Dermody Sarah S, Wright Aidan G C, Cheong JeeWon, Miller Karissa G, Muldoon Matthew F, Flory Janine D, Gianaros Peter J, Marsland Anna L, Manuck Stephen B
University of Pittsburgh.
University of Alabama.
J Pers. 2016 Dec;84(6):765-776. doi: 10.1111/jopy.12216. Epub 2015 Sep 4.
Varying associations are reported between Five-Factor Model (FFM) personality traits and cardiovascular disease risk. Here, we further examine dispositional correlates of cardiometabolic risk within a hierarchical model of personality that proposes higher-order traits of Stability (shared variance of Agreeableness, Conscientiousness, inverse Neuroticism) and Plasticity (Extraversion, Openness), and we test hypothesized mediation via biological and behavioral factors. In an observational study of 856 community volunteers aged 30-54 years (46% male, 86% Caucasian), latent variable FFM traits (using multiple-informant reports) and aggregated cardiometabolic risk (indicators: insulin resistance, dyslipidemia, blood pressure, adiposity) were estimated using confirmatory factor analysis (CFA). The cardiometabolic factor was regressed on each personality factor or higher-order trait. Cross-sectional indirect effects via systemic inflammation, cardiac autonomic control, and physical activity were tested. CFA models confirmed the Stability "meta-trait," but not Plasticity. Lower Stability was associated with heightened cardiometabolic risk. This association was accounted for by inflammation, autonomic function, and physical activity. Among FFM traits, only Openness was associated with risk over and above Stability, and, unlike Stability, this relationship was unexplained by the intervening variables. A Stability meta-trait covaries with midlife cardiometabolic risk, and this association is accounted for by three candidate biological and behavioral factors.
五因素模型（FFM）人格特质与心血管疾病风险之间存在不同的关联。在此，我们在一个人格层次模型中进一步研究心脏代谢风险的性格相关因素，该模型提出了稳定性（宜人性、尽责性、反向神经质的共同方差）和可塑性（外向性、开放性）的高阶特质，并通过生物学和行为因素检验假设的中介作用。在一项对856名年龄在30 - 54岁的社区志愿者的观察性研究中（46%为男性，86%为白种人），使用验证性因素分析（CFA）估计潜在变量FFM特质（使用多源报告）和综合心脏代谢风险（指标：胰岛素抵抗、血脂异常、血压、肥胖）。将心脏代谢因素对每个人格因素或高阶特质进行回归分析。通过全身炎症、心脏自主控制和身体活动测试横断面间接效应。CFA模型证实了稳定性“元特质”，但未证实可塑性。较低的稳定性与较高的心脏代谢风险相关。这种关联由炎症、自主功能和身体活动所解释。在FFM特质中，只有开放性与稳定性之外的风险相关，并且与稳定性不同，这种关系无法由干预变量解释。一种稳定性元特质与中年心脏代谢风险相关，并且这种关联由三个候选生物学和行为因素所解释。
