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Serum amyloid A for predicting prognosis in patients with newly diagnosed Crohn's disease
Qia Chen1,2, Xi Zhang1, Yizhe Tie1
1Gastroenterology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China.
Objective:
Serum amyloid A (SAA) was found to be positively correlated with the activity of Crohn's disease (CD); however, its prognostic value remains uncertain. Here, we examined its predictive ability in newly diagnosed CD and explored genetic association.
Methods:
This retrospective cohort study included patients newly diagnosed as CD at the First Affiliated Hospital of Sun Yat-sen University between June 2010 and March 2022. We employed receiver operating characteristic curve, Cox proportional hazard regression models and restricted cubic splines to investigate the prognostic performance of SAA for surgery and disease progression. To assess possible causality, a two-sample Mendelian randomisation (MR) of published genome-wide association study data was conducted.
Results:
During 2187.6 person-years (median age, 28 years, 72.4% male), 87 surgery and 153 disease progression events were documented. A 100-unit increment in SAA level generated 14% higher risk for surgery (adjusted HR (95% CI): 1.14 (1.05-1.23), p=0.001) and 12% for disease progression (1.12 (1.05-1.19), p<0.001). Baseline SAA level ≥89.2 mg/L led to significantly elevated risks for surgery (2.08 (1.31-3.28), p=0.002) and disease progression (1.72 (1.22-2.41), p=0.002). Such associations were assessed as linear. Adding SAA into a scheduled model significantly improved its predictive performances for surgery and disease progression (p for net reclassification indexes and integrated discrimination indexes <0.001). Unfortunately, no genetic causality between SAA and CD was observed in MR analysis. Sensitivity analyses showed robust results.
Conclusion:
Although causality was not found, baseline SAA level was an independent predictor of surgery and disease progression in newly diagnosed CD, and had additive benefit to existing prediction models.
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