Published online May 27, 2026. doi: 10.4240/wjgs.v18.i5.116822
Revised: December 23, 2025
Accepted: February 10, 2026
Published online: May 27, 2026
Processing time: 188 Days and 0.1 Hours
Visceral adiposity is implicated in colorectal cancer (CRC) progression and me
To investigate the impact of VFA on perioperative SML and its prognostic value in CRC patients.
A retrospective cohort of 389 CRC patients undergoing radical surgery was analyzed. VFA and skeletal muscle index (SMI) at the L3 level were quantified via preoperative and postoperative computed tomography scans. Multivariate logis
High VFA (≥ 100 cm2) was an independent risk factor for SML [odds ratio (OR) = 2.557, 95% confidence interval (CI): 1.518-4.305, P < 0.001] and independently predicted shorter overall survival (hazard ratio = 1.492, 95%CI: 1.069-2.208, P = 0.019) and relapse-free survival (hazard ratio = 1.638, 95%CI: 1.198-2.240, P = 0.002). Elevated preoperative SMI paradoxically increased SML susceptibility (OR = 1.082, P < 0.001), whereas higher body mass index reduced risk (OR = 0.846, P = 0.003). The high-VFA group exhibited greater intraoperative blood loss (median 100 mL vs 80 mL, P = 0.001) and prolonged hospitalization (median 16 days vs 14 days, P = 0.001). Kaplan-Meier analysis confirmed worse survival outcomes in patients with high VFA or SML.
Visceral obesity exacerbates perioperative muscle wasting and worsens long-term prognosis in CRC patients. Dynamic computed tomography monitoring of VFA and SMI trajectories offers actionable insights for personalized metabolic interventions, particularly in sarcopenic obesity.
Core Tip: This study identifies high visceral fat area as an independent driver of perioperative skeletal muscle loss and poor survival in colorectal cancer. Using dynamic computed tomography monitoring, we demonstrate that visceral obesity exacerbates muscle wasting through chronic inflammation. Paradoxically, higher preoperative muscle mass increased skeletal muscle loss risk, potentially indicating underlying myosteatosis. These findings redefine the fat-muscle axis in cancer prognosis and highlight the critical need for preoperative body composition assessment to guide metabolic interventions and improve long-term outcomes.
- Citation: Wang Y, Bu J, Zhan CY, Xu DL, Hu JQ, Zhang MH, Zhu KJ, Qi Y. Visceral obesity exacerbates perioperative muscle loss and impairs survival in colorectal cancer. World J Gastrointest Surg 2026; 18(5): 116822
- URL: https://www.wjgnet.com/1948-9366/full/v18/i5/116822.htm
- DOI: https://dx.doi.org/10.4240/wjgs.v18.i5.116822
Colorectal cancer (CRC) remains the third most common malignancy globally, with a rising burden evidenced by the latest GLOBOCAN estimates projecting over 2.2 million new cases and 1.1 million deaths in 2024[1]. Body mass index (BMI) ≥ 30 kg/m2 is a well-established risk factor, and the sarcopenic obesity phenotype - characterized by coexisting visceral fat area (VFA) and skeletal muscle loss (SML) - synergistically drives CRC progression through mechanisms involving chronic inflammation and insulin resistance[2,3]. Clinical evidence underscores the prognostic relevance of visceral adiposity: Patients with high VFA exhibit reduced response rates to bevacizumab-based chemotherapy (progression-free survival: 9 months vs 12 months)[4], while gastric cancer patients with low visceral fat index demonstrate significantly shorter overall survival (OS) following surgery[5]. These findings highlight the critical role of visceral fat metabolic activity in shaping tumor outcomes.
SML, a hallmark of cancer cachexia, forms a deleterious feedback loop with VFA: Visceral adipose tissue secretes pro-inflammatory cytokines [e.g., interleukin (IL)-6, tumor necrosis factor (TNF)-α], activating the ubiquitin-proteasome system and accelerating muscle proteolysis[6,7]. Conversely, reduced muscle mass impairs insulin-mediated glucose uptake, exacerbating metabolic dysregulation[8,9]. Although both preoperative skeletal muscle index (SMI) and VFA are recognized as independent predictors of CRC survival[10], a critical gap exists in understanding their dynamic interplay during the high-stress perioperative period and the consequent impact on accelerated postoperative muscle wasting.
Crucially, prior research has predominantly relied on static, single-timepoint body composition assessments[9-11], overlooking the temporal evolution of the metabolic microenvironment surrounding surgery. This study employed computed tomography (CT) imaging to quantitatively track VFA and SMI changes, aiming to address two key questions: (1) Does visceral obesity independently drive postoperative SML? and (2) How do dynamic interactions between VFA and SML influence survival prognosis? By elucidating the dynamic interplay between VFA and SML trajectories and their collective impact on survival, this study aims to fill a significant evidence gap and inform targeted perioperative metabolic interventions.
Patient enrollment: Patients who underwent radical resection for CRC at the Department of General Surgery, Chengdu Second People’s Hospital, between December 2014 and December 2020 were retrospectively enrolled.
Inclusion criteria: (1) Underwent radical CRC resection at the department during the study period; (2) Aged ≥ 18 years; (3) Pathologically confirmed CRC diagnosis post-surgery; (4) Underwent abdominal CT scanning at our institution within 2 weeks prior to surgery; (5) Complete hospitalization records with no missing clinical or laboratory data; and (6) Provided written informed consent for the use of their medical records in non-profit scientific research.
Exclusion criteria: (1) Patients undergoing emergency surgery; (2) CT images with significant artifacts/noise precluding software analysis; (3) Concurrent other malignancies; (4) Severe comorbidities (e.g., cardiac, pulmonary, or cerebrovascular diseases) or major organ dysfunction; (5) Prior neoadjuvant chemoradiotherapy[12]; (6) Preoperative BMI ≥ 30 kg/m2 (diagnosed obesity)[13]; (7) Pre-existing severe autoimmune disorders; (8) Concurrent hyperthyroidism, hypothyroidism, tuberculosis, or other conditions affecting basal metabolism; (9) Uncontrolled concurrent psychiatric disorders or lack of full legal capacity; and (10) Pre-existing sarcopenia, paralysis, long-term bedridden status, or inability to perform self-care.
Ethical approval: This study was approved by the Ethics Committee of Chengdu Second People’s Hospital, Approval No. [KY]PJ2024015. The data were de-identified. Data were systematically collected through multiple standardized approaches.
CT scanning parameters: Abdominal non-contrast CT scans were performed using a Siemens[5] 256-slice CT scanner (SENSATION 256; Siemens Healthiness, Germany). The use of non-contrast CT ensures consistent HU values for tissue segmentation, as intravenous contrast agents can significantly alter HU measurements and affect the accuracy of body composition quantification. Scanning parameters were standardized as follows: Tube voltage, 120 kVp; automatic tube current modulation (100-250 mA); slice thickness, 3 mm; reconstruction thickness, 1 mm; pitch, 0.8; matrix size, 512 × 512. The scanning field extended from the xiphoid process to the pubic symphysis.
Patient preparation: Patients fasted for 6 hours prior to scanning. Imaging was conducted with patients in the supine position, arms raised above the head, and images were acquired during end-inspiratory breath-holding to minimize motion artifacts.
Delineation: Axial CT images at the L3 vertebral level were analyzed using ImageJ software[14] (National Institutes of Health, NIH, United States). Tissue segmentation was performed using HU thresholds: Skeletal muscle area was defined as -29 HU to 150 HU, and VFA as -150 HU to -50 HU[6,15]. Two independent radiologists, each with over three years of experience in body composition analysis, manually delineated the regions of interests. Prior to the study, both operators underwent standardized training using a set of 20 reference CT images to ensure consistent application of HU thresholds. Each measurement was performed in triplicate, and the average value was used for analysis. Inter-observer reliability was assessed using the intraclass correlation coefficient, which showed excellent agreement for both skeletal muscle area and VFA (intraclass correlation coefficient > 0.9; Figure 1).
Data standardization and variable definitions: Key anthropometric and imaging parameters were standardized as follows: SMI: Calculated as skeletal muscle area divided by height squared (cm2/m2)[16-18]. Visceral-to-skeletal muscle ratio: Defined as VFA (cm2) divided by SMI, multiplied by 100 (%cm2/m2)[15]. Visceral obesity: Defined by a VFA threshold of ≥ 100 cm2[16]. Sarcopenia: Diagnosed using gender-specific SMI cutoffs (male: < 52.4 cm2/m2; female: < 38.5 cm2/m2)[17,18]. SML: Defined as a postoperative decline in SMI of ≥ 5% within 6 months[19]. The percentage of SML was calculated as: ΔSMI% = [(postoperative SMI - preoperative SMI)/preoperative SMI] × 100%.
Data collection: Baseline and perioperative data were systematically collected, including: Demographics: Age, sex, BMI. Comorbidities: Cardiovascular disease, chronic obstructive pulmonary disease, diabetes mellitus. Tumor characteristics: Tumor location, histological grade, Tumor-node-metastasis stage, lymphovascular invasion, perineural invasion. Perioperative variables: Surgical type (e.g., laparoscopic vs open), operation time, colostomy creation. Short-term outcomes: Postoperative complications (non-infectious: Anastomotic bleeding, ileus; infectious: Wound infection, intra-abdominal infection, pneumonia, urinary tract infection, anastomotic leak), flatus passage time, and length of hospital stay.
Primary endpoint: OS, defined as the time from surgery to death from any cause or the last follow-up visit.
Secondary endpoints: Relapse-free survival (RFS), defined as the time from surgery to tumor recurrence, metastasis, or last follow-up. Incidence of 30-day postoperative complications (as defined above).
Continuous variables were presented as mean ± SD or median (interquartile range). Normality of distribution was assessed using the Shapiro-Wilk test. Homogeneity of variances was evaluated with Levene’s test. For comparisons between two groups, the independent Student’s t-test was used for normally distributed data with equal variance; otherwise, the Mann-Whitney U test was applied. Categorical variables were compared using the χ2 test or Fisher’s exact test, as appropriate. Correlation analysis was performed using Spearman’s rank correlation test. Univariate and multivariate logistic regression analyzes were used to identify independent risk factors for SML. Survival analysis was conducted using Cox proportional hazards models, with survival curves visualized via Kaplan-Meier method (SPSS version 26.0 software). All statistical tests were two-sided, with a significance level set at P < 0.05.
A total of 495 patients were initially screened. Following application of inclusion and exclusion criteria, 389 patients who underwent radical resection for CRC were included in this retrospective cohort study. Baseline characteristics of the study population are summarized in Table 1 The cohort comprised 229 (58.87%) males and 160 (41.13%) females, with 187 (48.07%) patients aged > 65 years. Preoperative visceral obesity (VFA ≥ 100 cm2) was present in 214 (55.01%) patients, and preoperative sarcopenia (defined by gender-specific SMI cutoffs) was present in 147 (37.79%) patients. The patient enrollment flowchart is shown in Figure 2.
| Feature | All patients (n = 389) |
| Gender | |
| Male | 229 (58.87) |
| Female | 160 (41.13) |
| Age% | |
| ≤ 65 | 202 (51.93) |
| > 65 | 187 (48.07) |
| BMI (kg/m2) | 22.80 ± 3.17 |
| Cardiovascular disease | |
| None | 281 (72.24) |
| Present | 108 (27.76) |
| Respiratory disease | |
| None | 313 (80.46) |
| Present | 76 (19.54) |
| Diabetes | |
| None | 320 (82.26) |
| Present | 69 (17.74) |
| VFA (cm2) | 116.36 ± 62.98 |
| Visceral obesity, VFA ≥ 100 cm2 | |
| Yes | 214 (55.01) |
| No | 175 (44.99) |
| VSR (%, cm2/m2) | 2.64 ± 1.36 |
| Preoperative SMI (cm2/m2) | 43.92 ± 7.89 |
| Preoperative sarcopenia | |
| Yes | 147 (37.79) |
| No | 242 (62.21) |
| Postoperative SMI | 44.23 ± 8.56 |
| Change value (SMI%) | 0.011 ± 0.116 |
| SML% (ΔSMI ≥ -5%) | |
| No | 272 (69.92) |
| Yes | 117 (30.08) |
| Serum total protein(g/L) | 69.67 ± 7.33 |
| Preoperative albumin(g/L) | 41.24 ± 5.20 |
| Globulin level (g/L) | 28.41 ± 5.26 |
| Hemoglobin (g/L) | 120.60 ± 26.52 |
| Platelets (× 109/L) | 226.77 ± 91.14 |
| Neutrophil count (× 109/L) | 4.869 ± 2.34 |
| Lymphocyte count (× 109/L) | 1.436 ± 0.534 |
| Tumor location | |
| Sigmoid colon | 43 (11.05) |
| Colon | 95 (24.42) |
| Rectum | 251 (64.52) |
| Differentiation degree | |
| Low | 90 (23.14) |
| Moderate | 270 (69.41) |
| High | 29 (7.46) |
| ASA classification | |
| < III | 203 (52.19) |
| ≥ III | 186 (47.81) |
| Vascular nerve invasion | |
| None | 238 (61.18) |
| Present | 151 (38.82) |
| pTMN stage | |
| Stage I | 46 (11.83) |
| Stage II | 188 (48.33) |
| Stage III | 155 (39.85) |
| Postoperative adjuvant chemotherapy | |
| None | 208 (53.47) |
| Present | 181 (46.53) |
| Surgical approach | |
| Open | 190 (48.84) |
| Laparoscopic | 199 (51.16) |
| Intraoperative blood loss (mL) | 109.82 ± 105.65 |
| Operation time (minutes) | 215.21 ± 75.81 |
| Prophylactic ileostomy status | |
| None | 322 (82.78) |
| Present | 67(17.22) |
| LOS (days) | 15.00 (13.00-19.00) |
| Number of lymph nodes dissected (n) | 10.43 ± 10.00 |
| FPT (time to flatus, days) | 3.00 (2.00-3.00) |
| Complications | |
| None | 286 (73.52) |
| Present | 103 (26.48) |
| Postoperative infectious complications | |
| No | 298 (76.61) |
| Yes | 91 (23.39) |
| Postoperative non-infectious complications | |
| No | 360 (92.54) |
| Yes | 29 (7.46) |
Patients were stratified into high VFA (VFA ≥ 100 cm2, n = 214) and low VFA (VFA < 100 cm2, n = 175) groups. Comparative analysis revealed significant intergroup differences (Table 2). The high VFA group was older (P = 0.023), had higher BMI (P < 0.001), elevated visceral-to-skeletal muscle ratio (VSR; P < 0.001), and higher preoperative and postoperative SMI (both P < 0.001). Notably, the prevalence of preoperative sarcopenia was significantly higher in the high VFA group (43.93% vs 30.29%, P = 0.006), and the relative change in SMI (ΔSMI%) was significantly lower (median:
| Feature | SML | VFA | ||||
| No (n = 272) | Yes (n = 117) | P value | Low (n = 175) | High (n = 214) | P value | |
| Gender | 0.801 | 0.062 | ||||
| Male | 159 (58.46) | 70 (59.83) | 94 (53.71) | 135 (63.08) | ||
| Female | 113 (41.54) | 47 (40.17) | 81 (46.29) | 79 (36.92) | ||
| Age | 0.542 | 0.023 | ||||
| ≤ 65 | 144 (52.94) | 58 (49.57) | 102 (58.29) | 100 (46.73) | ||
| > 65 | 128 (47.06) | 59 (50.43) | 73 (41.71) | 114 (53.27) | ||
| BMI (kg/m2) | 22.74 ± 3.11 | 22.92 ± 3.30 | 0.623 | 21.34 ± 2.90 | 24.00 ± 2.87 | 0.000 |
| Cardiovascular disease | 0.905 | 0.001 | ||||
| None | 196 (72.06) | 85 (72.65) | 141 (80.57) | 140 (65.42) | ||
| Yes | 76 (27.94) | 32 (27.35) | 34 (19.43) | 74 (34.58) | ||
| Respiratory disease | 0.811 | 0.835 | ||||
| None | 218 (80.15) | 95 (81.20) | 140 (80.00) | 173 (80.84) | ||
| Yes | 54 (19.85) | 22 (18.80) | 35 (20.00) | 41 (19.16) | ||
| Diabetes | 0.943 | 0.000 | ||||
| None | 224 (82.35) | 96 (82.05) | 158 (90.29) | 162 (75.70) | ||
| Yes | 48 (17.65) | 21 (17.95) | 17 (9.71) | 52 (24.30) | ||
| VFA (cm2) | 99.45 (66.08-143.54) | 134.0 (81.30-174.40) | 0.001 | 66.27 (44.26-85.52) | 148.965 (126.16-181.58) | 0.000 |
| Preoperative SMI (cm2/m2) | 42.79 (37.7-47.2) | 45.09 (40.7-51.3) | 0.004 | 40.56 (36.11-45.60) | 45.035 (41.47-50.27) | 0.000 |
| Preoperative Sarcopenia | 0.026 | 0.006 | ||||
| Yes | 179 (65.81) | 63 (53.85) | 122 (69.71) | 120 (56.08) | ||
| No | 93 (34.19) | 54 (46.15) | 53 (30.29) | 94 (43.93) | ||
| Postoperative SMI (cm2/m2) | 45.41 (40.4-50.1) | 39.92 (35.0-45.4) | 0.000 | 42.45 (37.21-47.66) | 45.20 (40.06-49.65) | 0.001 |
| ΔSMI (%) | 0.04 (0.00-0.11) | -0.10 (-0.15 to -0.08) | 0.000 | 0.03 (-0.04 to 0.10) | -0.01 (-0.09 to 0.06) | 0.001 |
| VSR (%, cm2/m2) | 2.524 ± 1.295 | 2.898 ± 1.465 | 0.012 | 1.53 (1.02-1.95) | 3.25 (2.77-4.04) | 0.000 |
| Preoperative albumin (g/L) | 41.60 (37.4-44.7) | 42.70 (38.7-45.7) | 0.047 | 42.30 (37.70-45.10) | 41.50 (37.38-44.83) | 0.308 |
| Serum total protein (g/L) | 69.38 ± 7.62 | 70.34 ± 6.58 | 0.236 | 70.40 (65.40-75.40) | 69.95 (64.325-74.70) | 0.476 |
| Globulin level (g/L) | 28.250 (24.8-31.5) | 28.30 (25.6-31.2) | 0.798 | 28.40 (24.80-31.00) | 28.20 (25.08-31.70) | 0.875 |
| Hemoglobin (g/L) | 126.50 (105.0-140.0) | 130.00 (105.0-142.0) | 0.565 | 126.00 (105.00-140.00) | 128.50 (104.75-140.00) | 0.962 |
| Platelet (× 109/L) | 216.50 (167.3-278.8) | 204.00 (151.5-251.5) | 0.065 | 215.00 (168.00-277.00) | 210.00 (159.25-262.25) | 0.59 |
| Neutrophil count (× 109/L) | 4.43 (3.3-5.9) | 4.20 (3.2-5.6) | 0.234 | 4.40 (3.370-5.40) | 4.35 (3.192-5.92) | 0.687 |
| Lymphocyte count (× 109/L) | 1.380 (1.0-1.8) | 1.40 (1.0-1.8) | 0.686 | 1.34 (1.00-1.75) | 1.42 (1.07-1.79) | 0.161 |
| Tumor site | 0.923 | 0.818 | ||||
| Sigmoid colon | 30 (11.03) | 13 (11.11) | 18 (10.29) | 25 (11.68) | ||
| Rectum | 67 (24.63) | 28 (23.93) | 45 (25.71) | 50 (23.36) | ||
| Colon | 175 (64.34) | 76 (64.96) | 112 (64.00) | 139 (64.95) | ||
| Differentiation degree | 0.24 | 0.84 | ||||
| Low | 59 (21.69) | 31 (26.50) | 39 (22.29) | 51 (23.83) | ||
| Medium | 191 (70.22) | 79 (67.52) | 124 (70.86) | 146 (68.22) | ||
| High | 22 (8.09) | 7 (5.98) | 12 (6.86) | 17 (7.94) | ||
| Vascular nerve invasion | 0.206 | 0.091 | ||||
| None | 172 (63.24) | 66 (56.41) | 99 (56.57) | 139 (64.95) | ||
| Yes | 100 (36.76) | 51 (43.59) | 76 (43.43) | 75 (35.05) | ||
| Pathological grade (pTNM) | 0.096 | 0.063 | ||||
| Stage I | 33 (12.13) | 13 (11.11) | 19 (10.86) | 27 (12.62) | ||
| Stage II | 139 (51.10) | 49 (41.88) | 96 (54.86) | 92 (42.99) | ||
| Stage III | 100 (36.76) | 55 (47.01) | 60 (34.29) | 95 (44.39) | ||
| Postoperative adjuvant chemotherapy | 0.589 | 0.003 | ||||
| None | 100 (36.76) | 55 (47.01) | 79 (45.14) | 129 (60.28) | ||
| Yes | 129 (47.43) | 52 (44.44) | 96 (54.86) | 85 (39.72) | ||
| Surgical approach | 0.36 | 0.260 | ||||
| Open | 137 (50.37) | 53 (45.30) | 91 (52.00) | 99 (46.26) | ||
| Laparoscopic | 135 (49.63) | 64 (54.70) | 84 (48.00) | 115 (53.74) | ||
| Prophylactic ileostomy status | 0.047 | 0.598 | ||||
| None | 231 (85.24) | 90 (76.92) | 142 (81.61) | 179 (83.65) | ||
| Yes | 40 (14.76) | 27 (23.08) | 32 (18.39) | 35 (16.36) | ||
| Intraoperative blood loss (mL) | 100.00 (50.00-100.00) | 100.00 (50.00-100.00) | 0.569 | 0.947 | ||
| Operation time (minutes) | 200.00 (165.00-240.00) | 205.00 (165.00-257.50) | 0.735 | 91 (52.00) | 112 (52.34) | |
| ASA classification | 0.815 | 84 (48.00) | 102 (47.66) | |||
| < III | 143 (52.57) | 60 (51.28) | 80.0 (50.0-100.0) | 100.0 (50.0-150.00) | 0.001 | |
| ≥ III | 129 (47.43) | 57 (48.72) | 200 (165.0-245.0) | 204. (165.00-245.25) | 0.452 | |
| LOS (days) | 15.00 (13.00-19.00) | 15.00 (13.00-20.00) | 0.356 | 14.0 (13.0-18.0) | 16.0 (13.0-20.0) | 0.001 |
| Number of lymph nodes dissected (n) | 10.00 (5.00-14.00) | 10.00 (6.00-13.00) | 0.677 | 10.00 (6.0-14.0) | 10.00 (5.0-14.0) | 0.495 |
| FPT (days) | 3.00 (2.00-3.00) | 3.00 (2.00-3.00) | 0.782 | 3.00 (2.00-4.00) | 3.00 (2.00-3.00) | 0.199 |
| Complications | 0.613 | 0.757 | ||||
| None | 202 (74.26) | 84 (71.79) | 130 (74.29) | 156 (72.90) | ||
| Yes | 70 (25.74) | 33 (28.21) | 45 (25.71) | 58 (27.10) | ||
| Postoperative infectious complications | 0.721 | 0.427 | ||||
| No | 207 (76.10) | 91 (77.78) | 164 (93.71) | 196 (91.59) | ||
| Yes | 65 (23.90) | 26 (22.22) | 11 (6.29) | 18 (8.41) | ||
| Postoperative non-infectious complications | 0.338 | 0.798 | ||||
| No | 254 (93.38) | 106 (90.60) | 133 (76.00) | 165 (77.10) | ||
| Yes | 18 (6.62) | 11 (9.40) | 42 (24.00) | 49 (22.90) | ||
Patients were further categorized by postoperative SML occurrence (SML group: n = 117; No SML group: n = 272). As shown in Table 2, the SML group had significantly higher preoperative SMI (P = 0.004) but a marked postoperative SMI decline (median decrease: 10%, P < 0.001), consistent with the definition of muscle loss. Compared with the No SML group, the SML group had a higher prevalence of preoperative sarcopenia (53.85% vs 34.19%, P = 0.026) and larger VFA (median: 134.00 cm2 vs 99.45 cm2, P = 0.001). Additionally, the SML group exhibited higher VSR (P = 0.012) and slightly elevated preoperative albumin (P = 0.047). Surgically, SML patients were more likely to undergo prophylactic ileostomy (23.08% vs 14.76%, P = 0.047). No significant differences in overall or subtype complication rates were observed between groups.
Spearman correlation analysis revealed relationships between body composition parameters and SML (Figure 3). VFA showed a strong positive correlation with VSR (r = 0.95) and moderate positive correlation with BMI (r = 0.55). Preoperative SMI was moderately correlated with VFA (r = 0.39). SML exhibited weak positive correlations with VFA (r = 0.17), VSR (r = 0.14), and preoperative SMI (r = 0.15). BMI itself showed no significant correlation with SML (r = 0.04).
Univariate logistic regression identified preoperative SMI [odds ratio (OR) = 1.035, 95% confidence interval (CI): 1.007-1.064; P = 0.016], high VFA (OR = 2.110, 95%CI: 1.340-3.322; P = 0.001), and prophylactic ileostomy (OR = 1.733, 95%CI: 1.004-2.990; P = 0.048) as significant risk factors for SML. Age, sex, and BMI were not significant predictors at this stage. These variables, along with BMI (included for clinical relevance), were entered into a multivariate logistic regression model (P < 0.001; Table 3). Multivariate analysis confirmed high VFA (OR = 2.557, 95%CI: 1.518-4.305; P < 0.001) and higher preoperative SMI (OR = 1.082, 95%CI: 1.035-1.131; P < 0.001) as independent risk factors for SML. Prophylactic ileostomy remained an independent predictor (OR = 1.936, 95%CI: 1.086-3.449; P = 0.025). Notably, higher BMI was independently associated with reduced SML risk (OR = 0.846, 95%CI: 0.757-0.946; P = 0.003).
| SML | ||||
| Single factor OR (95%CI) | P value | Multivariate OR (95%CI) | P value | |
| Age | ||||
| ≤ 65 | Reference | |||
| > 65 | 1.144 (0.742-1.766) | 0.542 | ||
| Gender | ||||
| Female | Reference | |||
| Male | 0.945 (0.608-1.469) | 0.801 | ||
| BMI | 1.017 (0.95-1.089) | 0.622 | 0.846 (0.757-0.946) | 0.003 |
| VFA | ||||
| Low VFA | Reference | |||
| High VFA | 2.110 (1.34-3.322) | 0.001 | 2.557 (1.518-4.305) | 0.000 |
| Preoperative SMI | 1.035 (1.007-1.064) | 0.016 | 1.082(1.035-1.131) | 0.000 |
| Prophylactic ileostomy condition | ||||
| No | Reference | |||
| Yes | 1.733 (1.004-2.99) | 0.048 | 1.936 (1.086-3.449) | 0.025 |
Cox proportional hazards models evaluated the impact of VFA and SML on long-term survival (Figures 4 and 5). Kaplan-Meier survival curves demonstrated significantly worse outcomes for patients with high VFA and those who developed SML.
RFS: High VFA [hazard ratio (HR) = 1.638, 95%CI: 1.198-2.240; P = 0.002; Figure 4A] and SML (HR = 1.685, 95%CI: 1.241-2.289; P = 0.001; Figure 5A) was independently associated with poorer RFS.
OS: High VFA (HR = 1.492, 95%CI: 1.069-2.082; P = 0.019; Figure 4B) and SML (HR = 1.646, 95%CI: 1.184-2.290; P = 0.003; Figure 5B) were both independent predictors of shortened OS.
This study directly addresses two fundamental questions: (1) Whether visceral obesity independently drives postoperative SML in CRC patients; and (2) How dynamic interactions between VFA and SML influence survival outcomes. The data unequivocally demonstrate that high VFA is an independent risk factor for perioperative SML (OR = 2.557, 95%CI: 1.518-4.305, P < 0.001). Furthermore, the co-occurrence of high VFA and SML synergistically predicts worse survival, independently shortening OS (HR = 1.492, P = 0.019) and RFS (HR = 1.638, P = 0.002). Paradoxically, higher preoperative SMI increased SML susceptibility (OR = 1.082, P < 0.001), while elevated BMI reduced SML risk (OR = 0.846, P = 0.003). These results establish visceral obesity as a central driver of perioperative metabolic deterioration in CRC. Clinically, this metabolic dysregulation manifested in tangible short-term outcomes: High-VFA patients exhibited significantly greater intraoperative blood loss (median 100 mL vs 80 mL, P = 0.001) and prolonged hospitalization (median 16 days vs 14 days, P = 0.001).
The strong association between high VFA and SML supports the “visceral obesity-chronic inflammation-muscle catabolism” axis hypothesis[20-22]. Visceral adipose tissue secretes pro-inflammatory cytokines (e.g., IL-6, TNF-α), activating the ubiquitin-proteasome system and nuclear factor kappaB signaling, which directly accelerate muscle proteolysis[22-25]. This is corroborated by the significantly higher preoperative sarcopenia prevalence in high-VFA patients (43.93% vs 30.29%, P = 0.006), indicating chronic visceral adipose tissue-induced muscle erosion. Furthermore, beyond the classic inflammatory and ubiquitin-proteasome pathways, recent evidence implicates ferroptosis - an iron-dependent form of regulated cell death - in the pathogenesis of cancer-associated muscle wasting[26]. The short-term surgical outcomes - including increased intraoperative blood loss and prolonged hospital stay - closely align with a prothrombotic state and impaired wound healing environment driven by visceral adipose tissue-derived inflammation[27]. This pathological milieu further elucidates the mechanism behind delayed postoperative recovery in patients with high VFA: Pro-inflammatory cytokines[28-31], such as IL-6 and TNF-α released by visceral adipose tissue not only activate the coagulation system but also directly impair the wound healing process by inhibiting fibroblast proliferation and collagen deposition.
Preoperative high SMI as a risk factor: Contrary to conventional protective views of muscle mass, high SMI emerged as an independent risk factor for SML. This counterintuitive finding may reflect several non-mutually exclusive mechanisms: Metabolic overload hypothesis[32-35]: Muscle mass-function discrepancy - CT-derived SMI, while quantifying cross-sectional area, cannot differentiate intramuscular fat infiltration (myosteatosis), a key determinant of muscle quality and regenerative capacity. High SMI may paradoxically mask underlying myosteatosis, impairing the muscle’s ability to withstand catabolic stress post-surgery. Emerging evidence suggests that specific muscle fiber type composition (e.g., preferential loss or vulnerability of type II fibers) in individuals with high SMI might also contribute to accelerated wasting under stress. Muscle mass-function discrepancy[33,34,36-38]: CT-derived SMI cannot differentiate intramuscular fat infiltration (myosteatosis). High SMI may mask myosteatosis, impairing muscle regenerative capacity.
BMI as a protective factor: While BMI ≥ 25 kg/m2 is a known CRC risk factor, higher BMI reduced SML risk. This paradox highlights: Energy reserve hypothesis[39-43]: Subcutaneous fat may buffer energy demands, sparing muscle protein. Heterogeneity of sarcopenic obesity[44-48]: BMI fails to distinguish fat distribution. “Normal-weight obesity” (low BMI/high VFA) may drive SML risk, explaining why VFA - not BMI - emerged as the independent predictor.
Our finding that elevated preoperative SMI increases SML risk contradicts Tırnova et al[49], who reported preoperative sarcopenia as the primary risk factor for muscle wasting. This discrepancy highlights the complexity of body composition interactions and potential cohort differences. A key factor may be our exclusion of overtly obese patients (BMI ≥ 30 kg/m2). This exclusion likely enriched our ‘high-SMI’ group with individuals exhibiting ‘normal-weight obesity’ or ‘sarcopenic obesity precursor’ phenotypes - characterized by high muscle area but potentially compromised by visceral adiposity and/or intramuscular fat infiltration (myosteatosis), which CT-derived SMI alone cannot detect. In cohorts including obese patients, the detrimental effects of low muscle mass may dominate. Furthermore, the protective role of higher BMI against SML in our study, while seemingly paradoxical given general metabolic risks, aligns with surgical stress models emphasizing the importance of energy reserves during catabolic periods[44,49].
These results challenge the conventional focus on static body composition snapshots. Our longitudinal CT data demonstrate that dynamic VFA/SMI trajectories offer superior predictive value for perioperative metabolic risk compared to single-timepoint assessments. Critically, we identify VFA-driven SML as a key, potentially modifiable pathway linking visceral obesity to diminished survival. This finding refines the sarcopenic obesity paradigm by providing direct clinical evidence for visceral fat’s active role in driving perioperative muscle catabolism, as recently mechanistically detailed[22].
Risk stratification: VFA quantification via CT should be integrated into preoperative assessments to identify high-risk patients (VFA ≥ 100 cm2 + high SMI).
Intervention targets: Targeting the ‘fat-muscle axis’ holds therapeutic promise: Combining visceral fat reduction strategies (e.g., glucagon-like peptide 1 receptor agonists)[50], shown to effectively reduce visceral adipose tissue with muscle anabolic support (e.g., resistance training + β-hydroxy-β-methylbutyrate) may disrupt the vicious cycle of inflammation and catabolism[51].
ERAS protocol optimization: High-VFA patients required longer hospitalization (P = 0.001), suggesting tailored nutritional support and early mobilization in enhanced recovery pathways.
Key limitations directly pertinent to the study aims include: (1) Selection bias: Exclusion of obese patients (BMI ≥ 30 kg/m2) limits generalizability to all CRC subtypes, particularly those with sarcopenic obesity; (2) Unmeasured confounding: Lack of serial inflammatory biomarkers (e.g., IL-6, muscle RING finger 1) precludes mechanistic validation of the proposed catabolic pathways; (3) Follow-up duration: Median follow-up ≤ 5 years may underestimate long-term survival impacts of SML; and (4) Single-center design: Homogeneous surgical protocols may mask institution-specific confounding factors.
To address these limitations, we propose: Prospective multi-omics cohorts: Longitudinal studies integrating metabolomics (lipid signatures) and proteomics (ubiquitin-proteasome system markers) to define the “VFA to inflammation to SML” axis[52]. Predictive modeling: Machine learning algorithms using serial CT-derived VFA/SMI trajectories to identify high-risk patients before SML onset[53]. Targeted clinical trials: Preoperative: Randomized controlled trials testing glucagon-like peptide 1 agonists for VFA reduction[50]. Postoperative[54]: Combinations of ω-3 fatty acids (anti-inflammatory) and leucine/β-hydroxy-β-methylbutyrate (mammalian target of rapamycin activation) to preserve muscle mass. Extended follow-up: Survival analysis beyond 5 years to assess late recurrence and quality-of-life outcomes.
This study establishes visceral obesity as an independent driver of perioperative muscle wasting and poor survival in CRC, mediated through chronic inflammation and metabolic dysregulation. The paradoxical roles of BMI and preoperative muscle mass underscore the need for dynamic body composition monitoring. By translating these insights into predictive models and targeted “fat-muscle axis” interventions, we can optimize metabolic management for CRC patients - particularly those with sarcopenic obesity - to improve surgical resilience and long-term survival.
We would like to thank all participating hospitals and healthcare workers involved in collecting and recording patient data.
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