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. 2022 Aug 26;12(1):14581.
doi: 10.1038/s41598-022-17504-5.

Proteomic analyses do not reveal subclinical inflammation in fatigued patients with clinically quiescent inflammatory bowel disease

Affiliations

Proteomic analyses do not reveal subclinical inflammation in fatigued patients with clinically quiescent inflammatory bowel disease

Arno R Bourgonje et al. Sci Rep. .

Abstract

Fatigue is a common and clinically challenging symptom in patients with inflammatory bowel diseases (IBD), occurring in ~ 50% of patients with quiescent disease. In this study, we aimed to investigate whether fatigue in patients with clinically quiescent IBD is reflected by circulating inflammatory proteins, which might reflect ongoing subclinical inflammation. Ninety-two (92) different inflammation-related proteins were measured in plasma of 350 patients with clinically quiescent IBD. Quiescent IBD was defined as clinical (Harvey-Bradshaw Index < 5 or Simple Clinical Colitis Activity Index < 2.5) and biochemical remission (C-reactive protein < 5 mg/L and absence of anemia) at time of fatigue assessment. Leukemia inhibitory factor receptor (LIF-R) concentrations were inversely associated with severe fatigue, also after adjustment for confounding factors (nominal P < 0.05). Although solely LIF-R showed weak ability to discriminate between mild and severe fatigue (area under the curve [AUC] = 0.61, 95%CI: 0.53-0.69, P < 0.05), a combined set of the top seven (7) fatigue-associated proteins (all P < 0.10) was observed to have reasonable discriminative performance (AUC = 0.82 [95%CI: 0.74-0.91], P < 0.01). Fatigue in patients with IBD is not clearly reflected by distinct protein signatures, suggesting there is no subclinical inflammation defined by the studied inflammatory proteins. Future studies are warranted to investigate other proteomic markers that may reflect fatigue in clinically quiescent IBD.

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Conflict of interest statement

G.D. received research grants from Royal DSM and speaker’s fees from Janssen Pharmaceuticals, Takeda, Pfizer and Abbvie. R.K.W. acted as consultant for Takeda, received unrestricted research grants from Takeda, Johnson & Johnson, Tramedico and Ferring, and received speaker’s fees from MSD, Abbvie and Janssen Pharmaceuticals. A.R.B., S.J.W., S.H., H.M.v.D., M.C.V., K.N.F., E.A.M.F., J.N.S. and L.M.S. declare no potential conflict of interest.

Figures

Figure 1
Figure 1
(A) Patient-reported fatigue scores follow a rather normal distribution for patients with CD (red) and UC (purple). (B) Patient-reported psychological well-being scores show a negative (left-) skewed distribution. (C) Boxplots of fatigue scores plotted against psychological well-being scores, demonstrating that fatigue inversely correlates with psychological well-being.
Figure 2
Figure 2
Volcano plot demonstrating differentially abundant plasma proteins between patients with the lowest (Q1, range 0–3) and highest (Q4, range 6–10) fatigue scores. The red horizontal dashed line indicates the threshold for nominal significance (nominal P < 0.05), and the vertical black dashed line indicates zero difference between the groups. Abbreviations: Q1, first quartile; Q4, fourth quartile.
Figure 3
Figure 3
(AF) Top six (6) differentially abundant plasma proteins between mildly fatigued (Q1) patients and severely fatigued (Q4) patients. Abbreviations: CD5, T-cell surface glycoprotein CD5; CXCL10, C-X-C motif chemokine ligand 10; DNER, Delta and Notch-like epidermal growth factor-related receptor; GDNF, glial cell line-derived neurotrophic factor; leukemia inhibitory factor receptor; NPX, normalized protein expression; vascular endothelial growth factor A (VEGF-A).
Figure 4
Figure 4
(AC) Receiver operating characteristics (ROC) curves showing the discriminative value of (A) gender and smoking as relevant confounding factors and (B) a combined panel of seven (7) proteins (consisting of CD5, DNER, EN-RAGE, GDNF, IL-20RA, LIF-R, and VEGF-A) with regard to the presence of fatigue in patients with IBD. (C) When combining the seven proteins with gender and smoking as confounding factors, the classification performance increased. Abbreviations: AUC, area under the curve; Q1, first quartile; Q4, fourth quartile.

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