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BACKGROUND AND AIMS: Acute-on-chronic liver failure (ACLF) is a complication of cirrhosis characterized by multiple organ failure and high short-term mortality. The pathophysiology of ACLF involves elevated systemic inflammation leading to organ failure, along with immune dysfunction that heightens susceptibility to bacterial infections. However, it is unclear how these aspects are associated with recovery and nonrecovery in ACLF. APPROACH AND RESULTS: Here, we mapped the single-cell transcriptome of circulating immune cells from patients with ACLF and acute decompensated (AD) cirrhosis and healthy individuals. We further interrogate how these findings, as well as immunometabolic and functional profiles, associate with ACLF-recovery (ACLF-R) or nonrecovery (ACLF-NR). Our analysis unveiled 2 distinct states of classical monocytes (cMons). Hereto, ACLF-R cMons were characterized by transcripts associated with immune and stress tolerance, including anti-inflammatory genes such as RETN and LGALS1 . Additional metabolomic and functional validation experiments implicated an elevated oxidative phosphorylation metabolic program as well as an impaired ACLF-R cMon functionality. Interestingly, we observed a common stress-induced tolerant state, oxidative phosphorylation program, and blunted activation among lymphoid populations in patients with ACLF-R. Conversely, ACLF-NR cMon featured elevated expression of inflammatory and stress response genes such as VIM , LGALS2 , and TREM1 , along with blunted metabolic activity and increased functionality. CONCLUSIONS: This study identifies distinct immunometabolic cellular states that contribute to disease outcomes in patients with ACLF. Our findings provide valuable insights into the pathogenesis of ACLF, shedding light on factors driving either recovery or nonrecovery phenotypes, which may be harnessed as potential therapeutic targets in the future.
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BACKGROUND: Patient heterogeneity poses significant challenges for managing individuals and designing clinical trials, especially in complex diseases. Existing classifications rely on outcome-predicting scores, potentially overlooking crucial elements contributing to heterogeneity without necessarily impacting prognosis. METHODS: To address patient heterogeneity, we developed ClustALL, a computational pipeline that simultaneously faces diverse clinical data challenges like mixed types, missing values, and collinearity. ClustALL enables the unsupervised identification of patient stratifications while filtering for stratifications that are robust against minor variations in the population (population-based) and against limited adjustments in the algorithm's parameters (parameter-based). RESULTS: Applied to a European cohort of patients with acutely decompensated cirrhosis (n = 766), ClustALL identified five robust stratifications, using only data at hospital admission. All stratifications included markers of impaired liver function and number of organ dysfunction or failure, and most included precipitating events. When focusing on one of these stratifications, patients were categorized into three clusters characterized by typical clinical features; notably, the 3-cluster stratification showed a prognostic value. Re-assessment of patient stratification during follow-up delineated patients' outcomes, with further improvement of the prognostic value of the stratification. We validated these findings in an independent prospective multicentre cohort of patients from Latin America (n = 580). CONCLUSIONS: By applying ClustALL to patients with acutely decompensated cirrhosis, we identified three patient clusters. Following these clusters over time offers insights that could guide future clinical trial design. ClustALL is a novel and robust stratification method capable of addressing the multiple challenges of patient stratification in most complex diseases.