The Physiometrics of Inflammatory Proteins and Implications for Medical and Psychiatric Research: Investigation of Empirically-informed Inflammatory Composites

Abstract

Much inflammation research examines individual proteins; however, some studies have used summed score composites of all available inflammatory markers without first investigating dimensionality. Using three different samples (MIDUS-2: N = 1,255 adults, MIDUS-R: N =863 adults, and ACE: N = 315 adolescents), this study investigates the dimensionality of eight inflammatory proteins (C-reactive protein (CRP), interleukin (IL)-6, IL-8, IL-10, tumor necrosis factor-α (TNF-α), fibrinogen, E-selectin, and intercellular adhesion molecule (ICAM)-1) and compares the resulting factor structure to a) an “a priori” factor structure in which all inflammatory proteins equally load onto a single dimension (a technique that has been used previously) and b) proteins modeled individually (i.e., no latent variable) in terms of model fit, replicability, reliability, temporal stability, and their associations with medical history and depression symptoms. A hierarchical factor structure with two first-order factors (Factor 1A: CRP, IL-6, fibrinogen; Factor 2A: TNF-α, IL-8, IL-10, ICAM-1, IL-6) and a second-order general inflammation factor was identified in MIDUS-2 and replicated in MIDUS-R and partially replicated in ACE (which unfortunately only had CRP, IL-6, IL-8, IL-10, and TNF-α but, unlike the other two, has longitudinal data). Both the empirically-identified structure and modeling proteins individually fit the data better compared to the one-dimensional “a priori” structure. Results did not clearly indicate whether the empirically-identified factor structure or the individual proteins modeled without a latent variable had superior model fit. Modeling the empirically-identified factors and individual proteins (without a latent factor) as outcomes of medical diagnoses resulted in comparable conclusions, but modeling empirically-identified factors resulted in fewer results “lost” to correction for multiple comparisons. Importantly, when the factor scores were recreated in a longitudinal dataset, none of the individual proteins, the “a priori” factor, or the empirically-identified general inflammation factor significantly predicted concurrent depression symptoms in multilevel models. However, both empirically-identified first-order factors were significantly associated with depression, in opposite directions. Measurement properties are reported for the different aggregates and individual proteins as appropriate, which can be used in the design and interpretation of future studies. These results indicate that modeling inflammation as a unidimensional construct equally associated with all available proteins does not fit the data well. Instead, empirically-supported aggregates of inflammation, or individual inflammatory markers, should be used in accordance with theory. Further, the aggregation of shared variance achieved by constructing empirically-supported aggregates might increase predictive validity compared to other modeling choices, maximizing statistical power.