Malnutrition and visceral obesity predict adverse short-term and long-term outcomes in patients undergoing proctectomy for rectal cancer

doi:10.21203/rs.3.rs-2482567/v1

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Malnutrition and visceral obesity predict adverse short-term and long-term outcomes in patients undergoing proctectomy for rectal cancer

https://doi.org/10.21203/rs.3.rs-2482567/v1

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Background

There was no study explored the relationship of malnutrition and visceral obesity in rectal cancer patients following radical proctectomy. Therefore, we aimed to investigate the value of malnutrition and visceral obesity in patients with rectal cancer.

Methods

This study included patients with rectal cancer who underwent proctectomy. Malnutrition was defined by Global Leadership Initiative on Malnutrition (GLIM). Visceral obesity was measured by computed tomography (CT). Patients were classified into four groups according to the presence of malnutrition or visceral obesity. Univariate and multivariate logistic regression analyses were performed to evaluate the risk factors for postoperative complications. Univariate and multivariate cox regression analyses were performed to evaluate the risk factors for overall survival (OS). Kaplan-Meier survival curves and log-rank tests were performed by four groups in patients with rectal cancer.

Results

This study enrolled 624 patients. 204 (32.7%) patients in the well-nourished non-visceral obesity (WN) group, 264 (42.3%) patients in the well-nourished visceral obesity (WO) group, 114 (18.3%) patients in the malnourished non-visceral obesity (MN) group, and 42 (6.7%) patients in the malnourished visceral obesity (MO) group. In the multivariate cox regression analysis, Charlson comorbidity index (CCI), MN and MO were associated with postoperative complications. In the multivariate cox regression analysis, age, American Society of Anesthesiologists (ASA), tumor differentiation, tumor node metastasis (TNM), and MO were associated with worse OS.

Conclusion

This study demonstrated that the MO group had the higher postoperative complication and mortality rate and was a good indicator of poor prognosis in patients with rectal cancer.

Visceral obesity

GLIM

Malnutrition

Rectal cancer

Adverse outcomes

Malnutrition posed a major global health. Nutritional status was an important prognostic factor for patients with cancer [1]. Patients with cancer-related malnutrition increased postoperative complications and mortality [2]. Malnutrition was estimated to be responsible for 10–20% of deaths in patients with cancer rather than the tumor itself, because the effect on the progression and therapeutic responses of cancer [3, 4]. Thus, the nutritional status of patients with cancer should be assessed, and nutritional interventions should be provided as necessary in the perioperative period.

Increasingly pervasive obesogenic lifestyles had led to an epidemic of obesity worldwide [5]. The disease of obesity was characterized by an increase in adiposity, which adversely affected people health, but it was typically defined by body mass index (BMI) [6]. It was impossible for BMI to differentiate between visceral, subcutaneous, intermuscular, and intramuscular adiposity, as it was a generalized measure and did not take into account the composition of each compartment of the body. Therefore, obese individuals were highly heterogenous [7, 8]. The term visceral obesity referred to the accumulation of visceral adipose tissue (VAT), which was considered a more reliable indicator of obesity than BMI. [9, 10]. It had been demonstrated that visceral obesity had a negative impact on outcomes of patients with cancer, including longer operative times, higher intraoperative blood loss, longer hospital stays, higher postoperative complication, and higher mortality [11, 12].

According to the World Health Organization (WHO), “a double burden of malnutrition” existed when undernutrition, obesity, or diet-related noncommunicable disease coexisted, which was a real and growing global health challenge [13]. There were some studies that explored the impact of malnutrition on obese patients’ outcomes, but BMI remained the only surrogate for obesity to be used as a measure. [14–16]. There was no study explored the relationship of malnutrition and visceral obesity in rectal cancer patients following radical proctectomy. Therefore, we aimed to investigate the value of malnutrition and visceral obesity in patients with rectal cancer.

2.1 Patients

This study included patients with rectal cancer who underwent proctectomy between February 2013 and March 2019 at the Anorectal Surgery Department of the Second Affiliated Hospital of Wenzhou Medical University. Inclusion criteria: (1) age ≥ 18 years, (2) American Society of Anesthesiologists (ASA) grade ≤ III, and (3) planned to receive curative proctectomy. Exclusion criteria: (1) available preoperative abdominal computed tomography (CT) beyond one month, (2) missed data, or (3) received palliative surgery or emergency surgery. The data collection protocol for this study was approved by the Ethics Committee of the Second Affiliated Hospital of Wenzhou Medical University (LCKY2020–209).

2.2 Data collection

Data was collected as following: 1) general features, including age, gender, BMI, skeletal muscle index (SMI), visceral fat area (VFA), Charlson comorbidity index (CCI) [17], ASA grade, and previous abdominal surgery; 2) clinicopathological features, including tumor size, tumor location, tumor differentiation, tumor stage, node stage, and pathological tumor node metastasis (TNM) stage; and 3) postoperative short-term and long-term outcomes, including postoperative complications (according to the Clavien–Dindo classification grade [18]), postoperative hospital stay and mortality.

2.3 Assessment of SMI and VFA

Using specialized imaging software (INFINITT Healthcare Co, Ltd), preoperative abdominal CT images at the level of third lumbar vertebra were obtained to calculate muscle mass. Muscle mass was defined as the area shown in − 29 to + 150 Hounsfield unit (HU). VFA was defined as the area shown in − 150 to − 50 HU. The SMI was calculated as the area of muscle mass divided by the square of the height (m). Low SMI were determined by our previous study, < 40.8 cm2/m2 for males or < 34.9 cm2/m2 for females [19]. Visceral obesity was defined as VFA ≥ 100 cm2 in both males and females [20].

2.4 Assessment of malnutrition

Malnutrition was diagnosed by a two-step model Global Leadership Initiative in Malnutrition (GLIM) [21]. First step, Nutritional Risk Screening 2002 (NRS 2002) ≥ 3 to identify individuals who at risk of malnutrition. Second step, malnutrition was defined as one of three phenotypic criteria [non-volitional weight loss, low BMI and reduced muscle mass] met, because patients in this study had already met the etiologic criterion (disease burden) [21]. Non-volitional weight loss was defined as weight loss > 5% within 6 months or > 10% beyond 6 months. Low BMI was defined that patients aged ≥ 70 years and BMI < 18.5 kg/m², or aged < 70 years and BMI < 20 kg/m². Reduced muscle mass was defined as low SMI.

2.5 Follow-up

Follow-up with patients via telephone was regularly conducted 1 month after surgery, every 3 months for 2 years, and every 6 months thereafter until death, or the end of the study in August 2022, or more than 8 years. Overall survival (OS) was calculated from the date of surgery until the date of death.

2.6 Statistical analysis

For continuous variables, mean and standard deviation (SD) or median and interquartile range (IQR) were shown. Analysis of variance or Kruskal-Wallis H tests were used to compare continuous variables. Categorical variables were presented as number and proportion. Chi-squared or Fisher’s exact tests were used to compare categorical variables. Univariate and multivariate logistic regression analysis evaluated the relationship between the factors and postoperative complications. Kaplan-Meier survival curves, log-rank tests, and univariate and multivariate cox regression analysis clarified the association between factors and OS. Multivariate analysis was conducted on factors with P < 0.10 in the univariate analysis. Two-sided P < 0.05 is statistically significant. SPSS 26.0 and R software (version 4.2.1, https://cran.r-project.org) were used.

This study enrolled 624 patients with rectal cancer. Based on the diagnostic criteria, 306 (49.0%) patients were identified as visceral obesity and 156 (25.0%) patients were identified as malnutrition. The patients were classified into four groups according to the nutritional status and visceral obesity: 204 (32.7%) patients in the well-nourished non-visceral obesity (WN) group, 264 (42.3%) patients in the well-nourished visceral obesity (WO) group, 114 (18.3%) patients in the malnourished non-visceral obesity (MN) group, and 42 (6.7%) patients in the malnourished visceral obesity (MO) group.

Table 1 showed the clinical characteristics of patients with rectal cancer in each group. Among the four groups, there were significant differences in age (P < 0.001), SMI (P < 0.001), VFA (P < 0.001), BMI (P < 0.001), CCI (P = 0.001), and ASA grade (P < 0.001). There were no significant differences in gender, previous abdominal surgery, tumor size, tumor location, tumor differentiation, tumor stage, node stage, and TNM stage among the four groups.

Table 1

The patients’ clinical characteristics
Characteristics	Overall (n = 624)	WN (n = 204)	WO (n = 264)	MN (n = 114)	MO (n = 42)	P
Age, median (IQR), years	65 (58–74)	61 (53–67)	65 (58–73)	72 (63–77)	71 (62–80)	< 0.001^*
Gender						0.084
Female	245 (39.3)	88 (43.1)	89 (33.7)	52 (45.6)	16 (38.1)
Male	379 (60.7)	116 (56.9)	175 (66.7)	62 (54.4)	26 (61.9)
SMI, mean (SD), cm²/m²	42.61 (8.53)	42.76 (7.67)	45.02 (8.31)	37.37 (7.37)	41.00 (10.27)	< 0.001^*
VFA, median (IQR), cm²	98.9 (54.7–142.1)	64.8 (39.1–83.7)	143.9 (120.0–185.1)	38.6 (18.7–67.7)	135.0 (108.5–160.8)	< 0.001^*
BMI						< 0.001^*
< 18.5	72 (11.5)	0 (0)	0 (0)	65 (57.0)	7 (16.7)
18.5–23.9	362 (58.0)	165 (80.9)	123 (46.6)	48 (42.1)	26 (61.9)
≥ 24	190 (30.5)	39 (19.1)	141 (53.4)	1 (0.9)	9 (21.4)
CCI						0.001^*
0	427 (68.4)	154 (75.5)	158 (59.8)	87 (76.3)	28 (66.7)
≥ 1	197 (31.6)	50 (24.5)	106 (40.2)	27 (23.7)	14 (33.3)
ASA grade						< 0.001^*
I	63 (10.1)	31 (15.2)	16 (6.1)	15 (13.1)	1 (2.4)
II	460 (73.3)	155 (76.0)	204 (77.3)	75 (65.8)	26 (61.9)
III	101 (16.2)	18 (8.8)	44 (16.6)	24 (21.1)	15 (35.7)
Previous abdominal surgery						0.396
No	563 (90.2)	187 (91.7)	240 (90.9)	98 (86.0)	38 (90.5)
Yes	61 (9.8)	17 (8.3)	24 (9.1)	16 (14.0)	4 (9.5)
Tumor size, median (IQR), cm	4.0 (3.0–5.0)	4.0 (3.0–5.0)	4.0 (3.0–5.0)	4.0 (3.3–5.5)	4.5 (4.0-5.6)	0.064
Tumor location						0.544
Upper	487 (78.0)	166 (81.4)	202 (76.5)	86 (75.4)	33 (78.6)
Lower	137 (22.0)	38 (18.6)	62 (23.5)	28 (24.6)	9 (21.4)
Tumor differentiation						0.646
Well differentiated	543 (87.0)	177 (86.8)	230 (87.1)	97 (85.1)	39 (92.9)
Poorly differentiated	81 (13.0)	27 (13.2)	34 (12.9)	17 (14.9)	3 (7.1)
Tumor stage						0.739
Tis, T1	57 (9.2)	15 (7.4)	28 (10.6)	11 (9.6)	3 (7.1)
T2	155 (24.8)	58 (28.4)	66 (25.0)	24 (21.1)	7 (16.7)
T3	345 (55.3)	109 (53.4)	143 (54.2)	65 (57.0)	28 (66.7)
T4	67 (10.7)	22 (10.8)	27 (10.2)	14 (12.3)	4 (9.5)
Node stage						0.621
N0	366 (58.7)	113 (55.4)	160 (60.6)	66 (57.9)	27 (64.3)
N1	155 (24.8)	57 (27.9)	62 (23.5)	25 (21.9)	11 (26.2)
N2	103 (16.5)	34 (16.7)	42 (25.9)	23 (20.2)	4 (9.5)
TNM stage						0.201
Tis, I	173 (27.7)	56 (27.5)	79 (29.9)	31 (27.2)	7 (16.7)
II	190 (30.5)	55 (27.0)	80 (30.3)	35 (30.7)	20 (47.6)
III	261 (40.8)	93 (45.5)	105 (39.8)	48 (42.1)	15 (35.7)
Values in parentheses are percentages.
^*Statistically significant (P < 0.05).
IQR, interquartile range; SD, standard deviation; SMI, skeletal muscle index; VFA, Visceral fat area; BMI, body mass index; CCI, Charlson Comorbidity Index; ASA, American Society of Anesthesiologists; TNM, tumor node metastasis; WN, well-nourished non-visceral obesity; WO, well-nourished visceral obesity; MN, malnourished non-visceral obesity; MO, malnourished visceral obesity.

Table 2 showed the details of postoperative complications and postoperative hospital stay. The overall postoperative complication rate was 23.2% (n = 145). The postoperative complication rate of WN group 17.6% (n = 36), WO group 21.2% (n = 56), MN group 28.9% (n = 33), and MO group 47.6% (n = 42) were significant differences (P < 0.001). There were no significant differences in the postoperative hospital stay among the four groups (P = 0.194).

Table 2

Postoperative outcomes
	Overall (n = 624)	WN (n = 204)	WO (n = 264)	MN (n = 114)	MO (n = 42)	P
^aTotal complications	145 (23.2)	36 (17.6)	56 (21.2)	33 (28.9)	20 (47.6)	< 0.001^*
^bSevere complications	23 (3.7)	6 (2.9)	9 (3.4)	5 (4.4)	3 (7.1)	0.583
Detail of complications
Surgical complications	69 (11.1)	22 (10.8)	30 (11.4)	10 (8.8)	7 (16.7)	0.575
Intra-abdominal infection	13 (2.1)	5 (2.5)	4 (1.5)	2 (1.8)	2 (4.8)	0.554
Wound infection	17 (2.7)	5 (2.5)	8 (3.0)	2 (1.8)	2 (4.8)	0.750
Bleeding	5 (0.8)	2 (1.0)	3 (1.1)	0 (0.0)	0 (0.0)	0.634
Anastomotic leakage	10 (1.6)	2 (1.0)	6 (2.3)	1 (0.9)	1 (2.4)	0.616
Ileus	8 (1.3)	3 (1.5)	3 (1.1)	2 (1.8)	0 (0.0)	0.838
Gastrointestinal dysfunction	11 (1.8)	2 (1.0)	4 (1.5)	3 (2.6)	2 (4.8)	0.332
Ureteral fistula	2 (0.3)	1 (0.5)	1 (0.4)	0 (0.0)	0 (0.0)	0.870
Urinary retention	3 (0.5)	2 (1.0)	1 (0.4)	0 (0.0)	0 (0.0)	0.599
Medical complications	76 (12.2)	14 (6.9)	26 (9.8)	23 (20.2)	13 (31.0)	< 0.001^*
Pulmonary complications	7 (1.1)	3 (1.5)	3 (1.1)	0 (0.0)	1 (2.4)	0.548
Cardiac complications	5 (0.8)	0 (0.0)	1 (0.4)	1 (0.9)	3 (7.1)	< 0.001^*
Anemia	11 (1.8)	2 (1.0)	3 (1.1)	5 (4.4)	1 (2.4)	0.114
Persistent hypoalbuminemia	15 (2.4)	0 (0.0)	5 (1.9)	6 (5.3)	4 (9.5)	< 0.001^*
Cerebral infarction	2 (0.3)	0 (0.0)	0 (0.0)	2 (1.8)	0 (0.0)	0.030^*
Venous thrombosis	6 (1.0)	3 (1.5)	3 (1.1)	0 (0.0)	0 (0.0)	0.541
Sepsis	3 (0.5)	0 (0.0)	1 (0.4)	2 (1.8)	0 (0.0)	0.164
Hyperthermia	6 (1.0)	1 (0.5)	2 (0.8)	2 (1.8)	1 (2.4)	0.526
Urinary infection	21 (3.4)	5 (2.5)	8 (3.0)	5 (4.4)	3 (7.1)	0.420
Clavien-Dindo grade
I	13 (2.1)	8 (3.9)	4 (1.5)	1 (0.9)	0 (0.0)	0.138
II	122 (19.6)	30 (14.7)	47 (17.8)	28 (24.6)	17 (40.5)	0.001^*
III	15 (2.4)	6 (2.9)	6 (2.3)	3 (2.6)	0 (0.0)	0.722
IV	7 (1.1)	0 (0.0)	3 (1.1)	2 (1.8)	2 (4.8)	0.052
V	1 (0.2)	0 (0.0)	0 (0.0)	0 (0.0)	1 (2.4)	0.144
Postoperative hospital stay, median (IQR), days	16.4 (14.0–20.0)	16.4 (14.0–20.0)	16.0 (14.0–20.0)	16.4 (14.0–20.0)	17.7 (15.0–21.8)	0.194
Values in parentheses are percentages.
^*Statistically significant (P < 0.05).
^aTotal complications were defined as any adverse event corresponding to Clavien–Dindo grade II or higher, occurring within 30 days after surgery. If a patient had more than one type of complication, the complication with the highest grade was recorded.
^bSevere complications were defined as Clavien–Dindo grade III or higher.
WN, well-nourished non-visceral obesity; WO, well-nourished visceral obesity; MN, malnourished non-visceral obesity; MO, malnourished visceral obesity.

Table 3 showed the univariate and multivariate logistic regression analysis for factors associated with postoperative complications. In the multivariate analysis, CCI (odds ratio [OR]: 2.343, 95% confidence interval [CI]: 1.573–3.491; P < 0.001), MN (OR: 1.952, 95% CI: 1.126–3.383; P = 0.017) and MO (OR: 4.112, 95% CI: 2.007–8.426; P < 0.001) were associated with postoperative complications.

Table 3

Univariate and multivariate logistic regression analysis for factors associated with ^atotal postoperative complications.
	Univariate analysis	Multivariate analysis
Tools	HR (95% CI) P	HR (95% CI) P
Age
≥ 65/<65	1.488 (1.021–2.169) 0.039^*
Gender
Male/ Female	0.925 (0.634–1.352) 0.668
BMI
< 18.5/18.5–23.9	2.317 (1.356–3.960) 0.002^*
≥ 24/18.5–23.9	0.940 (0.610–1.449) 0.780
CCI
≥ 1/0	2.219 (1.512–3.257) <0.001^*	2.343 (1.573–3.491) <0.001^*
ASA grade
III/ I, II	1.781 (1.118–2.836) 0.015^*
Previous abdominal surgery
Yes/No	1.569 (0.881–2.794) 0.126
Tumor size
> 4/≤4	1.018 (0.700–1.481) 0.925
Tumor location
Lower/ Upper	0.907 (0.575–1.431) 0.674
Tumor differentiation
Poorly/Well	1.368 (0.811–2.309) 0.240
TNM stage
II/ Tis, I	0.983 (0.591–1.635) 0.947
III/ Tis, I	1.395 (0.882–2.205) 0.155
Malnourished obesity
WO/WN	1.256 (0.789–2.001) 0.336
MN/WN	1.901 (1.106–3.268) 0.020^*	1.952 (1.126–3.383) 0.017^*
MO/WN	4.242 (2.097–8.581) <0.001^*	4.112 (2.007–8.426) <0.001^*
^*Statistically significant (P < 0.05).
^aTotal postoperative complications were defined as any adverse event corresponding to Clavien–Dindo grade II or higher, occurring within 30 days after surgery. If a patient had more than one type of complication, the complication with the highest grade was recorded.
BMI, body mass index; CCI, Charlson Comorbidity Index; ASA, American Society of Anesthesiologists; TNM, tumor node metastasis; WN, well-nourished non-visceral obesity; WO, well-nourished visceral obesity; MN, malnourished non-visceral obesity; MO, malnourished visceral obesity.

The median follow-up time was 4.95 (3.38–6.72) years. There were 133 deaths (21.3%) during follow-up. Table 4 showed the univariate and multivariate cox regression analysis for factors associated with OS. In the multivariate cox regression analysis, age (OR: 2.029, 95% CI: 1.358–3.034; P = 0.001), ASA grade (OR: 1.989, 95% CI: 1.350–2.929; P < 0.001), tumor differentiation (OR: 2.075, 95% CI: 1.336–3.223; P = 0.001), TNM stage (II vs Tis, I OR: 2.243, 95% CI: 1.222–4.118; P = 0.009; III vs Tis, I, OR: 2.243, 95% CI: 2.310–7.003, P < 0.001), and MO (OR: 2.615, 95% CI: 1.404–4.871; P < 0.001) were associated with worse OS. Figure 1. showed the Kaplan-Meier curves for OS by the nutritional status and visceral obesity in patients with rectal cancer.

Table 4

Univariate and multivariate Cox regression analysis for factors associated with overall survival.
	Univariate analysis	Multivariate analysis
Tools	HR (95% CI) P	HR (95% CI) P
Age
≥ 65/<65	2.650 (1.818–3.863) <0.001^*	2.029 (1.358–3.034) 0.001^*
Gender
Male/ Female	0.883 (0.626–1.246) 0.478
BMI
< 18.5/18.5–23.9	1.285 (0.796–2.075) 0.305
≥ 24/18.5–23.9	0.635 (0.421–0.971) 0.036^*
CCI
≥ 1/0	1.308 (0.917–1.864) 0.138
ASA grade
III/ I, II	2.767 (1.928–3.972) <0.001^*	1.989 (1.350–2.929) <0.001^*
Previous abdominal surgery
Yes/No	1.148 (0.671–1.965) 0.614
Tumor size
> 4/≤4	1.241 (0.883–1.746) 0.213
Tumor location
Lower/ Upper	1.082 (0.727–1.610) 0.699
Tumor differentiation
Poorly/Well	2.324 (1.521–3.550) <0.001^*	2.075 (1.336–3.223) 0.001^*
TNM stage
II/ Tis, I	2.469 (1.352–4.511) 0.003^*	2.243 (1.222–4.118) 0.009^*
III/ Tis, I	4.384 (2.528–7.603) <0.001^*	4.022 (2.310–7.003) <0.001^*
Malnourished
WO/WN	1.282 (0.822–2.000) 0.273
MN/WN	1.961 (1.192–3.227) 0.008^*
MO/WN	3.759 (2.123–6.657) <0.001^*	2.615 (1.404–4.871) 0.002^*
^*Statistically significant (P < 0.05).
BMI, body mass index; CCI, Charlson Comorbidity Index; ASA, American Society of Anesthesiologists; TNM, tumor node metastasis; WN, well-nourished non-visceral obesity; WO, well-nourished visceral obesity; MN, malnourished non-visceral obesity; MO, malnourished visceral obesity.

To our knowledge, this is the first study to investigate the impact of visceral obesity and GLIM-defined malnutrition in patients with rectal cancer on short-term and long-term outcomes. This study demonstrated that patients with both visceral obesity and GLIM-defined malnutrition were more likely to suffer postoperative complications and poorer OS.

GLIM, the universal malnutrition criteria, was used in this study. The prevalence of GLIM-defined malnutrition was 25.0% in the present study. GLIM consisted of three phenotypic criteria and two etiologic criteria. In the present study, cancer diagnosis was used as the etiologic criteria. GLIM-defined malnutrition was diagnosed when at least one phenotypic criterion was met. Non-volitional weight loss as a phenotypic criterion was the most traditional indicator of malnutrition and was present in almost any malnutrition screening tools [21]. Skeletal muscle mass had attracted much attention in recent years. Many articles demonstrated the correlation between muscle mass and survival [19, 22]. When alternative phenotypic criteria were met, malnutrition may also be diagnosed despite high BMI values when other phenotypic criteria were met. Thus, the GLIM criteria offer a major conceptual advance in diagnosing malnutrition even in cases of high BMI and adiposity. Different combinations of phenotypic and etiologic criteria allowed for a wide range of applicability of GLIM [21]. Previous articles pointed out that GLIM-defined malnutrition was a predictor of OS [23, 24]. Therefore, we used GLIM to evaluate the nutritional status.

While BMI was a convenient way to measure body weight, it failed to distinguish between body composition, body fat distribution, fluid accumulation, and absolute weight gain, among other factors [8, 25]. Therefore, visceral obesity was used in this study and the prevalence of visceral obesity was 50.8%. Many trials had demonstrated that visceral obesity was associated with longer operative times, higher blood loss during surgery, longer postoperative hospital stays and higher postoperative complication rate after elective colorectal surgery [10, 11]. Furthermore, visceral obesity showed no influence on OS but was significantly associated with disease-free survival in patients with resectable colorectal cancer [12]. However, it had not been verified in this article, which may be caused by our separation of the MO group from total visceral obesity group.

It was no surprise that TNM stage and tumor differentiation correlated with survival. In this study, CCI was a significant predictor of postoperative complication, but showed no influence on OS. Furthermore, ASA index was a significant predictor of OS, but showed no influence on postoperative complication. It showed that preoperative comorbidity or not were related to postoperative complications, while preoperative comorbidity that limited activity was related to OS. Patients with low BMI had an effect on postoperative complications in the univariate analysis but did not appear in the multivariate analysis. The possible reason for this was that low BMI was one of the indicators for the diagnosis of malnutrition and was excluded as a confounding factor in the multivariate analysis. It had been reported that the association between BMI and colorectal cancer survival is U-shaped or J-shaped [26]. In our study, high BMI was a protective factor in the survival univariate analysis and was excluded in the multivariate analysis. In this study, MN group was a protective factor for OS in the univariate analysis and was excluded in the multivariate analysis. It may be caused by the fact that we cut out the MO group from the malnutrition group.

With the growth of urbanization and industrialization in Asia, food of low nutritional value was easily available and affordable, while sedentary lifestyles were promoted, contributing to the double burden of obesity and malnutrition [13]. Our study showed that visceral obesity and malnutrition were well evaluated by CT and GLIM. How to intervene in it is also a challenge. Previous studies have pointed out that preventing malnutrition should be a part of the obesity medical care plan, which included a lifestyle change, making the right food choices and avoiding unhealthy foods [27]. Fortified foods, such as specific vitamin supplements, should also be included to prevent shortages. It was not widely accepted that multi-model pre-habilitation should include exercise therapy, nutritional supplementation, and hematologic optimization before surgery [28]. Further research was needed to explain the factors shaping MO in patients, as well as appropriate treatment strategies.

This study has some limitations that should be considered. Firstly, despite our attempts to minimize confounding factors, the retrospective nature of our analysis posed a risk of selection bias. Secondly, we are supposed to keep in mind whether our cut-points are appropriate to define low SMI and visceral obesity. Several studies have established definitions for low SMI and visceral obesity according to different criteria [11, 29, 30]. Due to the lack of a uniform threshold for CT measured low SMI and visceral obesity, we applied cutoff values for SMI based on our previous study [19] and cutoff values for visceral obesity based on Japanese research [20]. There still remained the need for definitive criteria of CT measured low SMI and visceral obesity for Asian. Finally, this was a single-center study among Chinese patients with rectal cancer, which may not be applicable to other ethnic populations and regions. In the future, a multicenter prospective study in different populations is required to validate our findings.

In conclusion, this study demonstrated that the MO group had the higher postoperative complication and mortality rate and was a good indicator of poor prognosis in patients with rectal cancer. According to our findings, malnutrition and visceral obesity had a double burden in patients with rectal cancer, and health lifestyle programs and policies were required to promote nutritional health and reduce visceral obesity.

Author contributions

Conceptualization: Chen-Guo Zheng and Xi-Yi Chen, Methodology: Jie-Yu Liu, Formal analysis and investigation: Zhong-Lin Wang and Xi-Yi Chen, Writing - original draft preparation: Yi Lin and Chong-Jun Zhou, Writing - review and editing: Chen-Guo Zheng.

Funding

This study was funded by the National Natural Science Foundation of China (grant numbers 82274530).

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee of the Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University (LCKY2020–209).

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study

Conflicts of interest

The authors declare that they have no conflict of interest.

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No competing interests reported.

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Malnutrition and visceral obesity predict adverse short-term and long-term outcomes in patients undergoing proctectomy for rectal cancer

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Abstract

Background

Methods

Results

Conclusion

Figures

1. Introduction

2. Material & Methods

2.1 Patients

2.2 Data collection

2.3 Assessment of SMI and VFA

2.4 Assessment of malnutrition

2.5 Follow-up

2.6 Statistical analysis

3. Results

4. Discussion

5. Conclusions

Declarations

References

Additional Declarations

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