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Original Article

Sarcopenia, obesity and sarcopenic obesity: effects on liver function and volume in patients scheduled for major liver resection

Toine M. Lodewick1,2,3,*, Anjali A.J. Roeth1,3, Steven W.M. Olde Damink2,3,4, Patrick H. Alizai1,3, Ronald M. van Dam2,3, Nikolaus Gassler5, Mark Schneider1,3, Simon A.W.G. Dello2, Maximilian Schmeding1,3, Cornelis H.C. Dejong2,3 andUlf P. Neumann1,3

How to Cite

Lodewick, T. M., Roeth, A. A., Olde Damink, S. W., Alizai, P. H., van Dam, R. M., Gassler, N., Schneider, M., Dello, S. A., Schmeding, M., Dejong, C. H., and Neumann, U. P. (2015), Sarcopenia, obesity and sarcopenic obesity: effects on liver function and volume in patients scheduled for major liver resection. Journal of Cachexia, Sarcopenia and Muscle, doi: 10.1002/jcsm.12018.

Author Information

1

Department of Surgery, Division of General, Visceral and Transplantation Surgery, RWTH Aachen University, Aachen, Germany

2

Department of Surgery, Maastricht University Medical Centre & Nutrim School for Nutrition, Toxicology and Metabolism, Maastricht University, Maastricht, The Netherlands

3

Euregional HPB collaboration Aachen–Maastricht, Aachen–Maastricht, Germany–The Netherlands

4

Department of Surgery, Division of Surgery and Interventional Science, Royal Free Hospital, and University College London, London, United Kingdom

5

Institute of Pathology, RWTH Aachen University, Aachen, Germany

*Correspondence to: Toine M. Lodewick, MD, Department of Surgery, Maastricht University Medical Centre, P. Debyelaan 25, 6229 HX Maastricht, The Netherlands: Tel: +31 43 388 1501. Email: t.lodewick@maastrichtuniversity.nl


Introduction

In the past decade, indications for liver surgery have changed dramatically. This was mainly due to improvements in surgical technique and new insights in the field of oncology and chemotherapy, which led to larger liver resections.[1, 2] Despite more extensive preoperative assessment of patients undergoing major liver surgery, post-resectional liver failure still occurs and it remains the most frequent cause of death following major liver surgery.[3-5] Today, preoperative volumetric and, if needed, functional assessment of the liver are the cornerstones in the pursuit of safe resection liver surgery.[6-9]

As primary or secondary liver tumours often are accompanied by weight loss and cachexia, disturbances in body composition and metabolic state are now suggested to be risk factors for the development of major post-operative morbidity and post-resectional liver failure.[10] Recently, our group showed that depletion of muscle mass (i.e. sarcopenia) negatively influences total liver volume in patients undergoing liver surgery.[11] Several other studies have indicated that disturbances in body composition possibly have negative effects on outcome after liver surgery.[10, 12-16] The increased complication rates in patients with body composition disturbances (i.e. sarcopenia, obesity and sarcopenic obesity) might well be partially caused by impaired liver function.

Therefore, the aim of the present study was to explore whether total liver function and volume are influenced by sarcopenia, obesity and sarcopenic obesity in patients undergoing extensive preoperative assessment prior to potential liver surgery.

Materials and methods

Patients

This study was conducted according to the revised version of the Declaration of Helsinki (October 2008, Seoul). From January 2011 to December 2012, all consecutive patients undergoing a LiMAx[6, 7] liver function breath test and a CT scan as part of regular preoperative assessment in the Aachen University Hospital were included. Informed consent was obtained in every patient. The decision for LiMAx evaluation was based on clinical indications (such as resection of four or more liver segments and known or suspected fibrosis or cirrhosis) and was made by the responsible surgeons. Patients underwent extensive preoperative laboratory testing, and Child–Pugh[17] and model for end-stage liver disease (MELD)[18] scores were calculated. Jaundice was defined as a serum bilirubin level greater than 2.5 per decilitre.[19] Patients who underwent portal vein embolization (PVE) prior to resection were studied before the PVE procedure.

Methods

Liver function test

The LiMAx test was used to assess hepatocyte-specific metabolic function. This test is based on metabolization of 13C-labelled methacetin (Euriso-top, Saint-Aubin Cedex, France) by the cytochrome P450 1A2 enzyme in the liver.[6, 7] After intravenous injection, 13C-labelled methacetin is instantly metabolized, and the ratio between exhaled 13CO2 and normal non-enriched background 12CO2 is registered over a period of 60 min.[7]

Liver volumetry

A 2.4 GHz Intel Core 2 Duo MacBook (Apple Inc., Cupertino, CA, USA) with Osirix® software version 4.1.1 (http://www.osirix-viewer.com) was used for volumetric analysis of the liver. Liver contour was manually outlined by one researcher (T.M.L.) on transverse slices of the venous phase of routinely performed preoperative contrast-enhanced CT scans. Total liver volume (TLV) and tumour volume were measured as described earlier.[20] The non-tumour total liver volume (ntTLV) was calculated by subtracting tumour volume from TLV.

Body composition

Presence of sarcopenia was assessed through measurements of skeletal muscle areas by one single researcher (T.M.L.) with the use of the Osirix® programme on contrast-enhanced preoperative (or pre-PVE in case of a PVE) CT scans. A threshold range between −30 and 110 Hounsfield units was set to semiautomatically outline muscle areas at the transversal level of the third lumbar vertebra (L3) as recently described.[11] The mean of measurements on two adjacent CT slices at L3 level was used to calculate the L3 skeletal muscle index (L3 MI) by correcting it for height. Sarcopenia was defined as a L3 MI < 41 cm2/m2 in women, <43 cm2/m2 in men with a body mass index (BMI) of <25 and <53 cm2/m2 in men with a BMI of >25 as these cut-off values showed an association with mortality.[21] The ntTLV–bodyweight ratio (%) was calculated using the following formula: [ntTLV (mL)/bodyweight (g)] * 100%. Body surface area was estimated using the Mosteller formula,[22] {[height (cm) * weight (kg)]/3600}0.5. Total fat-free body mass (kg) was estimated as 0 · 30 * (skeletal muscle surface area at L3 in cm2) + 6.06.[23] Body-fat% was calculated as [body weight (kg) − fat-free body mass (kg)]/body weight (kg). Obesity was based on body-fat%; cut-off values for obesity were >49.6% for women and >37.5% for men, based on the top two body-fat% quintiles in our study as is conventional for studies evaluating sarcopenic obesity.[24-26] Sarcopenic obesity was defined as the presence of both sarcopenia and obesity according to our definitions.

Histopathology

One pathologist (N.G.) performed all pathologic examinations. Fibrosis of background liver tissue was classified using the Metavir score, which among others consists of a five-point fibrotic scale.[27] The degree of non-alcoholic steatohepatitis (NASH) was analysed using the NASH scoring system (NAS score).[28] Finally, sinusoidal dilatation was scored as a four-point scale as a measure of sinusoidal obstruction syndrome.[29]

Outcome after surgery

Post-operative morbidity was graded according to the Dindo–Clavien classification.[30] Complications with a grade ≥ 3a were considered major complications. Thirty-day and 90-day mortality were scored.

Statistical analysis

Data were analysed with SPSS version 18.0 (SPSS Inc., Chicago, IL) and Prism 5.0 for Macintosh (Graphpad software, Inc, San Diego, CA, USA). The data were expressed as median (range). Chi-square tests were used to analyse categorical data while continuous data were analysed with Mann–Whitney U tests. A level of P < 0.05 was considered statistically significant. Correlations between body composition factors and liver function or ntTLV were performed in patients with relatively healthy livers, that is, livers without cirrhosis (Metavir fibrotic scale Stage 4[27]), NASH (NAS score ≥ 5[28]) or severe sinusoidal dilatation (sinusoidal dilatation score = 3[29]). Also, patients without pathologic examination of liver tissue were excluded for correlation analysis. Correlations were calculated with Pearson's test. The resulting regression line was described as a linear equation, and the correlation coefficient (r) was calculated. Relevant clinicopathologic variables associated with liver function were examined using univariable and, where applicable, multivariable linear regression. For the multivariable models, a univariable inclusion criterion of P ≤ 0.15 was used.

Results

Patients

A total of 80 patients were included in the present study. The patient characteristics, body composition and liver-related measurements are presented in detail in Tables 1 and 2. Indications for potential liver resection were mostly cholangiocarcinoma (n = 28, 35.0%), colorectal liver metastases (n = 24, 30.0%) and hepatocellular carcinoma (n = 15, 18.8%).

Table 1. Patient characteristics
Variables, median (range)All n = 80Male n = 51Female n = 29P
  1. ASA, American society of anesthesiologists; PVE, portal vein embolization; BMI, body mass index.
Patient characteristics
Median age (years)66 (28–82)67 (28–82)64 (29–76)0.289
Percentage with ASA 3/453.951.158.60.521
Patients with PVE (%)34 (42.5)19 (37.3)15 (51.7)0.208
Weight (kg)80 (47–134)82 (52-109)72 (47–134)0.032
Height (cm)174 (155–205)176 (160–205)165 (155–180)<0.001
BMI (kg/m2)24.9 (18.7–46.4)24.6 (20.2–37.7)27.3 (18.7–46.4)0.837
BMI >30 kg/m2 (%)14 (17.5)5 (9.8)9 (31.0)0.016
Child–Pugh grade
Percentage with A82.183.779.30.627
Percentage with B17.916.320.70.627
MELD score7 (6–20)7 (6–20)7 (6–19)0.758
Indication (%)
Colorectal liver metastases24 (30.0)15 (29.4)9 (31.0)0.879
Other metastases6 (7.5)3 (5.9)3 (10.3)
Hepatocellular carcinoma15 (18.8)14 (27.5)1 (3.4)0.008
Cholangiocarcinoma28 (35.0)16 (31.4)12 (41.4)0.367
Gallbladder carcinoma1 (1.3)0 (0.0)1 (3.4)
Benign lesion5 (6.3)2 (3.9)3 (10.3)
Living donor liver transplant1 (1.3)1 (2.0)0 (0.0)
Table 2. Body composition and liver-related measurements
Variables, median (range)All n = 80Men n = 51Women n = 29P
  1. L3 MI, L3 skeletal muscle index; AST, aspartate transaminase; ALT, alanine transaminase; ntTLV, non-tumour total liver volume; NAS, non-alcoholic fatty liver disease (NAFLD) activity score.
Body composition
L3 MI (cm2/m2)45.3 (28.7–71.9)50.7 (31.9–68.3)41.6 (28.7–71.9)<0.001
Sarcopenia (%)31 (38.8)18 (35.3)13 (44.8)0.400
Fat-free body mass (kg)47.3 (31.7–75.9)54.2 (37.7–67.4)39.8 (31.7–75.9)<0.001
Fat mass (kg)29.0 (1.9–86.0)28.6 (1.9–45.6)29.2 (8.3–86.0)0.296
Body fat (%)36.5 (2.9–64.2)34.8 (2.9–49.7)43.5 (17.5–64.2)0.001
Obesity (%)32 (40.0)21 (41.2)11 (37.9)0.776
Sarcopenic obesity18 (22.5)15 (29.4)3 (10.3)0.050
Body surface area (m2)1.9 (1.4–2.5)2.0 (1.5–2.4)1.81 (1.42–2.52)0.001
Liver volume    
Total liver volume (mL)1680 (1067–3883)1844 (1142–3883)1537 (1067–2871)0.003
Tumour volume (mL)59 (0–2002)67 (0–2002)30 (0–290)0.159
Non-tumour TLV (mL)1571 (869–2852)1721 (1052–2708)1477 (869–2852)0.017
Liver function
LiMAx value (µg/kg/h)326 (95–684)337 (188–594)301 (95–684)0.086
LiMAx/ntTLV (µg/kg/h/mL)0.20 (0.06–0.47)0.19 (0.10–0.47)0.20 (0.06–0.44)0.908
Laboratory testing (normal)
Bilirubin (mg/dL) (1.2)0.7 (0.2–14.3)0.7 (0.2–5.6)0.7 (0.3–14.3)0.540
ALT (U/L) (50)32 (15–358)34 (15–164)32 (16–358)0.829
AST (U/L) (38)46 (14–224)43 (16–211)49 (14–224)0.423
INR (ratio)1.04 (0.82–1.45)1.05 (0.82–1.45)1.04 (0.90–1.24)0.338
C-reactive protein (mg/L) (<5)10 (1–187)9 (1–187)11 (1–172)0.208
Creatinin (mg/dL) (0.6–1.1)0.9 (0.5–3.8)0.9 (0.5–3.8)0.7 (0.5–1.5)<0.001
Albumin (g/L) (35–52)36.0 (19.5–45.8)36.5 (19.5–45.8)35.8 (22.6–42.7)0.379
Background liver
Metavir1 (0–6)1 (0–6)1 (0–4)0.242
Percentage cirrhosis (fibrosis score = 4)8.313.20.0
NAS1 (0–4)1 (0–4)1 (0–4)0.435
Percentage severe steatosis (NAS ≥ 5)0.00.00.0
Sinusoidal dilatation score0 (0–3)0 (0–3)0 (0–3)
Percentage severe dilatation (Grade 3)5.35.74.50.663
Percentage with severe background liver disease (Cirrhosis or NAS ≥ 5 or dilatation Grade 3)10.514.34.50.243

Influence of sarcopenia on liver volume and function

The median L3 MI was 50.7 (31.9–68.3) cm2/m2 in men and 41.6 (28.7–71.9) cm2/m2 in women. Based on the predefined criteria, 18 (35.3%) men and 13 (44.8%) women were sarcopenic (Table 2). Table 3 shows the features associated with sarcopenia, obesity and sarcopenic obesity. The median preoperative LiMAx value and non-tumour TLV were 326 (95–684) µg/kg/h and 1571 (869–2852) mL, respectively (Table 2). No statistically significant difference in liver function was observed between patients with or without sarcopenia [327 (95–684) µg/kg/h and 324 (125-594) µg/kg/h, respectively, P = 0.917]. Sarcopenic patients also had a comparable ntTLV compared with patients without sarcopenia [1518 (869–2581) vs. 1678 (1052–2852) mL, P = 0.215] (Table 3).

Table 3. Features associated with sarcopenia, obesity and sarcopenic obesity
 Sarcopenia Obesity Sarcopenic obesity 
Patient characteristicsNo (n = 49)Yes (n = 31)PNo (n = 48)Yes (n = 32)PNo (n = 62)Yes (n = 18)P
  1. BMI, body mass index; ntTLV, non-tumour total liver volume.
Median age (years)65 (28–80)67 (34–82)0.27765 (28–80)66 (37–82)0.18065 (28–80)72 (43–82)0.029
Sex, number of men (%)33 (67.3)18 (58.1)0.40030 (62.5)21 (65.6)0.77636 (58.1)15 (83.3)0.050
BMI (kg/m2)26.0 (19.6–46.4)24.2 (18.7–33.0)0.01623.6 (18.7–32.1)28.6 (21.8–46.4)<0.00124.3 (18.7–46.4–26.6 (21.8–33.0)0.324
Child–Pugh Grade A83.779.30.62785.476.70.32785.568.80.120
Child–Pugh Grade B16.320.70.62714.623.30.32714.531.30.120
MELD score7 (6–20)7 (6–19)0.6487 (6–20)9 (6–19)0.0157 (6–20)8 (6–19)0.093
Liver volume
Total liver volume (mL)1762 (1111–3883)1578 (1067–3290)0.1271592 (1067–3883)1831 (1142–3290)0.0841656 (1067–3883)1768 (1142–3290)0.637
Tumour volume (mL)28 (0–2002)63 (0–709)0.65950 (0–2002)72 (0–709)0.48150 (0–2002)72 (0–709)0.627
Non-tumour TLV (mL)1678 (1052–2852)1518 (869–2581)0.2151533 (869–2852)1694 (1116–2685)0.0791562 (869–2852)1638 (1116–2581)0.541
Non-tumour TLV–body weight ratio (%)2.02 (1.31–3.22)2.28 (1.34–3.19)0.1812.24 (1.43–3.22)1.97 (1.31–3.19)0.0622.06 (1.31–3.22)2.16 (1.34–3.19)1.000
Liver function
LiMAx value (µg/kg/h)324 (125–594)327 (95–684)0.917358 (96–684)295 (95–508)0.018333 (96–684)313 (95–490)0.378
LiMAx/ntTLV (µg/kg/h/mL)0.19 (0.06–0.47)0.21 (0.07–0.44)0.7070.22 (0.06–0.47)0.17 (0.07–0.32)0.0040.20 (0.06–0.47)0.18 (0.07–0.32)0.246
Laboratory testing (normal)
Bilirubin (mg/dL) (<1.2)0.6 (0.2–14.3)0.8 (0.3–5.6)0.3560.6 (0.2–4.3)0.8 (0.3–14.3)0.1400.6 (0.2–14.3)0.8 (0.3–5.6)0.162
ALT (U/L) (<50)35 (15–358)32 (15–234)0.61532 (15–234)39 (15–358)0.51636 (15–358)29 (15–121)0.341
AST (U/L) (<38)45 (14–224)46 (15–150)0.31145 (19–211)49 (14–224)0.96546 (14–224)40 (15–150)0.313
INR (ratio)1.06 (0.82–1.24)1.04 (0.90–1.45)0.7001.03 (0.82–1.19)1.06 (0.90–1.45)0.0381.04 (0.82–1.24)1.05 (0.90–1.45)0.190
C-reactive protein (mg/L) (<5)9 (1–172)11 (1–187)0.1078 (1–95)19 (1–187)0.0079 (1–172)14 (1–187)0.034
Creatinine (mg/dL) (0.6–1.1)0.9 (0.6–3.8)0.8 (0.5–2.3)0.1300.8 (0.5–3.8)0.9 (0.5–2.3)0.6230.8 (0.5–3.8)0.9 (0.5–2.3)0.373
Albumin (g/L) (35–52)36.7 (24.3–45.8)35.1 (19.5–45.8)0.13836.3 (22.6–45.8)35.7 (19.5–43.1)0.69336.6 (22.6–45.8)35.1 (19.5–41.7)0.313

Influence of obesity on liver volume and function

According to our cut-off body-fat% values for obesity, 11 (37.9%) women and 21 (41.2%) men were obese (Table 2). The L3 MI in women was comparable between the two groups. On the contrary, in obese men, the L3 MI was significantly smaller compared to that of non-obese men [42.9 (31.9–68.3) cm2/m2 vs. 53.4 (41.3–67.7) cm2/m2, P  < 0.001]. There was a trend towards larger liver volume in obese patients, with an ntTLV of 1694 (1116–2685) mL in obese and 1533 (869–2852) mL in non-obese patients (P = 0.079). Median liver function, as determined by LiMAx, was reduced in obese patients [295 (95–508) vs. 358 (96-684) µg/kg/h, P  = 0.018]. Moreover, the median liver function per millilitre ntTLV was significantly smaller in obese patients [0.17 (0.07–0.32) vs. 0.22 (0.06–0.47), P = 0.004] (Table 3).

Influence of sarcopenic obesity on liver volume and function

Eighteen (22.5%) patients met the criteria for sarcopenic obesity, and sarcopenic-obese patients were predominantly male (83.3%) (Table 2). Sarcopenic-obese patients were older than patients without sarcopenic obesity [72 (43–82) vs. 65 (28–80), P = 0.029]. NtTLV and LiMAx values were comparable between patients with and without sarcopenic obesity (Table 3).

Correlations between liver function, liver volume and body composition

Because of irresectable disease, histopathologic examination was not performed in 23 (28.8%) patients. Another six (10.5) patients had severe background liver disease and were also excluded for assessing possible correlations between liver volume, liver function and body composition (Figures 1 and 2). Therefore, 51 (63.8%) patients without severe background liver disease were analysed. We found no correlation between the LiMAx test and ntTLV (r = 0.06, P = 0.679) (Figure 1). Weight (r = −0.40, P = 0.003), body surface area (r = −0.32, P = 0.023), estimated body-fat% (r = −0.43, P < 0.002) and BMI (r = −0.47, P  < 0.001) showed a weak but significant negative correlation with the LiMAx test outcome. No correlation was found between the LiMAx test and L3 MI (r = 0.09, P = 0.550) or fat-free body mass (r = 0.09, P = 0.538) (Figure 2). A significant but weak correlation between the L3 MI and ntTLV was found (r = 0.41, P = 0.003). Moreover, fat-free body mass (r = 0.60, P < 0.001), body surface area (r = 0.66, P < 0.001), weight (r = 0.58, P < 0.001), height (P = 0.60, r < 0.001) and BMI (r = 0.29, P = 0.042) were all weak but significantly correlated with ntTLV (Figure 2).

Figure 1.

Figure 1.

Correlation between non-tumour total liver volume (TLV) and liver function (LiMAx).

Figure 2.

Figure 2.

Continued.

Histology

Cirrhosis was present in 8.3% of all patients, and all were men. None of the patients had NASH (Table 2). However, 21.1% of the patients had borderline NASH (NAS = 3–4). Of the non-obese and obese, 13.9% and 38.1% were considered as having borderline NASH (P = 0.036). Obese patients also showed a significantly higher preoperative C-reactive protein level [19 (1–187) vs. 8 (1–95) mg/L, P = 0.007] (Table 3). Severe sinusoidal dilatation as an indication for sinusoidal obstruction syndrome was present in 5.3% of the patients.

Predictors of decreased liver function LiMAx value

After univariable analysis, seven variables were considered significant negative prognostic factors for LiMAx liver function values, namely BMI (P = 0.001), obesity (P = 0.013), fat mass (P < 0.001), body-fat% (P < 0.001), body surface area (P = 0.022), INR (International Normalized Ratio) (P = 0.012) and sinusoidal dilatation (P = 0.019). One additional borderline significant (P ≤ 0.15) variable was selected for multivariable analysis, namely female sex (P = 0.118) (Table 4). Because of possible collinearity with body-fat%, five (borderline) significant negative prognostic factors were excluded for multivariable analysis, that is, BMI, obesity, fat mass, body surface area and NAS score. Using multivariable analysis, only body-fat% was identified as an independent negative prognostic factor influencing the liver function with a regression coefficient (standard error) of −3.2 (1.2), P  = 0.011. Presence of chemotherapy-induced sinusoidal dilatation also showed a tendency to decrease liver function with a regression coefficient of −34.4 (17.7), P = 0.057.

Table 4. Univariable and multivariable analysis of factors influencing LiMAx liver function values
 UnivariableMultivariable
Prognostic factorS (SE)PS (SE)P
  1. SE, standard error; NAS, non-alcoholic steatohepatitis.
  2. aExcluded from multivariable analysis due to possible collinearity.
Age (years)−0.1 (1.1)0.952  
Female sex−43.6 (27.6)0.118−19.0 (31.0)0.543
Liver volume (100 mL)−1.1 (3.1)0.730  
Body mass indexa−8.3 (2.4)0.001  
Obesitya−67.0 (26.5)0.013  
Fat-free body mass (kg)1.3 (1.3)0.330  
Fat mass (kg)a−3.7 (0.9)<0.001  
Body-fat%−4.0 (1.0)<0.001−3.2 (1.2)0.011
Body surface area (m2)a−139.8 (59.8)0.022  
Sarcopenia−6.2 (27.7)0.823  
L3 index (cm2/m2)1.4 (1.5)0.357  
Sarcopenic obesity−38.8 (32.0)0.229  
AST (U/L)0.1 (0.3)0.659  
ALT (U/L)0.1 (0.2)0.691  
Bili (mg/dL)−6.1 (7.6)0.422  
INR (ratio)−366.3 (142.7)0.012−53.9 (177.5)0.763
Albumin (g/L)−0.2 (2.5)0.936  
Child–Pugh grade1.9 (16.9)0.909  
MELD score−3.9 (4.5)0.388  
Metavir score−1.7 (8.9)0.846  
NAS scorea−22.7 (11.4)0.053  
Sinusoidal dilatation−44.9 (18.5)0.019−34.4 (17.7)0.057

Outcome after liver resection

Complications and survival were evaluated in 57 (71.2%) patients who had undergone liver resection. Complications and major complications occurred in 19 (33.3%) and 17 (29.8%) patients, respectively. Most frequent complications were intra-abdominal abscess (n = 8, 14.0%), bile leakage (n = 7, 12.3%), biloma (n = 4, 7.0%), sepsis (n = 4, 7.0%) and intra-abdominal haemorrhage (n  = 3, 5.3%). One patient developed post-resectional liver failure (1.8%), and another patient developed hepatic encephalopathy (1.8%). There were no differences in major complication rates between sarcopenic and non-sarcopenic patients (P = 0.392), obese and non-obese (P = 0.530) and patients with and without sarcopenic obesity (P = 0.765). Thirty-day and 90-day mortality rates were 3.5% (n = 2) and 10.5% (n = 6). There were also no significant differences in 90-day mortality rates between patients with and without sarcopenia (P = 0.624), obesity (P = 0.486) or sarcopenic obesity (P = 0.487).

Discussion

This study aimed to assess how liver function and volume relate to sarcopenia, obesity and sarcopenic obesity in patients undergoing extensive preoperative assessment prior to potential liver surgery. We showed that sarcopenic and sarcopenic-obese patients did not have diminished liver function compared with patients without sarcopenia or sarcopenic obesity, evidenced by comparable LiMAx values prior to surgery. Obese patients however showed significantly reduced LiMAx values compared with patients without obesity, and body-fat% was identified as an independent negative factor affecting liver function. Moreover, there were significant negative correlations between the LiMAx values and body-fat%, body surface area, weight and BMI, which confirmed that obesity influenced liver function. Differences in ntTLV between sarcopenic and non-sarcopenic, obese and non-obese and sarcopenic-obese and patients without sarcopenic obesity did not reach statistical significance.

Recently, we demonstrated that liver volume was associated with the L3 MI, whereby sarcopenic patients had smaller ntTLVs compared with patients without sarcopenia.[11] In the present study, we found comparable ntTLVs in patients with and without sarcopenia. Nevertheless, the L3 MI was correlated with ntTLV, indicating that muscle wasting is somehow associated with smaller livers. As only patients at risk of developing post-operative liver failure (i.e. large resections) underwent a LiMAx test, a selection bias may have influenced our findings. Whereas the majority of patients in our previous study suffered from colorectal cancer liver metastases, more patients with intrahepatic cholangiocarcinoma or Klatskin tumours were included in the present study. The difference in metabolic behaviour could explain the absence of a significant difference between the ntTLVs of sarcopenic and non-sarcopenic patients and lower correlation coefficient between L3 MI and liver volume in the present study (r = 0.41 vs. r = 0.64 in the previous study). This study also assessed (LiMAx) liver function values in relation with sarcopenia. The present data do not support the idea that the increased post-operative morbidity, earlier recurrence and shorter survival in sarcopenic patients[10, 12, 16] could be explained by a decline in preoperative liver function. However, sarcopenia or muscle wasting remains an important factor negatively influencing outcome through hypercatabolism, hypoanabolism and, as a result, reduced reserves.

Only few studies have been performed on the effect of obesity on morbidity, overall survival and disease-free survival in the surgical treatment of primary or secondary liver tumours. Recently, Cauchy et al. showed that the metabolic syndrome even in absence of overt steatosis adversely affected outcome.[14] Also, in other fields of oncologic surgery, obesity has been identified as an important factor affecting outcome.[31-34] In the present study, body-fat%, body surface area, BMI and weight all showed a significant negative correlation with liver function LiMAx values. Moreover, body-fat% was identified as an independent factor negatively affecting the liver function. The significantly decreased LiMAx values in obese patients were accompanied by an increase in borderline NASH as could be expected.[35, 36] We showed a trend that obese patients had larger livers and a positive correlation between liver volume and bodyweight, BMI and body surface area. Thus, obese patients have larger, although less functioning, livers probably due to deposition of fat, presumably increasing the risk of developing morbidity.

We found no disadvantageous consequences of sarcopenic obesity on liver volume or function. This is probably due to the small number of sarcopenic-obese patients and the heterogeneity of the indications for liver resection. However, it may be that sarcopenic-obese patients have an increased risk of post-operative morbidity as sarcopenia and obesity independently of one another proved to be risk factors for post-operative complications.[10, 14, 16] Differences in complication and mortality could however not be confirmed in this study, but this may relate to the sample size.

Body composition features have been calculated based on preoperative CT scans, body weight and length, and CT scanning is considered the gold standard for estimating muscle mass or lean body mass.[37] The use of body-fat% instead of BMI might be a better method of defining obesity as it prevents that muscular patients (with a BMI of >30) are incorrectly indicated as obese. Moreover, body-fat% is able to identify obesity in thin patients. The sample size and heterogeneity of our population are relative drawbacks of our study. Therefore, further investigations of the influence of body composition on short-term and long-term outcome after liver surgery are of major importance.

In conclusion, sarcopenia and sarcopenic obesity did not seem to influence liver volume or function negatively. However, obese patients have larger but less functional livers compared with those of non-obese patients. This indicates dissociation of function and volume most likely due to deposition of fat. Moreover, body-fat% seemed to be an independent factor affecting liver function negatively. The influence of obesity on morbidity after liver resection should therefore be taken into account as a part of routine preoperative assessment to prevent post-resectional liver failure especially in centres were no standard liver function evaluation is performed before major liver surgery.

Acknowledgements

The authors certify that they comply with the ethical guidelines for authorship and publishing of the Journal of Cachexia, Sarcopenia and Muscle (von Haehling S, Morley JE, Coats AJS, Anker SD. Ethical guidelines for authorship and publishing in the Journal of Cachexia, Sarcopenia and Muscle. J Cachexia Sarcopenia Muscle 2010;1:7–8.).

References

1 ONS. Mortality statistics: cause. England and Wales, 2007. LONDON, UK: National Statistics; 2007 [cited 2008.
Web of Science® Times Cited: 6
2(WCISU), T.W.C.a.I.a.S.U. Trends in incidence, 1985–2005. 2007.
3 Wild SH, Fischbacher CM, Brock A, Griffiths C, Bhopal R. Mortality from all cancers and lung, colorectal, breast and prostate cancer by country of birth in England and Wales, 2001–2003. Br J Cancer 2006; 94: 10791085.
CrossRef |
PubMed |
CAS
4 Coleman MP, Estčve J, Damiecki P, Arslan A, Renard H. Trends in cancer incidence and mortality. IARC Sci Publ 1993; 121: 1806.
PubMed
5 Moller H, Sandin F, Robinson D, Bray F, Klint S, Linklater KM et al. Colorectal cancer survival in socioeconomic groups in England: variation is mainly in the short term after diagnosis. Eur J Cancer 2012; 48: 4653.
CrossRef |
PubMed |
Web of Science® Times Cited: 10
6HQIP. National Bowel Cancer Audit - Annual Report 2013. 2013.
7 Kumamoto T, Nojiri K, Matsuyama R, Takeda K, Endo I. Impact of postoperative morbidity on long-term survival after resection for colorectal liver metastases. Ann Surg Oncol 2010.
PubMed
8 Law WL, Poon JT, Fan JK, Lo OS. Survival following laparoscopic versus open resection for colorectal cancer. Int J Colorectal Dis 2012; 27: 10771085.
CrossRef |
PubMed |
Web of Science® Times Cited: 14
9 Tekkis PP, Kessaris N, Kocher HM, Poloniecki JD, Lyttle J, Windsor AC. Evaluation of POSSUM and P-POSSUM scoring systems in patients undergoing colorectal surgery. Br J Surg 2003; 90: 340345.
Wiley Online Library |
PubMed |
CAS |
Web of Science® Times Cited: 68
10 Carlisle J, Swart M. Mid-term survival after abdominal aortic aneurysm surgery predicted by cardiopulmonary exercise testing. Br J Surg 2007; 94: 966969.
Wiley Online Library |
PubMed |
CAS
11 Older P, Hall A. Clinical review: how to identify high-risk surgical patients. Crit Care 2004; 8: 369372.
CrossRef |
PubMed |
Web of Science® Times Cited: 25
12 Older P, Hall A, Hader R. Cardiopulmonary exercise testing as a screening test for perioperative management of major surgery in the elderly. Chest 1999; 116: 355362.
CrossRef |
PubMed |
CAS
13 Balady GJ, Arena R, Sietsema K, Myers J, Coke L, Fletcher GF et al. Clinician's guide to cardiopulmonary exercise testing in adults: a scientific statement from the American Heart Association. Circulation 2010; 122: 191225.
CrossRef |
PubMed |
Web of Science® Times Cited: 250
14 Skalski J, Allison TG, Miller TD. The safety of cardiopulmonary exercise testing in a population with high-risk cardiovascular diseases. Circulation 2012; 126: 24652472.
CrossRef |
PubMed |
Web of Science® Times Cited: 12
15 Pichard C, Kyle UG, Morabia A, Perrier A, Vermeulen B, Unger P. Nutritional assessment: lean body mass depletion at hospital admission is associated with an increased length of stay. Am J Clin Nutr 2004; 79: 613618.
PubMed
16 Fearon KC. Cancer cachexia: developing multimodal therapy for a multidimensional problem. Eur J Cancer 2008; 44: 11241132.
CrossRef |
PubMed |
CAS
17 Meyerhardt JA, Giovannucci EL, Holmes MD, Chan AT, Chan JA, Colditz GA et al. Physical activity and survival after colorectal cancer diagnosis. J Clin Oncol 2006; 24: 35273534.
CrossRef |
PubMed |
Web of Science® Times Cited: 280
18 Fearon K, Strasser F, Anker SD, Bosaeus I, Bruera E, Fainsinger RL et al. Definition and classification of cancer cachexia: an international consensus. Lancet Oncol 2011; 12: 489495.
CrossRef |
PubMed |
Web of Science® Times Cited: 342
19 Fearon KC. Cancer cachexia and fat-muscle physiology. N Engl J Med 2011; 365: 565567.
CrossRef |
PubMed |
CAS
20 Weber MA, Kinscherf R, Krakowski-Roosen H, Aulmann M, Renk H, Künkele A et al. Myoglobin plasma level related to muscle mass and fiber composition - a clinical marker of muscle wasting? J Mol Med 2007; 85: 887896.
CrossRef |
PubMed |
CAS |
Web of Science® Times Cited: 11
21 Proctor DN Joyner MJ. Skeletal muscle mass and the reduction of VO2max in trained older subjects. J Appl Physiol (1985) 1997; 82: 14111415.
PubMed
22 Bassett DR Jr, Howley ET. Limiting factors for maximum oxygen uptake and determinants of endurance performance. Med Sci Sports Exerc 2000; 32: 7084.
CrossRef |
PubMed
23 Garth AK, Newsome CM, Simmance N, Crowe TC. Nutritional status, nutrition practices and post-operative complications in patients with gastrointestinal cancer. J Hum Nutr Diet 2010; 23: 393401.
Wiley Online Library |
PubMed
24 Brown SC, Abraham JS, Walsh S, Sykes PA. Risk factors and operative mortality in surgery for colorectal cancer. Ann R Coll Surg Engl 1991; 73: 269272.
PubMed
25 Brunelli A, Charloux A, Bolliger CT, Rocco G, Sculier JP, Varela G et al. ERS/ESTS clinical guidelines on fitness for radical therapy in lung cancer patients (surgery and chemo-radiotherapy). Eur Respir J 2009; 34: 1741.
CrossRef |
PubMed
26 Carli F, Schricker T. Modulation of the catabolic response to surgery. Nutrition 2000; 16: 777780.
CrossRef |
PubMed |
CAS |
Web of Science® Times Cited: 13
27 Forbes GB, Bruining GJ. Urinary creatinine excretion and lean body mass. Am J Clin Nutr 1976; 29: 13591366.
PubMed
28 Heymsfield SB, Arteaga C, McManus C, Smith J, Moffitt S. Measurement of muscle mass in humans: validity of the 24-hour urinary creatinine method. Am J Clin Nutr 1983; 37: 478494.
PubMed
29 Schutte JE, Longhurst JC, Gaffney FA, Bastian BC, Blomqvist CG. Total plasma creatinine: an accurate measure of total striated muscle mass. J Appl Physiol Respir Environ Exerc Physiol 1981; 51: 762766.
PubMed |
Web of Science® Times Cited: 58
30 Wittenberg BA, Wittenberg JB. Transport of oxygen in muscle. Annu Rev Physiol 1989; 51: 857878.
CrossRef |
PubMed |
CAS
31 Westerblad H, Bruton JD, Katz A. Skeletal muscle: energy metabolism, fiber types, fatigue and adaptability. Exp Cell Res 2010; 316: 30933099.
CrossRef |
PubMed |
CAS |
Web of Science® Times Cited: 24
32 Minetto MA, Lanfranco F, Botter A, Motta G, Mengozzi G, Giordano R et al. Do muscle fiber conduction slowing and decreased levels of circulating muscle proteins represent sensitive markers of steroid myopathy? A pilot study in Cushing's disease. Eur J Endocrinol 2011; 164: 985993.
CrossRef |
PubMed |
CAS |
Web of Science® Times Cited: 6
33 Cicoira M, Zanolla L, Franceschini L, Rossi A, Golia G, Zamboni M et al. Skeletal muscle mass independently predicts peak oxygen consumption and ventilatory response during exercise in noncachectic patients with chronic heart failure. J Am Coll Cardiol 2001; 37: 20802085.
CrossRef |
PubMed |
CAS
34 Holmes JD, Andrews DM, Durkin JL, Dowling JJ. Predicting in vivo soft tissue masses of the lower extremity using segment anthropometric measures and DXA. J Appl Biomech 2005; 21: 371382.
PubMed |
Web of Science® Times Cited: 11
35 Khan AA, Brown J, Faulkner K, Kendler D, Lentle B, Leslie W et al. Standards and guidelines for performing central dual X-ray densitometry from the Canadian Panel of International Society for Clinical Densitometry. J Clin Densitom 2002; 5: 435445.
CrossRef |
PubMed |
CAS
36 Khan AA, Colquhoun A, Hanley DA, Jankowski LG, Josse RG, Kendler DL et al. Standards and guidelines for technologists performing central dual-energy X-ray absorptiometry. J Clin Densitom 2007; 10: 189195.
CrossRef |
PubMed |
Web of Science® Times Cited: 3
37 Simonelli C, Adler RA, Blake GM, Caudill JP, Khan A, Leib E et al. Dual-energy X-ray absorptiometry technical issues: the 2007 isCd official positions. J Clin Densitom 2008; 11: 109122.
CrossRef |
PubMed
38 Heymsfield SB. Anthropometric measurements: application in hospitalized patients. Infusionstherapie 1990; 17(Suppl 3):4851.
PubMed |
Web of Science® Times Cited: 3
39 Heymsfield SB, Lichtman S. New approaches to body composition research: a reexamination of two-compartment model assumptions. Infusionstherapie 1990; 17 (Suppl 3):48.
PubMed
40 Heymsfield SB, Lichtman S, Baumgartner RN, Wang J, Kamen Y, Aliprantis A et al. Body composition of humans: comparison of two improved four-compartment models that differ in expense, technical complexity, and radiation exposure. Am J Clin Nutr 1990; 52: 5258.
PubMed |
Web of Science® Times Cited: 337
41 Heymsfield SB, Smith R, Aulet M, Bensen B, Lichtman S, Wang J et al. Appendicular skeletal muscle mass: measurement by dual-photon absorptiometry. Am J Clin Nutr 1990; 52: 214218.
PubMed |
Web of Science® Times Cited: 264
42 Heymsfield SB, Wang J, Aulet M, Kehayias J, Lichtman S, Kamen Y et al. Dual photon absorptiometry: validation of mineral and fat measurements. Basic Life Sci 1990; 55: 327337.
PubMed
43 Wittenberg JB, Wittenberg BA. Myoglobin function reassessed. J Exp Biol 2003; 206 (Pt 12):20112020.
CrossRef |
PubMed |
CAS |
Web of Science® Times Cited: 253
44 Noori N, Kopple JD, Kovesdy CP, Feroze U, Sim JJ, Murali SB et al. Mid-arm muscle circumference and quality of life and survival in maintenance hemodialysis patients. Clin J Am Soc Nephrol 2010; 5: 22582268.
CrossRef |
PubMed |
Web of Science® Times Cited: 63
45 Older P. Anaerobic threshold, is it a magic number to determine fitness for surgery? Perioperat Med (Lond) 2013; 2: 2.
CrossRef
46 Wilson RJ, Davies S, Yates D, Redman J, Stone M. Impaired functional capacity is associated with all-cause mortality after major elective intra-abdominal surgery. Br J Anaesth 2010; 105: 297303.
CrossRef |
PubMed