Abstract
Background
Hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF) is associated with dyslipidemia and inflammatory responses. However, the association between a novel comprehensive lipid parameter, the non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR), and clinical outcomes in HBV-ACLF remains unclear. This study aimed to investigate the relationship between NHHR and 28- and 90-day transplant-free (TF) mortality among patients with HBV-ACLF.
Methods
This study retrospectively enrolled 452 patients with HBV-ACLF from the First Affiliated Hospital of Anhui Medical University between January 2017 and June 2024. Clinical data at admission and follow-up outcomes at 28 and 90 days were collected. Multivariable Cox regression and restricted cubic splines (RCS) were employed to investigate the association between NHHR and 28- and 90-day TF mortality. A segmented Cox proportional hazards model was applied to examine the threshold effect. Stratified analysis was conducted to evaluate the relationship between NHHR and prognosis across different subgroups. Receiver operating characteristic (ROC) curve analysis was performed to compare the predictive performance of NHHR with other lipid indicators. Kaplan–Meier survival curves were compared between groups using the log-rank test.
Results
Multivariable Cox regression and RCS analyses revealed a nonlinear relationship between NHHR and the risk of mortality. A segmented Cox model was exploratorily fitted to approximate the apparent change in slope, yielding estimated change-points of 10.87 for 28-day and 11.21 for 90-day TF mortality. The findings remained generally consistent across subgroup analyses. Compared with conventional lipid parameters, NHHR showed the highest AUC for predicting 28-day and 90-day TF mortality (0.756 and 0.748), with similar 1,000-bootstrap optimism-corrected AUCs (0.757 and 0.749), and its performance was not significantly different from the MELD or COSSH-ACLF II scores (all DeLong P > 0.05).
Conclusion
Elevated NHHR was significantly associated with 28- and 90-day TF mortality in patients with HBV-ACLF. As an easily calculated index, NHHR may aid early risk stratification alongside established prognostic scores.
Clinical trial number
Not applicable.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12879-026-12646-7.
Keywords: Non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio, 28-day mortality, 90-day mortality, Prognosis, Acute-on‐chronic liver failure
Introduction
Acute-on-chronic liver failure (ACLF) is a distinct and reversible syndrome characterized by acute liver failure (TBil > 5 mg/dL and INR ≥ 1.5) in patients with chronic liver disease or cirrhosis, accompanied by ascites and/or hepatic encephalopathy within 28 days, with or without renal impairment (serum creatinine > 1.5 mg/dL). It is also associated with high 28-day mortality [1, 2]. Chronic hepatitis B virus (HBV) infection affects approximately 300 million people worldwide and represents one of the leading underlying causes of ACLF in HBV-endemic regions, particularly in the Asia–Pacific area [3, 4]. Mother-to-child transmission is the primary route of HBV infection in China and Southeast Asia, and maternal serum HBV DNA level (viral load) is an independent risk factor for intrauterine HBV infection [5]. Currently, there are no particular pharmacological treatments for ACLF. Therefore, early identification of patients with a poor prognosis is critical for initiating timely interventions and making decisions regarding liver transplantation. However, existing scoring systems (such as MELD and related scores) primarily reflect liver and kidney function as well as coagulation status, providing limited coverage for metabolic and nutritional dimensions such as blood lipids. There remains a lack of simple, readily available supplementary prognostic markers for early risk stratification.
The liver is the central organ for lipid metabolism, responsible for the synthesis, storage, transformation, and excretion of lipids. In chronic liver disease, particularly cirrhosis, hepatic lipid metabolic function is severely impaired, manifesting as an overall decrease in total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) levels [6]. Previous studies have indicated that decreased HDL-C is associated with adverse outcomes in liver failure, likely reflecting increased inflammatory burden and impaired immune/nutritional status [7]. Meanwhile, non-high-density lipoprotein cholesterol (non-HDL-C) reflects the total pool of atherosclerosis-related lipoproteins excluding HDL, thereby providing a complementary perspective on disordered lipid metabolism [8]. A novel composite lipid parameter, namely the non-HDL-C to HDL-C ratio (NHHR), has been associated with various diseases and demonstrates promising predictive value, including for cardiovascular diseases, liver fibrosis, sepsis, and cancer [9–12].
The key pathological processes of ACLF involve systemic inflammatory response and immune dysfunction, accompanied by metabolic reprogramming and alterations in lipoprotein profiles [13]. NHHR integrates information on both the decrease in protective HDL and the burden of non-HDL lipoproteins, which may more sensitively reflect the imbalance in inflammation, immune, and nutritional metabolism and thus correlate with short-term prognosis. However, the prognostic value of NHHR in ACLF, particularly among HBV-ACLF patients, has not been systematically investigated.
This study utilized data from patients with HBV-ACLF in our center to investigate the potential association between NHHR and 28- and 90-day outcomes, as well as its consistency across different population characteristics. As a readily available index at admission, NHHR may serve as a practical adjunct for early risk stratification and timely transplantation evaluation.
Materials and methods
Patients
This study retrospectively analyzed 854 patients diagnosed with ACLF in the Department of Infectious Diseases at the First Affiliated Hospital of Anhui Medical University between January 2017 and June 2024. The following were the requirements for inclusion: (1) diagnosis of ACLF according to the Asia-Pacific Association for the Study of the Liver (APASL) guidelines [14], defined as an acute hepatic insult in patients with underlying chronic liver disease presenting with jaundice and coagulopathy (serum bilirubin ≥ 5 mg/dL and INR ≥ 1.5 or prothrombin activity [PTA] < 40%), complicated within 4 weeks by clinical ascites and/or hepatic encephalopathy; (2) age ≥ 18 years; and (3) no prior use of lipid-lowering agents. Exclusion criteria included (1) ACLF without evidence of HBV infection; (2) comorbid systemic diseases, including malignancy, HIV, or pre-existing renal insufficiency; (3) pregnancy; (4) incomplete clinical data; and (5) undergoing liver transplantation or loss to follow-up during the observation period. Patients with HBV infection combined with other chronic liver diseases (e.g., alcohol-related, drug-induced, or autoimmune liver disease) were retained and categorized as the “HBV with other etiologies” subgroup. Cirrhosis was diagnosed based on prior liver biopsy when available; otherwise, it was determined from medical records using documented clinical/laboratory evidence together with endoscopic signs of portal hypertension (esophageal/gastric varices) and/or imaging findings (ultrasound, CT, or MRI) consistent with cirrhosis. Ultimately, 452 patients were included (Fig. 1). All patients received standardized treatment per current clinical guidelines after admission, including antiviral/antibiotic therapy, coagulation management, and nutritional support. During hospitalization, the initiation of artificial liver support system (ALSS) therapy was determined by the treating hepatology team based on disease severity and current guideline-based indications, together with patient/family consent. Loss to follow-up was defined as patients whose survival status could not be confirmed at the 28- or 90-day follow-up time point through review of inpatient/outpatient medical records or by telephone follow-up. Patients who underwent liver transplantation during the follow-up period were not included in the outcome analysis. The study protocol was approved by the Ethics Committee of the First Affiliated Hospital of Anhui Medical University (No: PJ2022-07-18) and was conducted in accordance with the Declaration of Helsinki and local institutional regulations. Informed consent was waived due to the retrospective design and anonymized data.
Fig. 1.
Flow chart of the study participants
Data collection
The data in this study were obtained from the hospital’s electronic medical record system. Demographic and clinical information was collected, including gender, age, presence of cirrhosis, HBV DNA level, HBeAg status, co-existing liver diseases, comorbidities (hypertension, diabetes, and coronary heart disease), and initiation of ALSS therapy during hospitalization. The first available laboratory results within 24 h after admission were recorded, including prothrombin time (PT), international normalized ratio (INR), total bilirubin (TBIL), albumin (ALB), alanine aminotransferase (ALT), aspartate aminotransferase (AST), creatinine (Cr), urea, sodium (Na), triglycerides (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), non-high-density lipoprotein cholesterol (non-HDL-C), and other relevant parameters. Non-HDL-C was calculated as TC − HDL-C, and NHHR was calculated as non-HDL-C/HDL-C. Disease severity scores, including the MELD score and the COSSH-ACLF II score, were calculated based on these laboratory results [15, 16]. The primary outcome of the study was defined as 28-day and 90-day transplant-free (TF) mortality.
Statistical analysis
Continuous variables were assessed for normality using the Shapiro–Wilk test. For normally distributed variables, the independent-samples t-test was used (mean ± SD); for non-normally distributed data, the Mann–Whitney U test was applied [M(IQR)]. Categorical variables were analyzed using the chi-square test and expressed as frequency and percentage (n, %). To further determine the association between NHHR levels and prognosis in HBV-ACLF, three Cox regression analyses were performed, denoted as Model 1 to Model 3. Model 1 was unadjusted for any covariates. Models 2 and 3 were adjusted for covariates selected based on clinical relevance. Model 2 was adjusted for age, gender, cirrhosis, HBV DNA (log10 IU/mL), HBeAg positivity, and co-existing liver diseases. Model 3 was the fully adjusted model, which further incorporated adjustments for ascites, gastrointestinal bleeding, bacterial infection, hepatic encephalopathy, hepatorenal syndrome, hypertension, diabetes, and coronary heart disease, ALSS therapy and MELD score based on Model 2. Multicollinearity among covariates included in the multivariable models was assessed using the variance inflation factor (VIF); all VIFs were < 5, indicating no evident multicollinearity. Time-to-event was calculated from the date of admission. Death was defined as the event, and patients alive at 28 or 90 days were censored at day 28 or day 90, respectively.
Subsequently, based on the covariates of Model 3, restricted cubic spline (RCS) curves and threshold effect analysis were utilized to investigate the putative nonlinear link between NHHR and HBV-ACLF prognosis, as well as to identify possible inflection points. Furthermore, stratified analyses were performed according to gender, age (< 50 years vs. ≥50 years), presence of other liver diseases (yes vs. no), cirrhosis (yes vs. no), HBV DNA load (< 4 vs. ≥4 log10 IU/ml), HBeAg status (positive vs. negative), ascites (yes vs. no), hepatic encephalopathy (yes vs. no), ALSS therapy (yes vs. no), and MELD score (< 28 vs. ≥28), to examine the association between NHHR and HBV-ACLF prognosis across different subgroups. The likelihood ratio test was further applied to evaluate potential interaction effects among these subgroups. Finally, receiver operating characteristic (ROC) curves and the area under the curve (AUC) with 95% confidence intervals were generated to assess the predictive performance of NHHR and other lipid parameters for 28-day and 90-day TF mortality, and AUCs were compared using the DeLong test. Internal validation was performed using 1,000 bootstrap resamples, and optimism-corrected AUCs were reported. The optimal cutoff value was determined based on the maximum Youden index, and patients were classified into low-risk and high-risk groups for short-term poor prognosis to facilitate clinical risk stratification and management.
All statistical analyses were performed using R version 4.4.2 and Storm Statistical Platform (http://www.medsta.cn/software). P < 0.05 was deemed significant.
Results
Patient characteristics
Among the 452 enrolled HBV-ACLF patients, the median age was 49.00 (40.00, 59.00) years, and 81.86% were male. The 28-day and 90-day TF mortality rates were 24.56% and 36.06%, respectively. Baseline lipid parameters are expressed as median (interquartile range). TG was 1.23 (0.98–1.61) mmol/L, TC was 2.28 (1.90–2.83) mmol/L, HDL-C was 0.21 (0.15–0.31) mmol/L, LDL-C was 1.23 (0.90–1.62) mmol/L, and non-HDL-C was 2.05 (1.73–2.51) mmol/L. The study population was then divided into three groups based on the NHHR tertiles: NHHR < 8.0 (T1), NHHR 8.0–11.5 (T2), and NHHR > 11.5 (T3). The clinical characteristics of each group are presented in Table 1. Among the three groups, patients in T3 had lower HDL-C (P < 0.001), TG (P = 0.035), and LDL-C (P = 0.037), whereas TC (P = 0.061) and non-HDL-C (P = 0.085) did not differ significantly across tertiles (Table 1). These patients also had more impaired liver function and coagulation profiles compared with those in T1 and T2. Given that NHHR = non-HDL-C/HDL-C, the marked decrease in HDL-C (P < 0.001) with no significant difference in non-HDL-C (P = 0.085) suggests that the higher NHHR in this cohort was largely driven by extremely low HDL-C. This group also demonstrated higher disease severity scores: the MELD score was 26.49 (23.38–31.94) in T3, versus 24.43 (22.26–27.14) in T1 and 24.84 (23.14–28.10) in T2 (P < 0.001); the COSSH-ACLF II score was 7.07 (6.35–8.07) in T3, compared to 6.58 (5.90–7.19) in T1 and 6.62 (5.96–7.39) in T2 (P < 0.001). Furthermore, the T3 group had the highest incidence of short-term poor prognosis, with 28-day and 90-day TF mortality rates of 44.37% and 58.94%, respectively, which were significantly higher than those in T1 (8.67% and 16.67%) and T2 (20.53% and 32.45%) (both P < 0.001) (Table 1).
Table 1.
Baseline characteristics by tertiles of NHHR
| Variables | Total (n = 452) | T1 (<8.0) n = 150 |
T2 (8.0-11.5) n = 151 |
T3 (>11.5) n = 151 |
P-value |
|---|---|---|---|---|---|
| Age (years) | 49.00 (40.00, 59.00) | 48.00 (38.00,59.00) | 49.00 (38.00,58.00) | 50.00 (43.00,60.00) | 0.426 |
| Male, n (%) | 370 (81.86) | 121 (80.67) | 124 (82.12) | 125 (82.78) | 0.888 |
| Liver cirrhosis, n (%) | 286 (63.27) | 91 (60.67) | 98 (64.90) | 97 (64.24) | 0.715 |
| HBV DNA, (log10 IU/ml) | 4.12 (3.10, 5.36) | 4.03 (2.82,5.60) | 4.13 (3.02,5.19) | 4.16 (3.20,5.44) | 0.544 |
| HBeAg positive, n (%) | 116 (25.66) | 35 (23.33) | 38 (25.17) | 43 (28.48) | 0.585 |
|
Causes of disease, n (%) |
0.830 | ||||
| HBV infection only | 369 (81.64) | 123 (82.00) | 125 (82.78) | 121 (80.13) | |
| HBV with other etiologies | 83 (18.36) | 27 (18.00) | 26 (17.22) | 30 (19.87) | |
| Complications, n (%) | |||||
| Ascites | 266 (58.85) | 82 (54.67) | 91 (60.26) | 93 (61.59) | 0.432 |
| Gastrointestinal bleeding | 13 (2.88) | 1 (0.67) | 3 (1.99) | 9 (5.96) | 0.027 |
| Bacterial infection | 138 (30.53) | 42 (28.00) | 53 (35.10) | 43 (28.48) | 0.326 |
| Hepatic encephalopathy | 61 (13.50) | 15 (10.00) | 18 (11.92) | 28 (18.54) | 0.075 |
| Hepatorenal syndrome | 25 (5.53) | 3 (2.00) | 12 (7.95) | 10 (6.62) | 0.060 |
| Common chronic diseases, n (%) | |||||
| Hypertension | 37 (8.19) | 9 (6.00) | 13 (8.61) | 15 (9.93) | 0.449 |
| Diabetes | 51 (11.28) | 12 (8.00) | 18 (11.92) | 21 (13.91) | 0.257 |
| Coronary heart disease | 17 (3.76) | 3 (2.00) | 6 (3.97) | 8 (5.30) | 0.318 |
| ALSS therapy, n (%) | 357 (78.98) | 108 (72.00) | 122 (80.79) | 127 (84.11) | 0.029 |
| Laboratory parameters | |||||
| PT (s) | 22.60 (19.48, 28.13) | 22.15 (19.00,25.43) | 22.30 (19.55,26.20) | 24.70 (19.70,34.30) | < 0.001 |
| INR | 1.99 (1.65, 2.59) | 1.92 (1.60,2.27) | 1.96 (1.66,2.39) | 2.26 (1.68,3.46) | < 0.001 |
| TBIL (umol/L) | 289.95 (212.00, 379.82) | 284.35 (198.45,363.11) | 291.10 (218.50,381.14) | 302.60 (224.84,386.10) | 0.048 |
| ALB (g/L) | 31.60 (28.80, 34.80) | 31.85 (28.88,35.68) | 32.10 (28.30,34.75) | 31.40 (29.20,34.00) | 0.622 |
| ALT (U/L) | 297.00 (110.50, 822.00) | 259.50 (74.75,821.50) | 281.00 (122.50,787.00) | 352.00 (124.00,839.00) | 0.327 |
| AST (U/L) | 219.50 (116.75, 555.00) | 197.00 (102.00,543.00) | 204.00 (116.00,568.00) | 276.00 (135.50,597.00) | 0.093 |
| Cr (umol/L) | 62.00 (51.55, 74.85) | 60.00 (51.32,68.80) | 63.30 (51.15,77.15) | 62.70 (52.00,76.00) | 0.124 |
| Serum urea (mmol/L) | 4.15 (3.10, 5.92) | 4.27 (3.34,5.42) | 4.08 (3.08,5.95) | 4.34 (2.91,6.11) | 0.916 |
| Na (mmol/L) | 135.95 (133.07, 138.62) | 136.65 (134.00,138.60) | 136.60 (133.40,139.20) | 134.80 (132.35,138.40) | 0.041 |
| TG (mmol/L) | 1.23 (0.98, 1.61) | 1.33 (0.98,1.66) | 1.24 (0.98,1.62) | 1.16 (0.99,1.40) | 0.035 |
| TC (mmol/L) | 2.28 (1.90, 2.83) | 2.34 (1.88,2.82) | 2.32 (2.01,3.07) | 2.16 (1.88,2.58) | 0.061 |
| HDL-C (mmol/L) | 0.21 (0.15, 0.31) | 0.32 (0.27,0.38) | 0.22 (0.18,0.29) | 0.13 (0.11,0.16) | < 0.001 |
| LDL-C (mmol/L) | 1.23 (0.90, 1.62) | 1.31 (0.95,1.62) | 1.32 (0.90,1.67) | 1.13 (0.90,1.38) | 0.037 |
| Non-HDL-C (mmol/L) | 2.05 (1.73, 2.51) | 2.01 (1.63,2.45) | 2.11 (1.81,2.78) | 2.03 (1.75,2.42) | 0.085 |
| WBC (109/L) | 6.65 (4.94, 8.97) | 6.25 (4.58,8.82) | 6.84 (5.05,8.76) | 7.02 (5.14,9.46) | 0.090 |
| Neutrophil (109/L) | 4.76 (3.24, 6.44) | 4.06 (2.93,6.17) | 4.80 (3.23,6.26) | 5.17 (3.64,7.35) | 0.009 |
| Lymphocyte (109/L) | 1.11 (0.79, 1.52) | 1.17 (0.85,1.61) | 1.11 (0.71,1.60) | 1.06 (0.79,1.35) | 0.121 |
| Haemoglobin (g/L) | 125.50 (112.75, 140.00) | 126.50 (111.00,141.00) | 125.00 (113.00,139.50) | 125.00 (113.00,136.50) | 0.863 |
| Platelet (109/L) | 102.50 (69.75, 138.00) | 94.00 (68.50,137.75) | 109.00 (71.00,142.50) | 104.00 (69.00,130.00) | 0.765 |
| Blood ammonia (umol/L) | 54.50 (38.30, 70.25) | 53.00 (38.00,70.75) | 52.00 (38.45,65.75) | 58.00 (40.00,75.50) | 0.165 |
| Severity scores | |||||
| MELD score | 25.22 (22.75, 28.73) | 24.43 (22.26,27.14) | 24.84 (23.14,28.10) | 26.49 (23.38,31.94) | < 0.001 |
| COSSH-ACLF II score | 6.72 (6.00, 7.50) | 6.58 (5.90,7.19) | 6.62 (5.96,7.39) | 7.07 (6.35,8.07) | < 0.001 |
| TF mortality, n (%) | |||||
| 28-day | 111 (24.56) | 13 (8.67) | 31 (20.53) | 67 (44.37) | < 0.001 |
| 90-day | 163 (36.06) | 25 (16.67) | 49 (32.45) | 89 (58.94) | < 0.001 |
Abbreviations: PT, prothrombin time; INR, international normalized ratio; TBIL, total bilirubin; ALB, albumin; ALT, alanine aminotransferase; AST, aspartate aminotransferase; Cr, creatinine; Na, sodium; TG, triglyceride; TC, total cholesterol; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; non-HDL-C, non-high-density lipoprotein cholesterol; LT-free mortality, transplant-free
The association between NHHR and short-term mortality in patients with HBV-ACLF
To explore the link between NHHR and short-term TF mortality, three adjusted Cox proportional hazards models were constructed for patients with HBV-ACLF, the outcomes of which are presented in Table 2. Univariable Cox regression analyses for the covariates included in the multivariable models are provided in Supplementary Table 1. In this cohort, there were 111 (24.56%) TF deaths at 28 days and 163 (36.06%) TF deaths at 90 days. After adjusting for confounders, higher NHHR was associated with increased 28- and 90-day TF mortality when examined as a continuous variable (NHHR is unitless; HR per 1-unit increase): adjusted hazard ratio (HR) = 1.09 (95% CI: 1.06–1.12; P < 0.001) for 28-day TF mortality, and adjusted HR = 1.09 (95% CI: 1.06–1.11; P < 0.001) for 90-day TF mortality. When analyzed as a categorical variable (by tertiles), in the fully adjusted model, the HRs and 95% CIs for 28-day TF mortality in T2 and T3 compared with T1 (reference) were 2.07 (1.07–4.01) and 4.23 (2.28–7.86), respectively. For 90-day TF mortality, the corresponding HRs (95% CIs) were 1.79 (1.10–2.93) and 3.77 (2.37–6.00), respectively. VIF diagnostics indicated no substantial multicollinearity in the fully adjusted model (all VIFs < 5; Table S2). Trend tests showed an increasing risk of TF mortality across NHHR tertiles for both 28-day (P for trend < 0.001) and 90-day outcomes (P for trend < 0.001). Overall, these results indicate that higher NHHR levels are associated with an increased risk of poor prognosis in patients with HBV-ACLF.
Table 2.
The adjusted effect of NHHR on 28-day and 90-day transplant-free mortality risk in HBV-ACLF patients
| Model 1 | Model 2 | Model 3 | ||||
|---|---|---|---|---|---|---|
| HR(95%CI) | P-value | HR(95%CI) | P-value | HR(95%CI) | P-value | |
| TF mortality (28-day) | ||||||
| NHHR | 1.10 (1.07, 1.12) | < 0.001 | 1.10 (1.07, 1.12) | < 0.001 | 1.09 (1.06, 1.12) | < 0.001 |
| Tertile | ||||||
| T1 (<8.0) | 1.00 (Reference) | 1.00 (Reference) | 1.00 (Reference) | |||
| T2 (8.0-11.5) | 2.50 (1.31, 4.77) | 0.006 | 2.53 (1.32, 4.84) | 0.005 | 2.07 (1.07, 4.01) | 0.030 |
| T3 (>11.5) | 6.39 (3.53, 11.58) | < 0.001 | 6.29 (3.47, 11.43) | < 0.001 | 4.23 (2.28, 7.86) | < 0.001 |
| P for trend | < 0.001 | < 0.001 | < 0.001 | |||
| TF mortality (90-day) | ||||||
| NHHR | 1.10 (1.08, 1.12) | < 0.001 | 1.09 (1.07, 1.12) | < 0.001 | 1.09 (1.06, 1.11) | < 0.001 |
| Tertile | ||||||
| T1 (<8.0) | 1.00 (Reference) | 1.00 (Reference) | 1.00 (Reference) | |||
| T2 (8.0-11.5) | 2.11 (1.30, 3.42) | 0.002 | 2.13 (1.32, 3.45) | 0.002 | 1.79 (1.10, 2.93) | 0.020 |
| T3 (>11.5) | 5.01 (3.21, 7.82) | < 0.001 | 4.99 (3.19, 7.80) | < 0.001 | 3.77 (2.37, 6.00) | < 0.001 |
| P for trend | < 0.001 | < 0.001 | < 0.001 | |||
HR: hazard ratio, CI: confidence interval
Model 1 was unadjusted
Model 2 was adjusted for age, sex, liver cirrhosis, HBV DNA, HBeAg positive, causes of disease
Model 3 was further adjusted for ascites, gastrointestinal bleeding, bacterial infection, hepatic encephalopathy, hepatorenal syndrome, hypertension, diabetes, and coronary heart disease, ALSS therapy and MELD score based on Model 2
Detection of nonlinear relationships
To explore the nonlinear relationship between NHHR and prognosis, we performed RCS analysis within a Cox proportional hazards model. The results showed that after adjusting for confounding factors, the RCS curves suggested a nonlinear association between NHHR and 28- and 90-day TF mortality (28-day: P for overall < 0.001, P for nonlinear = 0.046; 90-day: P for overall < 0.001, P for nonlinear = 0.012; Fig. 2). Based on the shape of the RCS curves, we observed a possible change in the slope of the association across the NHHR range. Overall, the curves showed an increasing risk with higher NHHR, with a steeper increase at approximately NHHR 10–15 and a more gradual increase at higher NHHR values, accompanied by wider confidence intervals in the upper range (Fig. 2). A segmented Cox model was exploratorily fitted to approximate the apparent change in slope, yielding estimated change-points of 10.87 for 28-day and 11.21 for 90-day TF mortality. The likelihood ratio test indicated that the piecewise model was statistically significant (Table 3). Specifically, below the inflection points, NHHR was positively associated with the risk of both endpoints: each unit increase in NHHR was associated with a 36% increase in the risk of 28-day TF mortality (HR = 1.36; 95% CI: 1.11–1.66; P = 0.003) and a 25% increase in the risk of 90-day TF mortality (HR = 1.25; 95% CI: 1.08–1.43; P = 0.002). However, above the inflection points, the associations remained statistically significant but were attenuated, with HRs of 1.05 (95% CI: 1.01–1.10; P = 0.026) for 28-day TF mortality and 1.06 (95% CI: 1.02–1.10; P = 0.006) for 90-day TF mortality.
Fig. 2.
RCS evaluated the nonlinear association of NHHR with 28-day (A) and 90-day (B) transplant-free mortality in HBV-ACLF patients
Table 3.
Threshold effect analysis of NHHR on 28-day and 90-day transplant-free mortality in HBV-ACLF patients
| NHHR | Adjusted HR (95% CI) | P value |
|---|---|---|
| TF mortality (28-day) | ||
| Standard linear model fitting | 1.09 (1.06, 1.12) | < 0.001 |
| Two-piecewise linear model fitting | ||
| Inflection point | 10.87 | |
| <10.87 | 1.36 (1.11, 1.66) | 0.003 |
| ≥ 10.87 | 1.05 (1.01, 1.10) | 0.026 |
| P for Log-likelihood ratio | 0.021 | |
| TF mortality (90-day) | ||
| Standard linear model fitting | 1.09 (1.06, 1.11) | < 0.001 |
| Two-piecewise linear model fitting | ||
| Inflection point | 11.21 | |
| <11.21 | 1.25 (1.08, 1.43) | 0.002 |
| ≥ 11.21 | 1.06 (1.02, 1.10) | 0.006 |
| P for Log-likelihood ratio | 0.023 |
Subgroup analysis
After adjusting for the same covariates as in Model 3, we conducted subgroup analyses based on gender, age (< 50 years vs. ≥50 years), presence of other liver diseases (yes vs. no), cirrhosis (yes vs. no), HBV DNA load (< 4 vs. ≥4 log10 IU/mL), HBeAg status (positive vs. negative), ascites (yes vs. no), hepatic encephalopathy (yes vs. no), ALSS therapy (yes vs. no), and MELD score (< 28 vs. ≥28) (Fig. 3). Event numbers for each outcome are provided in Fig. 3. Overall, the association between NHHR and 28-day and 90-day TF mortality remained directionally consistent across the predefined subgroups, and no significant interactions were observed (all P for interaction > 0.05). In addition, the proportional hazards assumption was assessed using Schoenfeld residuals (cox.zph), showing no evidence of violation for NHHR and the global test (28-day: NHHR P = 0.245; global P = 0.607; 90-day: NHHR P = 0.126; global P = 0.656; Table S3 and Figure S1).
Fig. 3.
Subgroup analysis of the association between NHHR and 28-day and 90-day transplant-free mortality in HBV-ACLF patients
The value of NHHR in predicting 28-day and 90-day TF mortality in patients with HBV-ACLF
To assess the predictive performance of lipid parameters for poor prognosis in HBV-ACLF, ROC curve analysis was conducted (Fig. 4). The results showed that NHHR demonstrated the highest AUC among all evaluated lipid parameters, which included TC, TG, HDL-C and LDL-C. For predicting 28-day TF mortality, the AUC of NHHR was 0.756 (95% CI: 0.705–0.808) (Fig. 4A), indicating moderate predictive accuracy. To evaluate robustness, we performed 1,000-bootstrap internal validation; the optimism-corrected AUC at day 28 was 0.757 (95% CI: 0.710–0.810). Further comparison with commonly used clinical prognostic scores showed no statistically significant difference between the predictive ability of NHHR and that of the MELD score (AUC: 0.780, 95% CI: 0.728–0.831; P = 0.475) or the COSSH-ACLF II score (AUC: 0.789, 95% CI: 0.740–0.838; P = 0.313). Similarly, for predicting 90-day TF mortality, NHHR achieved an AUC of 0.748 (95% CI: 0.700–0.796). Consistently, 1,000-bootstrap internal validation yielded an optimism-corrected AUC at day 90 of 0.749 (95% CI: 0.701–0.801). Its predictive performance was also not significantly different from that of the MELD score (AUC: 0.769, 95% CI: 0.724–0.815; P = 0.483) or the COSSH-ACLF II score (AUC: 0.783, 95% CI: 0.740–0.826; P = 0.256). In summary, NHHR showed moderate prognostic discrimination for short-term TF mortality and may serve as a convenient adjunct marker for early risk stratification.
Fig. 4.
ROC curves of TG, TC, HDL-C, LDL-C, NHHR, MELD score, and COSSH-ACLF II score for predicting 28-day and 90-day transplant-free mortality
Association of stratified NHHR with outcome of ACLF
Finally, based on the ROC curve analysis, an exploratory, data-driven cutoff value of NHHR for stratifying both 28-day and 90-day TF mortality was identified as 9.86. The corresponding sensitivity values were 80.2% and 76.1%, and the specificity values were 61.3% and 66.4%, respectively. Using this exploratory cutoff, patients were stratified into a low-risk group (NHHR < 9.86) and a high-risk group (NHHR ≥ 9.86). Cox proportional hazards regression showed that, compared with the low-risk group, the high-risk group was associated with a significantly higher risk of TF mortality at 28 days (Model 1: HR 4.97, 95% CI 3.11–7.92; Model 2: HR 4.93, 95% CI 3.09–7.87; Model 3: HR 3.65, 95% CI 2.25–5.93; all P < 0.001) and 90 days (Model 1: HR 4.38, 95% CI 3.05–6.28; Model 2: HR 4.42, 95% CI 3.08–6.35; Model 3: HR 3.58, 95% CI 2.45–5.21; all P < 0.001). Kaplan–Meier survival analysis further showed significant differences in survival between the two groups at both 28 and 90 days (log-rank test, P < 0.001) (Fig. 5).
Fig. 5.
Survival analysis of stratified NHHR for 28-day (A) and 90-day (B) transplant-free mortality in HBV-ACLF patients
Discussion
This study investigated the association between lipid profiles and short-term TF mortality risk in patients with HBV-ACLF, with a specific focus on NHHR. To our knowledge, a nonlinear relationship was identified between NHHR and short-term TF mortality in HBV-ACLF patients. An exploratory, data-driven threshold analysis suggested potential change-points at 10.87 for 28-day and 11.21 for 90-day TF mortality. Higher NHHR was associated with higher short-term TF mortality, with broadly consistent associations across subgroups. Taken together, NHHR is an inexpensive and readily available index that may support early risk stratification and prognostic assessment in HBV-ACLF.
Lipid profiles provide information about lipid metabolism and overall health and have been used in cardiovascular risk assessment [17]. NHHR has been proposed as an integrated lipid index derived from non-HDL-C and HDL-C, which may reflect the balance between atherogenic and anti-atherogenic lipoproteins [18, 19]. Previous studies have reported that NHHR may show better discrimination than traditional lipid parameters such as TC and LDL-C in evaluating atherosclerosis [20]. In addition, several studies have reported an association between NHHR and MASLD, suggesting that NHHR may also be related to hepatic metabolic conditions [21, 22]. In the present study, higher NHHR was associated with poorer 28- and 90-day transplant-free outcomes in patients with HBV-ACLF; to our knowledge, evidence in this specific population has been limited. Across NHHR tertiles, higher NHHR tended to co-occur with more severe clinical and laboratory profiles and higher short-term mortality. The slightly lower HRs in Model 3 likely reflect additional adjustment for disease severity and related covariates, suggesting that part of the crude association is explained by these factors. However, the association remained statistically significant, supporting NHHR as a potential adjunct marker for short-term risk stratification.
The association between elevated NHHR and poor prognosis in HBV-ACLF may involve multiple potential factors. Importantly, since NHHR is derived from non-HDL-C and HDL-C, the higher NHHR observed in this cohort appeared to be mainly driven by markedly lower HDL-C, with non-HDL-C showing no significant difference across tertiles. On the one hand, the liver is the primary organ responsible for synthesizing apolipoprotein ApoA-I, the core component of HDL-C. During HBV-ACLF, extensive hepatocyte injury may impair ApoA-I synthesis, potentially contributing to reduced production of nascent and functionally intact HDL-C particles [23]. HDL-C is integral to lipid metabolism and has also been reported to have protective roles in systemic inflammatory response syndrome (SIRS). It may exert anti-infection activity by neutralizing pathogens, attenuate inflammation through cytokine modulation, reduce oxidative stress via inhibition of lipid peroxidation, and help maintain immune homeostasis by regulating endothelial and leukocyte responses. These pleiotropic effects collectively may help mitigate the severity of SIRS [24, 25]. However, in the setting of intense inflammation and immune dysregulation in ACLF, the protective functions of HDL may be impaired. Although non-HDL-C, which includes LDL-C, may also decrease in ACLF, the decline may be less pronounced than that of HDL-C. This may be related to differences in metabolic pathways: the synthesis of non-HDL-C may be less dependent on hepatocyte functional integrity, and its clearance efficiency may be reduced under inflammatory conditions. As a result, non-HDL-C levels may remain relatively higher than HDL-C during ACLF, potentially contributing to an increased NHHR and reflecting a disturbed lipid–inflammatory milieu [26]. On the other hand, a higher NHHR may be linked to heightened inflammatory responses. Mechanistically, non-HDL-C particles—particularly VLDL remnants and oxidized LDL-C—can be taken up by macrophages, which may promote activation of the NLRP3 inflammasome and may increase the secretion of pro-inflammatory cytokines, including IL-1β and IL-18. This process may interact with SIRS in ACLF and potentially contribute to amplification of the inflammatory cascade [27]. Furthermore, low HDL-C levels may weaken its inhibitory effect on the TLR4/NF-κB pathway, potentially leading to enhanced inflammatory signaling and contributing to multiple organ dysfunction [28, 29].
ROC curve analysis suggested that NHHR provided moderate discrimination for 28- and 90-day TF mortality in this cohort. Previous studies have shown that conventional lipid markers are associated with liver function and disease progression. Taking TC as an example, its synthesis is highly dependent on hepatocyte function, and it may therefore partly reflect the liver’s synthetic capacity and nutritional status [30]. Furthermore, HDL-C has been regarded as a potential predictor of survival in ACLF [6, 31], possibly due to its reported roles in neutralizing bacterial toxins (e.g., LPS), facilitating clearance through hepatic/macrophage pathways, modulating systemic inflammation and oxidative stress, and maintaining endothelial barrier integrity [32, 33]. However, Wen et al. [34] reported that after multivariable adjustment, HDL-C was not an independent predictor of 90-day or 1-year mortality in HBV-ACLF. As an integrated index derived from non-HDL-C and HDL-C, NHHR may offer complementary prognostic information beyond individual lipid measures. In our dataset, a data-driven NHHR cut-off of 9.86 was identified for both 28- and 90-day TF mortality; this exploratory threshold may reflect clustering of early adverse events in ACLF and warrants external validation before broader clinical implementation. Given that NHHR can be readily calculated from routine lipid testing without additional cost, it may serve as a practical adjunct to support prognostic assessment and early risk stratification. Subgroup and interaction analyses involved multiple comparisons; after updated covariate adjustment, no statistically significant interactions were observed, and subgroup differences should be interpreted cautiously.
Given that MELD and COSSH-ACLF II scores are essential for prognostic assessment and key clinical decision-making in ACLF (e.g., liver transplantation evaluation), NHHR may provide complementary prognostic information as a practical adjunct marker. Easily derived from routine lipid measurements, NHHR is rapid and low-cost, which may support early risk stratification when score calculation is delayed or resources are limited, and help refine risk among patients with similar MELD/COSSH-ACLF II levels. Future prospective studies are warranted to evaluate its incremental value when combined with established scores. Although pregnant women were not included in this study, HBV infection during pregnancy is a challenging clinical scenario in which HBV DNA monitoring and peripartum antiviral prophylaxis are often used to reduce adverse outcomes and mother-to-child transmission [35–38]. The applicability of NHHR in this population warrants further validation. Extrapolation to complex settings such as decompensated cirrhosis with portal vein thrombosis should be cautious [39]. Retrospective data also suggest that cell-based therapies (e.g., bone marrow mononuclear cells, BMMNCs) may improve long-term survival in cirrhosis [40].
Although NHHR levels and short-term clinical outcomes in HBV-ACLF patients were thoroughly examined in this investigation, several limitations should be noted. First, as a retrospective, single-center investigation, the generalizability of our findings may be limited; although internal validation was performed using 1,000 bootstrap resamples, external validation in independent cohorts is still lacking. In addition, despite multivariable adjustment, residual confounding from unmeasured or incompletely measured factors cannot be fully excluded. Moreover, due to the retrospective design, alcohol consumption history could not be systematically collected and quantified, and confounding related to alcohol intake may not have been fully controlled. Second, this study only captured baseline NHHR values at admission, failing to dynamically reflect their changes throughout the disease course. Third, the endpoints were focused solely on short-term survival at 28 and 90 days, without assessing the predictive utility of NHHR for long-term outcomes, such as one-year survival, rehospitalization rates, or decompensation events in cirrhosis. Future studies should incorporate serial monitoring of NHHR trajectories and extend the follow-up duration to clarify its value in predicting long-term prognosis.
Conclusion
In conclusion, our findings suggest that higher NHHR is associated with increased 28- and 90-day transplant-free mortality in patients with HBV-ACLF. As an easily calculated index, NHHR may provide complementary information for early risk stratification alongside established prognostic scores.
Supplementary Information
Below is the link to the electronic supplementary material.
Acknowledgements
Not applicable.
Abbreviations
- HBV-ACLF
Hepatitis B virus-related acute-on-chronic liver failure
- NHHR
The non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio
- APASL
The Asia-Pacific Association for the Study of the Liver
- RCS
Restricted cubic splines
- ROC
Receiver operating characteristic
- ALSS
Artificial liver support system
- PT
Prothrombin time
- INR
International normalized ratio
- TBIL
Total bilirubin
- TG
Triglycerides
- TC
Total cholesterol
- HDL-C
High-density lipoprotein cholesterol
- LDL-C
Low-density lipoprotein cholesterol
- non-HDL-C
Non-high-density lipoprotein cholesterol
Author contributions
Z.L. conceived the study, gathered and analyzed the data, created tables and figures, and drafted the initial manuscript. S.D. and C.Z. collected the data. C.S. reviewed the statistical methods. Y.G. conceived the study and reviewed the final version. The article’s final version was approved by all authors who contributed to it.
Funding
This work was supported by Natural Science Foundation of China (No. 82370608), Anhui Provincial Natural Science Foundation (No. 2208085MH204), Anhui Education Department Natural Science Foundation (No. 2022AH040160), and Anhui Provincial Special Fund for Clinical Medical Research Transformation (No. 202304295107020040).
Data availability
The data supporting the findings of this study are available from the corresponding author upon reasonable request.
Declarations
Ethics approval and consent to participate
The study protocol was approved by the Ethics Committee of the First Affiliated Hospital of Anhui Medical University (No: PJ2022-07-18) and was conducted in accordance with the Declaration of Helsinki and local institutional regulations. Informed consent was waived due to the retrospective and anonymized nature of the study.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Supplementary Materials
Data Availability Statement
The data supporting the findings of this study are available from the corresponding author upon reasonable request.