Serum proteome signatures associated with liver steatosis in adolescents with obesity

Abstract

Purpose: Childhood obesity, a pressing global health issue, significantly increases the risk of metabolic complications, including metabolic dysfunction associated with steatotic liver disease (MASLD). Accurate non-invasive tests for early detection and screening of steatosis are crucial. In this study, we explored the serum proteome, identifying proteins as potential biomarkers for inclusion in non-invasive steatosis diagnosis tests.

Methods: Fifty-nine obese adolescents underwent ultrasonography to assess steatosis. Serum samples were collected and analyzed by targeted proteomics with the Proximity Extension Assay technology. Clinical and biochemical parameters were evaluated, and correlations among them, the individuated markers, and steatosis were performed. Receiver operating characteristic (ROC) curves were used to determine the steatosis diagnostic performance of the identified candidates, the fatty liver index (FLI), and their combination in a logistic regression model.

Results: Significant differences were observed between subjects with and without steatosis in various clinical and biochemical parameters. Gender-related differences in the serum proteome were also noted. Five circulating proteins, including Cathepsin O (CTSO), Cadherin 2 (CDH2), and Prolyl endopeptidase (FAP), were identified as biomarkers for steatosis. CDH2, CTSO, Leukocyte Immunoglobulin Like Receptor A5 (LILRA5), BMI, waist circumference, HOMA-IR, and FLI, among others, significantly correlated with the steatosis degree. CDH2, FAP, and LDL combined in a logit model achieved a diagnostic performance with an AUC of 0.91 (95% CI 0.75-0.97, 100% sensitivity, 84% specificity).

Conclusions: CDH2 and FAP combined with other clinical parameters, represent useful tools for accurate diagnosis of fatty liver, emphasizing the importance of integrating novel markers into diagnostic algorithms for MASLD.

Keywords: Cardiovascular; Circulating biomarkers; MAFLD; Metabolic; Pediatrics; Proteomics; Steatosis.

Fig. 1

Serum protein expression in obese adolescents with and without steatosis. The volcano plot shows the NPX difference between non-steatotic (n = 21) and steatotic (n = 37) patients on the x-axis and the –log10 of the nominal p-value on the y-axis. All p-values were adjusted for multiple testing using the Benjamini–Hochberg method and − log10 of the p-values was plotted

Fig. 2

Serum protein expression for proteins under study in obese adolescents with and without steatosis. Box plots depict the differences in NPX values for individuals without steatosis (S0, n = 21; Absence) has been compared with those with steatosis (S1, S2, S3, n = 37; Presence). A CTSO protein, B CDH2 protein, C LILRA5 protein, D SERPINB6 protein and E FAP protein, Values were presented as median with their respective 10–90 percentiles. Differences were considered statistical significants at p values less than 0.05. *p < 0.05, **p < 0.01, ***p < 0.001

Fig. 3

Serum protein expression differences according to gender for the proteins under study. Box plots show the variations according to gender, females (n = 28,) and males (n = 30). A CTSO protein, B FCRL1 protein, C ALDH1A1 protein, D FAP protein, E CLMP protein, and F APLP1 protein. Values were presented as median with their respective 10–90 percentiles. Differences were considered statistically significant at p-values less than 0.05. *p < 0.05, **p < 0.01, ***p < 0.001

Fig. 4

Receiver operating characteristic (ROC) curves for steatosis diagnosis in obese adolescents. A Logistic regression (logit) model combining the best 4 variables for steatosis outcome prediction. The logit model has the following equation: steatosis = -38.81 + 0.05*LDL + 3.21*CDH2 + 3.89*FAP, achieving a diagnostic performance with an area under the curve of 0.91 with a sensitivity of 100% and specificity of 84%. B ROCs for logit model and for each component of the model equation separately