Characterizing fatigue phenotypes with other symptoms and clinically relevant outcomes among people with multiple sclerosis.

PURPOSE: Fatigue is a common symptom of multiple sclerosis (MS) and can adversely affect all aspect of quality of life. The etiology of fatigue remains unclear, and its treatments are suboptimal. Characterizing the phenotypes of fatigued persons with MS may help advance research on fatigue's etiology and identify ways to personalize fatigue interventions to improve quality of life. The purpose of this study was to identify fatigue phenotypes; examine phenotype stability overtime; and characterize phenotypes by health and function, social and environmental determinants, psychosocial factors, and engagement in healthy behaviors. METHODS: We conducted a longitudinal study over a 3-month period with 289 fatigued participants with MS. To identify fatigue phenotypes and determine transition probabilities, we used latent profile and transition analyses with valid self-report measures of mental and physical fatigue severity, the mental and physical impact of fatigue, depression, anxiety, and sleep quality. We used ANOVAs and effect sizes to characterize differences among phenotypes. RESULTS: The best fitting model included six subgroups of participants: Mild Phenotype, Mild-to-Moderate Phenotype, Moderate-to-Severe Phenotype, Severe Phenotype, Fatigue-dominant Phenotype, and Mental Health-dominant Phenotype. The transition analysis indicated that phenotypic membership was highly stable. Variables with a large eta squared effect size included environmental barriers, self-efficacy, and fatigue catastrophizing. CONCLUSION: These results indicate that the magnitude of fatigue experienced may be more important to consider than the type of fatigue when characterizing fatigue phenotypes. Future research should explore whether tailoring interventions to environmental barriers, self-efficacy, and fatigue catastrophizing reduce the likelihood of transitioning to a more severe phenotype.

Predictors of fatigue self-management behaviors in adults with multiple sclerosis.

BACKGROUND: Fatigue is one of the most common and disabling symptoms in people with multiple sclerosis (MS). Fatigue self-management behaviors may be effective in reducing the impact of fatigue in people with MS. However, few studies have examined the factors that influence engagement in fatigue self-management behaviors. OBJECTIVE: Identify factors that directly and indirectly influence fatigue self-management behaviors. METHODS: Participants with MS (n = 287) completed online questionnaires at baseline and 6-weeks. Guided by the Self- and Family Management Framework, we examined the influence of health status, resources and environment, healthcare utilization, and self-management processes on fatigue self-management behaviors at 6-weeks. Multiple regression and path analyses were conducted. RESULTS: The final regression model variables accounted for 41.58% of the variance in fatigue self-management behaviors, which included outcome expectations (ß= 0.287), disability (ß= 0.265), environmental barriers (ß= 0.188), self-efficacy (ß= 0.153), symptom severity (ß= 0.113), living in an urban community (ß= -0.108), and living alone (ß= 0.103). Path analysis indicated that outcome expectations may mediate the relationship between disability levels and fatigue self-management behavior. CONCLUSIONS: Health status (i.e., disability and symptom severity), environmental factors (e.g., living situation), and self-management processes (i.e., self-efficacy and outcome expectations) may play an important role in influencing engagement in fatigue self-management behaviors.

Large-scale informatic analysis to algorithmically identify blood biomarkers of neurological damage.

The identification of precision blood biomarkers which can accurately indicate damage to brain tissue could yield molecular diagnostics with the potential to improve how we detect and treat neurological pathologies. However, a majority of candidate blood biomarkers for neurological damage that are studied today are proteins which were arbitrarily proposed several decades before the advent of high-throughput omic techniques, and it is unclear whether they represent the best possible targets relative to the remainder of the human proteome. Here, we leveraged mRNA expression data generated from nearly 12,000 human specimens to algorithmically evaluate over 17,000 protein-coding genes in terms of their potential to produce blood biomarkers for neurological damage based on their expression profiles both across the body and within the brain. The circulating levels of proteins associated with the top-ranked genes were then measured in blood sampled from a diverse cohort of patients diagnosed with a variety of acute and chronic neurological disorders, including ischemic stroke, hemorrhagic stroke, traumatic brain injury, Alzheimer's disease, and multiple sclerosis, and evaluated for their diagnostic performance. Our analysis identifies several previously unexplored candidate blood biomarkers of neurological damage with possible clinical utility, many of which whose presence in blood is likely linked to specific cell-level pathologic processes. Furthermore, our findings also suggest that many frequently cited previously proposed blood biomarkers exhibit expression profiles which could limit their diagnostic efficacy.

Analysis of Early Stroke-induced Changes in Circulating Leukocyte Counts using Transcriptomic Deconvolution.

Growing evidence suggests that stroke alters the phenotype of the peripheral immune system; better characterization of this response could provide new insights into stroke pathophysiology. In this investigation, we employed a deconvolution approach to informatically infer the cellular composition of the circulating leukocyte pool at multiple timepoints following stroke onset based on whole blood mRNA expression. Microarray data generated from the peripheral blood of 23 cardiovascular disease controls and 23 ischemic stroke patients at 3, 5, and 24 hours post-symptom onset were obtained from a public repository. Transcriptomic deconvolution was used to estimate the relative counts of nine leukocyte populations based on the expression of cell-specific transcripts, and cell counts were compared between groups across timepoints. Inferred counts of lymphoid cell populations including B-cells, CD4+ T-cells, CD8+ T-cells, γδ T-cells, and NK-cells were significantly lower in stroke samples relative to control samples. With respect to myeloid cell populations, inferred counts of neutrophils and monocytes were significantly higher in stroke samples compared to control samples, however inferred counts of eosinophils and dendritic cells were significantly lower. These collective differences were most dramatic in samples collected at 5 and 24 hours post-symptom onset. Findings were subsequently confirmed in a second dataset generated from an independent population of 24 controls and 39 ischemic stroke patients. Collectively, these results offer a comprehensive picture of the early stroke-induced changes to the complexion of the circulating leukocyte pool, and provide some of the first evidence that stroke triggers an acute decrease in eosinophil counts.