Analyses were performed using the SPSS statisti- cal package (V 13.0). Linear regression analysis was then applied to genotype frequency data. Statistical significance was set at P B 0.05.

determined by applying a genetic-demographic approach that allows the estimation of the survival function for candidate alleles and genotypes in cross- sectional samples of unrelated individuals (Yashin et al. 1999). A detailed description of the analytical procedure is reported elsewhere (Dato et al. 2007). Briefly, in order to take into account the changes in mortality experienced by all the cohorts considered in the study, a synthetic survival function of the general population is constructed. Then, the marginal survival functions S (x) and S (x) of carriers and non carriers a b of a certain genotype is determined according to the

Table 1 Distribution of the people who participated to the study, categorized according to gender and age

Age Age Age class 1 class 2 class 3 Men (years) \66 66–88 [88 Women (years) \73 73–91 [91 Age (mean ± SD) 53.0 ± 13.0 78.4 ± 6.1 99.6 ± 3.1 (years) N 463 300 326 (M, W) (191, 272) (147, 153) (82, 244)

Table 2 Genotype frequencies of KL-VS variant<colspec colnum="2" colname="c2" align="left/>

Whole subjects (1089) Age class 1 (n = 463) Age class 2 (n = 300) Age class 3 (n = 326) Genotypes wt/wt n (%) 348 (75.2%) 203 (67.7%) 236 (72.4%) KL-VS/wt n (%) 103 (22.2%) 94 (31.3%) 80 (24.4%) KL-VS/KL-VS n (%) 12 (2.6%) 3 (1.0%) 10 (3.1%) P value 0.008 0.428 Odds ratio (CI) 1.564 (1.126–2.174) 1.145 (0.819–1.601) Men (420) Age class 1 (n = 191) Age class 2 (n = 147) Age class 3 (n = 82) Genotypes wt/wt n (%) 142 (74.3%) 99 (67.3%) 59 (72.0%) KL-VS/wt n (%) 46 (24.1%) 46 (31.3%) 20 (24.4%) KL-VS/KL-VS n (%) 3 (1.6%) 2 (1.4%) 3 (3.7%) P value 0.336 0.553 Odds ratio (CI) 1.434 (0.885–2.324) 1.046 (0.571–1.919) Women (669) Age class 1 (n = 272) Age class 2 (n = 153) Age class 3 (n = 244) Genotypes wt/wt n (%) 206 (75.7%) 104 (68.0%) 177 (72.5%) KL-VS/wt n (%) 57 (21.0%) 48 (31.4%) 60 (24.6%) KL-VS/KL-VS n (%) 9 (3.3%) 1 (0.7%) 7 (2.9%) P value 0.018 0.605 Odds ratio (CI) 1.668 (1.063–2.617) 1.225 (0.809–1.854)

P = 0.008, Odds ratio (CI) 1.564 (1.126–2.174, P = 0.008) and P = 0.018, Odds ratio (CI) 1.668 (1.063–2.617, P = 0.026)

lived individuals (age class 3) and young controls (age class 1). Moreover, we observed that, when ranking the subjects according to the gender, the association was particularly evident in women, where it reached statistical significance, but not in men (see Table 2).

that the algorithm for the construction of the curves leads to an initial slope of the curve of heterozygous subjects, which is not due to an excessive mortality at very young age, but to the great excess of wt homozygous subjects before 70 years of age. To verify the statistical significance of the pattern shown in Fig. 1 we carried out pairwise heterogeneity tests between age classes as defined on the basis of the crosses between survival functions. Only the cross at 70 years of age reflects a statistically significant change of the population survival in the heterozygous subgroup with respect to the wt homozygous subgroup (P = 0.023). When stratified by gender (Fig. 1, inset A and B), the same trend was noted, being more evident for women (inset B) than for men (inset A).

The genetic-demographic analysis was carried out on the whole sample categorized according to the KL-VS polymorphism. Figure 1 shows the maximum likelihood estimates of the survival functions of heterozygous subjects (blue curve) and of wt homozy- gous subjects (red curve), compared to the general population (green curve). We noted that the survival function of heterozygous subjects crosses twice the survival function of the wt homozygous subgroup, roughly around the ages of 70 and 90 years. This trend suggests that the heterozygous genotype has a different effect on survival at different ages. In particular it indicates that heterozygous subjects have higher chance to survive beyond 70 years of age than the general population; on the other hand, among old subjects the chance of heterozygous subjects to survive beyond 90 years seems to be lower than the rest of the

Discussion

The studies regarding the role of Klotho gene variants in human longevity performed by the groups of Dietz

Survival function S(x) 0.9 +0.8 |07+0.4 |0.3}0.2 +04+  15 20 25 30 35 40 45 50 55 60 65age (years) a {eS n70 75 80 85 90 95 100 105 110

and Puca arrived to contrasting results. The group of Dietz basically reported a survival advantage for the KL-VS/wt heterozygous genotype in elderly people, while the group of Puca reported no difference between young and long-living subjects. In this study we analysed the distribution of Klotho KL-VS genotypes in a large group of Italian people of different age, from young to centenarians. We predicted in a previous work that changes in cohort mortality may influence the results of a genetic analysis (Yashin et al. 1999), and consequently we subdivided the study group into three age classes on the basis of demographic data, as indicated by Passarino et al. (2006), that considered gender specific life expectancies. This approach allowed us to compare genotype frequency without introducing arbitrary age classes, as in previous studies on TNF-alpha polymorphisms and human longevity (Cardelli et al. 2008). From previous studies it appears that many genotypes are associated with human longevity in a gender-specific fashion, leading to the hypothesis that men and women follow different strategies to attain longevity (Bonafe` et al. 2001; De Luca et al. 2001; Barbieri et al. 2004; Cederholm et al. 2007). In this case we did not observe any significant difference between the two genders, and the frequency distribution of the genotypes was remarkably similar in