ABSTRACT
Background & Aims
Hepatocellular carcinoma (HCC) is the third leading cause of cancer‐related death worldwide. Liver transplantation (LT) is a curative treatment option. We investigated survival outcomes based on recipient‐donor sex constellation (RDSC) following LT.
Methods
We performed a European Liver Transplant Registry analysis, including patients from 1988 to December 2022. The cohort was split into four RDSC groups: female donor female recipient (FDFR), female donor male recipient (FDMR), male donor female recipient (MDFR) and male donor male recipient (MDMR). Survival analysis, including death with recurrence, was performed.
Results
In 7601 LT for HCC with an overall median follow‐up of 22.6 months (5.8, 60.7), death was registered in 25.1% and, as primary cause of death, HCC tumour recurrence in 26.0%. There was no statistically significant difference on crude survival estimates among the different RDSC groups (log‐rank p = 0.66) with 10‐year overall survival (OS) of 54.5% in FDFR, 54.6% in FDMR, 59.1% in MDFR and 56.9% in MDMR. On multivariable analysis, RDSC showed a significant effect on OS (FDFR as reference): MDFR (aHR 0.72, p = 0.023). No significant difference was found for FDMR (aHR 0.98, p = 0.821) and MDMR (aHR 0.90, p= 0.288). Regarding overall registered causes of death, differences between RDSC groups were found in rejection (p = 0.017) and cardiovascular (p = 0.046) associated deaths.
Conclusions
In female recipients undergoing LT for HCC, male donor grafts were associated with a 28% reduction of mortality compared to female donor grafts.
Keywords: hepatocellular, liver transplantation, mortality, patient/donor sex, prognosis
Summary.
Hepatocellular carcinoma is a common malignancy, which occurs in the chronically diseased liver. Liver transplantation is a curative treatment option.
Our study reveals that the matching of donor and recipient sex affects the recipient's outcome.
The combination of male donor livers in female recipients results in the most favourable long‐term survival.
1. Introduction
Primary liver tumours are the sixth most common tumour type worldwide and the third leading cause of cancer‐related deaths [1]. Hepatocellular carcinoma (HCC) accounts for 80%–90% of all primary liver tumours and approximately 90% of HCC develop in chronic liver disease [2, 3]. Liver transplantation (LT) is the only true curative treatment option for very early to intermediate‐stage HCC [4].
The International Liver Transplantation Society (ILTS) recommends tumour biology, tumour (size and number), transplant benefit (quality of life and survival), waitlist composition and organ availability to be included when defining transplant criteria [5]. Surgical outcomes include a 10‐year overall survival (OS) of 54% [6] and a pooled HCC recurrence rate of 17% [7]. Different donor and recipient characteristics (e.g., age, liver size, ethnicity, body mass index, aetiology and comorbidities) are associated with outcome of LT after HCC [8]. These characteristics may differ between sexes. The liver is a sexually dimorphic organ, with over 1000 genes differentially expressed [9] with known discrepancies in androgenic and oestrogenic hepatic sex hormonal signalling pathways [8]. A sex disparity with regard to outcome after LT is suspected, as the female recipient sex is protective of mortality (aOR 0.89; 95% CI: 0.86, 0.92) [10] with conflicting data regarding the survival benefit [10, 11, 12, 13, 14, 15]. Males have a higher proportion of HCC tumour recurrence after LT as a cause of death than females (14.4% vs. 10.1%, p < 0.001), as observed in an European Liver Transplant Registry (ELTR) study [15]. Potentially impacting these observations are the four different categorizations of recipient and donor sex constellations (RDSC) [16], as they have differences in clinical characteristics. Currently, no assessment of the phenotype of RDSC as an independent factor for outcome after LT has been performed.
The aim of this study was to assess the independent association of RDSC with survival of patients undergoing LT for HCC. Primary endpoint was OS, with secondary endpoints including causes of death, particularly death with tumour recurrence. We hypothesise that, female recipients with a female donor liver have significantly better outcomes compared to the other RDSC.
2. Methods
This is a retrospective multicentric cohort study, using the ELTR database from 1988 to 2022. The methods of data accrual, maintenance, validation and robustness have been shown and described elsewhere [17, 18]. Data were collected in accordance with the General Data Protection Regulation, the European Union legislation and the ELTR privacy policy. The data used in the study were anonymous. The 1964 Helsinki Declaration and its later amendments and comparable ethical standards were adhered to. Reporting is undertaken in accordance with the ‘STrengthening the Reporting of OBservational studies in Epidemiology’ (STROBE) guidelines [19].
2.1. Sample Size
The ELTR is expected to offer a sufficiently large cohort for an adequately powered analysis based on recipient sex‐stratified differences of outcome (10.1% vs. 14.4% [15], alpha 0.05, power 80%, 1:1; required sample size 911 per group).
2.2. Selection of Patients
Only the first LT event for HCC was included (retransplantation events were excluded). Patients with fibrolamellar HCC, mixed HCC‐cholangiocellular carcinoma, and non‐specified liver malignancies, as well as incidental HCC diagnosis on histopathology, were excluded. Patients with incomplete RDSC information or incomplete histopathology data on nodule numbers/size were excluded.
2.3. Findings and Definitions
Patients were split into four groups according to the RDSC: female donor female recipient (FDFR), female donor male recipient (FDMR), male donor female recipient (MDFR) and male donor male recipient (MDMR). Multimodal pre‐transplant treatment was defined as receiving ≥ 2 different treatment modalities. Tumour burden was defined by number of nodules, maximum size of largest nodules, micro‐ and macrovascular invasion status and grouped as ‘within’ or ‘beyond’ Milan criteria [20]. Total tumour volume (TTV) was calculated assuming a spheric configuration of the largest nodule. Lost to follow‐up was defined as last status alive if equal or longer than 3 years up to 30 June 2023 (date of registry exempt). Available time periods were grouped into approximative decades (1988–1999, 2000–2009, 2010–2023).
Primary outcome was OS, defined as time from date of transplant to date of last outcome entered into the registry. Secondary outcome was defined as specific causes of death as registered as primary, secondary or tertiary causes. Level‐independent cause of death with HCC recurrence was assessed.
2.4. Statistical Analysis
Categorical variables were reported as numbers and percentages, continuous variables as medians and interquartile ranges (IQR). Categorical variables were compared using the chi‐squared test (or Fisher's exact test) and continuous using Mann–Whitney U (or Kruskal–Wallis rank sum test) in a comparative fashion. For survival analysis Kaplan–Meyer plots were used for visualisation and corresponding log‐rank tests were performed. Time‐to‐event analysis was performed using a Cox proportional hazard regression model. Multivariable analysis was performed by an a priori adjustment [8, 15] for: number of nodules, largest nodule size, vascular invasion, hepatitis C virus (HCV), hepatitis B virus (HBV), alcohol‐associated liver disease, number of treatments prior to LT, age difference between donor and recipient and recipient Body Mass Index (BMI). In deceased patients, a competing risk analysis was performed using cumulative incidence competing risk (CICR) plots. If values of endpoints within same recipient sex groups are observed, RDSC‐based sequence of results has been adjusted to allow for crude reporting, with no change towards significance testing. p‐values of 0.05 or less were considered statistically significant. Statistical analysis was performed using R software (R Foundation for Statistical Computing, Vienna, Austria, RRID:SCR_001905; version 4.3.1) with R Studio software (R Studio Inc. Boston, MA, RRID:SCR_000432; version 2023.06.0+421). The packages ‘tidyverse’ (version 2.0.0), ‘gtsummary’ (version 1.7.2), ‘survival’ (version 3.5–7), ‘survminer’ (version 0.4.9) and ‘cmprsk’ (version 2.2–11) were used.
3. Results
In the 34‐year study period, 36950 LT related to HCC were registered in the ELTR. For further analysis 7601 patients were included (Figure S1). In this cohort, the median recipient age was 58.6 years (IQR: 53.1, 63.4), 6310 (82.9%) male sex with a median BMI of 26.1 kg/m2 (IQR: 23.8, 28.9) (Table 1). 7428 (97.7%) patients had any type of concomitant cirrhosis of predominantly viral aetiology (4 757 [62.6%]) including 3064 (40.3%) patients with HCV. In 4784 (62.9%) patients a HCC specific treatment was performed prior to transplantation with 822 (10.7%) patients receiving two or more treatment modalities (Table 2). Chemoembolization (TACE) was the most common treatment, having been administered to 3362 (44.2%) patients.
TABLE 1.
Patient demographics and characteristics.
| Variable | Overall, N = 7601 a | FDFR, N = 743 b | FDMR, N = 2325 b | MDFR, N = 548 b | MDMR, N = 3985 b | p c |
|---|---|---|---|---|---|---|
| Recipient age [years] | 58.6 (53.1, 63.4) | 59.6 (53.6, 64.2) | 58.7 (53.1, 63.4) | 58.8 (51.7, 63.4) | 58.4 (53.2, 63.1) | 0.062 |
| Unknown | 9 | 0 | 3 | 0 | 6 | |
| Recipient Body Mass Index [kg/m2] | 26.1 (23.8, 28.9) | 25.0 (22.2, 28.0) | 25.6 (23.4, 28.1) | 25.8 (22.7, 29.7) | 26.6 (24.3, 29.3) | < 0.001 |
| Unknown | 655 | 55 | 200 | 55 | 345 | |
| Recipient blood group | ||||||
| A | 3309/7596 (43.6%) | 320/743 (43.1%) | 1046/2324 (45.0%) | 236/548 (43.1%) | 1707/3981 (42.9%) | 0.124 |
| AB | 433/7596 (5.7%) | 37/743 (5.0%) | 133/2324 (5.7%) | 36/548 (6.6%) | 227/3981 (5.7%) | |
| B | 988/7596 (13.0%) | 99/743 (13.3%) | 305/2324 (13.1%) | 89/548 (16.2%) | 495/3981 (12.4%) | |
| O | 2864/7596 (37.7%) | 287/743 (38.6%) | 838/2324 (36.1%) | 187/548 (34.1%) | 1552/3981 (39.0%) | |
| Pre‐transplant dialysis | 81/6505 (1.2%) | 8/646 (1.2%) | 24/1993 (1.2%) | 4/464 (0.9%) | 45/3402 (1.3%) | 0.864 |
| Pre‐transplant ascites | ||||||
| None | 4548/6911 (65.8%) | 440/679 (64.8%) | 1400/2129 (65.8%) | 301/495 (60.8%) | 2407/3608 (66.7%) | 0.017 |
| Controlled with medication | 1707/6911 (24.7%) | 186/679 (27.4%) | 540/2129 (25.4%) | 140/495 (28.3%) | 841/3608 (23.3%) | |
| Refractory (poorly controlled) | 485/6911 (7.0%) | 37/679 (5.4%) | 149/2129 (7.0%) | 43/495 (8.7%) | 256/3608 (7.1%) | |
| NA | 171/6911 (2.5%) | 16/679 (2.4%) | 40/2129 (1.9%) | 11/495 (2.2%) | 104/3608 (2.9%) | |
| Pre‐transplant encephalopathy | ||||||
| None | 5548/6745 (82.3%) | 533/659 (80.9%) | 1717/2071 (82.9%) | 396/485 (81.6%) | 2902/3530 (82.2%) | 0.076 |
| Grade I–II (controlled with medication) | 878/6745 (13.0%) | 98/659 (14.9%) | 254/2071 (12.3%) | 74/485 (15.3%) | 452/3530 (12.8%) | |
| Grade III–IV (refractory) | 120/6745 (1.8%) | 8/659 (1.2%) | 48/2071 (2.3%) | 4/485 (0.8%) | 60/3530 (1.7%) | |
| NA | 199/6745 (3.0%) | 20/659 (3.0%) | 52/2071 (2.5%) | 11/485 (2.3%) | 116/3530 (3.3%) | |
| UNOS status | ||||||
| Intensive care unit‐bound | 101/7213 (1.4%) | 12/701 (1.7%) | 34/2214 (1.5%) | 14/524 (2.7%) | 41/3774 (1.1%) | 0.002 |
| Continuous hospitalisation | 1224/7213 (17.0%) | 120/701 (17.1%) | 398/2214 (18.0%) | 74/524 (14.1%) | 632/3774 (16.7%) | |
| Continuous medical care | 3891/7213 (53.9%) | 345/701 (49.2%) | 1216/2214 (54.9%) | 276/524 (52.7%) | 2054/3774 (54.4%) | |
| At home with normal function | 1971/7213 (27.3%) | 220/701 (31.4%) | 557/2214 (25.2%) | 158/524 (30.2%) | 1036/3774 (27.5%) | |
| NA | 26/7213 (0.4%) | 4/701 (0.6%) | 9/2214 (0.4%) | 2/524 (0.4%) | 11/3774 (0.3%) | |
| Liver aetiology | ||||||
| Cirrhosis (any) | 7428/7601 (97.7%) | 703/743 (94.6%) | 2289/2325 (98.5%) | 520/548 (94.9%) | 3916/3985 (98.3%) | < 0.001 |
| Any viral disease | 4757/7601 (62.6%) | 435/743 (58.5%) | 1504/2325 (64.7%) | 333/548 (60.8%) | 2485/3985 (62.4%) | 0.015 |
| Hepatitis C virus (HCV) | 3064/7601 (40.3%) | 338/743 (45.5%) | 943/2325 (40.6%) | 229/548 (41.8%) | 1554/3985 (39.0%) | 0.008 |
| Hepatitis B virus (HBV) | 2137/7601 (28.1%) | 126/743 (17.0%) | 717/2325 (30.8%) | 120/548 (21.9%) | 1174/3985 (29.5%) | < 0.001 |
| Alcohol‐associated liver disease | 1371/7601 (18.0%) | 81/743 (10.9%) | 418/2325 (18.0%) | 67/548 (12.2%) | 805/3985 (20.2%) | < 0.001 |
| Metabolic associated disease | 305/7601 (4.0%) | 45/743 (6.1%) | 86/2325 (3.7%) | 24/548 (4.4%) | 150/3985 (3.8%) | 0.024 |
| Acute Liver failure | 142/7601 (1.9%) | 20/743 (2.7%) | 45/2325 (1.9%) | 9/548 (1.6%) | 68/3985 (1.7%) | 0.317 |
| Cholestatic liver disease | 60/7601 (0.8%) | 23/743 (3.1%) | 9/2325 (0.4%) | 12/548 (2.2%) | 16/3985 (0.4%) | < 0.001 |
| Primary biliary cholangitis | 36/7601 (0.5%) | 15/743 (2.0%) | 4/2325 (0.2%) | 10/548 (1.8%) | 7/3985 (0.2%) | < 0.001 |
| Primary sclerosing cholangitis | 14/7601 (0.2%) | 2/743 (0.3%) | 2/2325 (< 0.1%) | 2/548 (0.4%) | 8/3985 (0.2%) | 0.266 |
| Congenital disease | 7/7601 (< 0.1%) | 2/743 (0.3%) | 2/2325 (< 0.1%) | 1/548 (0.2%) | 2/3985 (< 0.1%) | 0.159 |
| Donor age [years] | 55.6 (40.1, 69.5) | 60.7 (46.6, 72.9) | 59.0 (45.8, 72.6) | 47.3 (30.6, 63.5) | 53.7 (37.0, 67.6) | < 0.001 |
| Unknown | 203 | 15 | 69 | 16 | 103 | |
| Donor body mass index [kg/m2] | 25.4 (23.4, 27.8) | 24.3 (22.0, 27.1) | 25.4 (23.0, 28.1) | 24.7 (23.1, 27.0) | 25.7 (23.9, 27.8) | < 0.001 |
| Unknown | 749 | 62 | 226 | 70 | 391 | |
| Donor blood group | ||||||
| A | 3177/7585 (41.9%) | 310/743 (41.7%) | 1004/2321 (43.3%) | 229/547 (41.9%) | 1634/3974 (41.2%) | |
| AB | 279/7585 (3.7%) | 25/743 (3.4%) | 89/2321 (3.8%) | 25/547 (4.6%) | 140/3974 (3.5%) | |
| B | 871/7585 (11.5%) | 81/743 (10.9%) | 273/2321 (11.8%) | 71/547 (13.0%) | 446/3974 (11.2%) | |
| NA | 5/7585 (< 0.1%) | 0/743 (0%) | 3/2321 (0.1%) | 0/547 (0%) | 2/3974 (< 0.1%) | |
| O | 3253/7585 (42.9%) | 327/743 (44.0%) | 952/2321 (41.0%) | 222/547 (40.6%) | 1752/3974 (44.1%) | |
| Age difference recipient donor [years] | −1.5 (−17.5, 11.6) | 3.0 (−11.0, 14.7) | 1.3 (−11.8, 14.0) | −6.4 (−23.9, 6.1) | −4.2 (−20.8, 9.6) | < 0.001 |
| Unknown | 211 | 15 | 72 | 16 | 108 | |
| Age difference categories | ||||||
| Donor ≥ 20 years younger | 1645/7390 (22.3%) | 117/728 (16.1%) | 348/2253 (15.4%) | 167/532 (31.4%) | 1013/3877 (26.1%) | < 0.001 |
| 20–10 years younger | 959/7390 (13.0%) | 74/728 (10.2%) | 275/2253 (12.2%) | 77/532 (14.5%) | 533/3877 (13.7%) | |
| 10–5 years younger | 630/7390 (8.5%) | 71/728 (9.8%) | 186/2253 (8.3%) | 37/532 (7.0%) | 336/3877 (8.7%) | |
| Within 5 years | 1420/7390 (19.2%) | 134/728 (18.4%) | 467/2253 (20.7%) | 105/532 (19.7%) | 714/3877 (18.4%) | |
| 5–10 years older | 680/7390 (9.2%) | 71/728 (9.8%) | 242/2253 (10.7%) | 32/532 (6.0%) | 335/3877 (8.6%) | |
| 10–20 years older | 1213/7390 (16.4%) | 147/728 (20.2%) | 420/2253 (18.6%) | 74/532 (13.9%) | 572/3877 (14.8%) | |
| ≥ 20 years older | 843/7390 (11.4%) | 114/728 (15.7%) | 315/2253 (14.0%) | 40/532 (7.5%) | 374/3877 (9.6%) | |
Note: p‐values of 0.05 or less were considered statistically significant and therefore presented as bold.
Abbreviations: FDFR, female donor female recipient; FDMR, female donor male recipient; HBV, hepatitis B virus; HCV, hepatitis C virus; MDFR, male donor female recipient; MDMR male donor male recipient; NA, not available.
Median (IQR); n/N (%).
Median (IQR) or frequency (%).
Kruskal–Wallis rank sum test, Fisher's exact test or Pearson's chi‐squared test.
TABLE 2.
Tumour characteristics on explant histology and pre‐transplant treatment.
| Variable | Overall, N = 7601 a | FDFR, N = 743 b | FDMR, N = 2325 b | MDFR, N = 548 b | MDMR, N = 3985 b | p c |
|---|---|---|---|---|---|---|
| Number of nodules | ||||||
| Median | 2.0 (1.0, 3.0) | 2.0 (1.0, 2.0) | 2.0 (1.0, 3.0) | 1.0 (1.0, 3.0) | 2.0 (1.0, 3.0) | < 0.001 |
| 1 | 3270/7601 (43.0%) | 370/743 (49.8%) | 965/2325 (41.5%) | 281/548 (51.3%) | 1654/3985 (41.5%) | |
| 2 | 1837/7601 (24.2%) | 191/743 (25.7%) | 569/2325 (24.5%) | 127/548 (23.2%) | 950/3985 (23.8%) | |
| 3 | 1157/7601 (15.2%) | 93/743 (12.5%) | 363/2325 (15.6%) | 63/548 (11.5%) | 638/3985 (16.0%) | |
| 4 | 494/7601 (6.5%) | 32/743 (4.3%) | 141/2325 (6.1%) | 34/548 (6.2%) | 287/3985 (7.2%) | |
| 5 | 264/7601 (3.5%) | 16/743 (2.2%) | 104/2325 (4.5%) | 13/548 (2.4%) | 131/3985 (3.3%) | |
| ≥ 6 | 579 /7601 (7.6%) | 41/743 (5.5%) | 183/2325 (7.9%) | 30/548 (5.5%) | 325/3985 (8.2%) | |
| Size of largest nodule [cm] | ||||||
| Median | 2.8 (2.0, 4.0) | 2.5 (1.7, 3.5) | 2.8 (2.0, 4.0) | 2.8 (2.0, 4.0) | 3.0 (2.0, 4.0) | < 0.001 |
| ≥ 5 cm | 1219/7601 (16.0%) | 93/743 (12.5%) | 371/2325 (16.0%) | 103/548 (18.8%) | 652/3985 (16.4%) | 0.017 |
| Total tumour volume (TTV) [cm3] | ||||||
| Median | 11.5 (4.2, 33.5) | 8.2 (2.6, 22.4) | 11.5 (4.2, 33.5) | 11.5 (4.2, 33.5) | 14.1 (4.2, 33.5) | < 0.001 |
| TTV ≤ 145cm3 | 7106/7601 (93.5%) | 702/743 (94.5%) | 2174/2325 (93.5%) | 511/548 (93.2%) | 3719/3985 (93.3%) | 0.698 |
| Vascular invasion status | ||||||
| None | 4810/6228 (77.2%) | 454/560 (81.1%) | 1494/1918 (77.9%) | 364/466 (78.1%) | 2498/3284 (76.1%) | 0.190 |
| Macrovascular | 275/6228 (4.4%) | 22/560 (3.9%) | 87/1918 (4.5%) | 18/466 (3.9%) | 148/3284 (4.5%) | |
| Microvascular | 1143/6228 (18.4%) | 84/560 (15.0%) | 337/1918 (17.6%) | 84/466 (18.0%) | 638/3284 (19.4%) | |
| Portal vein tumour thrombus | ||||||
| None | 5863/6234 (94.0%) | 518/549 (94.4%) | 1807/1919 (94.2%) | 424/463 (91.6%) | 3114/3303 (94.3%) | 0.139 |
| Present | 371/6234 (6.0%) | 31/549 (5.6%) | 112/1919 (5.8%) | 39/463 (8.4%) | 189/3303 (5.7%) | |
| Milan criteria (beyond) | 2822/6669 (42.3%) | 224/606 (37.0%) | 873/2044 (42.7%) | 177/491 (36.0%) | 1548/3528 (43.9%) | < 0.001 |
| Pre‐transplantation treatment | ||||||
| Chemoembolization (TACE) | 3362/7601 (44.2%) | 289/743 (38.9%) | 1076/2325 (46.3%) | 217/548 (39.6%) | 1780/3985 (44.7%) | 0.001 |
| Resection | 464/7601 (6.1%) | 47/743 (6.3%) | 150/2325 (6.5%) | 30/548 (5.5%) | 237/3985 (5.9%) | 0.776 |
| Radiofrequency (RFA) | 1335/7601 (17.6%) | 126/743 (17.0%) | 417/2325 (17.9%) | 58/548 (10.6%) | 734/3985 (18.4%) | < 0.001 |
| Percutaneous ethanol injection (PEI) | 182/7601 (2.4%) | 19/743 (2.6%) | 62/2325 (2.7%) | 12/548 (2.2%) | 89/3985 (2.2%) | 0.714 |
| Cryotherapy | 4/7601 (< 0.1%) | 1/743 (0.1%) | 2/2325 (< 0.1%) | 1/548 (0.2%) | 0/3985 (0%) | 0.033 |
| Radiotherapy (SBRT) | 107/7601 (1.4%) | 12/743 (1.6%) | 28/2325 (1.2%) | 11/548 (2.0%) | 56/3985 (1.4%) | 0.504 |
| Sorafenib | 19/7601 (0.2%) | 1/743 (0.1%) | 4/2325 (0.2%) | 0/548 (0.0%) | 14/3985 (0.4%) | 0.410 |
| Radioembolization (TARE) | 111/7601 (1.5%) | 8/743 (1.1%) | 38/2325 (1.6%) | 3/548 (0.5%) | 62/3985 (1.6%) | 0.197 |
| Other | 121/7601 (1.6%) | 9/743 (1.2%) | 34/2325 (1.5%) | 4/548 (0.7%) | 74/3985 (1.9%) | 0.150 |
| Number of treatment modalities | ||||||
| 0 | 2817/7601 (37.1%) | 310/743 (41.7%) | 824/2325 (35.4%) | 253/548 (46.2%) | 1430/3985 (35.9%) | |
| 1 | 3962/7601 (52.1%) | 363/743 (48.9%) | 1225/2325 (52.7%) | 258/548 (47.1%) | 2116/3985 (53.1%) | |
| 2 | 727/7601 (9.6%) | 61/743 (8.2%) | 242/2325 (10.4%) | 33/548 (6.0%) | 391/3985 (9.8%) | |
| 3 | 91/7601 (1.2%) | 9/743 (1.2%) | 34/2325 (1.5%) | 4/548 (0.7%) | 44/3985 (1.1%) | |
| 4 | 4/7601 (< 0.1%) | 0/743 (0.0%) | 0/2325 (0.0%) | 0/548 (0.0%) | 4/3985 (0.1%) | |
| Multimodal (≥ 2) treatments | 822/7601 (10.8%) | 70/743 (9.4%) | 276/2325 (11.9%) | 37/548 (6.8%) | 439/3985 (11.0%) | 0.003 |
Note: p‐values of 0.05 or less were considered statistically significant and therefore presented as bold.
Abbreviations: FDFR, female donor female recipient; FDMR, female donor male recipient; MDFR, male donor female recipient; MDMR male donor male recipient; PEI, percutaneous ethanol injection; RFA, radiofrequency ablation; SBRT, stereotactic body radiotherapy; TACE, transarterial chemoembolization; TARE, transarterial radioembolization; TTV, total tumour volume.
Median (IQR); n/N (%).
Median (IQR) or Frequency (%).
Kruskal–Wallis rank sum test; Fisher's exact test; Pearson's chi‐squared test.
For transplantation, a full‐sized graft was used in 6554 (86.5%) patients and a living donor graft was used in 856 (11.3%) (Table S1). Donor median age was 55.6 years (IQR: 40.1, 69.5) with a BMI of 25.4 kg/m2 (IQR: 23.4, 27.8). The median age difference between donor and recipient was −1.5 years (IQR: −17.5, 11.6). On liver explant histopathology, the number of nodules was one in 3270 (43.0%) with a median of 2 (IQR: 1, 3) (Table 2). The median size of the largest nodule was 2.8 cm (IQR: 2.0, 4.0) with tumours ≥ 5 cm found in 1219 (16.0%) patients. Correspondingly, the TTV of the largest nodule was 11.5 cm3 (IQR: 4.2, 33.5) with ≥ 145 cm3 in 495 (6.5%) patients. Vascular invasion was classified as microscopic in 1143 (18.4%) and as macroscopic in 275 (4.4%) patients, respectively. A portal vein tumour thrombus (PVTT) was registered in 371 (6.0%) patients. The tumour burden was classified as ‘beyond Milan criteria’ in 2822 (42.3%) patients.
3.1. Recipient Donor Sex Constellation
The cohort was split into four RDSC groups: 743 (9.8%) FDFR, 2325 (30.6%) FDMR, 548 (7.2%) MDFR and 3985 (52.4%) MDMR. The relative distribution across the four RDSC groups has remained stable between 2000 and 2022 (Figures S2 and S3). A significant difference across the four groups was found for recipient BMI, ascites, HBV, HCV, any cirrhosis, metabolic, alcohol and PBC (Table 1). Regarding donor‐specific variables, differences between RDSC groups for donor age, donor BMI, age difference between recipient and donor as well as graft size and procurement location (inside vs. outside of country) were found (Table 1, Table S1).
Male recipients had significantly more often pre‐LT TACE than female recipients (FDMR 46.3%, MDMR 44.7%, FDFR 38.5%, MDFR 39.6%; p = 0.001) (Table 2). Comparable results were seen for radiofrequency ablation (RFA) (FDMR 17.9%, MDMR 18.4%, FDFR 17.0%, MDFR 10.6%; p < 0.001). Assessing a combination of treatments, a multimodal pre‐transplant treatment was performed in significantly more male than female recipients (FDMR 11.9%, MDMR 11.0%, FDFR 9.4%, MDFR 6.8%; p = 0.003). For tumour characteristics on explant histopathology, significant differences were seen in number of nodules, size of main nodules and corresponding TTV between RDSC groups. Comparing tumour burden (beyond Milan criteria) significant differences were seen across the RDSC groups (FDMR 42.7%, MDMR 43.9%, FDFR 37.0%, MDFR 36.0%; p < 0.001).
3.2. Survival
Overall median follow‐up was 22.6 months (IQR: 5.8, 60.7), with no significant differences across the RDSC groups (p = 0.093) (Table 3). The follow‐up across time periods was for 1988–1999: 140.3 months (IQR: 25.7, 206.3), for 2000–2009: 54.3 months (IQR: 16.9, 100.5) and for 2010–2023: 14.8 months (IQR: 3.3, 36.0) (Figure S4). Overall 4651 (61.2%) patients are considered as lost to follow‐up. No significant difference was found for crude registered death incidence (FDMR 26.0%, MDMR 24.9%, FDFR 24.4%, MDFR 23.0%; p = 0.453). Time from transplant to death was not significantly different between the groups (FDMR 17.3 months [IQR: 4.8, 49.4], MDMR 17.3 months [IQR: 3.8, 46.4], FDFR 16.6 months [IQR: 3.8, 41.8], MDFR 18.6 months [IQR: 2.1, 38.3]; p = 0.739). Truncated for 10 years, no significant difference was found for estimates in the Kaplan–Meier analysis (log‐rank p = 0.66) (Figure 1A). 10‐year OS (FDMR 54.6% [95% CI: 51.2%, 58.3%], MDMR 56.9% [95% CI: 54.4%, 59.6%], FDFR 54.5% [95% CI: 48.2%, 61.5%], MDFR 59.1% [95% CI: 52.5%, 66.7%]) (Table S2a).
TABLE 3.
Outcome characteristics.
| Variable | Overall, N = 7601 a | FDFR, N = 743 b | FDMR, N = 2325 b | MDFR, N = 548 b | MDMR, N = 3985 b | p c |
|---|---|---|---|---|---|---|
| Follow‐up time [months] | 22.6 (5.8, 60.7) | 19.5 (5.2, 54.2) | 22.5 (6.2, 61.0) | 21.2 (4.8, 56.5) | 23.2 (5.8, 61.0) | 0.093 |
| Unknown | 17 | 1 | 7 | 2 | 7 | |
| Death | 1905/7601 (25.1%) | 181/743 (24.4%) | 605/2325 (26.0%) | 126/548 (23.0%) | 993/3985 (24.9%) | 0.453 |
| Time transplant to death [months] | 17.2 (3.8, 46.3) | 16.6 (3.8, 41.8) | 17.3 (4.8, 49.4) | 18.6 (2.1, 38.3) | 17.3 (3.8, 46.4) | 0.739 |
| Graft status at death | ||||||
| Death with functioning graft | 822/1212 (67.8%) | 59/97 (60.8%) | 288/406 (70.9%) | 51/89 (57.3%) | 424/620 (68.4%) | 0.035 |
| Death with chronic graft dysfunction | 390/1212 (32.2%) | 38/97 (39.2%) | 118/406 (29.1%) | 38/89 (42.7%) | 196/620 (31.6%) | |
| Cause of death | ||||||
| HCC recurrence | 496/1905 (26.0%) | 39/181 (21.5%) | 148/605 (24.5%) | 36/126 (28.6%) | 273/993 (27.5%) | 0.242 |
| Other liver related | 288/1905 (15.1%) | 32/181 (17.7%) | 91/605 (15.0%) | 21/126 (16.7%) | 144/993 (14.5%) | 0.692 |
| Infection | 250/1905 (13.1%) | 22/181 (12.2%) | 88/605 (14.5%) | 17/126 (13.5%) | 123/993 (12.4%) | 0.635 |
| Rejection | 198/1905 (10.4%) | 31/181 (17.1%) | 56/605 (9.3%) | 11/126 (8.7%) | 100/993 (10.1%) | 0.017 |
| Other tumour | 155/1905 (8.1%) | 8/181 (4.4%) | 61/605 (10.1%) | 8/126 (6.3%) | 78/993 (7.9%) | 0.070 |
| Cardiovascular complications | 117/1905 (6.1%) | 9/181 (5.0%) | 49/605 (8.1%) | 3/126 (2.4%) | 56/993 (5.6%) | 0.046 |
| Pulmonary | 71/1905 (3.7%) | 9/181 (5.0%) | 26/605 (4.3%) | 4/126 (3.2%) | 32/993 (3.2%) | 0.511 |
| Cerebrovascular complications | 42/1905 (2.2%) | 4/181 (2.2%) | 15/605 (2.5%) | 5/126 (4.0%) | 18/993 (1.8%) | 0.378 |
| Renal | 39/1905 (2.0%) | 3/181 (1.7%) | 11/605 (1.8%) | 2/126 (1.6%) | 23/993 (2.3%) | 0.928 |
| Gastrointestinal complications | 34/1905 (1.8%) | 1/181 (0.6%) | 11/605 (1.8%) | 0/126 (0.0%) | 22/993 (2.2%) | 0.221 |
| Other | 213/1905 (11.2%) | 25/181 (13.8%) | 67/605 (11.1%) | 17/126 (13.5%) | 104/993 (10.5%) | 0.485 |
| No data available | 104/1905 (5.5%) | 9/181 (5.0%) | 37/605 (6.1%) | 5/126 (4.0%) | 53/993 (5.3%) | 0.763 |
Note: p‐values of 0.05 or less were considered statistically significant and therefore presented as bold.
Abbreviations: FDFR, female donor female recipient; FDMR, female donor male recipient; HCC, hepatocellular carcinoma; MDFR, male donor female recipient; MDMR male donor male recipient.
Median (IQR); n/N (%).
Median (IQR) or frequency (%).
Kruskal–Wallis rank sum test; Fisher's exact test; Pearson's chi‐squared test.
FIGURE 1.
(A) Overall survival Kaplan–Meier plot stratified by recipient donor sex combination. (B) Adjusted (number of nodules, largest nodule size, vascular invasion status, hepatitis C virus, hepatitis B virus, alcohol‐associated liver disease, number of treatments prior to liver transplantation, age difference between donor and recipient and recipient body mass index) survival Kaplan–Meier analysis. FDFR, female donor female recipient; FDMR, female donor male recipient; MDFR, male donor female recipient; MDMR male donor male recipient; RDSC, recipient donor sex constellation.
A priori adjusted survival analysis using the Kaplan–Meier curve, shows a deviation of the groups with regard to survival estimates (Figure 1B). Starting approximately at 1 month after transplant (Figure S5) MDFR deviates from the other survival estimates while FDFR and FDMR are overlapping. In the multivariable Cox proportional hazard model regression analysis, a significant difference of the observed estimated effect in MDFR, showing a protective effect of this constellation (aHR 0.72; 95% CI: 0.54, 0.95; p = 0.023) (Table S2b). No significant difference was found of estimated effect of MDMR or FDMR, compared to FDFR.
The protective effect changed across the time periods for MDFR: for 1988–1999 (n = 21) aHR 0.26 (95% CI: 0.05, 1.48), 2000–2009 (n = 184) aHR 0.73 (95% CI; 0.49, 1.09) and 2010–2023 (n = 343) aHR 0.72 (95% CI: 0.47, 1.12).
3.3. Cause of Death
In patients with available graft status at death (n = 1212), death with dysfunctioning graft was registered in 390 (32.2%) patients (Table 3). MDFR had the highest proportion of graft dysfunction at death (FDMR 29.1%, MDMR 31.6%, FDFR 39.2%, MDFR 42.7%; p = 0.035). In contrast, MDFR had the lowest proportion of death with rejection (FDMR 9.3%, MDMR 10.1%, FDFR 17.1%, MDFR 8.7%; p = 0.017) and cardiovascular complications (FDMR 8.1%, MDMR 5.6%, FDFR 5.0%, MDFR 2.4%; p = 0.046).
Figure S6 displays the competing risk cumulative incidence plots of registered primary causes of death stratified by RDSC. While in FDFR, FDMR and MDMR HCC recurrence becomes the leading primary cause of death exceeding other causes around 15 months post‐transplant, in MDFR this interception is seen at around 18 months. Across all groups, infection and liver‐related problems are the leading causes of death within 12 months of transplant.
3.4. Death With Recurrence
Regarding differences in recurrence, the ELTR allows for comparative analysis for registered cause of death. HCC tumour recurrence was the most common cause and was registered in 496 (26.0%) patients. No statistically significant difference was found across the four RDSC groups (FDMR 24.5%, MDMR 27.5%, FDFR 21.5%, MDFR 28.6%; p = 0.242) with a deviation of lines in the cumulative incidence rate observed at 30 months (Figure 2A,B). No significant differences were seen between groups for the time‐dependent adjusted analysis (Table S2c).
FIGURE 2.
(A) Cumulative incidence rate of competing risk analysis for death with recurrence of hepatocellular carcinoma per recipient donor sex constellation. (B) Adjusted (number of nodules, largest nodule size, vascular invasion status, hepatitis C virus, hepatitis B virus, alcohol‐associated liver disease, number of treatments prior to liver transplantation, age difference between donor and recipient and recipient body mass index) cumulative incidence rate of death with recurrence of hepatocellular carcinoma per recipient donor sex constellation. FDFR, female donor female recipient; FDMR, female donor male recipient; MDFR, male donor female recipient; MDMR male donor male recipient.
4. Discussion
In this ELTR study, we were able to demonstrate that RDSC—specifically in female recipients—is an independent risk factor for mortality when adjusted for additional characteristics (MDFR compared to FDFR: aHR 0.72; p = 0.023). This observed effect was a priori adjusted for tumour‐, recipient and donor‐related variables. Receiving a male donor graft resulted in a 28% reduction of the hazard or risk of death in female recipients, with the risk reduction starting at the time of transplantation. Additionally, differences in causes of death were identified between the four RDSC groups. To the best of our knowledge, this is the first comparison of in‐depth RDSC outcome after LT for HCC.
Sex‐specific differences in immune cell activity, cytokines, humoral responses, metabolism and homeostasis are known [21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32]. Smiriglia et al. summarised sex‐differences of liver diseases in preclinical models [33]. Additionally, a sex‐disparity for ischemia–reperfusion injury is known, with less injury found in female mice [34], (possibly due to oestrogens increasing nitric oxide production). Sex‐related differences are also observed with regard to indications for LT [35, 36, 37], for which we adjusted in our analysis. Differences in pharmacokinetics and pharmacodynamics between sexes with resulting impact on immunosuppressive medication are known [38, 39, 40, 41, 42, 43], possibly further contributing to the observed differences. Previous groups have shown primary non‐function, vascular thrombosis, biliary anastomotic stricture and recurrent HCV occur more frequently in FDMR transplantations [37, 44].
Mechanistic hypotheses explaining the observed difference for MDFR, are likely to be manifold, including: (1) relative ‘oestrogen‐naive’ male donor organs might have marked proliferation of distinct cell lines and enhanced cell repair, (2) grafts of male donors tend to be larger than female grafts, possibly resulting in a more favourable graft size to recipient body ratio with additional blood‐flow associated physiological changes, and (3) interaction of the donors' hepatic innate immune system with the recipients' immune system (immunotolerance).
First, oestrogen (E2) interaction with the liver has been studied in preclinical models, possibly affecting outcome after liver surgery and transplantation. In a female bovine model (ovariectomized cows, relative oestrogen‐naive) the authors compared 14 days sex steroid hormone supplementation versus control animals (no substitution), and identified 172 upregulated and 173 downregulated genes in the liver by E2 and progesterone [45]. E2 has been shown to activate p53 and micro ribonucleic acid (miRNA)‐23a expression (among 35 other miRNA signatures) specifically in male‐derived liver cells [46]—controlling apoptosis and potentially influencing cell regeneration. E2 acts through the ER/Src/Shc/ERK pathway and is associated with cholangiocyte proliferation [47]. Moreover, it has been discussed that E2 may supresses HCC metastasis through decreasing interleukin 6 (IL‐6) and hepatocyte growth factor [48]. These proposed mechanisms are intriguing. However, while these hypotheses pivot around E2 levels, the impact of the (mainly) post‐menopausal status of liver recipients on E2 levels has yet to be elucidated. Furthermore, the average concentrations of bioavailable oestrogen has been reported in pre‐menopausal females to be E2 17 pg/mL, post‐menopausal females 3 pg/mL, young (20–39 years) males 13 pg/mL and elder males (≥ 60 years) 8 pg/mL [49, 50] making the idea of the ‘relative oestrogen‐naive male donor organs in female recipients’ less pronounced. As menopause most often occurs between the ages of 45–55 years, the majority of our recipients are likely to be post‐menopausal.
Second, female recipients receive significantly more often relatively large donor grafts (OR 1.82; p = 0.023) and relative small grafts (OR 11.30; p < 0.001) than male recipients [51]. While no significant association between donor sex and relative large grafts was reported (OR 0.77; p = 0.340) female donor sex is associated with relative smaller grafts (OR 2.39; p = 0.002). To prevent the graft size mismatch associated with ‘small‐for‐size’ or ‘small‐for‐flow’ syndromes, different techniques for portal blood flow modulation have been developed to decrease post‐LT marked portal venous blood flow [52]. Intraoperative decreased portal vein blood flow < 1000 mL/min has been reported to be associated with increased mortality (OR 0.54) [53] showing that too little portal venous flow may also be potentially harmful. Interestingly, graft volume to standard liver volume ratio increases over time after transplantation and higher portal venous and hepatic arterial blood flow correlate with graft volume increase [54].
Thirdly, inflammatory mechanisms within the liver are a complex multifactorial process between the donor graft innate liver immune system and the host's response which need to be maintained in homeostasis to allow for immunotolerance after transplantation [55, 56]. This is of particular interest, as we have observed a marked difference in rejection as a cause of death between MDFR and FDFR (9% vs. 17%). In murine models, males have lower numbers of Kupffer cells per liver tissue weight than females [57], while females show a significant increase in interferon alpha (IFN‐α), tumour necrosis factor alpha (TNF‐α) after toll‐like receptor 7 (TLR7)‐ligand stimulation [28] and higher IL‐6 [58] compared to males. Androgen receptor specific mRNA is expressed more in male models upon acute liver injury, mitigating the recruitment of migratory monocytes [58]. A potential intertwined mechanism is the ‘recipient‐derived hepatocyte repopulation’ which has been observed in the MDFR subgroup to occur in 100% [59].
The fact that only one recipient sex of LT for HCC has a marked effect of sex‐mismatch might explain why previous authors have failed or only identified a less pronounced effect compared to our findings. While four studies showed a worse survival in male recipients [10, 11, 12, 15] two studies showed identical results in female recipients [13, 14]. Schoening et al. reported comparable findings in an unadjusted analysis in patients receiving LT for any kind of indication (10 years graft survival MDFR 68%, FDFR 64%, MDMR 63% FDMR 56%) [35]. We incorporated known differences of recipient and donor derived factors by performing a multivariable analysis.
The observed marked difference could be integrated in the organ allocation process, contributing towards personalised medicine in transplantat oncology. In Europe the proportion of female donors increased over 10 years (+8%) [15], thus specifically affecting female recipients as per our shown results. The non‐significant differences of death with recurrence are possibly not a robust observation as the follow‐up (22.6 months) is less than the timepoint of observed deviation across groups (30 months). Additionally, the registry does not allow for per se recurrence analysis. As we have observed a difference of mortality rates, a competing risk analysis for recurrence with death across groups would be needed. Moreover, we were able to show, that categorisation of RDSC into four groups is a more robust approach rather than the established dichotomised analysis design for example, sex‐(mis)match or recipient sex‐based.
Our study has several strengths and limitations. As the observed incidence of the primary endpoint (25%) was higher than in the a priori calculated sample size, a sufficient numerical powered analysis was performed. The chosen multivariable Cox proportional hazard model regression analysis has been shown to result in comparable effect estimates compared to the propensity score matching method [60]. The risk of overfitting of our multivariable model is very low, as we adhered to Harrel's recommendations [61]. Biases due to the nature of the data originating from a registry may include selection bias, survivorship bias or information bias. A potential observer bias might be reflected by the median available follow up data of approximately 2 years. A differentiation between ‘lost to follow‐up’ and ‘lag of data entry’ (e.g., fewer entries in recent years as shown in Figure S2) is currently not possible and poses a known and frequently observed phenomenon in different national transplant registries [62]. This decrease in follow‐up data as well as a sample size issue influenced the time‐subgroup analysis. While we were able to adjust our analysis for liver disease aetiologies, no adjustment for comorbidities (e.g., diabetes, coronary artery disease or chronic kidney disease), immunosuppressive therapy, substance abuse status, perioperative blood loss and transfusion requirements as well as donor graft size, donor diseases and donor cause of death, was feasible in this study. A related potential attribution bias for comorbidities could not be adjusted for.
To conclude, our findings provide more insight into the relevant impact of organ and recipient sex combinations in the setting of transplant oncology—specifically for female recipients. Interestingly, female recipients show a benefit if they receive a sex‐mismatched organ with a risk reduction of mortality of 28%.
Conflicts of Interest
The ELTR is supported by a grant from Astellas, Novartis, Institut Georges Lopez, Sandoz, Chiesi and logistic support from the Paul Brousse Hospital (Assistance Publique—Hôpitaux de Paris). The Organ Sharing Organisations: the French ABM (Sami Djabbour), the Dutch NTS (Maaike de Wolf), the Eurotransplant Foundation (Marieke Van Meel), the Spanish RETH/ONT (Gloria de la Rosa), the UK‐Ireland NHSBT (Malcolm Fox) and the Scandiatransplatnt (Ilse Duus Weinreich) are acknowledged for the data cross‐check and sharing with the ELTR. Anja Lachenmayer declares relations to CAScination and Johnson&Johnson.
Supporting information
Data S1.
Acknowledgements
This project is endorsed by the ELITA (European Liver and Intestine Transplant Association) and performed using the European Liver Transplant Registry (ELTR). We thank all investigators and their participating center listed in the link http://www.eltr.org/spip.php?page=centers‐tous. The ELTR is supported by a grant from Astellas, Novartis, Institut Georges Lopez, Sandoz, Chiesi and logistic support from the Paul Brousse Hospital (Assistance Publique—Hôpitaux de Paris). The Organ Sharing Organisations: the French ABM (Sami Djabbour), the Dutch NTS (Maaike de Wolf), the Eurotransplant Foundation (Marieke Van Meel), the Spanish RETH/ONT (Gloria de la Rosa), the UK‐Ireland NHSBT (Malcolm Fox) and the Scandiatransplatnt (Ilse Duus Weinreich) are acknowledged for the data cross‐check and sharing with the ELTR.
Funding: The authors received no specific funding for this work.
Handling Editor: Alejandro Forner
Data Availability Statement
The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.
References
- 1. Sung H., Ferlay J., Siegel R. L., et al., “Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries,” CA: A Cancer Journal for Clinicians 71, no. 3 (2021): 209–249, 10.3322/caac.21660. [DOI] [PubMed] [Google Scholar]
- 2. Brown Z. J., Tsilimigras D. I., Ruff S. M., et al., “Management of Hepatocellular Carcinoma: A Review,” JAMA Surgery 15 (2023): 410, 10.1001/JAMASURG.2022.7989. [DOI] [PubMed] [Google Scholar]
- 3. Llovet J. M., Kelley R. K., Villanueva A., et al., “Hepatocellular Carcinoma,” Nature Reviews Disease Primers 7 (2021): 1, 10.1038/S41572-020-00240-3. [DOI] [PubMed] [Google Scholar]
- 4. Reig M., Forner A., Rimola J., et al., “BCLC Strategy for Prognosis Prediction and Treatment Recommendation: The 2022 Update,” Journal of Hepatology 76, no. 3 (2022): 681–693, 10.1016/J.JHEP.2021.11.018. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5. Mehta N., Bhangui P., Yao F. Y., et al., “Liver Transplantation for Hepatocellular Carcinoma. Working Group Report From the ILTS Transplant Oncology Consensus Conference,” Transplantation 104, no. 6 (2020): 1136–1142, 10.1097/TP.0000000000003174. [DOI] [PubMed] [Google Scholar]
- 6. Lai Q., Viveiros A., Iesari S., et al., “Prognostic Factors for 10‐Year Survival in Patients With Hepatocellular Cancer Receiving Liver Transplantation,” Frontiers in Oncology 12 (2022): 1–11, 10.3389/fonc.2022.877107. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7. Bzeizi K. I., Abdullah M., Vidyasagar K., Alqahthani S. A., and Broering D., “Hepatocellular Carcinoma Recurrence and Mortality Rate Post Liver Transplantation: Meta‐Analysis and Systematic Review of Real‐World Evidence,” Cancers (Basel) 14, no. 20 (2022): 5114, 10.3390/cancers14205114. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8. Han S., Yang J. D., Sinn D. H., et al., “Risk of Post‐Transplant Hepatocellular Carcinoma Recurrence Is Higher in Recipients of Livers From Male Than Female Living Donors,” Annals of Surgery 16 (2017): 1043–1050, 10.1097/SLA.0000000000002318. [DOI] [PubMed] [Google Scholar]
- 9. Lefebvre P. and Staels B., “Hepatic Sexual Dimorphism–Implications for Non‐alcoholic Fatty Liver Disease,” Nature Reviews. Endocrinology 17, no. 11 (2021): 662–670, 10.1038/s41574-021-00538-6. [DOI] [PubMed] [Google Scholar]
- 10. Tejada S., Martinez‐Reviejo R., Nogueira T. A., et al., “The Effect of Sex Inequality on Solid Organ Transplantation: A Systematic Review and Meta‐Analysis,” European Journal of Internal Medicine 109 (2022): 58–67, 10.1016/J.EJIM.2022.12.009. [DOI] [PubMed] [Google Scholar]
- 11. Berenguer M., Di Maira T., Baumann U., et al., “Characteristics, Trends, and Outcomes of Liver Transplantation for Primary Sclerosing Cholangitis in Female Versus Male Patients: An Analysis From the European Liver Transplant Registry,” Transplantation 105, no. 10 (2021): 2255–2262, 10.1097/TP.0000000000003542. [DOI] [PubMed] [Google Scholar]
- 12. Gabbay U., Issachar A., Cohen‐Naftaly M., Brown M., and Nesher E., “Gender Specific Survival Rates After Deceased Donor Liver Transplantation: A Retrospective Cohort,” Annals of Medicine and Surgery 79 (2022): 79, 10.1016/j.amsu.2022.103933. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. Serrano M. T., Sabroso S., Esteban L. M., et al., “Mortality and Causes of Death After Liver Transplantation: Analysis of Sex Differences in a Large Nationwide Cohort,” Transplant International 35 (2022): 1, 10.3389/ti.2022.10263. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14. Leibovici‐Weissman Y., Anchel N., Nesher E., Leshno M., and Shlomai A., “Early Post‐Liver Transplantation Infections and Their Effect on Long‐Term Survival,” Transplant Infectious Disease 23, no. 4 (2021): e13673, 10.1111/TID.13673. [DOI] [PubMed] [Google Scholar]
- 15. Germani G., Zeni N., Zanetto A., et al., “Influence of Donor and Recipient Gender on Liver Transplantation Outcomes in Europe,” Liver International 40, no. 8 (2020): 1961–1971, 10.1111/LIV.14510. [DOI] [PubMed] [Google Scholar]
- 16. Magyar C. T. J., Gretener C. P., Baldi P., et al., “Recipient Donor Sex Combinations in Solid Organ Transplantation and Impact on Clinical Outcome: A Scoping Review,” Clinical Transplantation 38, no. 5 (2024): e15312, 10.1111/ctr.15312. [DOI] [PubMed] [Google Scholar]
- 17. Adam R., Karam V., Delvart V., et al., “Evolution of Indications and Results of Liver Transplantation in Europe. A Report From the European Liver Transplant Registry (ELTR),” Journal of Hepatology 57, no. 3 (2012): 675–688, 10.1016/j.jhep.2012.04.015. [DOI] [PubMed] [Google Scholar]
- 18. Karam V., Gunson B., Roggen F., et al., “Quality Control of the European Liver Transplant Registry: Results of Audit Visits to the Contributing Centers,” Transplantation 75, no. 12 (2003): 2167–2173, 10.1097/01.TP.0000080271.20145.07. [DOI] [PubMed] [Google Scholar]
- 19. von Elm E., Altman D. G., Egger M., Pocock S. J., Gøtzsche P. C., and Vandenbroucke J. P., “Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement: Guidelines for Reporting Observational Studies,” BMJ 335, no. 7624 (2007): 806–808, 10.1136/BMJ.39335.541782.AD. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20. Mazzaferro V., Regalia E., Doci R., et al., “Liver Transplantation for the Treatment of Small Hepatocellular Carcinomas in Patients With Cirrhosis,” New England Journal of Medicine 334, no. 11 (1996): 693–699, 10.1056/NEJM199603143341104. [DOI] [PubMed] [Google Scholar]
- 21. Klein S. L. and Flanagan K. L., “Sex Differences in Immune Responses,” Nature Reviews. Immunology 16, no. 10 (2016): 626–638, 10.1038/NRI.2016.90. [DOI] [PubMed] [Google Scholar]
- 22. Furman D., Hejblum B. P., Simon N., et al., “Systems Analysis of Sex Differences Reveals an Immunosuppressive Role for Testosterone in the Response to Influenza Vaccination,” Proceedings of the National Academy of Sciences of the United States of America 111, no. 2 (2014): 869–874, 10.1073/PNAS.1321060111. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23. Wikby A., Månsson I. A., Johansson B., Strindhall J., and Nilsson S. E., “The Immune Risk Profile Is Associated With Age and Gender: Findings From Three Swedish Population Studies of Individuals 20‐100 Years of Age,” Biogerontology 9, no. 5 (2008): 299–308, 10.1007/S10522-008-9138-6. [DOI] [PubMed] [Google Scholar]
- 24. Bupp M. R. G., Potluri T., Fink A. L., and Klein S. L., “The Confluence of Sex Hormones and Aging on Immunity,” Frontiers in Immunology 9 (2018): 9, 10.3389/FIMMU.2018.01269. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25. Huang Z., Chen B., Liu X., et al., “Effects of Sex and Aging on the Immune Cell Landscape as Assessed by Single‐Cell Transcriptomic Analysis,” Proceedings of the National Academy of Sciences of the United States of America 118, no. 33 (2021): e2023216118, 10.1073/PNAS.2023216118/-/DCSUPPLEMENTAL. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26. Griesbeck M., Ziegler S., Laffont S., et al., “Sex Differences in Plasmacytoid Dendritic Cell Levels of IRF5 Drive Higher IFN‐α Production in Women,” Journal of Immunology 195, no. 11 (2015): 5327–5336, 10.4049/JIMMUNOL.1501684. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27. Berghöfer B., Frommer T., Haley G., Fink L., Bein G., and Hackstein H., “TLR7 Ligands Induce Higher IFN‐Alpha Production in Females,” Journal of Immunology 177, no. 4 (2006): 2088–2096, 10.4049/JIMMUNOL.177.4.2088. [DOI] [PubMed] [Google Scholar]
- 28. Laffont S., Rouquié N., Azar P., et al., “X‐Chromosome Complement and Estrogen Receptor Signaling Independently Contribute to the Enhanced TLR7‐Mediated IFN‐α Production of Plasmacytoid Dendritic Cells From Women,” Journal of Immunology 193, no. 11 (2014): 5444–5452, 10.4049/JIMMUNOL.1303400. [DOI] [PubMed] [Google Scholar]
- 29. Hewagama A., Patel D., Yarlagadda S., Strickland F. M., and Richardson B. C., “Stronger Inflammatory/Cytotoxic T‐Cell Response in Women Identified by Microarray Analysis,” Genes and Immunity 10, no. 5 (2009): 509–516, 10.1038/GENE.2009.12. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30. Souyris M., Cenac C., Azar P., et al., “TLR7 Escapes X Chromosome Inactivation in Immune Cells,” Science Immunology 3, no. 19 (2018): eaap8855, 10.1126/SCIIMMUNOL.AAP8855. [DOI] [PubMed] [Google Scholar]
- 31. Pisitkun P., Deane J. A., Difilippantonio M. J., Tarasenko T., Satterthwaite A. B., and Bolland S., “Autoreactive B Cell Responses to RNA‐Related Antigens due to TLR7 Gene Duplication,” Science 312, no. 5780 (2006): 1669–1672, 10.1126/SCIENCE.1124978. [DOI] [PubMed] [Google Scholar]
- 32. Kur P., Kolasa‐Wołosiuk A., Misiakiewicz‐Has K., and Wiszniewska B., “Sex Hormone‐Dependent Physiology and Diseases of Liver,” International Journal of Environmental Research and Public Health 17, no. 8 (2020): 2620, 10.3390/ijerph17082620. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33. Smiriglia A., Lorito N., Serra M., Perra A., Morandi A., and Kowalik M. A., “Sex Difference in Liver Diseases: How Preclinical Models Help to Dissect the Sex‐Related Mechanisms Sustaining NAFLD and Hepatocellular Carcinoma,” iScience 26, no. 12 (2023): 108363, 10.1016/j.isci.2023.108363. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34. Sayaf K., Gabbia D., Russo F. P., and De Martin S., “The Role of Sex in Acute and Chronic Liver Damage,” International Journal of Molecular Sciences 23, no. 18 (2022): 10654, 10.3390/ijms231810654. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35. Schoening W. N., Helbig M., Buescher N., et al., “Gender Matches in Liver Transplant Allocation: Matched and Mismatched Male‐Female Donor‐Recipient Combinations; Long‐Term Follow‐Up of More Than 2000 Patients at a Single Center,” Experimental and Clinical Transplantation 14, no. 2 (2016): 184–190, 10.6002/ECT.2015.0211. [DOI] [PubMed] [Google Scholar]
- 36. Cullaro G., Sarkar M., and Lai J. C., “Sex‐Based Disparities in Delisting for Being “Too Sick” for Liver Transplantation,” American Journal of Transplantation 18, no. 5 (2018): 1214–1219, 10.1111/AJT.14608. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37. Croome K. P., Segal D., Hernandez‐Alejandro R., Adams P. C., Thomson A., and Chandok N., “Female Donor to Male Recipient Gender Discordance Results in Inferior Graft Survival: A Prospective Study of 1,042 Liver Transplants,” Journal of Hepato‐Biliary‐Pancreatic Sciences 21 (2013): 269–274, 10.1002/jhbp.40. [DOI] [PubMed] [Google Scholar]
- 38. Katz‐Greenberg G. and Shah S., “Sex and Gender Differences in Kidney Transplantation,” Seminars in Nephrology 42, no. 2 (2022): 219–229, 10.1016/J.SEMNEPHROL.2022.04.011. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39. Momper J. D., Misel M. L., and McKay D. B., “Sex Differences in Transplantation,” Transplantation Reviews 31, no. 3 (2017): 145–150, 10.1016/J.TRRE.2017.02.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40. Tornatore K. M., Meaney C. J., Wilding G. E., et al., “Influence of Sex and Race on Mycophenolic Acid Pharmacokinetics in Stable African American and Caucasian Renal Transplant Recipients,” Clinical Pharmacokinetics 54, no. 4 (2015): 423–434, 10.1007/S40262-014-0213-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41. Van Hest R. M., Van Gelder T., Vulto A. G., and Mathot R. A. A., “Population Pharmacokinetics of Mycophenolic Acid in Renal Transplant Recipients,” Clinical Pharmacokinetics 44, no. 10 (2005): 1083–1096, 10.2165/00003088-200544100-00006. [DOI] [PubMed] [Google Scholar]
- 42. Zimmerman J. J., “Exposure‐Response Relationships and Drug Interactions of Sirolimus,” AAPS Journal 6, no. 4 (2004): 1–12, 10.1208/AAPSJ060428. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43. Velickovi‐Radovanovi R., Mikov M., Paunovi G., et al., “Gender Differences in Pharmacokinetics of Tacrolimus and Their Clinical Significance in Kidney Transplant Recipients,” Gender Medicine 8, no. 1 (2011): 23–31, 10.1016/J.GENM.2011.01.003. [DOI] [PubMed] [Google Scholar]
- 44. Verdonk R. C., Buis C. I., Porte R. J., et al., “Anastomotic Biliary Strictures After Liver Transplantation: Causes and Consequences,” Liver Transplantation 12, no. 5 (2006): 726–735, 10.1002/lt.20714. [DOI] [PubMed] [Google Scholar]
- 45. Piccinato C. A., Rosa G. J. M., N'Jai A. U., Jefcoate C. R., and Wiltbank M. C., “Estradiol and Progesterone Exhibit Similar Patterns of Hepatic Gene Expression Regulation in the Bovine Model,” PLoS One 8, no. 9 (2013): e73552, 10.1371/journal.pone.0073552. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 46. Huang F. Y., Wong D. K. H., Seto W. K., Lai C. L., and Yuen M. F., “Estradiol Induces Apoptosis via Activation of miRNA‐23a and p53: Implication for Gender Difference in Liver Cancer Development,” Oncotarget 6, no. 33 (2015): 34941–34952, 10.18632/oncotarget.5472. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 47. Alvaro D., Onori P., Metalli V. D., et al., “Intracellular Pathways Mediating Estrogen‐Induced Cholangiocyte Proliferation in the Rat,” Hepatology 36, no. 2 (2002): 297–304, 10.1053/jhep.2002.34741. [DOI] [PubMed] [Google Scholar]
- 48. Wang Y. C., Xu G. L., Jia W. D., et al., “Estrogen Suppresses Metastasis in Rat Hepatocellular Carcinoma Through Decreasing Interleukin‐6 and Hepatocyte Growth Factor Expression,” Inflammation 35, no. 1 (2012): 143–149, 10.1007/s10753-011-9299-3. [DOI] [PubMed] [Google Scholar]
- 49. Khosla S., Melton L. J., Robb R. A., et al., “Relationship of Volumetric BMD and Structural Parameters at Different Skeletal Sites to Sex Steroid Levels in Men,” Journal of Bone and Mineral Research 20, no. 5 (2005): 730–740, 10.1359/JBMR.041228. [DOI] [PubMed] [Google Scholar]
- 50. Khosla S., Riggs B. L., Robb R. A., et al., “Relationship of Volumetric Bone Density and Structural Parameters at Different Skeletal Sites to Sex Steroid Levels in Women,” Journal of Clinical Endocrinology and Metabolism 90, no. 9 (2005): 5096–5103, 10.1210/jc.2005-0396. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 51. Kubal C. A., Mihaylov P., Fridell J., et al., “Donor‐Recipient Body Size Mismatch Has no Impact on Outcomes After Deceased Donor Whole Liver Transplantation: Role of Donor Liver Size Measurement,” Clinical Transplantation 35, no. 6 (2021): e14299, 10.1111/ctr.14299. [DOI] [PubMed] [Google Scholar]
- 52. Azoulay D., Feray C., Lim C., et al., “A Systematic Review of Auxiliary Liver Transplantation of Small‐For‐Size Grafts in Patients With Chronic Liver Disease,” JHEP Reports 4, no. 4 (2022): 100447, 10.1016/j.jhepr.2022.100447. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 53. Spitzer A. L., Dick A. A. S., Bakthavatsalam R., et al., “Intraoperative Portal Vein Blood Flow Predicts Allograft and Patient Survival Following Liver Transplantation,” HPB 12, no. 3 (2010): 166–173, 10.1111/j.1477-2574.2009.00137.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 54. Arslan A., Romano A., Wang Q., Wang B., Brismar T. B., and Nowak G., “Volumetric Graft Changes After Liver Transplantation: Evidence of Adaptation to Recipient Body Size,” American Journal of Physiology‐Gastrointestinal and Liver Physiology 325, no. 5 (2023): G398–G406, 10.1152/ajpgi.00040.2023. [DOI] [PubMed] [Google Scholar]
- 55. Pérez‐Escobar J., Jimenez J. V., Rodríguez‐Aguilar E. F., et al., “Immunotolerance in Liver Transplantation: A Primer for the Clinician,” Annals of Hepatology 28, no. 1 (2023): 100760, 10.1016/j.aohep.2022.100760. [DOI] [PubMed] [Google Scholar]
- 56. Xiao T., Lee J., Gauntner T. D., Velegraki M., Lathia J. D., and Li Z., “Hallmarks of Sex Bias in Immuno‐Oncology: Mechanisms and Therapeutic Implications,” Nature Reviews. Cancer 24, no. 5 (2024): 338–355, 10.1038/s41568-024-00680-z. [DOI] [PubMed] [Google Scholar]
- 57. Marcos R., Lopes C., Malhão F., et al., “Stereological Assessment of Sexual Dimorphism in the Rat Liver Reveals Differences in Hepatocytes and Kupffer Cells but Not Hepatic Stellate Cells,” Journal of Anatomy 228, no. 6 (2016): 996–1005, 10.1111/joa.12448. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 58. Bizzaro D., Crescenzi M., Di Liddo R., et al., “Sex‐Dependent Differences in Inflammatory Responses During Liver Regeneration in a Murine Model of Acute Liver Injury,” Clinical Science 132, no. 2 (2018): 255–272, 10.1042/CS20171260. [DOI] [PubMed] [Google Scholar]
- 59. Idilman R., Erden E., Kuzu I., et al., “Recipient‐Derived Hepatocytes in Sex‐Mismatched Liver Allografts After Liver Transplantation: Early Versus Late Transplant Biopsies,” Transplantation 78, no. 11 (2004): 1647–1652, 10.1097/01.TP.0000144055.78462.4F. [DOI] [PubMed] [Google Scholar]
- 60. Fu E. L., Groenwold R. H. H., Zoccali C., Jager K. J., van Diepen M., and Dekker F. W., “Merits and Caveats of Propensity Scores to Adjust for Confounding,” Nephrology, Dialysis, Transplantation 34, no. 10 (2019): 1629–1635, 10.1093/ndt/gfy283. [DOI] [PubMed] [Google Scholar]
- 61. Harrell F. E., Regression Modeling Strategies: With Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis, 2nd ed. (Cham, Switzerland: Springer International Publishing, 2015), https://books.google.ca/books?id=sQ90rgEACAAJ. [Google Scholar]
- 62. European Commission , “Report From the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions on the Implementation of Directive 2010/53/EU of the European Parliament and of the Council of 7 July 2010 on S,” 2022, https://eur‐lex.europa.eu/legal‐content/EN/TXT/HTML/?uri=CELEX%3A52022DC0671.
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data S1.
Data Availability Statement
The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.