selaM dna selameF gnomA deidutS sessalC egA ni 2252401sr 35PT fo noitubirtsiD ycneuqerF .1 elbaT )2252401sr( grA27orP 35PT )% ,n( elellA)% ,n( epytoneG 2 nosirapmoC 1 nosirapmoC )2 ssalC egA sv 3 ssalC egA()1 ssalC egA sv 2 ssalC egA(EWH*R*P*R/*R*P/*R*P/*PssalC egA nemoW evitiddaevitiddA092.0)%0.96( 162)%0.13( 711)%0.64( 78)%0.64( 78)%0.8( 51)981 = n( 1 600. = p ;475.0 Ro nommoC071. = p ;518.0 Ro nommoC )*R/*R sv *P/*R + *P/*P( tnanimoD)*R/*R sv *P/*R + *P/*P( tnanimoD803.0)%0.37( 904)%0.72( 151)%1.25( 641)%8.14( 711)%1.6( 71)082 = n( 2 600. = p ;)038.0–713.0( 315.0 Ro491.0 = p ;)331.1–145.0( 387.0 Ro )*R/*R + *P/*R sv *P/*P( evisseceR)*R/*R + *P/*R sv *P/*P( evisseceR669.0)%5.28( 561)%5.71( 53)%0.86( 86)%0.92( 92)%0.3( 3)001 = n( 3 832. = p ;)966.1–731.0( 974.0 Ro134. = p ;)145.1–563.0( 057.0 Ro )*R sv *P( elellA)*R sv *P( elellA 800. = p ;)668.0–183.0( 575.0 Ro581. = p ;)790.1–816.0( 428.0 Ro252.0)%4.37( 538)%6.62( 303)%9.25( 103)%9.04( 332)%2.6( 53)965 = n( latoT neM evitiddaevitiddA624.0)%2.17( 502)%8.82( 38)%3.94( 17)%8.34( 36)%9.6( 01)441 = n( 1 545. = p ;809.0 Ro nommoC523. = p ;268.0 Ro nommoC )*R/*R sv *P/*R+*P/*P( tnanimoD)*R/*R sv *P/*R+*P/*P( tnanimoD 574. = p ;)643.1–825.0( 348.0 Ro782. = p ;)502.1–335.0( 108.0 Ro727.0)%3.47( 583)%7.52( 331)%8.45( 241)%0.93( 101)%2.6( 61)952 = n( 2 )*R/*R + *P/*R sv *P/*P( evisseceR)*R/*R + *P/*R sv *P/*P( evisseceR 000.1 = p ;)085.2–273.0( 089.0 Ro467. = p ;)999.1–093.0( 288.0 Ro197.0)%5.67( 351)%5.32( 74)%0.95( 95)%0.53( 53)%0.6( 6)001 = n( 3 )*R sv *P( elellA)*R sv *P( elellA 745. = p ;)303.1–706.0( 988.0 Ro433. = p ;)871.1–816.0( 358.0 Ro385.0)%9.37( 347)%1.62( 362)%1.45( 272)%6.93( 991)%3.6( 23)305 = n( latoT .stnapicitrap dlo raey-19> :3 ssalC egA dna ;dlo sraey-19 ot -37 :2 ssalC egA ;dlo sraey-37< :1 ssalC egA*—nemoW :seton .stnapicitrap dlo raey-88> :3 ssalC egA dna ;dlo sraey-88 ot -66 :2 ssalC egA ;dlo sraey-66< :1 ssalC egA*—neM

selaM dna selameF gnomA deidutS sessalC egA ni 2252401sr 35PT fo noitubirtsiD ycneuqerF .1 elbaT

  090.80706SH)sw 05040302o4      ao 20, 2 so os @ 7 (8 so 100110 40 20 30 40506070 BH Age (years) Age (years)

from http://biomedgerontology.oxfordjournals.org/ at Univ of Iowa-Law Library on August 8, 2015

Figure 1. Maximum likelihood estimates of the survival functions of P72* carriers (P*/P* plus P*/R*) participants (Sa(x); blue curve) and R*/R* homozygotes (Sb(x); red curve) aged between 18 and 106 years. The synthetic survival function of the Italian population (Ss(x); green curve), is reported for reference. (A) Women, (difference between survival curves, p = .115). (B) Men (difference between survival curves, p = .930).

meta-analysis also showed that female patients with liver cancer had a significantly lower frequency of R*/R* and a higher frequency of P* allele carriers than females without cancer (22). The same analysis yielded no significant result in males (22). Gender-specific differences were also found in colon cancer, where the P72* carriers compared with R*/R* were associated with an increased risk of proximal colon cancers in women though not with distal colon, whereas among men, the same genotypes were associated with an increased risk of distal colon cancers, but not proximal colon cancers (23). Moreover, it has been reported that breast cancer patients with P*/P* genotype have a poorer survival (24,25).

using a cross-sectional approach on a healthy population divided into three sex-specific age classes according to demographic information and accounting for the differ- ent survival rates of men and women. We also applied the GD approach to our data, thus providing a novel view of the effect of rs1042522 on human longevity. By monitor- ing the survival function of each genotype at different ages, the GD approach allows to identify the alleles/genotypes that are favorable or unfavorable to longevity without intro- ducing arbitrary age classes, taking into account also the cohort effects in mortality changes (15). Moreover, the GD approach allows to determine the age where a certain geno- type affects survival chance, also revealing sex-specific allelic effects and possibly pleiotropic effect of an allele during the individual life (15).

A very important element contributing to the gender- specific association detected here is the estrogen hormone. A strong relationship of estrogen signaling and estrogen receptor with p53 has been documented, where ERα and p53 physically interact, leading to inhibition of p53 func- tion in cell culture and xenograft models (26). Thus, it is conceivable that in women, the inhibition of p53 function can be stronger than in men, especially in TP53*P allele carriers, leading to a significant impairment of apoptotic mechanisms. This event may be associated to an increased risk of cancer, immune senescence, and autoimmunity (27). In this context, it is remarkable that women display an increased risk for autoimmune diseases compared with men (28).

Several suggestions can be made about the gender- specific association of TP53*P72 allele with life expec- tancy. First, we have to take into account the difference between the two polymorphic p53 variants: the P72 is weaker than the R72 variant in inducing apoptosis and sup- pressing cellular transformation but appears to be better at initiating senescence and cell cycle arrest (17,18).