Lifestyle and nutrition related to male longevity in Sardinia: an ecological study

Nutr Metab Cardiovasc Dis. 2013 Mar;23(3):212-9. doi: 10.1016/j.numecd.2011.05.004. Epub 2011 Sep 29.


Background and aims: A demographic analysis in the Mediterranean island of Sardinia revealed marked differences in extreme longevity across the 377 municipalities and particularly identified a mountain inner area where the proportion of oldest subjects among male population has one of the highest validated value worldwide. The cause(s) of this unequal distribution of male longevity may be attributed to a concurrence of environmental, lifestyle and genetic factors.

Methods and results: In this study we focussed on some lifestyle and nutrition variables recorded in the island's population in early decades of 20th century, when agricultural and pastoral economy was still prevalent, and try to verify through ecological spatial models if they may account for the variability in male longevity. By computing the Extreme Longevity Index (the proportion of newborns in a given municipality who reach age 100) the island's territory was divided in two areas with relatively higher and lower level of population longevity. Most nutritional variables do not show any significant difference between these two areas whereas a significant difference was found with respect to pastoralism (P = 0.0001), physical activity estimated by the average slope of the territory in each municipality (P = 0.0001), and average daily distance required by the active population to reach the usual workplace (P = 0.0001).

Conclusion: Overall, these findings suggest that factors affecting the average energy expenditure of male population such as occupational activity and geographic characteristics of the area where the population mainly resides, are important in explaining the spatial variation of Sardinian extreme longevity.

Publication types

  • Comparative Study

MeSH terms

  • Demography
  • Environment
  • Humans
  • Italy / epidemiology
  • Life Style*
  • Logistic Models
  • Longevity*
  • Male
  • Motor Activity
  • Nutritional Status*
  • Occupations
  • Prevalence
  • Risk Factors
  • Socioeconomic Factors