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High visceral adipose tissue area is independently associated with early allograft dysfunction in liver transplantation recipients: a propensity score analysis



To evaluate the association between adipose tissue distribution and early allograft dysfunction (EAD) in liver transplantation (LT) recipients.


A total of 175 patients who received LT from April 2015 to September 2020 were enrolled in this retrospective study. The areas of abdominal adipose tissue and skeletal muscle of all patients were measured based on the preoperative CT images. The appropriate statistical methods including the propensity score-matched (PSM) analysis were performed to identify the association between adipose tissue distribution and EAD.


Of 175 LT recipients, 55 patients (31.4%) finally developed EAD. The multivariate logistic analysis revealed that preoperative serum albumin (odds ratio (OR) 0.34, 95% confidence interval (CI) 0.17–0.70), platelet–lymphocyte ratio (OR 2.35, 95% CI 1.18–4.79), and visceral adipose tissue (VAT) area (OR 3.17, 95% CI 1.56–6.43) were independent associated with EAD. After PSM analysis, VAT area was still significantly associated with EAD (OR 3.95, 95% CI 1.16–13.51). In survival analysis, no significant difference was identified in one-year graft failure (log-rank: p = 0.487), and conversely result was identified in overall survival (OS) (log-rank: p = 0.012; hazard ratio (HR) 4.10, 95% CI 1.27–13.16).


LT recipients with high VAT area have higher risk for the occurrence of EAD, and high VAT area might have certain clinical value for predicting the poor OS of patients. For LT candidates with large amount of VAT, the clinicians can take clinical interventions by suggesting physical and nutritional treatments to improve outcomes after LT.

Key points

  • Liver transplantation (LT) recipients with high visceral adipose tissue (VAT) area were more likely to develop early allograft dysfunction.

  • High VAT area may be associated with poor OS in LT recipients.

  • LT candidates with high VAT area may be targets for timely therapeutic interventions.


Liver transplantation (LT) is the only effective therapeutic way for patients with end-stage liver disease [1]. In the past decades, several developments in surgical techniques, immunosuppression, and perioperative care have dramatically improved the postoperative outcomes [2, 3]. However, a considerable number of patients still develop graft insufficiency and failure after LT due to various kinds of postoperative complications, which may cause declines of survival rate and quality of life of patients.

EAD is a common and critical complication after LT, which indicates early graft function insufficiency and adversely influences patient and graft survival [4,5,6]. EAD was defined as the presence of at least one of the following criteria: total bilirubin ≥ 10 mg/dL on postoperative day 7; international normalized ratio (INR) ≥ 1.6 on postoperative day 7; and alanine aminotransferase (ALT) or aspartate aminotransferase (AST) > 2000 IU/L within the first postoperative 7 days [7]. Previous studies have shown that multiple factors lead to the occurrence of EAD, among which the recipient-related factors play an important role in the development of EAD [8,9,10]. The early prediction of EAD is helpful to discern recipients who may develop poor outcomes and stratify recipients for personalized treatment.

Obesity is known to be one of the important risk factors associated with poor post-transplant outcomes in terms of increased morbidity, which has long been considered a relative contraindication for organ transplantation [11,12,13,14]. Obesity was defined by the presence of excessive body fat accumulation to the extent that can lead to a variety of diseases and pathologies, including type 2 diabetes, hypertension, cardiovascular disease and nonalcoholic steatohepatitis, and several cancers [15]. The body mass index (BMI) has been considered as a standard of measuring obesity for a long time. Multiple previous studies have shown that BMI demonstrates association with outcomes after LT [16, 17]. However, recent reports have shown that the adipose tissue distribution measured by axial computed tomography (CT) imaging could distinguish between visceral and subcutaneous fat, which can provide more accurate and direct measurement of obesity, over and above the BMI. Many researches have revealed the fat distribution correlates significantly with complications and mortality in various kinds of disease [18,19,20,21]. To date, few studies have directly investigated measured the association between body fat distribution and EAD after LT. Thus, this retrospective study aims to determine the association between preoperative adipose tissue distribution and EAD in LT recipients and further explore the value of the most meaningful body distribution indicator for predicting overall survival (OS) and one-year graft failure of patients.


Study population

The Institutional Review Board of local hospital approved this study and the requirement for patient informed consent was waived. This study retrospectively evaluated 459 adult patients (age ≥ 18 years) who underwent LT from April 2015 to September 2020. 284 patients were excluded based on the following criteria: (1) patients without preoperative abdominal CT scans (n = 257); (2) patients with preoperative abdominal CT scans which is more than 3 months (n = 14); (3) patients with insufficient scanning coverage of subcutaneous adipose tissue (SAT) during CT examination (n = 7); and (4) patients who received re-transplantation or combined liver and kidney transplantation (n = 6). Finally, 175 patients were included in this study and their CT images were analyzed (Fig. 1). For patients with multiple abdominal CT examinations within 3 months before LT, CT scan with the shortest time interval between imaging and the operation was used for this analysis.

Fig. 1
figure 1

Flowchart of the study population

Data collection

Clinical information of all patients, including baseline characteristics and perioperative laboratory variables were extracted from electronic medical records. BMI (weight in kg divided by height in meters squared) was based on weight and height measured during this hospital stay within one week prior to surgery. Patients were followed from the date of operation until April 10, 2021, or death. Follow-up time varied from 8 to 72 months, and the mean follow-up time was 28 months. Of 175 LT recipients, 134 patients were tracked until the end of follow-up. The patients who were tracked for less than one year form the date of operation to the end of follow-up were excluded in one-year graft survival analysis (n = 30) (Fig. 2).

Fig. 2
figure 2

Flowchart of the follow-up

Outcome parameters

The primary outcome measurement for this study was EAD, which was defined as the presence of at least one of the following criteria: (1) bilirubin ≥ 10 mg/dL on postoperative day 7; (2) international normalized ratio (INR) ≥ 1.6 on postoperative day 7; (3) alanine aminotransferase (ALT) or aspartate aminotransferase (AST) > 2000 IU/L within the first postoperative 7 days [7]. The secondary outcomes were overall survival and one-year graft failure. One-year graft failure was defined as re-transplantation or death, whichever was first in one year after LT [22].

CT protocol

All preoperative abdominal CT examinations were performed at our institution using CT scanners (Discovery CT 750 HD, GE Healthcare, USA; Somatom Force/Somatom Definition AS+, Siemens Healthineers, Germany; uCT 780, United Imaging, China) in a supine, feet‐first position. Intravenous contrast media 370 mg/mL iopromide (Ultravist 370, Bayer Schering Pharma, Berlin, Germany) was administered at a flow rate of 3.5 mL/s, followed by a 20-mL saline flush. The total contrast volume was 1.5 mL/kg. Contrast material was injected through the ante-cubital vein with an 18-gauge intravenous cannula using a dual-head injector (Stellant, Medrad, CO, USA), each with an injection time of 20 s.

The time of arterial phase scanning was determined when a threshold enhancement of 120 HU was achieved in the abdominal aorta. Portal phase imaging was initiated 25–30 s after the completion of arterial phase scanning. The main CT scanning parameters were as follows: tube voltage, 120 kV; automatic tube current modulation; scanning thickness, 10 mm; table speed, 39.37 mm/rotation; rotation time, 0.5 s; detector pitch, 0.984:1; matrix size = 512 × 512. All CT images were then reconstructed with a slice thickness of 5 mm.

CT measurements

The cross-sectional CT image at the level of third lumbar vertebra (L3) to quantify skeletal muscle and abdominal adipose tissue area was analyzed [23]. Preoperative CT images were analyzed using ImageJ software ( A semi-automated method defined by density window was used to measure the body composition variables by manually outlining of the border of the muscle and excluding of the bowel contents. Skeletal muscles include the psoas, erector spinae, quadratus lumborum, transversus abdominis, latissimus dorsi, external and internal obliques, and rectus abdominis [24]. Tissue Hounsfield unit (HU) thresholds were employed as follows: − 29 HU to 150 HU for skeletal muscle area (SMA), − 190 HU to − 30 HU for SAT area and intramuscular adipose tissue (IMAT) area, and − 150 HU to − 50 HU for visceral adipose tissue (VAT) area [25, 26]. The different portions of adipose tissue distribution at L3 are shown in Fig. 3. The mean attenuation of the entire skeletal muscle (SMD) was also measured. The increase in IMAT and correspondingly decline of SMD represent decrease in strength and muscle quality [26]. The visceral to subcutaneous adipose tissue area ratio (VSR) was also calculated, and it was usually used to reflect visceral adiposity [19].

Fig. 3
figure 3

Cross‐sectional computed tomography images at the level of the third lumbar vertebra. A Subcutaneous adipose tissue area (yellow) was quantified using − 190 to − 30 Hounsfield units (HU). B Visceral adipose tissue area (red) was quantified using − 150 to − 50 HU. C Skeletal muscle area (green) was quantified using − 29 to 150 HU. D Intramuscular adipose tissue area (blue) was quantified using − 190 to − 30 HU

Statistical analysis

Continuous variables were presented as median (interquartile range, IQR) and were compared by an independent sample Student t test or Mann–Whitney U test. Categorical variables were presented as number (percentage) and compared by the Chi-squared analysis or Fisher’s exact test. Variables with p ≤ 0.10 in univariate analysis were considered candidates for multivariate logistic regression analysis to identify the risk factors associated with EAD. To further demonstrate the relationship between VAT area and EAD, one-to-one propensity-score matching (PSM) method (n = 45 pairs) was used to minimize the impact of selection bias and potential confounding factors between different groups. The following variables were entered into the propensity model: age, gender, BMI, serum albumin, platelet–lymphocyte ratio (PLR). The propensity scores for subjects with and without EAD were matched within a caliper of 0.02. The ideal cut-of value of was set as the value maximizing the Youden index according to the receiver operating characteristic (ROC) curve. Cumulative rates of overall survival and one-year graft failure were calculated using Kaplan–Meier method and log-rank test. The hazard ratio (HR) was obtained by Cox univariate regression analysis. Statistical analysis was performed with SPSS version 25 statistical software (Chicago, IL, USA) and MedCalc ( All tests were two-sided, and values of p ≤ 0.05 were considered statistically significant.


Patient characteristics

Out of all 459 LT cases performed within the given time frame, a total of 175 recipients were enrolled in this study and EAD occurred in 55 (31.4%) cases. The clinical and body composition characteristics of patients in the two groups are summarized in Table 1. Median recipient age at transplant was 49 (42–54) [IQR] years, and 81.1% (n = 142) of patients were male. No significant differences in age and gender were found between the two groups (p = 0.435; p = 0.069, respectively). The median BMI value was 22.5 (20.3–24.7) [IQR] kg/m2 in all patients, and the EAD group was significantly more likely to have higher BMI value than No EAD group (23.2 vs 22.0, p = 0.019). The most common reasons for LT were hepatocellular carcinoma (45.7%), viral hepatitis infection (38.3%), followed by primary biliary cirrhosis and autoimmune liver disease (8.0%), other diseases (4.6%), and alcoholic cirrhosis (3.4%). The hepatic features including Child–Pugh score and MELD score were slightly higher in the EAD group than in the No EAD group, but the difference was not statistically significant (7 vs 6, p = 0.189; 13 vs 12, p = 0.507, respectively).

Table 1 Clinical and body composition characteristics of the patients

Body composition analysis

The median SAT and VAT area were 90.6 (61.5–131.5) [IQR] cm2 and 77.9 (49.3–120.7) [IQR] cm2 in all patients, respectively. Significant differences were observed between EAD group and No EAD group in SAT and VAT area (109.3 vs 84.6, p = 0.013; 101.0 vs 69.6, p < 0.001, respectively). There were no significant differences between EAD group and No EAD group in SMA and VSR (143.4 vs 132.2, p = 0.075; 0.95 vs 0.86, p = 0.084, respectively). The EAD group has higher IMAT area and correspondingly lower SMD value, but the differences were not statistically significant (3.4 vs 2.9, p = 0.068; 43.0 vs 44.0, p = 0.350, respectively).

Multivariate logistic analysis of risk factors for EAD

Nine of the examined variables with p value close to 0.10 in univariate analysis including gender, BMI, preoperative serum albumin, PLR, SMA, SAT, VAT, VSR, and IMAT were applied to multivariate analysis to identify the risk factors associated with EAD (p = 0.069; p = 0.019; p = 0.085; p = 0.100; p = 0.075; p = 0.013; p < 0.001; p = 0.084; p = 0.068, respectively) (Table 2). However, on multivariate analysis, only preoperative serum albumin (odds ratio (OR) 0.34, 95% confidence interval (CI) 0.17–0.70, p = 0.004), PLR (OR 2.35, 95% CI 1.18–4.79, p = 0.018), VAT area were independently associated with EAD and the VAT area was with the biggest OR among the three variables (OR 3.17, 95% CI 1.56–6.43, p = 0.001).

Table 2 Multivariate analysis of risk factors for early allograft dysfunction (EAD)

Propensity score-matched analysis

PSM analysis was performed to minimize the potential bias and further verify the accuracy of VAT area as risk factor of EAD. Patients were matched for age, gender, BMI, serum albumin, and PLR. Finally, 45 pairs of patients were selected using one-to-one PSM method (Table 3). Five of examined variables with p value close to 0.10 in univariate analysis including Child–Pugh class, SMD, VAT, VSR, and IMAT were applied to multivariate analysis (p = 0.085; p = 0.059; p = 0.047; p = 0.042; p = 0.042, respectively), and VAT area was still independently associated with EAD in multivariate analysis (OR 3.95, 95% CI 1.16–13.51, p = 0.029) (Table 4). Besides, SMD exhibited significant association with EAD in multivariate analysis after PSM (OR 3.84, 95% CI 1.23–12.00, p = 0.020).

Table 3 Clinical and body composition characteristics of patients after propensity scoring-matched (PSM) analysis
Table 4 Multivariate analysis of risk factors for early allograft dysfunction after propensity scoring-matched (PSM) analysis

Survival analysis

The VAT area of 85.2 cm2 was the optimal cut-off value with the highest Youden’s index (Youden’s index = 0.25, sensitivity = 64%, specificity = 62%) for predicting postoperative EAD. In one-year graft failure analysis, 45 patients were classified into high VAT area group among which 6 patients were confirmed graft failure and 59 patients were classified into low VAT area group among which 5 patients were confirmed graft failure. Kaplan–Meier analysis revealed that no significant difference was identified in one-year graft failure between the two groups (average survival time: 11.0 vs 10.9, p = 0.487) (Fig. 4a). In OS analysis, 61 patients were classified into high VAT area group among which 51 patients were alive until the end of the follow-up and 73 patients were classified into low VAT area group among which 69 patients were alive until the end of the follow-up. Kaplan–Meier analysis revealed that the difference was significantly between the two groups (average survival time 67.0 vs 53.5, p = 0.012) (Fig. 4b). The risk of death in the high VAT area group was 4.10 times that of the low VAT area group (HR 4.10, 95% CI 1.27–13.16, p = 0.018).

Fig. 4
figure 4

Kaplan–Meier curves of one-year graft failure and overall survival (OS) in high and low visceral adipose tissue (VAT) area group. A Kaplan–Meier curves of one-year graft failure in patients with high and low VAT area. B Kaplan–Meier curves of OS in patients with high and low VAT area


EAD is a critical complication after LT, which contributes to high mortality. Therefore, a better understanding of the risk factors associated with EAD can help to improve pre-LT patient management and post-LT outcome. In this study, we comprehensively analyzed the impact of adipose tissue distribution on outcomes in patients who underwent LT and determined that VAT area was independently associated with the development of EAD. In addition, we found that recipients with high visceral fat area have worse OS compared with those with relatively low visceral fat area. Therefore, the early identification of those patients who have excess visceral fat may not only prompt a therapeutic intervention, but also warn of an increased risk of poor outcomes.

Limited value of BMI

Our current results indicated that 31.4% of the recipients occurred EAD after LT, which was consistent with the range of 5.2–38.7% reported in previous studies [4,5,6,7,8, 27]. In this study, EAD group tend to have slightly higher BMI value than No EAD group before PSM analysis. At present, the relationship between BMI and prognostic outcome post-transplant needs further verification. Although some studies have revealed an association between pretransplant high BMI value of recipients and poor survival after LT for both adult and pediatric recipients [15, 28, 29], recent studies reported that BMI was not associated with higher risk of post-transplant vascular and biliary complications, graft loss, and death [16, 30,31,32]. The inconsistent results could be attributed to different BMI grouping criteria and the limitations of BMI value in predicting outcomes after LT including the overestimated influence of fluid accumulation or systemic edema and inability to discriminate different components of body composition. Therefore, the application of BMI to reflect obesity and assess prognosis is limited. Body composition measurement based on CT images can provide more accurate information and may be regarded as a useful tool with prognostic value in recipients after LT.

Potential fat distribution indices

Many recent reports have indicated that excessive accumulation of abdominal adipose tissue significantly correlates with postoperative outcomes, including complications and mortality in patients with various cancers [19, 20, 33, 34]. In this study, we observed significant differences between the two groups in the SAT and VAT area before PSM analysis. However, the difference was no longer significant in the SAT area after PSM analysis. The different results between VAT and SAT were on the basis of their differences in anatomical location, cellular, molecular, and metabolic activity [35]. Our results also found that the EAD group have significantly higher IMAT area than No EAD group after PSM analysis and the SMD exhibited significant association with EAD in multivariate analysis after PSM analysis. However, the SMA between two groups was not significantly different before and after PSM analysis. Although many previous studies have reported that low muscle mass was significantly associated with survival in patients who suffered from hepatocellular carcinoma or received LT [36,37,38], recent studies tend to show that muscle quality rather than muscle quantity was identified as a prognostic marker in LT recipients [24, 39]. IMAT is thought to begin to increase when lipids intake exceeds the disposal capacity of adipose tissue and muscle, and the increase represents the decline in muscle strength and quality [40]. The accumulation of IMAT may be associated with a muscle-to-liver cross-talking that the secretion of pro-inflammatory cytokines would increase and concentrations of myokines would decrease, which may in turn lead to systemic inflammation with unfavorable immune response and restricted graft regeneration [41]. In the study of Czigany et al. [24], the researchers have reported that patients with high IMAT accumulation and correspondingly low SMD, rather than reduced muscle mass, had significantly higher post-transplant complication rates and poor perioperative outcomes, which was similar with our results.

Meaningful clinical parameters

In this study, multivariate analysis showed that serum albumin, PLR, VAT area were significantly associated with EAD before PSM analysis. The relationship between serum albumin and post-transplant outcomes remains controversial. Many previous studies have demonstrated that there is no significant correlation between preoperative serum albumin and patient or graft survival [42, 43]. However, in the studies of Hiroi et al. [44] and Bernardi et al. [45], serum albumin can influence short-term outcomes following LT and the albumin administration to patients on wait-listed for LT should be strengthen, which was in line with our results. They hold the view that maintaining high serum albumin level reflects good nutritional status and can reduce the amount of fluid collection in abdominal or thoracic cavity, which could resist the catabolic state induced by surgical stress and inflammatory response in the early postoperative period. As for PLR, our findings stay consistent with several observations of the PLR prognostic role in patients undergoing LT [46, 47]. Pravisani et al. [47] reported that pre-LT and post-LT PLR has shown clear associations with short- or long-term outcomes and HCC recurrence, which can be used as inflammatory and nutritional biomarkers to offer reliable prognostic information after LT. Elevated PLR reflects the more severer liver inflammation and worse nutritional status, and this may be the reason why PLR was significantly associated with EAD [48].

Remarkable performance of VAT

Previous studies have revealed that high VAT area measured by CT is associated with greater risk of post-transplant complications and outcomes. For instance, in the study of Kamo et al. [49], the authors found that incidence of post-transplant bacteremia was significantly higher in patients with high visceral fat area. Terjimanian et al. [50] reported that excessive visceral fat was associated with a shorter one-year and five-year survival after LT. According to Montano-Loza et al. [51], increased visceral fat area was significantly associated with post-transplant tumor recurrence on 78 hepatocellular carcinoma liver transplant recipients. Our results showed that visceral, instead of the subcutaneous adipose deposition, worked as a significant risk factor for the development of EAD. After adjustment for potential confounders with PSM analysis, the independent association between VAT area and EAD still exists. In addition, our study also found that VAT area might also have certain clinical value for predicting the OS of recipients.

Prior researches have shown that VAT compared with SAT contains a larger number of inflammatory and immune cells. The cells will release more pro‐inflammatory cytokines such as tumor necrosis factor (TNF)-ɑ, interleukin (IL)-6, IL-1b and monocyte chemoattractant protein-1 to create a pro‐inflammatory microenvironment that potentially impairs immune function [52]. However, anti-inflammatory cytokines such as adiponectin is more highly secreted form SAT [53]. Thus, patients with high amount of adipose tissue in the visceral region are more easily to be in a state of chronic inflammation status. On the other hand, the pro‐inflammatory cytokines especially TNF-ɑ released by VAT plays an important role in the hepatic ischemia/reperfusion injury (IRI), which is considered to serve as pivotal mechanisms of influencing early and long-term results of the organ transplantation [54, 55]. Therefore, high VAT area can upregulate the release of pro‐inflammatory cytokines that contribute to the IRI, thereby promoting a higher incidence rate of EAD and having a negative impact on the long-term outcomes. In addition, it has been believed that VAT exerts damaging metabolic effects. Excessive accumulation of VAT has high rate of insulin resistance by provoking greater toxic-free fatty acids (FFA) release [56]. FFAs and adipokines secreted from VAT can flow into the liver through the portal vein and directly mediate the metabolic changes and injury of the graft [57]. As reported in previous researches, the accumulation of VAT contributes to increased risk of metabolic syndromes such as cardiovascular events, hyperlipidemia, and diabetes mellitus [15, 58], which may deteriorate the healthy status and indirectly lead to the decline of the OS rate of high VAT group.

This study has several important limitations. First, the number of patients was small in our study, and female patients account for only 18.9% in the cohort; further studies with multi-center larger sample size are needed to confirm the results of this study. Second, this study was retrospective, patients who did not receive an abdominal CT scan within 3 months before LT were not included in the study, and this may have caused selection bias. Although PSM analysis was used to reduce the bias, the results may be affected by unconsidered factors. Third, due to the lack of donor and operation-related data, we are unable to analyze the risk factors of EAD comprehensively. Additionally, the follow-up time is short, and the direct effect of high VAT area on worse OS and graft survival are needed to be testified based on long-term follow-up in the future.

In conclusion, LT recipients with a high amount of visceral fat were more likely to develop EAD. It also seems to have certain clinical value for predicting poor long-term prognosis of patients who underwent LT. More importantly, liver transplant candidates with high VAT area may be targets for timely therapeutic intervention to improve short- and long-term outcomes.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.



Alkaline phosphatase


Alanine aminotransferase


Aspartate aminotransferase


Body mass index


Confidence interval


Computed tomography


Early allograft dysfunction


Free fatty acid


Hepatitis B virus


Hepatitis C virus


Hazard ratio


Hounsfield units




Intramuscular adipose tissue


International normalized ratio


Interquartile range


Ischemic-reperfusion injury


Lactate dehydrogenase


Liver transplantation


Model for end-stage liver disease


Neutrophil–lymphocyte ratio


Odds ratio


Overall survival


Platelet–lymphocyte ratio


Propensity-score matching


The receiver operating characteristic


Subcutaneous adipose tissue


Skeletal muscle area


The mean attenuation of skeletal muscle


Tumor necrosis factor ɑ


Visceral adipose tissue


Visceral to subcutaneous adipose tissue area ratio


  1. Brown RS Jr (2008) Live donors in liver transplantation. Gastroenterology 134(6):1802–1813.

    Article  PubMed  Google Scholar 

  2. Starzl TE, Iwatsuki S, Esquivel CO et al (1985) Refinements in the surgical technique of liver transplantation. Semin Liver Dis 5(4):349–356.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Farkas S, Hackl C, Schlitt HJ (2014) Overview of the indications and contraindications for liver transplantation. Cold Spring Harb Perspect Med 4(5):a015602.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Bastos-Neves D, Salvalaggio PRO, Almeida MD (2019) Risk factors, surgical complications and graft survival in liver transplant recipients with early allograft dysfunction. Hepatobiliary Pancreat Dis Int 18(5):423–429.

    Article  PubMed  Google Scholar 

  5. Pomposelli JJ, Goodrich NP, Emond JC et al (2016) Patterns of early allograft dysfunction in adult live donor liver transplantation: the A2ALL experience. Transplantation 100(7):1490–1499.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Hoyer DP, Paul A, Gallinat A et al (2015) Donor information based prediction of early allograft dysfunction and outcome in liver transplantation. Liver Int 35(1):156–163.

    Article  PubMed  Google Scholar 

  7. Olthoff KM, Kulik L, Samstein B et al (2010) Validation of a current definition of early allograft dysfunction in liver transplant recipients and analysis of risk factors. Liver Transpl 16(8):943–949.

    Article  PubMed  Google Scholar 

  8. Deschenes M (2013) Early allograft dysfunction: causes, recognition, and management. Liver Transpl 19(Suppl 2):S6-8.

    Article  PubMed  Google Scholar 

  9. Huesing-Kabar A, Dohna CZ, Heinzow H et al (2018) Risk factors for allograft failure in liver transplant recipients. Z Gastroenterol 56(7):745–751.

    Article  PubMed  Google Scholar 

  10. Wagener G, Raffel B, Young AT et al (2013) Predicting early allograft failure and mortality after liver transplantation: the role of the postoperative model for end-stage liver disease score. Liver Transpl 19(5):534–542.

    Article  PubMed  Google Scholar 

  11. Hasse J (2007) Pretransplant obesity: a weighty issue affecting transplant candidacy and outcomes. Nutr Clin Pract 22(5):494–504.

    Article  PubMed  Google Scholar 

  12. Jomphe V, Mailhot G, Damphousse V et al (2018) The impact of waiting list BMI changes on the short-term outcomes of lung transplantation. Transplantation 102(2):318–325.

    Article  PubMed  Google Scholar 

  13. Liese J, Bottner N, Buttner S et al (2018) Influence of the recipient body mass index on the outcomes after kidney transplantation. Langenbecks Arch Surg 403(1):73–82.

    Article  PubMed  Google Scholar 

  14. Macshut M, Kaido T, Yao S et al (2021) Visceral adiposity is an independent risk factor for high intra-operative blood loss during living-donor liver transplantation; could preoperative rehabilitation and nutritional therapy mitigate that risk? Clin Nutr 40(3):956–965.

    Article  PubMed  Google Scholar 

  15. Smith CJ, Ryckman KK (2015) Epigenetic and developmental influences on the risk of obesity, diabetes, and metabolic syndrome. Diabetes Metab Syndr Obes 8:295–302.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Giorgakis E, Tedeschi M, Bonaccorsi-Riani E et al (2017) The effect of recipient body mass index and its extremes on survival and graft vascular and biliary complications after liver transplantation: a single center retrospective study. Ann Transplant 22:611–621.

    Article  PubMed  Google Scholar 

  17. Hakeem AR, Cockbain AJ, Raza SS et al (2013) Increased morbidity in overweight and obese liver transplant recipients: a single-center experience of 1325 patients from the United Kingdom. Liver Transpl 19(5):551–562.

    Article  PubMed  Google Scholar 

  18. Peng YC, Wu CH, Tien YW et al (2021) Preoperative sarcopenia is associated with poor overall survival in pancreatic cancer patients following pancreaticoduodenectomy. Eur Radiol 31(4):2472–2481.

    Article  PubMed  Google Scholar 

  19. Ebadi M, Bhanji RA, Tandon P et al (2020) Review article: prognostic significance of body composition abnormalities in patients with cirrhosis. Aliment Pharmacol Ther 52(4):600–618.

    Article  PubMed  Google Scholar 

  20. Yoshikawa K, Shimada M, Kurita N et al (2011) Visceral fat area is superior to body mass index as a predictive factor for risk with laparoscopy-assisted gastrectomy for gastric cancer. Surg Endosc 25(12):3825–3830.

    Article  PubMed  Google Scholar 

  21. Yang Y, Ding L, Zou X et al (2020) Visceral adiposity and high intramuscular fat deposition independently predict critical illness in patients with SARS-CoV-2. Obesity (Silver Spring) 28(11):2040–2048.

    Article  CAS  Google Scholar 

  22. Kwon HM, Kim KS, Park J et al (2020) von Willebrand factor to protein C ratio-related thrombogenicity with systemic inflammation is predictive of graft dysfunction after liver transplantation: retrospective cohort study. Int J Surg 84:109–116.

    Article  PubMed  Google Scholar 

  23. Fang H, Berg E, Cheng X et al (2018) How to best assess abdominal obesity. Curr Opin Clin Nutr Metab Care 21(5):360–365.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Czigany Z, Kramp W, Bednarsch J et al (2020) Myosteatosis to predict inferior perioperative outcome in patients undergoing orthotopic liver transplantation. Am J Transplant 20(2):493–503.

    Article  PubMed  Google Scholar 

  25. Mitsiopoulos N, Baumgartner RN, Heymsfield SB et al (1985) (1998) Cadaver validation of skeletal muscle measurement by magnetic resonance imaging and computerized tomography. J Appl Physiol (1985) 85(1):115–122.

  26. Tanaka M, Okada H, Hashimoto Y et al (2020) Relationship between nonalcoholic fatty liver disease and muscle quality as well as quantity evaluated by computed tomography. Liver Int 40(1):120–130.

    Article  PubMed  Google Scholar 

  27. Agopian VG, Harlander-Locke MP, Markovic D et al (2018) Evaluation of early allograft function using the liver graft assessment following transplantation risk score model. JAMA Surg 153(5):436–444.

    Article  PubMed  Google Scholar 

  28. Nair S, Verma S, Thuluvath PJ (2002) Obesity and its effect on survival in patients undergoing orthotopic liver transplantation in the United States. Hepatology 35(1):105–109.

    Article  PubMed  Google Scholar 

  29. Dick AA, Perkins JD, Spitzer AL et al (2010) Impact of obesity on children undergoing liver transplantation. Liver Transpl 16(11):1296–1302.

    Article  PubMed  Google Scholar 

  30. Shi Y, Huang B, Deng R et al (2019) The Association of obesity with vascular complications after liver transplantation. BMC Gastroenterol 19(1):39.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Bambha KM, Dodge JL, Gralla J et al (2015) Low, rather than high, body mass index confers increased risk for post-liver transplant death and graft loss: risk modulated by model for end-stage liver disease. Liver Transpl 21(10):1286–1294.

    Article  PubMed  Google Scholar 

  32. Singhal A, Wilson GC, Wima K et al (2015) Impact of recipient morbid obesity on outcomes after liver transplantation. Transpl Int 28(2):148–155.

    Article  PubMed  Google Scholar 

  33. Almasaudi AS, Dolan RD, McSorley ST et al (2019) Relationship between computed tomography-derived body composition, sex, and post-operative complications in patients with colorectal cancer. Eur J Clin Nutr 73(11):1450–1457.

    Article  PubMed  Google Scholar 

  34. Tamura T, Sakurai K, Nambara M et al (2019) Adverse effects of preoperative sarcopenia on postoperative complications of patients with gastric cancer. Anticancer Res 39(2):987–992.

    Article  CAS  PubMed  Google Scholar 

  35. Wajchenberg BL (2000) Subcutaneous and visceral adipose tissue: their relation to the metabolic syndrome. Endocr Rev 21(6):697–738.

    Article  CAS  PubMed  Google Scholar 

  36. Pinto Dos Santos D, Kloeckner R, Koch S et al (2020) Sarcopenia as prognostic factor for survival after orthotopic liver transplantation. Eur J Gastroenterol Hepatol 32(5):626–634.

    Article  CAS  PubMed  Google Scholar 

  37. Fujiwara N, Nakagawa H, Kudo Y et al (2015) Sarcopenia, intramuscular fat deposition, and visceral adiposity independently predict the outcomes of hepatocellular carcinoma. J Hepatol 63(1):131–140.

    Article  CAS  PubMed  Google Scholar 

  38. Hamaguchi Y, Kaido T, Okumura S et al (2014) Impact of quality as well as quantity of skeletal muscle on outcomes after liver transplantation. Liver Transpl 20(11):1413–1419.

    Article  PubMed  Google Scholar 

  39. Czigany Z, Kramp W, Lurje I et al (2021) The role of recipient myosteatosis in graft and patient survival after deceased donor liver transplantation. J Cachexia Sarcopenia Muscle 12(2):358–367.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Stephens NA, Skipworth RJ, Macdonald AJ et al (2011) Intramyocellular lipid droplets increase with progression of cachexia in cancer patients. J Cachexia Sarcopenia Muscle 2(2):111–117.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Hamaguchi Y, Kaido T, Okumura S et al (2018) Proposal for new selection criteria considering pre-transplant muscularity and visceral adiposity in living donor liver transplantation. J Cachexia Sarcopenia Muscle 9(2):246–254.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Tanaka T, Renner EL, Selzner N et al (2013) The impact of obesity as determined by modified body mass index on long-term outcome after liver transplantation: Canadian single-center experience. Transplant Proc 45(6):2288–2294.

    Article  CAS  PubMed  Google Scholar 

  43. Wong CS, Lee WC, Jenq CC et al (2010) Scoring short-term mortality after liver transplantation. Liver Transpl 16(2):138–146.

    Article  PubMed  Google Scholar 

  44. Hiroi K, Matsusaki T, Kaku R et al (2019) Postoperative course of serum albumin levels and organ dysfunction after liver transplantation. Transplant Proc 51(8):2750–2754.

    Article  CAS  PubMed  Google Scholar 

  45. Bernardi M, Zaccherini G, Caraceni P (2019) Pro: the role of albumin in pre-liver transplant management. Liver Transpl 25(1):128–134.

    Article  PubMed  Google Scholar 

  46. Nylec M, Derbisz K, Chrzaszcz P et al (2020) Preoperative neutrophil-to-lymphocyte ratio as an independent predictor of 1-year graft loss and mortality after orthotopic liver transplantation. Transplant Proc 52(8):2472–2476.

    Article  CAS  PubMed  Google Scholar 

  47. Pravisani R, Mocchegiani F, Isola M et al (2021) Postoperative trends and prognostic values of inflammatory and nutritional biomarkers after liver transplantation for hepatocellular carcinoma. Cancers (Basel) 13(3):513.

    Article  CAS  PubMed Central  Google Scholar 

  48. Kanikarla Marie P, Fowlkes NW, Afshar-Kharghan V et al (2021) The provocative roles of platelets in liver disease and cancer. Front Oncol 11:643815.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Kamo N, Kaido T, Miyachi Y et al (2020) Preoperative abnormal body composition is closely related to bacteremia after living donor liver transplantation. Nutrition 77:110798.

    Article  CAS  PubMed  Google Scholar 

  50. Terjimanian MN, Harbaugh CM, Hussain A et al (2016) Abdominal adiposity, body composition and survival after liver transplantation. Clin Transplant 30(3):289–294.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Montano-Loza AJ, Mazurak VC, Ebadi M et al (2018) Visceral adiposity increases risk for hepatocellular carcinoma in male patients with cirrhosis and recurrence after liver transplant. Hepatology 67(3):914–923.

    Article  PubMed  Google Scholar 

  52. Wronska A, Kmiec Z (2012) Structural and biochemical characteristics of various white adipose tissue depots. Acta Physiol (Oxf) 205(2):194–208.

    Article  CAS  Google Scholar 

  53. Fain JN, Madan AK, Hiler ML et al (2004) Comparison of the release of adipokines by adipose tissue, adipose tissue matrix, and adipocytes from visceral and subcutaneous abdominal adipose tissues of obese humans. Endocrinology 145(5):2273–2282.

    Article  CAS  PubMed  Google Scholar 

  54. Zhou J, Chen J, Wei Q et al (2020) The role of ischemia/reperfusion injury in early hepatic allograft dysfunction. Liver Transpl 26(8):1034–1048.

    Article  PubMed  Google Scholar 

  55. Ito T, Naini BV, Markovic D et al (2021) Ischemia-reperfusion injury and its relationship with early allograft dysfunction in liver transplant patients. Am J Transplant 21(2):614–625.

    Article  CAS  PubMed  Google Scholar 

  56. Ibrahim MM (2010) Subcutaneous and visceral adipose tissue: structural and functional differences. Obes Rev 11(1):11–18.

    Article  PubMed  Google Scholar 

  57. Nielsen S, Guo Z, Johnson CM et al (2004) Splanchnic lipolysis in human obesity. J Clin Invest 113(11):1582–1588.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Tchernof A, Despres JP (2013) Pathophysiology of human visceral obesity: an update. Physiol Rev 93(1):359–404.

    Article  CAS  PubMed  Google Scholar 

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We thank Yang Yang, whose important contributions to this study were indispensable to its success.


This study has received funding by the National Natural Science Foundation of China (NSFC) No. 82071889, 81771801, 82071890, 82001786.

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Authors and Affiliations



ZL contributed to conceptualization, methodology, writing—review and editing and funding acquisition; JL was involved in statistical analysis and writing—review and editing. GY was involved in data analysis, literature search, and writing original draft. SL and PL were involved in formal analysis, data curation. GC was involved in conceptualization and data curation. YS and YL was involved in investigation and funding acquisition; XH was involved in writing—review and editing. DH was involved in methodology, supervision, writing—review and editing. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Jiali Li or Zhen Li.

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This retrospective study was approved by the Ethics Committee of Tongji Hospital, and the informed consent of all patients was waived.

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This manuscript is approved by all patients for publication.

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The authors declare that they have no competing interests.

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Yuan, G., Li, S., Liang, P. et al. High visceral adipose tissue area is independently associated with early allograft dysfunction in liver transplantation recipients: a propensity score analysis. Insights Imaging 13, 165 (2022).

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  • Tomography (X-ray computed)
  • Body composition
  • Liver transplantation
  • Allograft
  • Propensity score