Italy. The UCP1 gene is involved in thermogenesis, a process that dissipates energy as heat in response to cold stress and excess caloric intake. This process has potential for controlling body mass and cellular fat metabolism. The study aimed to investigate the impact of UCP1 variability on survival since regulation of metabolism is one of the key pathways in lifespan extension. The results showed that two polymorphisms (A-3826G and C-3740A) located in the upstream promoter region of UCP1 affected gene expression and were correlated with human longevity. The A-C haplotype favored survival in the elderly, and transfection experiments showed that it had higher luciferase activity than the G-A haplotype. Interestingly, the different UCP1 haplotypes responded differently to hormonal stimuli. The study suggests a correlation between UCP1 activity and human survival, indicating the intricacy of mechanisms involved in energy production, storage, and consumption as key factors for understanding human aging and longevity.Italy. The functional relevance of the two polymorphisms in the context of haplotypes was also explored.

2.2. Genetic and statistical analyses

The Hardy-Weinberg equilibrium (HWE) was tested using an approach based on 10,000 random allele permutations for each sample (Weir, 1996). An expectation maximization (EM) algorithm, implemented in the Arlequin software package (Excoffier et al., 2005), was used to obtain maximum likelihood estimation of multilocus haplotype frequencies for UCP1 genotypes. Standard deviations of the haplotype frequencies were estimated by a parametric bootstrap procedure (1000 replications). The D linkage disequilibrium coefficient was assessed using the Arlequin software package (Excoffier et al., 2005). Diplotypes were constructed based on the population genotypes in the UCP1 gene using a pseudo-Bayesian approach (ELB algorithm) implemented in the Arlequin software package (Excoffier et al., 2005).

In order to model haplotype/diplotype frequency as a function of age, we carried out a binomial logistic regression analysis, as previously reported by Tan and co-workers (Tan et al., 2001). For this purpose, the following model was used:

2. Materials and methods

In Eq. (1), g[(x)] represents a logit transformation of the ith frequency of carriers of haplotype h at age x, (x). Thus, the effect of haplotype h on survival is represented by the slope parameter i: if it is significantly different from 0, the frequency of carrier i1 of haplotype h goes up if N0 or down if b0.

2.1. Subjects and genotyping

A total of 910 unrelated individuals (age range: 40-109 years), consisting of 475 women and 435 men, participated in the study. The subjects were born and currently living in Calabria (southern Italy), and their parents and grandparents were native to the same area. They were collected during several recruitment campaigns (De Rango et al., 2010). All the subjects gave their informed consent to use their anonymous genetic and phenotypic data for genetic studies on aging and longevity.

For the diplotype frequencies, the previous model can be reformulated as:

The UCP1 A-3826G polymorphism was genotyped by genomic PCR and BclI-RFLP analysis as previously described (Oppert et al., 1994). To genotype the UCP1 C-3737A polymorphism, 35 μl of the amplified DNA as above was digested by the restriction enzyme DdeI (3 U) for 3 h at 37°C as recommended by the manufacturer. The fragments were separated by electrophoresis on a 2% agarose gel and stained with ethidium bromide.

Where the effect of diplotype h/h on survival is represented by the slope parameter i: if it is significantly different from 0, the frequency ij1 of carriers of diplotype h/h goes up if N0 or down if b0.

The binomial logistic regression model has been applied to estimate the effect of each of the three reconstructed haplotypes (A-C, G-A, and G-C) and relevant diplotypes on survival.

Table 2

Diplotype frequency distributions in the analyzed sample categorized according to defined age classes.

Diplotype Females Males Age class 1 Age class 2 Age class 3 Age class 1 Age class 2 Age class 3 N=200 N=83 N=113 N=104 N=128 N=87 (40-72 years) (73-91 years) (N>91 years) (40-65 years) (66-88 years) (N>88 years) A-C/A-C 108 (54.0%) 99 (63.1%) 69 (58.5%) 63 (54.3%) 119 (55.3%) 64 (61.5%) A-C/G-A 27 (13.5%) 17 (10.8%) 14 (11.9%) 21 (18.1%) 45 (20.9%) 9 (8.7%) A-C/G-C 53 (26.5%) 30 (19.1%) 29 (26.4%) 22 (19.0%) 43 (20.0%) 25 (24.0%) G-A/G-A 2 (1.0%) 2 (1.3%) 2 (1.7%) 3 (2.6%) 0 (0.0%) 1 (1.0%) G-A/G-C 4 (2.0%) 8 (5.the text appears to be discussing scientific results, it is difficult to understand the context and meaning of the text without additional information. Therefore, I will focus on correcting any spelling, grammar, and formatting errors in the text while preserving the original content as much as possible.

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