Metabolomics to identify fingerprints of carotid atherosclerosis in nonobese metabolic dysfunction-associated fatty liver disease

Background/aims Nonobese metabolic dysfunction-associated fatty liver disease (MAFLD) is paradoxically associated with improved metabolic and pathological features at diagnosis but similar cardiovascular diseases (CVD) prognosis to obese MAFLD. We aimed to utilize the metabolomics to identify the potential metabolite profiles accounting for this phenomenon.Methods This prospective multicenter cross-sectional study was conducted in China enrolling derivation and validation cohorts. Liquid chromatography coupled with mass spectrometry and gas chromatography-mass spectrometry were applied to perform a metabolomics measurement.Results The study involved 120 MAFLD patients and 60 non-MAFLD controls in the derivation cohort. Controls were divided into two groups according to the presence of carotid atherosclerosis (CAS). The MAFLD group was further divided into nonobese MAFLD with/without CAS groups and obese MAFLD with/without CAS groups. Fifty-six metabolites were statistically significant for discriminating the six groups. Among the top 10 metabolites related to CAS in nonobese MAFLD, only phosphatidylethanolamine (PE 20:2/16:0), phosphatidylglycerol (PG 18:0/20:4) and de novo lipogenesis (16:0/18:2n-6) achieved significant areas under the ROC curve (AUCs, 0.67, p = 0.03; 0.79, p = 0.02; 0.63, p = 0.03, respectively). The combination of these three metabolites and liver stiffness achieved a significantly higher AUC (0.92, p < 0.01). In obese MAFLD patients, cystine was found to be significant with an AUC of 0.69 (p = 0.015), followed by sphingomyelin (SM 16:1/18:1) (0.71, p = 0.004) and de novo lipogenesis (16:0/18:2n-6) (0.73, p = 0.004). The combination of these three metabolites, liver fat content and age attained a significantly higher AUC of 0.91 (p < 0.001). The AUCs of these metabolites remained highly significant in the independent validation cohorts involving 200 MAFLD patients and 90 controls.Conclusions Diagnostic models combining different metabolites according to BMI categories could raise the accuracy of identifying subclinical CAS.(GRAPHICS)

Automated summary

Metabolomics was used to identify potential metabolite profiles in nonobese metabolic dysfunction-associated fatty liver disease (MAFLD). Diagnostic models combining different metabolites according to BMI categories could improve the accuracy of identifying subclinical carotid atherosclerosis (CAS).