Moriarity, D. P. | Search bioRxiv

bioRxiv2026

Inflammatory Proteins are Highly Variable During a Natural Stressor: A Call for Intensive Longitudinal Blood Microsampling

Much research investigates how inflammation might play a role in stress-mediated diseases such as heart disease and depression. However, standard longitudinal designs often have measurement lags of months or years between assessments. This status quo is at odds with evidence that inflammatory biology and stress are both highly dynamic and would require much shorter time lags to quantify change as it naturally occurs, with minimal risk of confounding or effects diluting over time. Inspired by the use of intensive longitudinal data collection in psychological research, this study collected high frequency (every 3 days) blood microsamples over 22 days as 86 incoming college students transitioned onto campus (total observations = 622 blood samples). Samples were analyzed for CRP, TNF-, IFN-{gamma}, IL-1{beta}, IL-2, IL-4, IL-6, IL-10, IL-12p70, and IL-17A. Results using both bivariate correlation and intra-class correlation coefficients (which can be used for future power analyses for longitudinal research with these proteins) demonstrate low reliability. Notably, hierarchical linear models demonstrated that, even at this frequency of assessment, most proteins were characterized by less than 6% stable between-person differences. These results provide compelling evidence that investment in intensive longitudinal data collection of immune proteins is critical for understanding how inflammatory biology might function in psychosocial models of risk and resilience.

immunology↗

bioRxiv2021

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

Most research testing the association between inflammation and health outcomes (e.g., heart disease, diabetes, depression) has focused on individual proteins; however, some studies have used summed composites of inflammatory markers without first investigating dimensionality. Using two different samples (MIDUS-2: N = 1,255 adults, MIDUS-R: N = 863 adults), this study investigates the dimensionality of eight inflammatory proteins (C-reactive protein (CRP), interleukin (IL)-6, IL-8, IL-10, tumor necrosis factor- (TNF-), fibrinogen, E-selectin, and intercellular adhesion molecule (ICAM)-1) and compared the resulting factor structure to a) an "a priori"/tau-equivalent factor structure in which all inflammatory proteins equally load onto a single dimension (comparable to the summed composites) and b) proteins modeled individually (i.e., no latent variable) in terms of model fit, replicability, reliability, and their associations with health outcomes. An exploratory factor analysis indicated a two-factor structure (Factor 1: CRP and fibrinogen; Factor 2: IL-8 and IL-10) in MIDUS-2 and was replicated in MIDUS-R. Results did not clearly indicate whether the empirically-identified factor structure or the individual proteins modeled without a latent variable had superior model fit, but both strongly outperformed the "a priori"/tau-equivalent structure (which did not achieve acceptable model fit in any models). Modeling the empirically-identified factors and individual proteins (without a latent factor) as outcomes of medical diagnoses resulted in comparable conclusions. However, modeling individual proteins resulted in findings more robust to correction for multiple comparisons despite more conservative adjustments. Further, reliability for all latent variables was poor. These results indicate that modeling inflammation as a unidimensional construct equally associated with all available proteins does not fit the data well. Instead, individual inflammatory proteins or, potentially (if empirically supported and biologically-plausible) empirically-identified inflammatory factors should be used in accordance with theory.

immunology↗