Identification of clinical phenotypes associated with poor prognosis in patients with nonalcoholic fatty liver disease via unsupervised machine learning

Abstract

Background and aims: Both fibrosis status and body weight are important for assessing prognosis in nonalcoholic fatty liver disease (NAFLD). The aim of this study was to identify population clusters for specific clinical outcomes based on fibrosis-4 (FIB-4) index and body mass index (BMI) using an unsupervised machine learning method.

Methods: We conducted a multicenter study of 1335 biopsy-proven NAFLD patients from Japan. Using the Gaussian mixture model to divide the cohort into clusters based on FIB-4 index and BMI, we investigated prognosis for these clusters.

Results: The cohort consisted of 223 cases (16.0%) with advanced fibrosis (F3-4) as assessed from liver biopsy. Median values of BMI and FIB-4 index were 27.3 kg/m² and 1.67. The patients were divided into four clusters by Bayesian information criterion, and all-cause mortality was highest in cluster d, followed by cluster b (P = 0.001). Regarding the characteristics of each cluster, clusters d and b presented a high FIB-4 index (median 5.23 and 2.23), cluster a presented the lowest FIB-4 index (median 0.78), and cluster c was associated with moderate FIB-4 level (median 1.30) and highest BMI (median 34.3 kg/m² ). Clusters a and c had lower mortality rates than clusters b and d. However, all-cause of death in clusters a and c was unrelated to liver disease.

Conclusions: Our clustering approach found that the FIB-4 index is an important predictor of mortality in NAFLD patients regardless of BMI. Additionally, non-liver-related diseases were identified as the causes of death in NAFLD patients with low FIB-4 index.

Keywords: BMI; FIB-4 index; NAFLD; NIT; Unsupervised machine learning.