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Low skeletal muscle radiodensity is the best predictor for short-term major surgical complications in gastrointestinal surgical cancer: A cohort study
Low skeletal muscle radiodensity is the best predictor for short-term major surgical complications in gastrointestinal surgical cancer: A cohort study

Competing Interests: The authors have declared that no competing interests exist.

Article Type: research-article Article History
Abstract

The aim of this study was to evaluate whether body composition, muscle function, and their association are predictive factors for short-term postoperative complications in patients with gastric and colorectal cancer. A prospective cohort study was conducted with patients undergoing resection of gastric and colorectal tumors. Nutritional status was assessed using Patient-Generated Subjective Global Assessment (PG-SGA) and anthropometric techniques. Low handgrip strength (HGS) was observed when <16kg for women, and <27kg for men. Computed tomography images were used to measure visceral adipose tissue, skeletal muscle index (SMI), and skeletal muscle radiodensity (SMD). Complications of grade II or above (according to Clavien-Dindo’s classification) were considered in a follow-up period of up to 30 days after surgery. Major complications were defined when they reached grade III or above. A total of 84 patients were analyzed (57.1% female, 59.7 ± 12.6 years) and 19% were diagnosed with low HGS + low SMI or SMD. Postoperative complications occurred in 51.2%, and these patients presented significantly longer duration of surgery and hospital stay. Major complications were observed in 16.7% of the total number of patients. Binary logistic regression adjusted by age, sex, and tumor staging showed that low SMD, low HGS + low SMI or SMD, and obesity were independent risk factors for postoperative complications, but only low SMD was an independent risk factor for major postoperative complications. Low SMD is an independent risk factor for short-term major complications following surgery in patients with gastric and colorectal cancer.

de Carvalho,Gonzalez,de Sousa,das Virgens,de Medeiros,Oliveira,Dantas,Trussardi Fayh,and Lanza: Low skeletal muscle radiodensity is the best predictor for short-term major surgical complications in gastrointestinal surgical cancer: A cohort study

Introduction

Cancer is one of the leading causes of death worldwide, with an upward incidence due to population growth and aging, as well as the adoption of already proven carcinogenic habits such as smoking, inadequate diet, and physical inactivity [1, 2]. The global estimates by the International Agency for Research on Cancer (IARC) show that more than 18 million new cases of cancer were diagnosed in 2018, and about 9 million deaths occurred [3]. In Brazil, 625 thousand new cases are predicted to occur in each year in the 2020–2022 period, with gastric and colorectal tumors among the top 10 most occurring types [4].

Surgery is considered the cornerstone in the treatment of gastric and colorectal cancer, allowing staging of the disease, verifying its extension, and removing all visible tumors. However, major surgeries are associated with a higher frequency of postoperative complications and greater morbidity, with a negative impact on short and long-term outcomes [5]. To prevent or minimize the occurrence of such complications, the impact of nutritional status, body composition, and functional capacity alterations in the pre-surgical period has been investigated [68].

Malnutrition is frequent in patients with gastric and colorectal tumors [911] due to the combination of effects related to disease progression, host response to the tumor(s), treatment symptoms and the direct effect of mechanical obstruction caused by the tumor, with consequent malabsorption of nutrients [12, 13]. Muscle wasting associated with malnutrition are frequent in cancer patients and may lead to the development of secondary sarcopenia, associated with adverse outcomes [1416]. Sarcopenia can occur even in the absence of weight and fat loss, and it can, therefore, go undetected in patients who are overweight or obese [1719]. Also, sarcopenia is associated with negative outcomes, mainly a higher rate of post-surgical complications, longer hospital stays and a worse prognosis after cancer surgery [6, 15].

Various techniques may be used to estimate body composition, but analysis of Computed Tomography (CT) images obtained as part of the routine treatment has emerged as the preferred one [17]. CT images can evaluate skeletal muscle mass and the amount and distribution of adipose tissue (subcutaneous vs. visceral) and tissue-specific radiodensity values [8, 20, 21]. Reduced skeletal muscle radiodensity (SMD), referred to as myosteatosis, reflects intramuscular fat infiltration, and it can also directly affect survival [22, 23] and prognosis of cancer patients [24, 25].

Although the role of muscle mass in the prognosis of cancer patients undergoing surgical treatment is well established in the literature [8, 15, 26, 27], a discrepancy between studies in determining the degree of severity of the complications and of the muscle mass impairment contributes to a confusing interpretation of the results. Furthermore, recent systematic reviews with meta-analyses showed that the effect of low muscle mass on the risk of complications and mortality in cancer patients may vary according to the type of cancer or complication severity [28, 29], but both studies used only skeletal muscle index (SMI) to verify the effect, disregarding other CT-determined body components, as SMD and visceral adipose tissue. Our hypothesis is that, as in primary sarcopenia, the presence of an impaired physical function, such as low handgrip strength (HGS), combined with other CT-determined muscle abnormalities (low SMD or low SMI) may represent more severe sarcopenia and improve the risk prediction, making it more sensitive to identify the higher-risk patient prone to short-term and more severe complications. Thus, the aim of this study was to evaluate whether body composition, muscle function, and CT-determined muscle abnormalities are predictive factors for postoperative complications in patients with gastric and colorectal cancer.

Materials and methods

Design and subjects

A prospective cohort study of patients undergoing elective open gastric and colorectal cancer resection was conducted between December 2017 and December 2018 in a single center, in Brazil. Patients with histopathological gastric and colorectal cancer diagnosis were included. Patients without CT scans available for, at least, three months before the date of the surgical procedure or whose analysis was impaired (absence of the third lumbar vertebra in the image, presence of ascites or edema) or undergoing palliative surgery (only exploratory laparotomy and biopsy) were excluded. The sample size was calculated according to a previous study that found 27.4% of total postsurgical complications in 376 colorectal cancer patients [8]. Considering a standard error of 10%, it was necessary to evaluate, at least, 76 patients (G*Power®, version 3.1.9.2; Institute for Experimental Psychology in Dusseldorf, Germany). The study protocol was approved by the Research Ethics Committee from the Onofre Lopes University Hospital (protocol number 73316117.8.0000.5292) and all participants signed the written informed consent form in the admission before surgery.

Procedures

Clinical and demographic data were obtained from the digital records at the Hospital one day before the surgical procedure: age, sex, ethnicity, presence of comorbidities (diabetes and/or hypertension), primary site of the tumor, neoadjuvant treatment with chemotherapy and/or radiotherapy and functional capacity by ECOG-PS (Eastern Cooperative Oncology Group Performance Status). Information about the duration and type of surgery performed, length of hospital stay and the occurrence of post-surgical complications was collected from medical records at the end of the follow-up. Patients were staged according to the eighth edition of the American Joint Committee on Cancer (AJCC) staging manual [30], based on the histopathological report.

Nutritional status and muscle strength

Nutritional status was assessed using the anthropometric technique and subjective evaluation. Body Mass Index (BMI) was calculated from weight and height squared in meters, and then, patients were classified according to the WHO criteria [31]. An inelastic tape (Sanny®, Brazil) was used for calf circumference (CC) measurement, and individuals were seated with their legs positioned at a 90° angle. The cut-off point validated for this population was adopted, which indicates a low CC when the value is equal or less than 33 cm for women and 34 cm for men [32].

The Patient-Generated Subjective Global Assessment (PG-SGA) was also applied, in which the patient is classified as well-nourished (PG-SGA A), suspected or moderately undernourished (PG-SGA B), or severely malnourished (PG-SGA C) [33, 34]. Patients classified as B and C in the present study were grouped and classified as malnourished.

Cachexia was defined according to the criteria proposed by Fearon [35] of involuntary weight loss > 5% over the last 6 months (in the absence of simple starvation), or BMI less than 20kg/m2 and any weight loss > 2% in the last 6 months, or low SMI associated with any weight loss > 2%.

Handgrip strength (HGS, kg) was measured one day before surgery, using a calibrated dynamometer (Jamar®) with the dominant hand. Patients were instructed to sit on a bed holding the dynamometer in their hand comfortably, with their arm resting at a 90° angle with the forearm and were then instructed to squeeze the dynamometer handle at maximum strength for at least 3 seconds. After three attempts with a minimum rest period of 60 seconds between them, the highest recorded value was used as maximum muscular strength [36]. The categorization of low muscle strength (an indicator of muscle function) was performed according to the following cut-off points: < 16kg for women and < 27kg for men [37].

Computed tomography images

CT images available in the hospital system (up to 90 days prior to the surgical procedure) were used for the analysis of body composition. A single transverse slice CT image at the third lumbar vertebra (L3) was analyzed using the Slice-O-matic software (v5.0, Tomovision®, Montreal, Canada). The tissues were demarcated using the Hounsfield Units (HU) thresholds of -29 to +150 for skeletal muscle and -150 to -50 for Visceral Adipose Tissue (VAT). The Skeletal Muscle Index (SMI, cm2/m2) was calculated through the total muscle area, corrected by the body surface. Skeletal muscle radiodensity quantifies the average radiation attenuation rate (HU) and it is a radiological measure of the extent of lipid contained within the muscle. Low SMI and low SMD (quantitative and qualitative muscle abnormalities) were defined according to the cut-off points proposed by Martin et al. [25], in a cohort study with adult patients with a diagnosis of gastrointestinal or lung cancer: for men, SMI < 43 cm2/m2 when BMI < 25 kg/m2 or SMI < 53 cm2/m2 when BMI ≥ 25 kg/m2; for women, SMI < 41 cm2/m2, regardless of BMI. Low SMD < 41 HU when BMI < 24.9 kg/m2 and < 33 HU when BMI ≥ 25 kg/m2 for both genders. These cutoff points were chosen for being the most widely used in the literature and based on the similarities between patients (gastrointestinal cancer with advanced staging). Visceral obesity was evaluated from the amount of VAT at the L3 level and defined from the cut-off point of 163.8cm2 for men and 80.1cm2 for women, proposed by Doyle et al. [38] in a population of patients with gastrointestinal cancer, similar to the present study. All analyzes were performed by a single trained expert, blinded to the outcome.

Outcome

The postoperative course was observed for 30 days after surgery. In case of discharged, these monitoring was carried out by observing whether there was re-hospitalization within this period for treatment of surgery complications, based on the records in the electronic hospital record. The Clavien-Dindo Classification (CDC) [39] was used, which classifies the surgical complications in degrees from I to V according to the severity. The translated and adapted version of the scale to Brazilian Portuguese was used in our study [40]. Complications of grade II or above were considered in this study, and they include infectious processes treated with antibiotics, need for blood transfusion and parenteral nutrition. Complications of grade III or higher were considered severe, including surgical re-interventions for correcting fistulas, intra-abdominal abscess and evisceration, admissions in ICU for treating abdominal sepsis, in addition to death.

Statistical analysis

Normal distribution of the continuous variables was verified by the Kolmogorov-Smirnov test. The groups with and without postoperative complications were compared using the statistical package SPSS version 25.0 (IBM®, Chicago, IL, USA). Continuous data with a normal distribution were compared using Student’s t-test for independent samples and are expressed as mean and standard deviation (SD). Data with non-normal distribution are expressed as median or interquartile range (IQR) and were tested for statistical differences by the Mann-Whitney U test. The categorical data are expressed in absolute and relative frequency (%). Pearson’s chi-squared test was used for the bivariate association analysis between postoperative complications and risk factors, and relative risk was calculated with a 95% confidence interval. The body composition and nutritional status variables that presented p < 0.20 were tested in a logistic binary regression, adjusted for confounding variables (age, sex and tumor stage) to evaluate the Odds Ratio for post-surgical complications. A p-value < 0.05 was considered significant.

Results

The overall characteristics of the sample are shown in Table 1. Eighty-four (84) patients with mean age of 59.7 ± 12.58 years were included in the study, the majority being female, and non-Caucasian. Of the total, 65.5% had colorectal cancer, and stages II and III were the most prevalent. Only 17.8% had ECOG-PS higher than 1 and 23.8% of the patients had undergone neoadjuvant chemotherapy and radiotherapy. The median length of hospital stay was of 5 days (IQR: 4.0–8.7), and the time between CT and the surgery was of 34 days (IQR: 23.2–48.7). More than a half of the patients (51.2%) had complications of grade II or higher during the follow-up period and 14 patients (16.7%) had severe complications (grade III or higher), and 6 patients (7.2%) died. No statistically significant differences were found when comparing the prevalence of low SMD between patients with or without previous treatment (15% vs 17,2% respectively, p = 0.053) and tumor site (17.2% for gastric cancer vs 16.4% for colorectal cancer, p = 0.011). There was also no statistically significant difference between low SMI between patients with or without previous treatment (10% vs 20,3% respectively, p = 1.105) and tumor site (13,8% for gastric cancer vs 20% for colorectal cancer, p = 0.499).

Table 1
Demographic and clinical characteristics of patients who underwent gastric and colorectal cancer surgery (n = 84).
Characteristicsn%
Sex
Female4857.1
Male3642.9
Ethnicity
Caucasian6273.8
Non-Caucasian2226.2
Tumor site
Gastric2934.5
Colon/rectum5565.5
TNM stage
I1821.4
II2125.0
III2631.0
IV1619.0
Unknown33.6
ECOG-PS
03845.2
13136.9
2910.7
367.1
Neoadjuvant treatment
Yes2023.8
No6476.2
Duration of surgery (min)
<1201113.1
120–2395059.5
≥2402327.4
Postoperative complications ≥ grade II
Yes4351.2
No4148.8
Postoperative complications ≥ grade III
Yes1416.7
No7083.3

Data expressed in absolute and relative frequency (%).

ECOG-PS: Eastern Cooperative Oncology Group Performance Status.

Bivariate analysis (Tables 2 and 3) showed that postoperative complications were associated with the site of the tumor being in the stomach (p = 0.018), tumors of stage III and IV (p = 0.033), presence of obesity (p = 0.011), low SMD (p = 0.025), low function (HGS) + muscle impairment (low SMI or SMD) (p = 0.034), and visceral obesity (p = 0.030). No significant association was observed between postoperative complications and the other variables. In addition, patients who developed postoperative complications presented higher amounts of VAT (123.7 ± 84.5 cm2 vs 89.7 ±64.6 cm2, p = 0.042), longer duration of surgery (median of 195 min vs 150min, p = 0.002) and length of hospital stay (median of 8 days vs 5 days, p <0.001).

Table 2
Demographic and clinical characteristics of patients who underwent gastric and colorectal cancer surgery and associations with 30-day postoperative complications (n = 84).
Characteristicsn (%)Complications n (%)RR (95% CI)p
Sex0.801
 Male36 (42.9)19 (52.8)1
 Female48 (57.1)24 (50.0)0.95 (0.62;1.44)
Age0.996
 < 60 years41 (48.8)21 (51.2)1
 > 60 years43 (51.2)22 (51.2)1.00 (0.66;1.52)
Comorbidities0.666
 None41 (48.8)20 (48.8)1
 Diabetes/hypertension43 (51.2)23 (53.5)1.10 (0.72;1.67)
TNM stagea0.033
 I-II39 (46.4)16 (37.8)1
 III-IV42 (50.0)25 (61.9)1.64 (1.02;2.63)
Neoadjuvant treatment0.903
 No64 (76.2)33 (51.6)1
 Yes20 (23.8)10 (50.0)0.97 (0.59;1.60)

RR: relative risk; 95% CI: 95% confidence interval.

aTNM stage was unknown in three patients.

Table 3
Pre-operative nutritional and body composition parameters of patients who underwent gastric and colorectal cancer surgery and associations with 30-day postoperative complications (n = 84).
Characteristicsn (%)Complications n (%)RR (95% CI)P
PG-SGA0.346
 B or C33 (39.3)19 (57.6)1
 A51 (60.7)24 (47.1)0.82 (0.54;1.24)
Calf circumference0.503
 Normal70 (83.3)35 (81.4)1
 Lowa14 (16.7)8 (18.6)1.14 (0.69;1.90)
Low HGSb0.204
 No58 (69.0)27 (46.6)1
 Yes26 (31.0)16 (61.5)1.32 (0.88;1.99)
Cachexia0.467
 No52 (61.9)25 (48.1)1
 Yes32 (38.1)18 (56.3)1.17 (0.77;1.77)
Low SMIc0.186
 No69 (82.1)32 (45.7)1
 Yes15 (17.9)10 (66.7)1.39 (0.90;2.15)
Low SMDd0.025
 No70 (83.3)33 (47.8)1
 Yes14 (16.7)11 (78.6)1.72 (1.18;2.50)
Low Function + Muscle impairmente0.034
 No68 (81.0)31 (45.6)1
 Yes16 (19.0)12 (75.0)1.65 (1.12;2.42)
Obesityf0.011
 No66 (78.6)29 (43.9)1
 Yes18 (21.4)14 (77.8)1.77 (1.23;2.56)
Visceral obesityg0.030
 No47 (56.0)19 (40.4)1
 Yes37 (44.0)24 (64.9)1.70 (1.03;2.80)

PG-SGA: Patient-Generated Subjective Global Assessment; HGS: handgrip strength; SMI: skeletal muscle index; SMD: skeletal muscle radiodensity; RR: relative risk; 95% CI: 95% confidence interval.

aLow calf circumference: ≤ 33 cm for females and ≤ 34cm for males;

bLow HGS: < 16kg for females and < 27kg for males;

cLow SMI: < 43 cm2/m2 (BMI < 25 kg/m2) or SMI < 53 cm2/m2 (BMI ≥ 25 kg/m2) for males; SMI < 41 cm2/m2 for females;

dLow SMD: SMD < 41 HU (BMI < 24.9 kg/m2) or < 33 HU (BMI ≥25 kg/m2);

eLow function + muscle impairment: low HGS + low SMI or low SMD;

fObesity: BMI ≥ 30 kg/m2;

gVisceral obesity: VAT > 80.1cm2 for females and > 163.8 cm2 for males.

Logistic binary regression was used to analyze the association between body composition variables and complications within 30 days after surgery, adjusted or not for confounding factors (Tables 3 and 4). For complications of grade II or above (Table 4), individuals with obesity (BMI ≥ 30 kg/m2), visceral obesity, low SMD and low function + muscle impairment presented more chances of having postoperative complications, even with the adjustment for confounding variables (age, sex, and tumor staging).

Table 4
Logistic binary regression model analysis of factors associated with postoperative complications within 30 days after surgery in patients with gastric and colorectal cancer (n = 84).
Complications Grade ≥ 2 (n = 43; 51.2%)
Independent variablesUnadjustedAdjusteda
OR (95% CI)OR (95% CI)
BMIp = 0.016p = 0.014
BMI < 30 kg/m2(n = 18)11
BMI ≥ 30 kg/m2 (n = 66)4.47 (1.33;15.02)5.16 (1.39;19.21)
Visceral obesityp = 0.026p = 0.020
VAT ≤ 163.8/80.1 cm2 (n = 56)11
VAT > 163.8/80.1cm2 (n = 37)1.70 (1.03; 2.76)1.24 (1.02; 2.55)
SMIp = 0.192p = 0.190
Adequate SMI (n = 69)11
Low SMIb (n = 15)2.18 (0.68;7.05)2.38 (0.65;8.75)
SMDp = 0.034p = 0.015
Adequate SMD (n = 70)11
Low SMDc (n = 14)4.35 (1.12;16.97)7.82 (1.5;40.88)
Low Function + Muscle impairmentdp = 0.042p = 0.022
No (n = 68)11
Yes (n = 16)3.58 (1.05;12.23)5.74 (1.28;25.64)

OR: Odds ratio; CI: confidence interval; BMI: body mass index; VAT: visceral adipose tissue SMI: skeletal muscle index; SMD: skeletal muscle radiodensity.

aAdjusted model: by age, gender and tumor stage;

bLow SMI: < 43 cm2/m2 (BMI < 25 kg/m2) or SMI < 53 cm2/m2 (BMI ≥ 25 kg/m2) for men; SMI < 41 cm2/m2 for women;

cLow SMD: SMD < 41 HU (BMI < 24.9 kg/m2) or < 33 HU (BMI ≥ 25 kg/m2);

dLow function + muscle impairment: low HGS + low SMI or low SMD.

When serious complications were considered (CDC ≥ III) (Table 5), obesity and SMD were predictive factors of postoperative complications, but after adjustments for confounding variables, only SMD remained as a predictive factor of risk.

Table 5
Logistic binary regression model analysis of factors associated with severe postoperative complications within 30 days after surgery in patients with gastric and colorectal cancer (n = 84).
Complications Grade ≥ 3 (n = 14; 16.7%)
Independent variablesUnadjustedAdjusteda
OR (95% CI)OR (95% CI)
BMIp = 0.04p = 0.079
BMI < 30 kg/m2(n = 18)11
BMI ≥30 kg/m2 (n = 66)3.63 (1.06;12.37)3.67 (0.86;15.65)
Visceral obesityp = 0.095p = 0.083
VAT ≤163.8/80.1 cm2 (n = 56)11
VAT >163.8/80.1cm2 (n = 37)1.18 (0.96;1.45)1.14 (0.87; 1.33)
SMIp = 0.703p = 0.727
Adequate SMI (n = 69)11
Low SMIb (n = 15)1.32 (0.32;5.45)1.32 (0.28;6.29)
SMDp = 0.045p = 0.046
Adequate SMD (n = 70)11
Low SMDc (n = 14)3.77 (1.03;13.79)5.62 (1.03;30.54)
Low Function + Muscle impairmentdp = 0.326p = 0.383
No (n = 68)11
Yes (n = 16)1.93 (0.52;7.21)1.99 (0.42;9.37)

OR: Odds ratio; CI: confidence interval; BMI: body mass index; SMI: skeletal muscle index; SMD: skeletal muscle radiodensity.

aAdjusted model: by age, gender and tumor stage;

bLow SMI: < 43 cm2/m2 (BMI < 25 kg/m2) or SMI < 53 cm2/m2 (BMI ≥ 25 kg/m2) for men; SMI < 41 cm2/m2 for women;

cLow SMD: SMD < 41 HU (BMI < 24.9 kg/m2) or < 33 HU (BMI ≥ 25 kg/m2);

dLow function + muscle impairment: low HGS + low SMI or low SMD.

Discussion

The preoperative nutritional assessment has gained importance in the treatment of patients undergoing surgery in order to identify risk factors that may be predictors of worse outcomes [15]. The main finding of this study was that low SMD was the best predictive factor associated with severe short-term postoperative complications in patients with gastric and colorectal cancer. For general surgical complications (CDC scale ≥ II), the presence of obesity, visceral obesity, and the presence of low function + muscle impairment also showed a significant association of risk.

Although there is no doubt that muscle mass depletion and malnutrition are considered surgical risks, methodological differences between studies can lead to confusion in analysis and result interpretation. The inclusion of patients with different surgical objectives (curative or palliative), type of surgery (laparoscopic or open procedure), and the number of surgical procedures in the same surgery, for example, can lead to inappropriate conclusions. Thus, in the present study, we included only patients that underwent curative surgery in an open procedure, and with that, we have obtained a more homogeneous sample.

Another factor that leads to confusion in the interpretation of the result between different studies is the fact that the presence of complications is determined by the CDC scale. While some studies consider surgical complications when CDC scale ≥ II [8, 21, 41, 42], others consider complications to be major only when CDC scale ≥ III [29, 4345]. Therefore, in the present study, we analyzed the impact of the variables of nutritional status and body composition on the prediction of surgical risk using both classifications (general and major complications). In a retrospective study with 115 patients who underwent initial hepatectomy for colorectal liver metastasis, Horii et al. [43] described general (CDC scale ≥ II) and major (CDC scale ≥ III) complications in their patients, but the multivariate analysis was performed only with patients with major complications.

In the present study, patients classified as obese by BMI had a higher risk of general complications. In fact, more than half of the sample was overweight or obese, and it is interesting to remember that these characteristics are risk factors for the development of colorectal and gastric cancer [4]. Performing a surgical procedure in an obese patient requires more attention when manipulating a more voluminous mesentery with also increased difficulty in properly visualizing and identifying the surgical planes and blood vessels, and this peculiarity may explain the greater risk of complications for them [8]. However, the BMI value in the evaluation of hospitalized patients is questionable in the literature [46, 47], because they may have muscle mass deficit (sarcopenic obesity). In the present study, no patient was classified with this condition.

Our results showed that PG-SGA and cachexia were not associated with short-term surgical complications. Although about 38% of patients had cachexia, most of the patients were classified as well-nourished by PG-SGA. The good nutritional status found in the sample can also be attributed to the fact that the minority of the patients had previously undergone chemotherapy and radiotherapy, treatments with a negative impact on the nutritional status [46]. As the definition of cachexia allows different factors (weight loss, sarcopenia, and inflammation) to be present whether isolated or associated [35], and weight loss is an information that relies on memory, further studies are needed to investigate the effects of the different components on the surgical prognosis.

Although the risk associated with sarcopenia is not prohibitive for surgery, patients with low SMI/SMD require closer vigilance during their postoperative course. Several studies have evaluated body composition related to post-surgical complications in cancer patients. Recently, Xiao et al. showed that both low SMI and low SMD were associated with a higher risk of postsurgical complications, and short-term and long-term mortality in patients with colorectal cancer [48]. In a similar population of the same nationality, using the same cut-off points for low SMI, Maurício et al. [42] observed that low SMI and low muscle strength were associated with short-term postoperative complications in colorectal cancer patients. These results are different from those of the present study, probably because they have not included gastric cancer patients and the follow-up of these patients was curtailed by their hospital discharge, as well as in the majority of the studies [7, 26, 42, 49]. Jun Lu et al. [21] followed patients with gastric cancer after gastrectomy for a mean period of 64 months, and found that the SMI and SMD were associated with complications and a worse prognosis. However, the authors did not evaluate muscle strength. Margadant et al. [23], as in the present study, followed-up 373 older adult patients with colorectal cancer for 30 days after surgery, and observed that low SMD was associated with major postoperative complications.

Recently, Tankel et al. [50] published results of their retrospective single-center study with 580 surgical colorectal patients. Similar to the present study, considering major complications, low SMD was a predictor of complications, but not low SMI. SMD reflects the lipid content of the muscle, and SMI reflects the skeletal muscle volume or mass and it is measured as the skeletal muscle area divided by body height squared [51]. One possible explanation for this could be that the increase in lipid content of the muscle (myosteatosis) occurs before the decline in the size of muscle mass, and the deterioration of muscle is often occult, particularly in the presence of obesity [48]. Also, CT-based calculation allows for early detection of reduction in HU (SMD) while the muscle area remains unchanged; thus decrease in SMD is detected earlier than the corresponding decrease in SMI [52]. On the same way, Xiao et al. [24] observed that low SMD, but not low SMI, were associated with pre-existing comorbidities, suggesting a pioneer shared mechanism between them and fat infiltration into the muscle.

Regarding body adiposity, few studies have evaluated the effects of visceral fat on postoperative complications. Although the present study found that the patients who developed general complications had a higher amount of VAT compared to patients without them, this result was not observed for major complications. Chen et al. [8] evaluated the effects of visceral fat on post-surgical complications in patients with colorectal cancer, and they also observed statistically significant associations. They can be attributed to the fact that VAT secretes cytokines that systemically alter the immune, metabolic and endocrine system, influencing the body response to surgical stress, and its excess leads to an exacerbation of the post-surgical acute phase inflammatory response, affecting the immune system and resulting in worse outcomes [53].

Our study has some possible limitations. Although it is recognized that contrast infusion during CT analysis can increase muscle radiodensity measurements [54], we do not control this factor. However, because of the magnitudes of the differences are relatively small, the effect of the sweep phase on the accuracy of the results must be determined. The cut-off points adopted for classifying low SMI and low SMD were not proposed for the Brazilian population, and ethnic characteristics may influence this analysis. We recognize that the population in our study included only patients who performed open surgical procedure, not including laparoscopic surgery, and thus, future studies comparing these different surgical techniques are necessary. Finally, it is relevant to highlight that this is a single-center study with a small number of patients with heterogeneous cancer sites, so the results need to be analyzed with caution before being extrapolated to the general population.

In conclusion, the findings suggest that the presence of obesity, visceral obesity, low function + muscle impairment, and low SMD were associated and may predict an increased risk of short-term postoperative complications following gastric and colorectal cancer surgery, even in a sample with low nutritional risk. Low SMD also identifies individuals at risk of major complications. Thus, it is recommended to evaluate body composition by CT images, when available, before performing the surgical procedure, which may help to screen patients at higher risk of developing complications, thereby helping the multidisciplinary team to prepare the patient and reduce the risks arising from this therapeutic modality. The development of automated body composition analysis may be a reality in the next future, making possible the inclusion of body composition assessment from the CT-images as a routine in pre-operative care. For this, it is suggested that hospitals and the multidisciplinary teams invest in technology, training and time to analyze the images, aimed at improving patient care and supporting prior nutritional interventions to reduce surgical risk. Also, we suggest that future studies focus on comparing the role of muscle mass abnormalities on adverse outcomes between surgeries and less invasive procedures during cancer treatment.

Acknowledgements

The authors would like to thank all the patients for their willingness to participate in the study, as well as all the employees of the Dr. Luiz Antônio Hospital who directly or indirectly contributed to the study.

References

LATorre, FBray, RLSiegel, JFerlay, JLortet-tieulent, AJemal. Global Cancer Statistics, 2012. Ca Cancer J Clin [Internet]. 2015;65(2):87108. 10.3322/caac.21262

FBray, JFerlay, ISoerjomataram, RLSiegel, LATorre, AJemal. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin [Internet]. 2018 11;68(6):394424. 10.3322/caac.21492

JFerlay, MColombet, ISoerjomataram, CMathers, DMParkin, MPiñeros, et al Estimating the global cancer incidence and mortality in 2018: GLOBOCAN sources and methods. Int J Cancer [Internet]. 2018;00:113. 10.1002/ijc.31937

INCA. Instituto Nacional de Câncer José de Alencar Gomes da Silva. Estimativa 2020: incidência de câncer no Brasil. INCA. Instituto Nacional de Câncer José de Alencar Gomes da Silva, editor. Rio de Janeiro: Ministério da Saúde; 2019. 1–120 p.

STMcSorley, DHBlack, PGHorgan, DCMcMillan. The relationship between tumour stage, systemic inflammation, body composition and survival in patients with colorectal cancer. Clin Nutr. 2018; 37(4):127985. 10.1016/j.clnu.2017.05.017

GMalietzis, OAziz, NMBagnall, NJohns, KCFearon, JTJenkins. The role of body composition evaluation by computerized tomography in determining colorectal cancer treatment outcomes: A systematic review. Eur J Surg Oncol [Internet]. 2015;41(2):18696. 10.1016/j.ejso.2014.10.056

JHärter, SPOrlandi, MCGonzalez. Nutritional and functional factors as prognostic of surgical cancer patients. Support Care Cancer [Internet]. 2017 8 16;25(8):252530. 10.1007/s00520-017-3661-4

WChen, XChen, LMa, FZhang, JLin, CZhuang, et al Impact of Visceral Obesity and Sarcopenia on Short ‑ Term Outcomes After Colorectal Cancer Surgery. Dig Dis Sci. 2018;63(6):162030. 10.1007/s10620-018-5019-2

JRBroughman, GRWilliams, AMDeal, HYu, KANyrop, SMAlston, et al Prevalence of sarcopenia in older patients with colorectal cancer. J Geriatr Oncol [Internet]. 2015 11;6(6):4425. 10.1016/j.jgo.2015.08.005

10 

HTamagawa, TAoyama, KIguchi, HFujikawa, NYukawa, YRino, et al Preoperative evaluation of skeletal muscle mass in the risk assessment for the short ‑ term outcome of elderly colorectal cancer patients undergoing colectomy. Mol Clin Oncol. 2018;8(6):77984. 10.3892/mco.2018.1607

11 

SKKamarajah, JBundred, BHLTan. Body composition assessment and sarcopenia in patients with gastric cancer: a systematic review and meta-analysis. Gastric Cancer [Internet]. 2019 1 1;22(1):1022. 10.1007/s10120-018-0882-2

12 

ZAversa, PCostelli, MMuscaritoli. Cancer-induced muscle wasting: latest findings in prevention and treatment. Ther Adv Med Oncol [Internet]. 2017 5 8;9(5):36982. 10.1177/1758834017698643

13 

PEPorporato. Understanding cachexia as a cancer metabolism syndrome. Oncogenesis [Internet]. 2016;5(2):e20010. 10.1038/oncsis.2016.3

14 

Svon Haehling, JEMorley, SDAnker. An overview of sarcopenia: facts and numbers on prevalence and clinical impact. J Cachexia Sarcopenia Muscle [Internet]. 2010 12;1(2):12933. 10.1007/s13539-010-0014-2

15 

CSimonsen, Pde Heer, EDBjerre, CSuetta, PHojman, BKPedersen, et al Sarcopenia and Postoperative Complication Risk in Gastrointestinal Surgical Oncology. Ann Surg [Internet]. 2018 7;268(1):5869. 10.1097/SLA.0000000000002679

16 

AJCruz-Jentoft, JPBaeyens, JMBauer, YBoirie, TCederholm, FLandi, et al Sarcopenia: European consensus on definition and diagnosis: Report of the European Working Group on Sarcopenia in Older People. Age Ageing [Internet]. 2010 7 1;39(4):41223. 10.1093/ageing/afq034

17 

CMPrado, SJCushen, CEOrsso, AMRyan. Sarcopenia and cachexia in the era of obesity: clinical and nutritional impact. Proc Nutr Soc [Internet]. 2016 5 8;75(2):18898. 10.1017/S0029665115004279

18 

CMPrado, JRLieffers, LJMcCargar, TReiman, MBSawyer, LMartin, et al Prevalence and clinical implications of sarcopenic obesity in patients with solid tumours of the respiratory and gastrointestinal tracts: a population-based study. Lancet Oncol [Internet]. 2008 7;9(7):62935. 10.1016/S1470-2045(08)70153-0

19 

LMartin, IGioulbasanis, PSenesse, VEBaracos. Cancer-Associated Malnutrition and CT-Defined Sarcopenia and Myosteatosis Are Endemic in Overweight and Obese Patients. J Parenter Enter Nutr [Internet]. 2019 4 22;0(0):110. 10.1002/jpen.1597

20 

JAubrey, NEsfandiari, VEBaracos, FAButeau, JFrenette, CTPutman, et al Measurement of skeletal muscle radiation attenuation and basis of its biological variation. Acta Physiol [Internet]. 2014 3;210(3):48997. 10.1111/apha.12224

21 

JLu, ZZheng, PLi, JXie, JWang, J-XLin, et al A Novel Preoperative Skeletal Muscle Measure as a Predictor of Postoperative Complications, Long-Term Survival and Tumor Recurrence for Patients with Gastric Cancer After Radical Gastrectomy. Ann Surg Oncol [Internet]. 2018;25(2):43948. 10.1245/s10434-017-6269-5

22 

CHKroenke, CMPrado, JAMeyerhardt, EKWeltzien, JXiao, EMCespedes Feliciano, et al Muscle radiodensity and mortality in patients with colorectal cancer. Cancer [Internet]. 2018 7 15;124(14):300815. 10.1002/cncr.31405

23 

CCMargadant, ERJBruns, DAMSloothaak, Pvan Duijvendijk, AFvan Raamt, HJvan der Zaag, et al Lower muscle density is associated with major postoperative complications in older patients after surgery for colorectal cancer. Eur J Surg Oncol [Internet]. 2016;42(11):16549. 10.1016/j.ejso.2016.05.040

24 

JXiao, BJCaan, EWeltzien, EMCespedes Feliciano, CHKroenke, JAMeyerhardt, et al Associations of pre-existing co-morbidities with skeletal muscle mass and radiodensity in patients with non-metastatic colorectal cancer. J Cachexia Sarcopenia Muscle [Internet]. 2018 8; 9(4):654663. 10.1002/jcsm.12301

25 

LMartin, LBirdsell, NMacDonald, TReiman, MTClandinin, LJMcCargar, et al Cancer Cachexia in the Age of Obesity: Skeletal Muscle Depletion Is a Powerful Prognostic Factor, Independent of Body Mass Index. J Clin Oncol [Internet]. 2013 4 20;31(12):153947. 10.1200/JCO.2012.45.2722

26 

SZhang, STan, YJiang, QXi, QMeng, QZhuang, et al Sarcopenia as a predictor of poor surgical and oncologic outcomes after abdominal surgery for digestive tract cancer: A prospective cohort study. Clin Nutr [Internet]. 2019; 38(6): 288188. 10.1016/j.clnu.2018.12.025

27 

BJCaan, JAMeyerhardt, CHKroenke, SAlexeeff, JXiao, EWeltzien, et al Explaining the Obesity Paradox: The Association between Body Composition and Colorectal Cancer Survival (C-SCANS Study). Cancer Epidemiol Biomarkers Prev [Internet]. 2017 7;26(7):100815. 10.1158/1055-9965.EPI-17-0200

28 

DPapaconstantinou, KVretakakou, APaspala, EPMisiakos, CNastos, PPatapis, et al The impact of preoperative sarcopenia on postoperative complications following esophagectomy for esophageal neoplasia: a systematic review and meta-analysis. Dis Esophagus. 7 2020;33(7):111. 10.1093/dote/doaa002

29 

ASBorggreve, RBDen Boer, GIVan Boxel, PADe Jong, WBVeldhuis, ESteenhagen, et al The Predictive Value of Low Muscle Mass as Measured on CT Scans for Postoperative Complications and Mortality in Gastric Cancer Patients: A Systematic Review and Meta-Analysis. J Clin Med. 2020;9(1):199.

30 

AJCC. 8th AJCC Cancer Staging Form Supplement 6–2018 update. AJCC Cancer Staging Manual, 8th Ed. 2018;(Junio):99–105.

31 

WHO. Physical status: the use and interpretation of anthropometry. Report of a WHO Expert Committee. Vol. 854, World Health Organization technical report series. 1995. p. 1–452.

32 

TGBarbosa-Silva, RMBielemann, MCGonzalez, AMBMenezes. Prevalence of sarcopenia among community-dwelling elderly of a medium-sized South American city: results of the COMO VAI? study. J Cachexia Sarcopenia Muscle [Internet]. 2016 5;7(2):13643. 10.1002/jcsm.12049

33 

JBauer, SCapra, MFerguson. Use of the scored Patient-Generated Subjective Global Assessment (PG-SGA) as a nutrition assessment tool in patients with cancer. Eur J Clin Nutr [Internet]. 2002 8 19;56(8):77985. 10.1038/sj.ejcn.1601412

34 

MCGonzalez, LRBorges, DHSilveira, MCAssunção, SPOrlandi. Validação da versão em português da avaliação subjetiva global produzida pelo paciente. Rev Bras Nutr Clínica. 2010;1028.

35 

KFearon, FStrasser, SDAnker, IBosaeus, EBruera, RLFainsinger, et al Definition and classification of cancer cachexia: An international consensus. Lancet Oncol [Internet]. 2011;12(5):48995. 10.1016/S1470-2045(10)70218-7

36 

JMBoadella, PPKuijer, JKSluiter, MHFrings-Dresen. Effect of self-selected handgrip position on maximal handgrip strength. Arch Phys Med Rehabil [Internet]. 2005 2;86(2):32831. 10.1016/j.apmr.2004.05.003

37 

AJCruz-Jentoft, GBahat, JBauer, YBoirie, OBruyère, TCederholm, et al Sarcopenia: revised European consensus on definition and diagnosis. Age Ageing [Internet]. 2019 1 1;48(1):1631. 10.1093/ageing/afy169

38 

SLDoyle, AMBennett, CLDonohoe, AMMongan, JMHoward, FELithander, et al Establishing computed tomography–defined visceral fat area thresholds for use in obesity-related cancer research. Nutr Res [Internet]. 2013 3;33(3):1719. 10.1016/j.nutres.2012.12.007

39 

DDindo, NDemartines, P-AClavien. Classification of Surgical Complications. Ann Surg [Internet]. 2004 8;240(2):20513. 10.1097/01.sla.0000133083.54934.ae

40 

LFMoreira, MCMPessôa, DSMattana, FFSchmitz, BSVolkweis, JLAntoniazzi, et al Cultural adaptation and the Clavien-Dindo surgical complications classification translated to Brazilian Portuguese. Rev Col Bras Cir [Internet]. 2016;43(3):1418. 10.1590/0100-69912016003001

41 

TTamura, KSakurai, MNambara, YMiki, TToyokawa, NKubo, et al Adverse Effects of Preoperative Sarcopenia on Postoperative Complications of Patients With Gastric Cancer. Anticancer Res. 2019;39:98792. 10.21873/anticanres.13203

42 

SFMaurício, JXiao, CMPrado, MCGonzalez, MITDCorreia. Different nutritional assessment tools as predictors of postoperative complications in patients undergoing colorectal cancer resection. Clin Nutr [Internet]. 2018 10;37(5):15051511. 10.1016/j.clnu.2017.08.026

43 

NHorii, YSawda, TKumamoto, NTsuchiya, TMurakami, YYabushita, et al Impact of intramuscular adipose tissue content on short- and long-term outcomes of hepatectomy for colorectal liver metastasis: a retrospective analysis. World J Surg Oncol. 2020;18(1):68 10.1186/s12957-020-01836-5

44 

SIBril, TFPezier, BMTijink, LMJanssen, WWBraunius, RDe Bree. Preoperative low skeletal muscle mass as a risk factor for pharyngocutaneous fistula and decreased overall survival in patients undergoing total laryngectomy. Head Neck. 2019;41(6):174555. 10.1002/hed.25638

45 

JYang, TZhang, DFeng, XDai, TLv, XWang, et al A new diagnostic index for sarcopenia and its association with short-term postoperative complications in patients undergoing surgery for colorectal cancer. Color Dis. 2019;21(5):53847. 10.1111/codi.14558

46 

LEDaly, CMPrado, AMRyan. A window beneath the skin: how computed tomography assessment of body composition can assist in the identification of hidden wasting conditions in oncology that profoundly impact outcomes. Proc Nutr Soc [Internet]. 2018 5 10;77(2):13551. 10.1017/S0029665118000046

47 

ÉBNí Bhuachalla, LEDaly, DGPower, SJCushen, PMacEneaney, AMRyan. Computed tomography diagnosed cachexia and sarcopenia in 725 oncology patients: is nutritional screening capturing hidden malnutrition? J Cachexia Sarcopenia Muscle [Internet]. 2018 4;9(2):295305. 10.1002/jcsm.12258

48 

JXiao, BJCaan, EMCFeliciano, JAMeyerhardt, PDPeng, VEBaracos, et al Association of Low Muscle Mass and Low Muscle Radiodensity With Morbidity and Mortality for Colon Cancer Surgery. JAMA Surg. 2020 8 12;e202497 10.1001/jamasurg.2020.2497

49 

AFukuta, BSc, TSMS, SMMS, DMakiura, DPh, et al Impact of preoperative cachexia on postoperative length of stay in elderly patients with gastrointestinal cancer. Nutrition [Internet]. 2019;58:658. 10.1016/j.nut.2018.06.022

50 

JTankel, SYellinek, EVainberg, YDavid, DGreenman, JKinross, et al Sarcopenia defined by muscle quality rather than quantity predicts complications following laparoscopic right hemicolectomy. Int J Color Dis. 2020;35(1):8594. 10.1007/s00384-019-03423-x

51 

NHayashi, YAndo, BGyawali, TShimokata, OMaeda, MFukaya, et al Low skeletal muscle density is associated with poor survival in patients who receive chemotherapy for metastatic gastric cancer. Oncol Rep. 2016;35:172731. 10.3892/or.2015.4475

52 

BHGoodpaster, DEKelley, FLThaete, JHe, RRoss. Skeletal muscle attenuation determined by computed tomography is associated with skeletal muscle lipid content. J Appl Physiol. 2000;89(1):10410. 10.1152/jappl.2000.89.1.104

53 

YZhang, JPWang, XLWang, HTian, TTGao, LMTang, et al Computed tomography–quantified body composition predicts short-term outcomes after gastrectomy in gastric cancer. Curr Oncol [Internet]. 2018 11 2;25(5):41122. 10.3747/co.25.4014

54 

KERollins, HJavanmard-Emamghissi, AAwwad, IAMacdonald, KCHFearon, DNLobo. Body composition measurement using computed tomography: does the phase of the scan matter? Nutrition. 2017;41:3744. 10.1016/j.nut.2017.02.011