The Relationship Between Social Status and Atherosclerosis in Male and Female Monkeys as Revealed by Meta-Analysis

Am J Primatol. 2009 Sep;71(9):732-41. doi: 10.1002/ajp.20707.


More than 25 years ago our laboratory reported sex-dependent relationships between social status and coronary artery atherosclerosis among cholesterol-fed cynomolgus monkeys (Macaca fascicularis) maintained in social groups of four to six animals each. Dominant males developed more atherosclerosis than subordinates, but only if housed in recurrently reorganized social groups. In contrast, dominant females developed significantly less atherosclerosis than subordinates, irrespective of social setting. Although we have continued to study these associations, no confirmatory investigations have been reported by other laboratories or using other atherosclerosis-susceptible monkey species. Accordingly, we conducted a meta-analysis of all relevant data sources developed in our laboratory since 1982 to determine whether the originally reported relationships between social status and atherosclerosis reflected robust associations. The sentinel (first) studies were composed of 16 females and 27 males. The current meta-analysis encompassed 419 animals (200 females and 219 males) derived from 11 separate investigations. The results confirmed that, among males, dominant individuals developed more extensive atherosclerosis than subordinates when housed in recurrently reorganized (unstable) social groups in which an estrogen-implanted female was also present. Dominant males in stable social groups tended to have less atherosclerosis than similarly housed subordinates, but this effect was not significant. On the contrary, we found that dominant females developed reliably less atherosclerosis than subordinates.

Publication types

  • Meta-Analysis
  • Research Support, N.I.H., Extramural
  • Review

MeSH terms

  • Animals
  • Atherosclerosis / diagnosis
  • Atherosclerosis / epidemiology*
  • Female
  • Hierarchy, Social*
  • Macaca fascicularis
  • Male
  • Regression Analysis
  • Sex Factors