Sarcopenic obesity is the combination of reduced fat-free mass (FFM) and increased fat mass (FM) with advancing age but there is lack of clear criteria for its identification. The purposes of the present investigation were: 1) to determine the prevalence of postmenopausal women with reduced FFM relative to their FM and height, and 2) to examine whether there are associations between the proposed classification and health-related variables. A total of 607 women were included in this cross-sectional study and were separated into two subsets: 258 older women with a mean age of 66.8 ± 5.6 years and 349 young women aged 18-40 years (mean age, 29.0 ± 7.5 years). All volunteers underwent body composition assessment by dual-energy X-ray absorptiometry. The FFM index relative to FM and height was calculated and the cutoff value corresponded to two standard deviations below the mean of the young reference group. To examine the clinical significance of the classification, all older participants underwent measurements of quadriceps strength and cardiorespiratory fitness. Values were compared between those who were classified as low FFM or not, using an independent samples t-test and correlations were examined. The cutoff corresponded to a residual of -3.4 and generated a sarcopenic obesity prevalence of 19.8% that was associated with reduced muscle strength and aerobic fitness among the older participants. Also, the index correlated significantly with the health-related fitness variables. The results demonstrated reduced functional capacity for those below the proposed cutoff and suggested applicability of the approach as a definition for sarcopenic obesity.

Os dados são provenientes de 234 touros da raça Nelore participantes de um teste de progênie, no período de 1996 a 2003. A diferença esperada na progênie (DEP) de sete características: peso aos 120 e 210 dias, efeito materno (DMPP120 e DMPP210), peso e perímetro escrotal aos 365 e 450 dias, efeito direto (DDP365, DDP450, DDPE365 e DDPE450) e idade ao primeiro parto (DDIPP) foi utilizada para classificar os animais em três grupos, assim como identificar quais as características possuíram maior poder discriminatório na formação de cada grupo. Para tanto, foram utilizados procedimentos estatísticos multivariados de análise de agrupamentos k-médias e componentes principais. Os resultados evidenciaram que, dos três grupos formados, dois se destacaram quanto aos valores médios das DEPs. A importância desses dois grupos de touros foi confirmada pela análise de componentes principais, que associou a eles valores superiores de DEPs diretas de peso e perímetro escrotal. A quantidade da variabilidade original retida pelos dois primeiros componentes principais foi de 70,22%. Estes procedimentos mostraram-se eficientes e constituíram importantes ferramentas para classificar touros, discriminar variáveis, bem como resumir informações multivariadas, podendo ser usados como auxílio valioso na seleção de reprodutores para uso nos programas de melhoramento genético.

The data set is from 234 Nelore bulls which participate of a progeny test in the period from 1996 to 2003. The expected progeny diference (EPDs) of seven economic traits, weights at 120 e 210 days of age, maternal effect (DMPP120 and DMPP210), weights and scrotal circumferences at 365 e 450 days of age, direct effects (DDP365, DDP450, DDPE365 and DDPE450), and age at first calving (DDIPP), were used in order to classify these animals in three groups and verify which EPDs showed greatest discriminating power in forming the groups. The statistical applied techniques were: k-means clusters analysis and principal components analysis. From the three groups formed, two of them stood out in relation to values of the EPDs means. Evidence of the importance of these two groups was observed in the principal component analysis that associate to them higher values of direct EPDs of weight and scrotal circumference. The two first principal components accounted for 70.22% of the total original variability. Both techniques could be an important tools to sire classification, variable discrimination, as well as to resume multivariate information, and they could be applied to help selection in animal breeding program.