Plasma GLP-1 and metabolic dynamics during human liver regeneration and their association with posthepatectomy liver failure

Introduction

The liver’s ability to regenerate after substantial tissue loss is the foundation of oncologic liver surgery. Understanding the limits of how much tissue loss the remaining liver can compensate for maintaining homeostasis, and accurately estimating its regenerative capacity preoperatively, is critical to identify patients eligible for major liver surgery. Posthepatectomy liver failure (PHLF) still remains the leading cause of short-term mortality following major hepatectomy (1-3). Underlying liver diseases, increasingly prevalent in patients, critically impact hepatic functional reserve and regenerative capacity (4,5). Additionally, accumulating evidence suggests that alterations in intermediary metabolism significantly impact hepatic regeneration (6,7). In this context, lipid metabolism, providing for the increased energy demands and structural components necessary for membrane synthesis, is of utmost importance (7). Rodent models have demonstrated significant changes in lipid metabolism after induction of liver regeneration, involving not only hepatic de-novo synthesis, hepatic fatty acid trafficking and lipid oxidation, but also affecting peripheral lipid mobilization (7-9). Increased flux of lipids and fatty acids to the liver result in transient liver steatosis, crucially required for sufficient liver regeneration and disruption or delay of this process has been proven detrimental (10,11).

Although extensively studied in murine models, little is known about regulators of such and post-operative dynamics of lipid metabolism in humans following hepatectomy. In the context of hepatic regeneration, to our knowledge, these have never been reported. Previously, we suggested an association of the enteroendocrine L-cell derived incretin hormones glucagon-like peptide-1 (GLP-1) and glucagon-like peptide-2 (GLP-2) in regulating lipid metabolism (6). In particular, both have been attributed to opposing effects on lipid and (apo)lipoprotein mobilization and metabolism. While GLP-1 exerts a decreasing effect on lipid absorption, plasma levels and hepatic fatty content, GLP-2 has pro-lipidemic effects and is critically regulated during liver regeneration (6). In rodent partial hepatectomy models, GLP-1 administration was reported to be detrimental (12), while GLP-2 administration has shown to improve liver regeneration (13).

Acknowledging that therapeutic targets for treating PHLF have not yet been identified, preoperative risk stratification remains paramount (14). Importantly, the majority of approaches for preoperative liver function assessment primarily focus on the liver’s excretory or synthetic functions, neglecting the metabolic aspects of hepatic regeneration.

We aimed to investigate perioperative dynamics of circulating metabolites in functional and dysfunctional liver regeneration in patients undergoing partial hepatectomy by utilizing an unbiased metabolomic approach. Furthermore, given the fact that little is known about regulators critically affecting alterations in lipid metabolism during liver regeneration, we evaluated plasma level dynamics of GLP-1 and GLP-2 in relation to the histologic phenotype of metabolic dysfunction-associated steatotic liver disease (MASLD) and PHLF. We further assessed the predictive potential of baseline GLP-1 plasma levels, aiming to reflect the metabolic component of hepatic regeneration and ultimately compared it to established predictors of PHLF (15-18). We present this article in accordance with the STROBE reporting checklist (available at https://hbsn.amegroups.com/article/view/10.21037/hbsn-24-464/rc) (19).

Methods

Study populations

This study included 156 patients from two institutions. Eighty-one patients underwent liver resection at the Clinicum Landstrasse, Vienna from January 2012 to January 2018 (cohort 1) and 75 patients had liver resections at State Hospital Wiener Neustadt, Austria between July 2018 and December 2023 (cohort 2). Patient selection for inclusion was conducted according to plasma sample availability in our prospectively maintained biobank. All patients underwent liver resections for colorectal liver metastasis (CRLM), hepatocellular carcinoma (HCC), intrahepatic or perihilar cholangiocarcinoma carcinoma (iCCA/pCCA), other secondary liver tumors or benign liver lesions. None of the patients had a documented history of chronic excessive alcohol consumption. Patients with type II diabetes mellitus did not take any antidiabetic drugs within 24 hours prior to surgery (except short-acting insulin for immediate blood glucose correction), and none of the patients had used GLP-1 analogs. Resections were classified as minor (<3 anatomical segments) or major (≥3 anatomical segments) resections according to the Brisbane 2000 nomenclature (20). Good Scientific Practice in accordance with guidelines and principles of the Declaration of Helsinki (as revised in 2013) were followed. Blood collection was conducted after obtaining written informed consent of patients and approval by the institutional ethics committees (Ethics Committee of the Medical University of Vienna: EK 16-253-0117 and Ethics Committee Niederösterreich: GS-1-EK-4/568-2018).

Definition of outcome parameters

PHLF according to the definition of the International Study Group of Liver Surgery (ISGLS) was defined as clinical endpoint. In brief, elevation of serum bilirubin (SB) level exceeding the ULON and a prolonged prothrombin time (PT) on POD5 or beyond was classified as PHLF. If SB or PT were preoperatively elevated, any further increase until POD5 or beyond was considered for PHLF classification. Patients who were discharged before POD5 due to excellent clinical presentation were classified as not having PHLF (21).

Postoperative morbidity was graded according to the Dindo et al. classification. Severe morbidity was noted if invasive complication management was required, with the most severe complication taken as the reference (22).

Blood collection and processing

For the first cohort, blood was collected into prechilled citrate, theophylline, adenosine and dipyridamole (CTAD) tubes prior to surgery (PRE), on POD1 and POD5 and immediately placed on ice. In the second cohort, blood was collected following the same protocol as in cohort 1 and drawn into EDTA tubes additionally containing a DPP4-inhibitor (10 µmol/L, Sigma Aldrich, St. Louis, MO, USA) and aprotinin (500 KIU/mL, Sigma Aldrich, St. Louis, MO, USA) for protease inhibition, according to established protocols (23). Blood was drawn after a minimum of 8 hours of fasting, consistently at the same time each morning at the specified timepoints. For both cohorts, plasma preparation was carried out as previously described (24). In brief, drawn blood samples were chilled on ice and further processed by two immediate centrifugation steps within 20 min after acquisition, including 10 min, 1,000 g at 4 ℃ and 10,000 g for 10 min at 4 ℃ for further purification from cell detritus and platelets. Plasma was stored in aliquots at −80 ℃ until further use.

Metabolomic analysis

Metabolites were analyzed using a targeted quantitative metabolomics assay with internal quality control (Biocrates Absolute IDQ p180 kit from Biocrates Life Science, Innsbruck, Austria). This independently validated kit includes analyses of 76 phosphatidylcholines (PCs), 14 lysophosphatidylcholines, 15 sphingomyelins (SMs), 40 acylcarnitines, 21 amino acids, and 19 biogenic amines (25). Sample preparation and quality control were conducted according to the vendor’s manual (Archimed Life Science, Vienna, Austria).

GLP-1, GLP-2 and standard plasma lipid parameters

Commercially available ELISA kits were used to determine plasma concentrations of total GLP-1 (7–36 and 9–36) (ALPCO, Salem, NH, USA) and total GLP-2 (1–33 and 3–33) (Merck Millipore, Burlington, MA) following the manufacturers’ instructions. Routine laboratory parameters, including alanine aminotransferase (ALT), aspartate aminotransferase (AST), γ-glutamyltransferase (γ-GT), alkaline phosphatase (AP), bilirubin, PT, triglycerides, total cholesterol, low-density lipoprotein cholesterol (LDLc), high-density lipoprotein cholesterol (HDLc), apolipoprotein A1 (ApoA1), and apolipoprotein B (ApoB), were measured perioperatively in fasting plasma.

Histological analysis

The histological phenotype of MASLD was evaluated in tumor distant liver tissue from resection specimens. Two trained clinical pathologists, blinded for patient’s clinical outcome analyzed hematoxylin/eosin dyed formalin-fixed paraffin-embedded (FFPE) tissue slices by employing the NAFLD activity score (NAS) (26). Briefly, by allocating points for histological findings of steatosis, hepatocyte ballooning and inflammation activity, groups were stratified and named according to the Delphi consensus nomenclature noMASLD (0–2 patients) and MASLD (≥3 patients). Fibrosis was assessed employing the Kleiner et al. classification (27).

Statistical analysis

The analysis was based on non-parametric tests due to the small sample sizes in rare clinical outcome groups and to increase robustness regarding outliers. Partial least squares discriminant analysis (PLS-DA) was used to identify the most relevant postoperatively changed metabolites and their association with PHLF. Metabolic profiles were hierarchically clustered based on Euclidean distance with average linkage using Genesis (TU Graz, Austria, version 1.8.1, 2017). The Mann-Whitney U test or Kruskal-Wallis test was applied for the comparison of means. The chi-square and Fisher exact test were used to analyze group differences regarding metabolic clusters and histological phenotypes. Associations between variables were evaluated using Spearman correlation. The odds ratios of variables potentially associated with PHLF were calculated using univariate binary regression modeling. Sensitivity and specificity for PHLF risk prediction were calculated using receiver operating characteristic (ROC) and in order to avoid overfitting, leave-one-out cross validation (LOOCV) was performed. Total GLP-1 values were log₂ transformed to approximate normal distribution. The optimal cutoffs for PHLF prediction were determined by calculating the Youden index (J). Collinearity between the evaluated models was assessed and ruled out, as indicated by a variance inflation factor (VIF) of 1. Model goodness of fit was evaluated based on minimal Akaike information criterion (AIC). A P-value of less than 0.05 was considered statistically significant. Statistical analyses were conducted using SPSS^® version 20 (IBM, Armonk, NY, USA), GraphPad Prism 8.0.2 (GraphPad Software Inc., San Diego, CA, USA), and R (R Foundation for Statistical Computing, Vienna, Austria, version 3.6.1, 2019).

Results

Patient demographics

In this study, a total of 156 patients were included and evaluated according to technical considerations in two separate cohorts of 81 and 75 patients. The median age of the total cohort was 64.9 years at the time of surgery and 69% were men. Severe underlying fibrosis was observed in 19% of patients in cohort 1 and 16% in cohort 2, with higher prevalence in patients with HCC, at 42% and 56%, respectively. Patient characteristics are displayed in Table 1. Our first cohort was stratified for their alterations of their metabolic profile until the first and fifth postoperative day (POD) (Cluster A and Cluster B). The second cohort was stratified in subgroups according to the presence of MASLD histologic phenotype and PHLF. Both cohorts were comparable to each other, except major liver resections were more often performed in the first cohort than in the second (70% vs. 37%; P<0.001). PHLF occurred in 27% in cohort 1 and in 16% in cohort 2 (P=0.42).

Alterations of circulating metabolites during liver regeneration

By analyzing postoperative changes of 180 circulating metabolites in 81 patients, we were able to delineate 44 metabolites that changed significantly during liver regeneration. Among those, we found 12 proteinogenic amino acids and 23 phospholipid species significantly altered within the first POD (all P<0.05). Utilizing an unsupervised hierarchical clustering approach, we identified 2 metabolomic clusters displaying most similar variance, including 31 patients in cluster A and 50 patients in cluster B. Cluster A was associated with an increase in circulating amino acids, while phospholipids were markedly decreased compared to baseline level. Among the decreased ten identified PC species, 80% had polyunsaturated primary acyl chains, while only 17% of the 12 SM metabolites had 2 or more double bounds. Interestingly, the incidence of PHLF was significantly higher in cluster A compared to cluster B (39% vs. 10%; P=0.004). When assessing PHLF incidence in the subgroups of major resections, 48% of patients in cluster A and 9% in cluster B developed PHLF (P=0.01). Additionally, the incidence of steatosis >33% was lower (4%) in cluster A than in cluster B (26%, P=0.04) and histologic phenotype of MASLD was present in histological specimens of tumor distant liver tissue in 40% and 24%, respectively (P=0.31) (Figure 1).

Figure 1 Changes in circulating metabolites until POD1. (A) Heatmap of 44 significantly changed metabolites from baseline until POD1. (B) Partial least squares-discriminant analysis indicates the distribution of the 44 metabolites according to PHLF development. PHLF, posthepatectomy liver failure; HCC, hepatocellular carcinoma; CCA, cholangiocarcinoma; CRLM, colorectal cancer liver metastasis; MASLD, metabolic dysfunction-associated steatotic liver disease; n.a., detail not available; POD1, postoperative day 1; PRE, prior to surgery; PLS-DA, partial least squares discriminant analysis.

Differences in lipid metabolism aggravate till POD5

When evaluating changes of circulating metabolites until POD5 in 38 patients (samples available for patients that had not left the hospital), 40 metabolites remained significantly altered. Thirty-five of these were lipid species, including 20 PCs, 9 SMs and 6 Acyl-carnitines (ACs). Interestingly, 15 PCs were polyunsaturated with 12 PCs having 4 or more double bounds. Except for PCaaC36:5, all other phospholipids were decreased in cluster A. On the contrary, all 6 ACs were increased in Cluster A. Two were medium-chain length ACs (6–12 carbon acyl-residues) and 3 had >12 carbon acyl-residues (long-chain). Of note, 93% of all PHLFs in this cohort (P<0.001) were associated with the metabolic profile of Cluster A. Again, intriguingly, cluster A showed a lower rate of baseline >33% steatosis (6% in cluster A vs. 37% in cluster B; P=0.04) and a similar rate of MASLD (21% cluster A vs. 38% cluster B; P=0.44) (Figure 2).

Figure 2 Changes in circulating metabolites until POD5. (A) Heatmap of the 40 unsupervised hierarchical clustered metabolites according to their significant changes in 38 patients with accessible plasma samples until POD5. (B) Partial least squares-discriminant analysis of the 40 metabolites in PHLF. PHLF, posthepatectomy liver failure; HCC, hepatocellular carcinoma; CCA, cholangiocarcinoma; CRLM, colorectal cancer liver metastasis; MASLD, metabolic dysfunction-associated steatotic liver disease; n.a., detail not available; POD, postoperative day; PRE, prior to surgery; PLS-DA, partial least squares discriminant analysis.

Perioperative dynamics of standard plasma lipid parameters

We further aimed to confirm, if the observed alterations of lipid metabolites would be reflected standard blood lipid parameters during liver regeneration. Accordingly, we investigated perioperative triglyceride, total cholesterol, HDLc, LDLc, apolipoprotein A1 and apolipoprotein B trajectories in patients stratified for their histological phenotype of MASLD and PHLF perioperatively during liver regeneration. We observed a consistent postoperative decrease in all parameters until the first POD. While triglyceride levels tended to increase again until POD5 in all patients, this recovery was delayed in PHLF (Figure 3A). Plasma total cholesterol, HDLc, LDLc, apolipoprotein A1 and apolipoprotein B further decreased, with significant differences between groups at certain time points (as illustrated in Figure 3). Except of triglycerides, PHLF was associated with the lowest circulating levels of total cholesterol, HDLc, LDLc, apolipoprotein A1 and apolipoprotein B, already present at baseline level preoperatively (Figure 3B-3F).

Figure 3 Perioperative standard plasma lipid parameter dynamics stratified for histologic MASLD phenotype and PHLF. (A-F, left panels) Postoperative dynamics of plasma triglycerides, total cholesterol, HDLc, ApoA1, LDLc and ApoB. (A-F, right panels) Preoperative differences of indicated lipid parameters according to PHLF development. *, P<0.05; **, P<0.005; ***, P<0.0005. MASLD, metabolic dysfunction-associated steatotic liver disease; PHLF, posthepatectomy liver failure; PRE, preoperative; POD, postoperative day; HDLc, high-density lipoprotein cholesterol; LDLc, low-density lipoprotein cholesterol; ApoA1, apolipoprotein A1; ApoB, apolipoprotein B.

When summarizing patients who did not develop PHLF and compare baseline lipid levels to PHLF patients, HDLc and apoA1 level were already preoperatively significantly decreased in PHLF (P=0.01 and P=0.005, respectively). PHLF development was not related to patients BMI, MASLD histology, preoperative chemotherapy, but related to diabetes [odds ratio (OR) 3.85, 95% CI: 1.06–13.91; P=0.04] (Table S1).

GLP-1 and GLP-2 are linked to circulating lipid dynamics during liver regeneration

Given the suggested involvement of GLP-1 and GLP-2 in regulating lipid mobilization and trafficking during liver regeneration, we further evaluated perioperative GLP-1 and GLP-2 plasma concentrations in patients stratified according to their histological phenotype of tumor distant liver tissue and PHLF. In PHLF patients, GLP-1 was already preoperatively 3-fold elevated compared to patients without the histological phenotype of MASLD (noMASLD) (P=0.009, Figure 4A) and 2.3 times increased compared to MASLD patients, although statistically not significant (P=0.19, Figure 4A). In all patients GLP-1 level decreased until the first POD and further reached baseline level until POD5. The magnitude of change between groups were similar (right panel of Figure 4A). On the other hand, baseline GLP-2 concentrations were not elevated in PHLF patients, but differed between noMASLD and MASLD (P=0.03). Postoperatively, GLP-2 level increased markedly when PHLF occurred (2.1-fold in PHLF vs. 1.1-fold in noMASLD vs. 1.0-fold in MASLD, P=0.002 and P=0.10, respectively), most prominent upon POD5 (2.5-fold in PHLF vs. 1.6-fold in noMASLD vs. 1.2-fold in MASLD, P=0.02 and P=0.005, respectively) (Figure 4).

Figure 4 Perioperative total GLP-1 and total GLP-2 dynamics. (A, left) Total GLP-1 and (B, left) total GLP-2 concentrations at baseline, on POD1 and POD5 according to the histological phenotype of MASLD and PHLF. (A + B, right) Comparison of absolute magnitude of total GLP-1 and total GLP-2 change until POD1 and POD5. *, P<0.05; **, P<0.005. GLP-1, glucagon-like peptide-1; GLP-2, glucagon-like peptide-2; PRE, preoperative; MASLD, metabolic dysfunction-associated steatotic liver disease; PHLF, posthepatectomy liver failure; POD, postoperative day.

When correlating total GLP-1 and -2 plasma levels with standard plasma lipid parameters in the entire cohort 2, our data surprisingly revealed an inverse correlation of lipid parameters with GLPs. Our findings depicted a positive correlation of triglycerides with total GLP-1 and an inverse correlation of total GLP-2 with total cholesterol, HDLc, LDLc, apolipoprotein A1 and apolipoprotein B. This relation was even stronger in the cohort that developed PHLF (Table S2).

Preoperative GLP-1 level predict risk for PHLF

We further compared baseline GLP-1 in patients that did and did not develop PHLF. We found a significant elevation of GLP-1 levels already preoperatively in patients with PHLF (P=0.02) (Figure 5). Of note, preoperative GLP-1 level did not appear to be associated with diabetes, steatosis, fibrosis or MASLD (Figure S1).

Figure 5 Preoperative total GLP-1 plasma level. Baseline total GLP-1 plasma level in patients who did and did not develop PHLF. **, P<0.005. GLP-1, glucagon-like peptide-1; PHLF, posthepatectomy liver failure.

We further assessed baseline GLP-1 level for its PHLF predictive potential and compared it to the combined APRI + ALBI score, that we previously identified as strong preoperative predictor for PHLF.

ROC analysis confirmed the predictive potential of the APRI + ALBI score [area under the curve (AUC): 0.804, 95% confidence interval (CI): 0.614–0.961, bootstrap]. Furthermore, we evaluated log₂(GLP-1): AUC: 0.753 (95% CI: 0.614–0.961, bootstrap) and integrated it into the APRI + ALBI | log₂(GLP-1) model, observing an improved prediction for PHLF [AUC: 0.833 (0.660–0.964, bootstrap)] reaching a superior sensitivity of 67%, specificity of 91% with a PPV of 67% and a NPV of 91% (Figure 6, Table 2).

Figure 6 Prediction of PHLF. (A) ROC evaluation of the combined APRI + ALBI score, and regression curve of the logistic regression model’s P-score illustrating the relationship of PHLF and the APRI + ALBI score (B) and log₂(GLP-1). (C) ROC analysis of the integrated APRI + ALBI | log₂(GLP-1) model; light blue dot indicating cut-off according to the Youden index (left panel); scatter blot of the combined APRI + ALBI | log₂(GLP-1) model, light blue line indicating model’s cut-off for identification of PHLF patients (red dots). PHLF, posthepatectomy liver failure; APRI, aspartate aminotransferase to platelet ratio index; ALBI, albumin-bilirubin score; AUC, area under the curve; LOOCV, leave one out cross validation; Log₂(GLP-1), log₂ transformation of non-normal distributed GLP-1 plasma values; ROC, receiver operating characteristic.

Discussion

This study highlights the role of lipid metabolism homeostasis in human liver regeneration across two independent cohorts. We identified significant alterations in circulating lipid species during hepatic regeneration in all patients, but also striking differences between patients with and without PHLF. Importantly, we found these processes to be independent from advanced hepatic steatosis or the histologic phenotype of MASLD. Additionally, we found correlations with metabolic regulators GLP-1 and GLP-2, which are crucial for and affected by hepatic regeneration. Recognizing the link between PHLF development and lipid homeostasis, we identified significant preoperative differences in GLP-1, HDLc, and ApoA1. We found GLP-1 plasma levels to be a strong predictor of PHLF in our exploratory analyses, highlighting the metabolic aspect of liver function. Integrating GLP-1 levels into the APRI + ALBI score, a strongly validated prediction model that does not include metabolic parameters, we were able to improve predictive performance and offer a more holistic model for PHLF risk stratification.

Rodent studies show significant metabolic changes in lipid homeostasis during liver regeneration, including lipid mobilization from peripheral storage and fatty acid flux into hepatocytes. These changes provide metabolites for beta-oxidation and structural components. Importantly, these adaptations are global and occur independently of the liver injury model used (7-11). Our metabolomic data of human samples closely mirrored these observations, revealing a significant decrease in circulating PCs, especially those with polyunsaturated acyl-chains (PUFA-PCs), in cases of delayed or impaired liver regeneration. PCs, crucial for cell homeostasis and viability, are synthesized via the Kennedy (CDP-choline) pathway and the Lands cycle ubiquitously across all cell types (28-30). However, hepatocytes specifically use the phosphatidylethanolamine N-methyltransferase (PEMT) pathway, accounting for about 30% of liver PC synthesis and the primary source of circulating PUFA-PCs (31,32). Liver derived lipids, including SMs, must be packaged into very-low-density lipoproteins (VLDL) to enter circulation and are exchanged between HDL and LDL particles for liver recycling, a process critically dependent on PC synthesis (33). Common rodent liver injury models often disrupt PC synthesis by dietary choline depletion, underscoring the importance of PC homeostasis (34-36). Our data showed decreased perioperative HDLc and ApoA1 level in PHLF patients, potentially reflecting compromised liver function associated with impaired liver regeneration.

Previous reports have shown down-regulation of the PEMT pathway during liver regeneration, reducing hepatic PUFA-PC content and secretion (37). Our data indicated a postoperative decrease in circulating PUFA-PCs and SMs, along with decreased levels of ApoA1, HDLc, and total cholesterol, especially in patients with insufficient liver regeneration, reflecting a shift of plasmatic lipids into hepatocytes and decreased de novo synthesis during regeneration (9).

Although the baseline metabolomic profile was not distinctly associated with PHLF development, our observation of differences in preoperative standard plasma lipid parameters in the second cohort suggests a disruption of underlying metabolic homeostasis that predisposes to impaired liver regeneration. Of note, we did not observe alterations of circulating metabolites associated with the histological phenotype of MASLD or advanced steatosis, which were even less prevalent in patients who developed PHLF.

While a number of cellular regulators are known to influence hepatocyte lipid metabolism during liver regeneration, little is known about the systemic effectors that orchestrate lipid mobilization and trafficking during this process (7). GLP-1 and GLP-2 have been extensively studied in the context of metabolic disorders, particularly in diabetes and short bowel syndrome, respectively (38,39). Their roles in liver regeneration on multiple levels have been discussed, including their metabolism-regulating effects across various models and in humans (6,12,13). Briefly, as effectors of the gut-liver axis, GLP-1 and GLP-2 secretion from enteroendocrine L-cells is regulated not only by luminal nutrient exposure, but also during fasting via bile acids and interleukin-6, both of which are altered during liver regeneration after hepatectomy (6). Physiologically, GLP-1 and GLP-2 have opposing effects on plasma and hepatic lipid metabolism. GLP-1 decreases plasma lipid levels and hepatic lipid content (40,41), while GLP-2 mobilizes lipids from peripheral stores (42), upregulates lipid mobilization via chylomicron and VLDL synthesis, thereby increasing plasma lipid levels and ultimately hepatic lipid content, both in rodents and humans (40,43-45). Interestingly, in rodents, perioperative GLP-1 supplementation has been reported to negatively affect hepatocyte proliferation, potentially by reducing postoperative hepatic lipid accumulation, a process repeatedly shown to be critical for effective liver regeneration (12). Mechanistically, GLP-1 increases protein kinase A (PKA) activity, leading to phosphorylation of Akt and AMP-activated protein kinase (AMPK), the master regulator of cellular energy homeostasis. This promotes fatty acid β-oxidation, lipid breakdown, and inhibition of hepatic lipogenesis, ultimately reducing hepatic lipid content (46-49). In contrast, GLP-2 supplementation has been shown to support liver regeneration in rodents (13), potentially through additional mechanisms beyond its lipogenic effects, including immunological regulation within the gut-liver axis and improved mesenteric perfusion, as we have discussed in more detail elsewhere (6).

Our data further elucidate the relationship between GLP-1 and GLP-2 levels and standard lipid parameter dynamics during liver regeneration. Interestingly, the plasma concentrations of GLP-1 and GLP-2 exhibited an inverse relationship with plasma lipid levels, which is contrary to their expected physiological effects. This suggests an adaptive postoperative downregulation of GLP-1 and upregulation of GLP-2 secretion, most prominent in PHLF, independent of the liver tissue’s histological phenotype.

Maintaining lipid homeostasis is a critical aspect of overall liver function, and its adaptive capacity is essential for effective liver regeneration. Established preoperative liver function assessments to predict PHLF risk have been largely neglecting lipid metabolic processes. Our observation of elevated baseline GLP-1 levels in patients who developed PHLF, independent of severe fibrosis, diabetes, hepatic steatosis or histological MASLD phenotype, suggests an unfavorable preoperative homeostatic state. This is particularly intriguing, as GLP-1 levels are typically decreased in type 2 diabetes and obesity, both key features of metabolic syndrome, frequently associated hepatic steatosis/MASLD (50,51). Given this preoperative association, along with previously reported detrimental effects of GLP-1 on liver regeneration in rodent models, the perioperative use of GLP-1 analogs, which are widely used for weight reduction in obesity (52) and are highly effective at reducing hepatic lipid content (53), raises concerns about their safety in the context of liver surgery.

Leveraging baseline GLP-1 levels as a predictive marker from the metabolic component of liver health and integrating it into the combined APRI + ALBI score, which we previously have documented to represent the most comprehensive stratifier for assessing global liver function and PHLF risk (16,17), demonstrated a superior model to predict PHLF.

In interpreting these study results, several limitations must be acknowledged. First, it is important to acknowledge that our study population is quite heterogeneous in terms of tumor type, multimodal therapies, and the extent of resection, all of which must be considered when interpreting our results. Additionally, potential pharmacological side effects that may influence metabolism should also be taken into account as a confounding factor. Secondly, we did evaluate two separate cohorts, one with detailed metabolic profiling and one with detailed routine lipid parameter profile. While it might be perceived as strength of the study that we observed similar processes in these two independent cohorts, we are unable to directly correlate metabolic assessments with lipid profiles. Thirdly, while our GLP-1-based PHLF prediction model is the first to reflect the metabolic component of liver regeneration, it is important to emphasize that this analysis is exploratory in nature. The findings are constrained by the small cohort size, highlighting the need for validation and further investigation in larger-scale studies. Moreover, whether GLP-1 is causatively involved in the pathophysiology of PHLF or merely reflects the metabolic steady state requires further clarification.

Conclusions

In summary, our data provide evidence that significant alterations in lipid metabolism occur during human liver regeneration, as reflected by changes in circulating plasma lipids, most distinct in cases of impaired liver regeneration. We identified an association between the dynamics of plasma GLP-1, GLP-2 and plasma lipid levels, as well as with PHLF, which was not associated with the histological liver tissue phenotypes of severe steatosis or MASLD. Preoperative GLP-1 levels were vital to predict PHLF, highlighting the necessity of including lipid metabolism parameters in global liver function and PHLF risk assessments.

Acknowledgments

Funding: This study was supported by the Austrian government “Bürgermeisterfonds, Vienna, Austria” (18129); NIH (R01DK122813); the Austrian Science Fund (FWF) (P-32064); and “City of Vienna Fund for Innovative Interdisciplinary Cancer Research” (21041). M.A. was personally supported by means of the IHPBA Kenneth Warren Fellowship during conducting this study. The funding organizations had no influence on study design, data management or publication of this article.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://hbsn.amegroups.com/article/view/10.21037/hbsn-24-464/rc

Data Sharing Statement: Available at https://hbsn.amegroups.com/article/view/10.21037/hbsn-24-464/dss

Peer Review File: Available at https://hbsn.amegroups.com/article/view/10.21037/hbsn-24-464/prf

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://hbsn.amegroups.com/article/view/10.21037/hbsn-24-464/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by the institutional ethics committees (Ethics Committee of the Medical University of Vienna: EK 16-253-0117 and Ethics Committee Niederösterreich: GS-1-EK-4/568-2018) and individual consent for this retrospective analysis was obtained.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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