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dc.contributor.advisorAlloy, Lauren B.
dc.creatorMoriarity, Daniel
dc.date.accessioned2021-09-14T15:37:34Z
dc.date.available2021-09-14T15:37:34Z
dc.date.issued2022
dc.identifier.urihttp://hdl.handle.net/20.500.12613/6943
dc.description.abstractMost psychoneuroimmunology research examines individual proteins; however, some studies have used summed score composites of all available inflammatory markers without evaluating the appropriateness of this decision. 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.
dc.format.extent117 pages
dc.language.isoeng
dc.publisherTemple University. Libraries
dc.relation.ispartofTheses and Dissertations
dc.rightsIN COPYRIGHT- This Rights Statement can be used for an Item that is in copyright. Using this statement implies that the organization making this Item available has determined that the Item is in copyright and either is the rights-holder, has obtained permission from the rights-holder(s) to make their Work(s) available, or makes the Item available under an exception or limitation to copyright (including Fair Use) that entitles it to make the Item available.
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.subjectImmunology
dc.subjectAggregate
dc.subjectComposite
dc.subjectImmunology
dc.subjectInflammation
dc.subjectLatent variable modeling
dc.subjectMethodology
dc.titleThe Physiometrics of Inflammation and Implications for Medical and Psychiatric Research: Toward Empirically-informed Inflammatory Composites
dc.typeText
dc.type.genreThesis/Dissertation
dc.contributor.committeememberEllman, Lauren M.
dc.contributor.committeememberOlino, Thomas
dc.contributor.committeememberMcCloskey, Michael S.
dc.contributor.committeememberSmith, David V.
dc.contributor.committeememberBangasser, Debra A.
dc.description.departmentPsychology
dc.relation.doihttp://dx.doi.org/10.34944/dspace/6925
dc.ada.noteFor Americans with Disabilities Act (ADA) accommodation, including help with reading this content, please contact scholarshare@temple.edu
dc.description.degreePh.D.
dc.identifier.proqst14554
dc.creator.orcid0000-0001-8678-7307
dc.date.updated2021-09-13T16:04:12Z
dc.embargo.lift09/13/2022
dc.identifier.filenameMoriarity_temple_0225E_14554.pdf


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