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Table 1

Luhrmann et al., 2010; St-Onge and Gallagher, 2010). This indicates that in aging, resting metabolism may decrease through other mechanisms such as reduction in the mass and metabolic rate of individual organs and tissues. An increase in resting metabolic rate, as is observed in pathological conditions, accelerates aging and leads to early mortality (Ruggiero et al., 2008; Jumpertz et al., 2011). Consistently, a reduction in metabolic rate, such as in caloric restriction, improvesseveral biologicalparametersand has a favorable impact on longevity in both animal models and humans (Mattson and Wan, 2005). Notwithstanding the above considerations, the link between metabolic rate and aging and longevity is far from straightforward. Rizzo et al. (2005) showed that compared to aged subjects, healthy long-lived subjects display energy expenditure parametersthat are closer to the values of healthy middle-aged adults.

Frequency distribution of two-marker haplotypes in the UCP1 region in the analyzed sample categorized according to the defined age classes. Standard errors (SE) of haplotype frequencies were calculated through 1000 bootstrap resampling cycles.

Age groups Females Haplotypes Age class 1 Age class 2 Age class 3 2n=400 2n=341 2n=236 (40–72 years) (73–91 years) (N91 years) Rel. freq.±SE Rel. freq.±SE Rel. freq.±SE A-C 0.741±0.022 0.780±0.022 0.764±0.028 G-A 0.088±0.014 0.092±0.016 0.081±0.017 G-C 0.171±0.019 0.127±0.019 0.153±0.023 A-A – – – Males Haplotypes Age class 1 Age class 2 Age class 3 2n=232 2n=430 2n=208 (40–65 years) (66–88 years) (N88 years) Rel. Freq. ± SE Rel. Freq. ± SE Rel. Freq. ± SE A-C 0.728±0.029 0.760±0.020 0.773±0.030 G-A 0.134±0.022 0.110±0.016 0.076±0.019 G-C 0.139±0.024 0.131±0.016 0.152±0.026 A-A – – –

It is also worth noting that aging is characterized by the progressive increase in oxidative stress due to the production of ROS during cellular metabolism. The accumulation over time of oxidative damage to cellular components affects normal cell functioning, and leads to impaired ability to maintain baseline tissue energy homeostasis, and to the inability to respond adequately to physiological needs (Frisard and Ravussin, 2006). Therefore, metabolic control and stress response are interconnected processes that likely contribute to the aging phenomenon.

2000)

According to the to survive (Brand, 2000) uncoupling modulates the aging process by affecting the body's metabolic rate (mitochondrial proton leak accounts for 20–30% of whole-body resting energy expenditure, see Harper et al., 2002), and by protecting from oxidative stress. In this scenario, the UCP1 uncoupling activity in BAT mitochondria, as well as the uncoupling activity of other UCPs in different tissues, may be considered a physiological process that by linking metabolic rate and ROS production, can modulate the rate of human aging and lifespan. It is worth noting that in a previous report we showed that a functional polymorphism located in the promoter region of the muscle-specific UCP3 gene (rs1800849), significantly affected hand grip strength, a predictor of disability and mortality, in an elderly population from southern Italy (Crocco et al., 2011). Our report suggested a correlation between the uncoupling process and the regulation of muscle performance in the elderly. Thus, the goal of this study, was to test the hypothesis that aging and longevity are influenced by UCP1 variants. Two polymorphisms, A-3826G and C-3740A, falling in the upstream enhancer region of UCP1, were studied in a cohort of middle aged and oldest old subjects from southern 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 by using a approach based on 10,000 random allele permutations 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 replica- tions). The D′ linkage disequilibrium coefficient was assessed using 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[π (x)] represents a logit transformation of the i frequency of the carriers of the haplotype h at age x, π (x). Thus, the i i effect of the haplotype h on survival is represented by the slope i parameter β : if it is significantly different from 0 the frequency of the i1 carrier of the haplotype h goes up if β N0, or down if β b0. i i1 i1

2.1. Subjects and genotyping

A total of 910 (475 women and 435 men) unrelated individuals (age range: 40–109 years) participated in the present study. The subjects, born and currently living in Calabria (southern Italy), and whose parents and grandparents were native of the same area, were collected during several campaigns of recruitment (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: