Association between Metabolic Disorders and Cholangiocarcinoma: Impact of a Postulated Risk Factor with Rising Incidence

Citation: Da Fonseca, L.G.; Hashizume, P.H.; de Oliveira, I.S.; Izquierdo-Sanchez, L.; Saud, L.R.d.C.; Xerfan, M.P.; Alves, V.A.F.; de Mello, E.S.; Herman, P.; Banales, J.M.; et al. Association between Metabolic Disorders and Cholangiocarcinoma: Impact of a Postulated Risk Factor with Rising Incidence. Cancers 2022, 14, 3483. https://doi.org/10.3390/ cancers14143483 Academic Editors: Sven Loosen and Christoph Roderburg Received: 25 May 2022 Accepted: 12 July 2022 Published: 18 July 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). cancers Article Association between Metabolic Disorders and Cholangiocarcinoma: Impact of a Postulated Risk Factor with Rising Incidence Leonardo G. Da Fonseca 1,2,*,† , Pedro H. Hashizume 1,†, Irai Santana de Oliveira 3, Laura Izquierdo-Sanchez 4, Lisa Rodrigues da Cunha Saud 2,5, Mariana Pinheiro Xerfan 5, Venancio Avancini Ferreira Alves 2,6, Evandro Sobroza de Mello 2,6, Paulo Herman 2,7, Jesus M. Banales 4,8, Claudia P. Oliveira 2,5 and Flair J. Carrilho 2,5 1Clinical Oncology, Instituto do Cancer do Estado de São Paulo, School of Medicine, University of São Paulo, São Paulo 01246-000, Brazil; [email protected] 2São Paulo Clínicas Liver Cancer Group—Instituto do Cancer do Estado de São Paulo, 255 ICHC—9th Floor, Room 9159, São Paulo 05403-000, Brazil; [email protected] (L.R.d.C.S.); [email protected] (V.A.F.A.); [email protected] (E.S.d.M.); [email protected] (P.H.); [email protected] (C.P.O.); [email protected] (F.J.C.) 3Department of Radiology, Instituto do Cancer do Estado de São Paulo, School of Medicine, University of São Paulo, São Paulo 01246-000, Brazil; [email protected] 4Department of Liver and Gastrointestinal Diseases, Biodonostia Health Research Institute—Donostia University Hospital, University of the Basque Country (UPV/EHU), CIBERehd, Ikerbasque, 48009 San Sebastian , Spain; [email protected]g (L.I.-S.); [email protected]g (J.M.B.) 5Department of Gastroenterology, Division of Clinical Gastroenterology and Hepatology, School of Medicine, Hospital das Clinicas, University of São Paulo, São Paulo 05403-000, Brazil; [email protected] 6Department of Pathology, University of São Paulo School of Medicine, São Paulo 05403-000, Brazil 7Department of Gastroenterology, Division of Digestive Surgery, School of Medicine, Hospital das Clinicas, University of São Paulo, São Paulo 04021-001, Brazil 8 Department of Biochemistry and Genetics, School of Sciences, University of Navarra, 31080 Pamplona, Spain *Correspondence: [email protected] † These authors contributed equally to this work. Simple Summary: A potential relationship between cholangiocarcinoma and metabolic disorders has been suggested, but there is a lack of published data. This study aimed to describe the prevalence of metabolic disorders in a cohort of 122 patients with cholangiocarcinoma and report clinical outcomes. We found a prevalence of 42.6% of metabolic disorders. There was no significant difference in overall survival between patients with or without metabolic disorders, although there was a better survival in the subgroup of patients undergoing surgical resection. This indicates a need to better explore the association between cholangiocarcinoma in a metabolic background. Abstract: Introduction and objectives: The incidence of cholangiocarcinoma (CCA) has been increasing globally. Although a concomitant increase in the incidence of metabolic disorders might suggest a causal relationship, the data are scarce. We aimed to describe the prevalence of metabolic disorders in patients with CCA and report the clinical features and outcomes. Patients and Methods: Retrospective study including patients with CCA. Patients were divided into: (1) past history of diabetes or/and overweight/obesity (“metabolic disorder group”) and (2) without any of these features (“non-metabolic-disorder group”). A Cox regression model was used to determine the prognostic factors. Results: 122 patients were included. In total, 36 (29.5%) had overweight/obesity, 24 (19.7%) had diabetes, and 8 (6.6%) had both. A total of 29 (23.8%) patients had resectable disease and received upfront surgery. A total of 104 (85.2%) received chemotherapy for advanced/recurrent disease. The overall survival of the cohort was 14.3 months (95% CI: 10.1–17.3). ECOG-PS 0 ( p< 0.0001 ), resectable disease (p= 0.018) and absence of vascular invasion (p= 0.048) were independently associated with better prognosis. The “metabolic disorder group” (n= 52) had a median survival of 15.5 months (95% CI 10.9–33.9) vs. 11.5 months (95% CI 8.4–16.5) in the “non-metabolic-disorder group” (n= 70) (HR: 1.10; 95% CI 0.62–1.94). Patients with resectable disease in the “metabolic group” had longer survival than patients in the “non-metabolic group” (43.4 months (95% CI 33.9-NR) vs. 21.8 months Cancers 2022,14, 3483. https://doi.org/10.3390/cancers14143483 https://www.mdpi.com/journal/cancers Cancers 2022,14, 3483 2 of 14 (95% CI 8.6–26.9); HR = 0.12, 95% CI 0.03–0.59). Conclusion: Metabolic disorders are frequent among CCA patients. Underlying metabolic comorbidities may be associated with prognosis in resectable CCA. There is a need to explore the mechanism that drives CCA carcinogenesis in a metabolic background. Keywords: liver cancer; cholangiocarcinoma; metabolic syndrome; diabetes; obesity 1. Introduction Cholangiocarcinoma (CAA) represents a heterogeneous entity not only because of the anatomical site of origin (intra-hepatic, perihilar and distal CCA) but also due to differences in molecular features that are being increasingly explored in recent studies [1]. CCA has been associated with several hepatobiliary diseases, such as primary sclerosing cholangitis (PSC), primary biliary cholangitis (PBC), cholelithiasis, viral hepatitis infection, liver fluke infestation, cirrhosis and inflammatory bowel disease. However, these risk factors do not explain either the amount of CCA cases or the increasing incidence. This scenario raises concern about the impact of underexplored factors beyond these etiologies [2]. Non-alcoholic fatty liver disease (NAFLD) is recognized as an emerging risk factor for chronic liver disease, cirrhosis and liver cancer, especially hepatocellular carcinoma (HCC) [ 3 ]. It is suggested that NAFLD-related HCC may carry a worse prognosis and poorer response to systemic treatments compared to other etiologies [ 4 ]. There have been efforts toward the characterization of patients with metabolic disorders who are at risk of developing cirrhosis and primary liver cancer. Recently, a more practical and inclusive approach suggested the nomenclature of metabolic-associated fatty liver disease (MAFLD) [ 5 ]. The MAFLD definition highlights the importance of overweight/obesity and type 2 diabetes mellitus as key additional factors to a steatotic liver. Liver disease related to metabolic factors characterizes the hepatic manifestation of a multisystem disorder, which is heterogeneous in its course and outcomes. Recent research highlights the role of oxidative stress and lipotoxicity in the progression of liver disease and liver fat deposition [ 6 ]. Hyperinsulinemia and insulin resistance, which occur in the context of DM and obesity, are associated with both fat deposition and malignant cell transformation [ 7 ]. Insulin is found in bile and stimulates cell proliferation of cholestatic cells, which may promote cholangiocarcinogeneis [ 6 ]. Leptin, a hormone secreted by adipous tissue, may play a role in cholangiocytes’ transformation, growth and migration [ 8 ]. Therefore, a biological background supports a potential association between metabolic disorders and CCA. Concomitant increases in the incidence of metabolic disorders and CCA may indicate a causal relationship between these diseases. However, there is a paucity of data supporting this assumption. Considering the potential role of metabolic dysfunction in the carcinogenesis of CCA and its impact on prognosis, the aim of this study is to describe the prevalence of clinical metabolic disorders (such as diabetes and overweight/obesity) in a cohort of patients with CCA and to report the clinical features and outcomes of patients with CCA and metabolic-associated background. 2. Materials and Methods 2.1. Study Design and Participants We evaluated a retrospective cohort of patients diagnosed with CCA from October 2013 to January 2021 treated at “Instituto do Cancer do Estado de Sao Paulo” (Brazil). All patients included in this study had confirmed histologic diagnosis of CCA obtained through percutaneous biopsy, fine-needle aspiration or surgical resection. Clinical characteristics, past medical history, underlying liver disease, treatments and outcomes were collected from the medical records. Since the histological classification was significantly modified in Cancers 2022,14, 3483 3 of 14 the WHO Classification of Tumors in 2019 [ 9 ], a histological review as well as assessment of new immunohistochemical markers are under study and will be reported afterward. Patients were further divided into two groups: (group 1) patients with a past medical history (confirmed or self-reported) of diabetes mellitus (DM) or/and body mass index (BMI) of ≥ 25 kg/m 2 and no past history of liver disease, and (group 2) patients without any of the features mentioned. Among the patients in group 1 (“metabolic disorder group”), the imaging features, treatment and outcomes were detailed. The study was approved by the institutional ethics committee (protocol number 3.807.496). 2.2. Management and Treatment Protocol According to the local protocol, all patients referred to our institution have CCA diagnosis confirmed by reassessment of the external tissue sample or newly obtained samples. Baseline evaluation consists of performance status assessment using the Eastern Cooperative Oncology group (ECOG) scale, past medical history, comorbidities, liver function tests and general laboratory parameters. Radiological studies are performed to assess the loco-regional or distant spread, staging and resectability. Imaging consists of chest, abdomen and pelvis computed tomography (CT) scans. Whenever required, liver magnetic resonance imaging (MRI) or cholangiopancreatography are performed. Serum tumor markers, such as carbohydrate antigen (CA) 19–9 and carcinoembryonic antigen (CEA), are routinely collected at the baseline assessment and periodically during the treatment and follow-up. Treatments are provided according to the local protocol, which is in line with the main guidelines adopted globally [ 10 ]. Briefly, the treatment strategy varies for each type of CCA depending on its site of origin. Patients with local disease who are potential candidates for resections are usually discussed in weekly multidisciplinary tumor boards for surgery indication. After resection, no adjuvant treatment is routinely offered, and patients are followed with imaging assessment every 6 months. Patients with locally advanced/unresectable disease and those who have distant metastasis are considered for systemic treatment. Patients with ECOG performance status of 0–2 with no organ dysfunctions are suitable for first-line chemotherapy with cisplatin-gemcitabine combination, according to the ABC-02 trial [ 11 ]. Alternatively, some patients may start gemcitabine monotherapy or other regimens (gemcitabine-oxaliplatin or fluoropyrimidine-oxaliplatin) as first-line treatment at the physician’s discretion. After progression to first-line therapy, patients who are fit for subsequent treatment are often considered for receiving 5-fluorouracil-based regimens as a second-line treatment. Whenever required, candidates for systemic treatment receive biliary drainage. 2.3. Patients with a Background of Metabolic Disorders Patients with a past medical history of DM and/or BMI of ≥ 25 kg/m 2 were selected from the total cohort and grouped as a “metabolic disorder” group. Clinical characteristics and outcomes of this specific group were analyzed. Baseline images were also evaluated in order to assess the prevalence of steatosis. The manual mean liver attenuation was measured in Hounsfield units (HU) by using a simple and previously validated technique, which consists of the placement of a ROI over a representative parenchymal portion of the right hepatic lobe. The criteria used for defining steatosis were liver attenuation ≤40 HU on unenhanced CT images. Although many criteria have been previously used to determine liver steatosis with variable sensitivity and specificity, it is suggested that a liver attenuation value ≤ 40 HU represents the most accurate criterion for detecting moderate-to-severe disease [12,13]. 2.4. Statistical Analysis Descriptive methods were used to analyze the incidence of risk factors in the total cohort and to report baseline and demographic features. Continuous variables were expressed as mean, median, ranges or interquartile ranges (IQR). Categorical variables Cancers 2022,14, 3483 4 of 14 were expressed as frequency. Comparisons between the group of interest (“metabolic disorder group”) and the group of patients with no background of metabolic disorders were performed. Categorical variables were compared using the χ2-test or Fischer’s exact test when appropriate. Continuous variables were compared using Student’s t-test. Overall survival (OS) was estimated using the Kaplan–Meier method, and curves were compared by using log-rank. For the analysis including the whole cohort, a Cox regression model, including variables that showed significance in the univariate analysis, was performed to evaluate the independent prognostic factors and calculate the hazard ratios (HR) and 95% confidence intervals (CI). For time-to-event analysis including only patients submitted for surgery (recurrence-free survival and overall survival from surgery), a Cox regression model was performed, including variables associated with outcomes in patients with CCA submitted for resection [ 14 – 16 ]: primary site (intrahepatic, perihilar or distal), vascular invasion (yes or no), status of resection margin (R0, R1 or R2), baseline CA 19.9 (< or ≥150 U/mL ), nodal disease (N1 or N0), number of nodules (unior multinodular) and ECOG-PS (0, 1 and ≥ 2). A p< 0.05 was considered significant. Data were evaluated using the STATA software version 15.0. 3. Results 3.1. Baseline Characteristics From October 2013 to January 2021, 122 patients with confirmed diagnosis of CCA were included in the present analysis. The median age was 62 years (IQR 55–67), 72 (59%) were female, and the predominant primary site was intrahepatic CCA (n= 48; 39%) followed by distal CCA (n= 41, 33.6%) and perihilar CCA (n= 30, 24.6%). History of alcohol consumption was reported by 20 (16.4%) patients and smoking by 54 (44.3%). Regarding metabolic factors, the median BMI was 23 kg/m 2 [IQR 20–26], 23 (18.9%) patients had BMI 25–30 kg/m 2 , 13 (10.7%) patients had BMI ≥ 30 kg/m 2 , 24 (19.7%) patients had DM, and 8 (6.6%) patients had both DM and BMI ≥ 25 kg/m 2 . The other risk factors found were PSC in two (1.7%) patients, cirrhosis in one (0.8%), viral hepatitis in four (3.2%) and human immunodeficiency virus (HIV) infection in two (1.6%) patients. The majority of patients had a performance status of 0–1 (n= 84; 68.9%), and 65 (53.8%) had metastatic/unresectable disease at diagnosis. Median gamma-glutamyl transferase (GGT = 423 U/L) and alkaline phosphates (ALP = 272 U/L) were higher than the upper limit of the reference range ( 30 U/L and 150 U/L, respectively), while the other median laboratory parameters were within the normal range or only slightly altered. Regarding patients with DM, 23 patients were classified as having type II DM, and 1 patient had type I DM. The median time from DM diagnosis to CCA diagnosis was 6 years (IQR: 2.5–12.2 years), and all patients reported regular follow-up since diagnosis. At CCA diagnosis, 18 (75%) patients were using metformin, 9 (37.5%) patients were using glicazide, 4 (16.7%) were using glibenclamide, and 3 patients were using dapaglifozin (12.5%). Eleven (45.8%) patients reported routine use of insulin, while three (12.5%) patients reported previous use before CCA diagnosis. More than one anti-DM drug was required for 15 (62.5%) patients, and 2 (8.3%) patients reported DM control with dietary and lifestyle habits without current medication. Baseline hemoglobin A1C (within 3 months of CCA diagnosis) was available for 19 (79.1%) patients, with a median of 7.3% (IQR, 6.41–8.24%). In the non-metabolic-disorder group, 17 (24.3%) patients had available hemoglobin A1C with a median of 5.3 (IQR 5.1–5.6). Regarding the therapeutic strategy, 29 (23.8%) patients were treated with upfront curative-intent resection. Chemotherapy was delivered to 104 patients, given that cisplatingemcitabine was the most used regimen (n= 98; 94.2%) followed by gemcitabine-oxaliplatin (n= 3; 2.9%), gemcitabine monotherapy (n= 2; 1.9%) and mFLOX (n= 1; 0.9%). Biliary drainage was required in 19 (15.5%) and stenting in 66 (54.1%) patients. Table 1shows the baseline characteristics of the entire cohort and subgroups. Cancers 2022,14, 3483 5 of 14 Table 1. Baseline features of the total cohort and subgroups according to the coexistence of metabolic disorders. Variables Total Metabolic Disorder Group Non-Metabolic-Disorder Group pValue n(%) 122 52 (42.6%) 70 (57.4%) Median age, years (IQR) 62 (55–67) 64 (56–68.5) 59 (53–64) 0.199 Gender 0.048 Woman, n(%) 72 (59.0%) 36 (69.2%) 36 (51.4%) Man, n(%) 50 (40.9%) 16 (30.8%) 34 (48.6%) Conditions Weight, kilograms (IQR) 59 (51.7–68) 67.5 (59.9–79) 55 (48–59) <0.0001 Height, centimeters (IQR) 159 (153–166) 157.5 (152.5–164.5) 160 (154–168) 0.1991 BMI, kg/m2(IQR) 23 (20–26) 26 (24.5–29.5) 21 (18–23) <0.0001 Smoking, n(%) 54 (44.3%) 13 (25%) 41 (58.6%) <0.0001 Alcohol, n(%) 20 (16.4%) 6 (11.6%) 14 (20%) 0.212 PSC, n(%) 2 (1.7%) 0 (0%) 2 (2.9%) 0.219 Cholelithiasis, n(%) 11 (9.0%) 4 (7.7%) 7 (10%) 0.354 Cirrhosis, n(%) 1 (0.8%) 0 (0%) 1 (1.4%) 0.387 Viral hepatitis 4 (3.2%) 2 (3.8%) 2 (2.9%) 0.742 HIV 2 (1.6%) 0 (0%) 2 (2.9%) 0.219 Primary site 0.691 Intrahepatic 48 (39.3%) 18 (34.6%) 30 (42.9%) Perihilar 30 (24.6%) 13 (25%) 17 (24.3%) Distal 41 (33.6%) 19 (36.5%) 22 (31.4%) Undefined 3 (2.5%) 2 (3.9%) 1 (1.4%) Performance status 0.636 0–1, n(%) 84 (68.9) 37 (71.1%) 47 (67.1%) 2–4, n(%) 38 (31.1%) 15 (28.9%) 23 (32.9%) Number of liver nodules 0.124 Unicentric, n(%) 73 (59.9%) 37 (71.2%) 36 (51.4%) Multicentric, n(%) 44 (36.1%) 13 (25%) 31 (44.3%) Non-Applicable, n(%) 5 (4.1%) 2 (3.9%) 3 (4.3%) Vascular invasion, n(%) 44 (36.1%) 17 (32.7%) 27 (38.6%) 0.576 Clinical positive node, n(%) 55 (45.1%) 26 (50%) 29 (41.4%) 0.178 Distant metastasis, n(%) 61 (50%) 25 (48.1%) 36 (51.4%) 0.595 Tumor status (pT and/or cT) 0.100 T1, n(%) 9 (7.4%) 7 (13.5%) 2 (2.9%) T2, n(%) 39 (32.0%) 21 (40.4%) 18 (25.7%) T3, n(%) 42 (34.4%) 14 (26.9%) 28 (40%) T4, n(%) 25 (20.4%) 7 (13.5%) 18 (25.7%) Tx, n(%) 7 (5.7%) 3 (5.8%) 4 (5.7%) Nodal status (pN or cN) 0.541 Cancers 2022,14, 3483 6 of 14 Table 1. Cont. Variables Total Metabolic Disorder Group Non-Metabolic-Disorder Group pValue N0, n(%) 51 (41.8%) 18 (34.6%) 33 (47.1%) N1, n(%) 53 (43.4%) 24 (46.2%) 29 (41.4%) N2, n(%) 4 (3.3%) 3 (5.8%) 1 (1.4%) Nx, n(%) 14 (11.5%) 7 (13.5%) 7 (10%) Metastasis (cM and/or pM) 0.768 M0, n(%) 57 (46.7%) 27 (51.9%) 30 (42.9%) M1, n(%) 65 (53.8%) 25 (48.1%) 40 (57.1%) Growth pattern 0.174 Mass Forming, n(%) 72 (59.0%) 27 (51.9%) 45 (64.3%) Periductal infiltrating, n(%) 23 (18.9%) 10 (19.2%) 13 (18.6%) Intraductular growth, n(%) 6 (4.9%) 1 (1.9%) 5 (7.1%) Not available, n(%) 22 (18.0%) 15 (28.5%) 7 (10.0%) Laboratory AST, U/L median (IQR) 44.5 (29–76) 43.5 (24.7–57.2) 46 (32–75.5) 0.848 ALT, U/L median (IQR) 44 (30–68) 45 (29–74) 44.5 (30–76.8) 0.202 ALP, U/L median (IQR) 272 (141–569) 226 (143–429) 289 (134.5–588) 0.189 GGT, U/L median (IQR) 423 (158–752) 413 (132.7–630.5) 423 (165–793) 0.098 Bilirubin, mg/dL median (IQR) 1.03 (0.45–4.50) 0.98 (0.43–4.01) 1.25 (0.45–4.48) 0.707 Cholesterol, mg/dL median (IQR) 148 (132–201) 201 (181–213) 141 (130–143.5) 0.404 LDL, mg/dL median (IQR) 93 (94–129) 127 (109–140) 87 (74–91) 0.383 TG mg/dL, median (IQR) 118.5 (92–188) 132 (112–199) 92 (71–114) 0.777 AFP, ng/mL median (IQR) 3.1 (2–5.7) 3 (2.2–4.0) 3.35 (2.05–5.37) 0.157 CEA, ng/L median (IQR) 3.61 (2.08–14.5) 3.62 (2.05–12.87) 3.65 (2.1–16.87) 0.223 CA19.9, ng/L median (IQR) 145 (35.6–614) 112.45 (38.82–461.75) 151.85 (26.64–675.2) 0.855 Glucose, mg/dL median (IQR) 104 (100–106) 111 (98.5–125) 95 (83–107.5) 0.542 Albumin, g/dL median (IQR) 3.6 (3.3–4.1) 3.5 (3.1–4.1) 3.7 (3.3–4.1) 0.164 Neutrophils, /mm3median (IQR) 6620 (5800–7400) 6600 (5800–7400) 6700 (5700–7405) 0.621 Lymphocytes, /mm3median (IQR) 2100 (1400–2950) 2300 (1500–3020) 1900 (1395–2950) 0.701 Hemoglobin, g/dL median (IQR) 11.9 (10.8–12.9) 12.05 (11.0–13.07) 11.6 (10.4–12.7) 0.456 Platelets, 103median (IQR) 270 (201–351) 279 (185–347) 266 (212–354) 0.173 INR, median (IQR) 1.15 (1.05–1.26) 1.13 (1.05–1.24) 1.16 (1.05–1.28) 0.576 Therapeutic management Curative intent resection, n(%) 29 (23.8%) 15 (28.8%) 14 (20%) 0.256 Recurrence after surgery, n(%) 21 (17.2%) 8 (15.4%) 13 (18.6%) 0.039 Chemotherapy, n(%) 104 (85.2%) 45 (76.9%) 59 (84.3%) 0.256 Best supportive care, n(%) 11 (9%) 1 (1.9%) 10 (14.3%) 0.018 Biliary stent, n(%) 66 (54.1%) 25 (48.1%) 41 (58.6%) 0.25 Biliary drainage, n(%) 19 (15.5%) 9 (17.3%) 10 (14.3%) 0.328 IQR: interquartile range; BMI: body mass index; PSC: Primary Sclerosing cholangitis; HIV: human deficiency virus; AST: aspartate aminotransferase; ALT: alanine aminotransferase; ALP: alkaline phosphatase; GGT: gammaglutamyl transferase; Bili: bilirubin; LDL: low-density lipoprotein; TG: triglycerides; AFP: alpha-fetoprotein; CEA: carcinoembrionic antigen; INR: international normalized ratio; U: unit; ng: nanograms; L: liter; dL: deciliter; mg: milligrams. Cancers 2022,14, 3483 7 of 14 3.2. Comparison between Groups According to Metabolic Factors The group of patients with metabolic factors (“metabolic disorder group”) accounted for 52 (42.6%) patients, while the other 70 (57.4%) patients represented the “non-metabolic group”. As expected, the metabolic group had higher median weight (67.5 vs. 55.0 kg; p< 0.0001 ) and higher median BMI (26.0 vs. 21.0 kg/m 2 ,p< 0.0001). Moreover, the metabolic group had a higher prevalence of women (69.2 vs. 36%; p= 0.048) and nonsmokers (75% vs. 41.4%; p< 0.0001). On the other hand, there was no difference regarding prognostic factors, such as ECOG-PS, metastatic disease, positive lymph node, primary site and choice of therapeutic strategies, between “metabolic disorder” and “non-metabolic-disorder” groups. (Table 1). The median liver attenuation in the “metabolic disorder group” was 56 UH (IQR: 46.2–56.2). Only one patient in the “metabolic disorder group” met the radiologic criteria for hepatic steatosis, with a liver attenuation of 36 UH. 3.3. Clinical Outcomes and Overall Survival The median OS of the “metabolic disorder” group was 15.5 months (95% CI 10.9–33.9 ) compared to 11.5 months for the “non-metabolic-disorder” group (95% CI 8.4–16.5; univariate p= 0.048). In the univariate analysis, other variables were associated with better OS: CA19.9 < 150 U/mL (p= 0.0003); diabetes mellitus (p= 0.04); ECOG-PS 0 (p< 0.001); absence of metastasis (p= 0.0012); absence of vascular invasion (p= 0.001); and upfront resection (p< 0.001). In the multivariate analysis, ECOG-PS (p< 0.0001), surgery (p= 0.018) and absence of vascular invasion (p= 0.048) were independently associated with survival, while there was no significant survival difference between the “metabolic disorder” and “non-metabolic-disorder” groups in the multivariate analysis (adjusted HR: 1.09; 95% CI: 0.62–1.94). (Table 2and Figure 1). Table 2. Survival by subgroups and uniand multivariate analysis. Subgroups (n)nMedian Overall Survival (95% CI) Univariate pHR (95% CI) Multivariate, pValue Man 50 11.5 (8.6–20.6) 0.88 Woman 72 14.6 (9.4–17.4) Obesity 13 15.5 (5.8–NR) 0.77 No obesity 109 13.4 (9.6–17.3) Diabetes 24 22.8 (5.8–38.2) 0.04 0.54 (0.25–1.15), p= 0.111 No diabetes 98 11.9 (9.4–15.5) Smoking 54 10.1 (7.4–15.3) 0.17 No smoking 68 16.5 (11.1–21.8) Alcohol 20 16.2 (5.7–22.4) 0.93 No alcohol 102 14.0 (9.6–17.3) Undefined primary site 3 3.5 (3.5–NR) 0.539 Distal 41 20.6 (14.8–25.3) Intrahepatic 48 10.5 (7.4–14.6) Perihilar 30 8.9 (5.4–26.7) PS0 42 16.8 (14.2–29.2) <0.001 1.86 (1.43–2.41), p< 0.0001 Cancers 2022,14, 3483 8 of 14 Table 2. Cont. Subgroups (n)nMedian Overall Survival (95% CI) Univariate pHR (95% CI) Multivariate, pValue PS1 42 13.4 (9.4–20.6) PS2 25 11.1 (5.5–22.0) PS3 12 3.0 (2.1–5.4) PS4 1 NR (NR–NR) Vascular invasion 44 5.9 (4.5–11.4) 0.0012 1.61 (1.01–2.58), p= 0.048 No vascular invasion 74 16.5 (11.9–22.4) Node positive 55 8.9 (5.8–15.3) 0.14 Node negative 62 16.5 (10.9–26.9) Metastasis 61 8.9 (5.4–14.0) 0.001 1.47 (0.91–2.41), p= 0.118 No metastasis 59 21.8 (13.4–26.9) Biliary stent 66 13.4 (7.2–17.3) 0.07 No biliary stent 56 15.5 (9.6–25.3) Resection 29 34.2 (22.0–43.5) <0.0001 0.42 (0.21–0.86), p= 0.018 No resection 93 10.5 (7.5–14.0) Family history of neoplasia 50 20.7 (11.9–25.2) 0.11 No family history of neoplasia 67 11.5 (7.7–16.2) CA19.9 < 150 U/mL 64 17.5 (11.5–22.4) 0.0003 1.47 (0.91–2.36), p= 0.112 CA19.9 ≥150 U/mL 58 8.1 (5.7–14.3) Metabolic disorder group 52 15.5 (10.9–33.9) 0.048 1.09 (0.62–1.94), p= 0.745 Non-metabolic-disorder group 70 11.5 (8.4–16.5) PS: performance status; HR: Hazard ratio; CI: confidence interval; NR: not-reached; U/mL: Unit/milliliter. Cancers 2022, 14, x 7 of 13 disorder” groups in the multivariate analysis (adjusted HR: 1.09; 95%CI: 0.62–1.94). (Table 2 and Figure 1). Table 2. Survival by subgroups and uniand multivariate analysis. Subgroups (n) n Median Overall Survival (95%CI) Univariate p HR (95%CI) Multivariate, p Value Man 50 11.5 (8.6–20.6) 0.88 Woman 72 14.6 (9.4–17.4) Obesity 13 15.5 (5.8–NR) 0.77 No obesity 109 13.4 (9.6–17.3) Diabetes 24 22.8 (5.8–38.2) 0.04 0.54 (0.25–1.15), p = 0.111 No diabetes 98 11.9 (9.4–15.5) Smoking 54 10.1 (7.4–15.3) 0.17 No smoking 68 16.5 (11.1–21.8) Alcohol 20 16.2 (5.7–22.4) 0.93 No alcohol 102 14.0 (9.6–17.3) Undefined primary site 3 3.5 (3.5–NR) 0.539 Distal 41 20.6 (14.8–25.3) Intrahepatic 48 10.5 (7.4–14.6) Perihilar 30 8.9 (5.4–26.7) PS 0 42 16.8 (14.2–29.2) <0.001 1.86 (1.43–2.41), p < 0.0001 PS1 42 13.4 (9.4–20.6) PS2 25 11.1 (5.5–22.0) PS3 12 3.0 (2.1–5.4) PS4 1 NR (NR–NR) Vascular invasion 44 5.9 (4.5–11.4) 0.0012 1.61 (1.01–2.58), p = 0.048 No vascular invasion 74 16.5 (11.9–22.4) Node positive 55 8.9 (5.8–15.3) 0.14 Node negative 62 16.5 (10.9–26.9) Metastasis 61 8.9 (5.4–14.0) 0.001 1.47 (0.91–2.41), pp = 0.118 No metastasis 59 21.8 (13.4–26.9) Biliary stent 66 13.4 (7.2–17.3) 0.07 No biliary stent 56 15.5 (9.6–25.3) Resection 29 34.2 (22.0–43.5) <0.0001 0.42 (0.21–0.86), p = 0.018 No resection 93 10.5 (7.5–14.0) Family history of neoplasia 50 20.7 (11.9–25.2) 0.11 No family history of neoplasia 67 11.5 (7.7–16.2) CA19.9 < 150 U/mL 64 17.5 (11.5–22.4) 0.0003 1.47 (0.91–2.36), p = 0.112 CA19.9 ≥ 150 U/mL 58 8.1 (5.7–14.3) Metabolic disorder group 52 15.5 (10.9–33.9) 0.048 1.09 (0.62–1.94), p = 0.745 Non-metabolic-disorder group 70 11.5 (8.4–16.5) PS: performance status; HR: Hazard ratio; CI: confidence interval; NR: not-reached; U/mL: Unit/milliliter. 52 15 5 1 0Group = Metabolic 70 19 2 1 0Group = No metabolic Number at risk 020 40 60 80 Time (months) Group = No metabolic Group = Metabolic 51 15 5 1 0Group = Metabolic 60 17 2 1 0Group = No metabolic Number at risk 020 40 60 80 Time (months) Group = No metabolic Group = Metabolic Figure 1. Kaplan–Meier curves showing: ( Left ) overall survival according to subgroup with metabolic disorders vs. no metabolic disorders. The median overall survival of the “metabolic disorder” group was 15.5 months (95% CI 10.9–33.9), and the median OS of the “non-metabolic-disorder” group was 11.5 months (95% CI 8.4–16.5), adjusted HR: 1.09 (0.62–1.94), p= 0.745. ( Right ) overall survival according to subgroup with metabolic disorders vs. no metabolic disorders, excluding patients managed with best supportive care. The median overall survival of the “metabolic disorder” group was 15.5 months (95% CI 11.4–33.9), and the median OS of the “non-metabolic-disorder” group was 14.0 months (95% CI 9.4–17.3), adjusted HR: 1.32 (0.74–2.35), p= 0.351. There was no correlation between the group (metabolic vs. non-metabolic) and the tumor site ( χ2 = 1.46; p= 0.691). Interestingly, the median overall survival for patients with intrahepatic cholangiocarcinoma and in the metabolic group (n= 30) was 7.7 months (95% CI 3.1–11.9), and for patients with intrahepatic cholangiocarcinoma and in the “non- Cancers 2022,14, 3483 9 of 14 metabolic” group (n= 18), it was 15.5 months (95% CI 8.2—not reached) (univariate p= 0.006 ). However, after performing a Cox regression model including only patients with intrahepatic cholangiocarcinoma, the metabolic group was not associated with prognosis (HR = 0.43, 95% CI 0.13–1.42). 3.4. Treatment Strategies and Clinical Outcomes Regarding patients who were submitted for upfront surgery (n= 29), 21 (75%) presented disease recurrence with a median time to recurrence of 14 months (95% CI 8.2–19.8). Patients in the “metabolic disorder” group (n= 8) had significantly longer relapse-free survival (15.6 months, 95% CI 8.2–24.6) compared to patients in the “non-metabolic group” (13.5 months, 95% CI 3.5–19.8, multivariate p= 0.02). Accordingly, the median overall survival of the metabolic group after resection was significantly higher than the non-metabolic group (43.4 [95% CI 33.9-NR] vs. 21.8 months [95% CI 8.6–26.9]; adjusted HR = 0.23, 95% CI 0.06–0.86; p= 0.029) (Figure 2). Cancers 2022, 14, x 8 of 13 Figure 1. Kaplan–Meier curves showing: (Left) overall survival according to subgroup with metabolic disorders vs. no metabolic disorders. The median overall survival of the “metabolic disorder” group was 15.5 months (95%CI 10.9–33.9), and the median OS of the “non-metabolic-disorder” group was 11.5 months (95%CI 8.4–16.5), adjusted HR: 1.09 (0.62–1.94), p = 0.745. (Right) overall survival according to subgroup with metabolic disorders vs. no metabolic disorders, excluding patients managed with best supportive care. The median overall survival of the “metabolic disorder” group was 15.5 months (95%CI 11.4–33.9), and the median OS of the “non-metabolic-disorder” group was 14.0 months (95%CI 9.4–17.3), adjusted HR: 1.32 (0.74–2.35), p = 0.351. There was no correlation between the group (metabolic vs. non-metabolic) and the tumor site (χ2 = 1.46; p = 0.691). Interestingly, the median overall survival for patients with intrahepatic cholangiocarcinoma and in the metabolic group (n = 30) was 7.7 months (95% CI 3.1–11.9), and for patients with intrahepatic cholangiocarcinoma and in the “nonmetabolic” group (n = 18), it was 15.5 months (95%CI 8.2—not reached) (univariate p = 0.006). However, after performing a Cox regression model including only patients with intrahepatic cholangiocarcinoma, the metabolic group was not associated with prognosis (HR = 0.43, 95% CI 0.13–1.42). 3.4. Treatment Strategies and Clinical Outcomes Regarding patients who were submitted for upfront surgery (n = 29), 21 (75%) presented disease recurrence with a median time to recurrence of 14 months (95%CI 8.2– 19.8). Patients in the “metabolic disorder” group (n = 8) had significantly longer relapsefree survival (15.6 months, 95%CI 8.2–24.6) compared to patients in the “non-metabolic group” (13.5 months, 95%CI 3.5–19.8, multivariate p = 0.02). Accordingly, the median overall survival of the metabolic group after resection was significantly higher than the non-metabolic group (43.4 [95%CI 33.9-NR] vs. 21.8 months [95%CI 8.6–26.9]; adjusted HR = 0.23, 95%CI 0.06–0.86; p = 0.029) (Figure 2). Figure 2. Overall survival. (Left) Patients treated with chemotherapy: the median OS was 11.5 months (95%CI 8.4–14.8), and there was no difference between metabolic and non-metabolic groups (11.5 months [95%CI 5.8–15.3] vs. 11.5 months [95%CI 6.7–16.2]; p = 0.406). (Right) Patients treated with surgery: the median of the metabolic group was significantly better than that of the non-metabolic group (43.4 [95%CI 33.9-NR] vs. 21.8 months [95%CI 8.6–26.9]; HR = 0.23, 95%CI 0.06–0.86; p = 0.029. Considering the patients who were treated with chemotherapy, the median OS was 11.5 months (95%CI 8.4–14.8), and there was no difference between the metabolic and nonmetabolic groups (11.5 months [95%CI 5.8–15.3] vs. 11.5 months [95%CI 6.7–16.2]) with an adjusted HR = 0.82 (95%CI 0.52–1.30; p = 0.406). We observed a higher rate of patients managed with best supportive care in the nonmetabolic-disorder group (14.3% vs. 1%, p = 0.018). Therefore, we also performed a survival analysis excluding these patients. After excluding patients managed with best supportive care, the median OS for the non-metabolic group (n = 60) was 14.01 months (95% CI 9.42–17.29) vs. 15.47 (95% CI 11.40–33.92) for the metabolic group (n = 51); HR: 45 13 4 1 0Group = Metabolic 59 16 2 1 0Group = No metabolic Number at risk 020 40 60 80 Time (months) Group = No metabolic Group = Metabolic 15 10 4 1 0Group = Metabolic 14 8 1 1 0Group = No metabolic Number at risk 020 40 60 80 Time (months) Group = No metabolic Group = Metabolic Figure 2. Overall survival. ( Left ) Patients treated with chemotherapy: the median OS was 11.5 months (95% CI 8.4–14.8), and there was no difference between metabolic and non-metabolic groups (11.5 months [95% CI 5.8–15.3] vs. 11.5 months [95% CI 6.7–16.2]; p= 0.406). ( Right ) Patients treated with surgery: the median of the metabolic group was significantly better than that of the non-metabolic group (43.4 [95% CI 33.9-NR] vs. 21.8 months [95% CI 8.6–26.9]; HR = 0.23, 95% CI 0.06–0.86; p= 0.029. Considering the patients who were treated with chemotherapy, the median OS was 11.5 months (95% CI 8.4–14.8), and there was no difference between the metabolic and non-metabolic groups (11.5 months [95% CI 5.8–15.3] vs. 11.5 months [95% CI 6.7–16.2]) with an adjusted HR = 0.82 (95% CI 0.52–1.30; p= 0.406). We observed a higher rate of patients managed with best supportive care in the non-metabolic-disorder group (14.3% vs. 1%, p= 0.018). Therefore, we also performed a survival analysis excluding these patients. After excluding patients managed with best supportive care, the median OS for the non-metabolic group (n= 60) was 14.01 months (95% CI 9.42–17.29) vs. 15.47 (95% CI 11.40–33.92) for the metabolic group (n= 51); HR: 1.32 (95% CI 0.74–2.35), p= 0.351 Figure 1. Accordingly, we also performed a Cox regression model excluding patients treated with best supportive care and observed that the same variables remained independently associated with survival (ECOG-PS: p= 0.017; surgery: p= 0.03; and absence of vascular invasion: p= 0.032). Detailed results regarding OS, progression-free survival and response rate are shown in Table 3.