Abstract
BackgroundPatients with severe acute pancreatitis (SAP) have a high mortality, thus early diagnosis and interventions are critical for improving survival. However, conventional tests are limited in acute pancreatitis (AP) stratification. We aimed to assess AP severity by integrating the informative clinical measurements with cell free DNA (cfDNA) methylation markers.MethodsOne hundred and seventy-five blood samples were collected from 61 AP patients at multiple time points, plus 24 samples from healthy individuals. Genome-wide cfDNA methylation profiles of all samples were characterized with reduced representative bisulfite sequencing. Clinical blood tests covering 93 biomarkers were performed on AP patients within 24 h. SAP predication models were built based on cfDNA methylation and conventional blood biomarkers separately and in combination.ResultsWe identified 565 and 59 cfDNA methylation markers informative for acute pancreatitis and its severity. These markers were used to develop prediction models for AP and SAP with area under the receiver operating characteristic of 0.92 and 0.81, respectively. Twelve blood biomarkers were systematically screened for a predictor of SAP with a sensitivity of 87.5% for SAP, and a specificity of 100% in mild acute pancreatitis, significantly higher than existing blood tests. An expanded model integrating 12 conventional blood biomarkers with 59 cfDNA methylation markers further improved the SAP prediction sensitivity to 92.2%.ConclusionsThese findings have demonstrated that accurate prediction of SAP by the integration of conventional and novel blood molecular markers, paving the way for early and effective SAP intervention through a non-invasive rapid diagnostic test.
Highlights
Patients with severe acute pancreatitis (SAP) have a high mortality, early diagnosis and interven‐ tions are critical for improving survival
To improve the power of detecting subtle methylation differences in plasma DNA, we focused on a set of Methylation Haplotype Blocks (MHBs) in which local CpG methylation status are coordinated along single DNA molecules, such that tissue-specific signals are easier to detect with a haplotype-based scoring scheme [25]
We plotted the arithmetic average of Unmethylated Haplotype Load (uMHL) values of these MHBs for both Mild acute pancreatitis (MAP) and SAP training samples for comparison (Fig. 3B), and the results showed that SAP samples have significantly higher average uMHL scores than MAP cases (p = 2.83 × 10–11, Welch’s t-test), demonstrating that these MHBs (Additional file 2: Table S6) are less methylated in SAP samples than in MAP samples, and that the average uMHL scores can be used to differentiate MAP and SAP plasma samples
Summary
Patients with severe acute pancreatitis (SAP) have a high mortality, early diagnosis and interven‐ tions are critical for improving survival. While generally useful, so far all of them have been shown to predict SAP with moderate accuracy between 0.6 to 0.8 [10, 11], some of which perform better at specificity over sensitivity in diagnoses, or vice versa [12] Some systems, such as APACHE-II, which requires 16 tests to complete to predict AP severity, are complicated and hard to implement in typical clinical settings. Some, such as Ranson’s scores, require a minimum of 48 h after hospitalization to predict SAP, limiting the time window to initiate medical intervention [13]. Imaging-based systems are less objective because interpretation relies on inspectors’ personal experiences [5], and enhanced CT, which is essential to identify localized pancreas complications, may complicate treatment by causing deterioration in pancreatic microcirculatory disturbance [14]
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