collected in different research fields (such as epidemiology, genetics, anthropology sociology, environmental studies, among others) as supported by recent and relevant papers in the field of evolutionary medicine published in Lancet in 2017.289–291
Footnote
Nonstandard Abbreviations and Acronyms
- AD
- Alzheimer disease
- APOE
- apolipoprotein E
- CHIP
- clonal hematopoiesis of indeterminate potential
- CVD
- cardiovascular diseases
- FOXO3
- Forkhead box O3
- G×E
- gene-environment interactions
- GEHA
- Genetics of Healthy Ageing
- GM
- gut microbiota
- GWAS
- genome-wide association study
- GWLS
- genome-wide linkage study
- IGF
- insulin-like growth factor
- IL6
- interleukin 6
- MR
- Mendelian randomization
- mtDNA
- mitochondrial DNA
- SNPs
- single-nucleotide polymorphisms
- T2D
- type 2 diabetes
Supplemental Material
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Acknowledgment
We thank also Dr Rita Ostan for her contribution in Figure 1 of this article.
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Published online: 13 September 2018
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This study was supported by the European Union’s Seventh Framework Programme to C. Franceschi (grant number 602757, HUMAN); by the European Union’s H2020 Project to C. Franceschi and P. Garagnani (grant number 634821, PROPAG-AGING); by JPco-fuND to C. Franceschi (ADAGE) and by a grant of the Ministry of Education and Science of the Russian Federation Agreement No. 074-02-2018-330 DPM-AGEING Digitalized and Personalized Medicine of Healthy Aging at Lobachevsky State University of Nizhny Novgorod to C. Franceschi.
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