Serum Albumin-Creatinine Ratio Predicts Sarcopenia and Poor Outcomes in Patients with Colorectal Cancer

doi:10.21203/rs.3.rs-7897966/v1

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Research Article

Serum Albumin-Creatinine Ratio Predicts Sarcopenia and Poor Outcomes in Patients with Colorectal Cancer

https://doi.org/10.21203/rs.3.rs-7897966/v1

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Background: This study was designed to explore the prognostic significance of the serum albumin-creatinine ratio (ACR) in patients with colorectal cancer (CRC).

Methods: Kaplan-Meier survival analysis was utilized to evaluate overall survival (OS) and progression-free survival (PFS). Cox proportional hazards regression models were applied to identify independent prognostic factors. A nomogram based on the ACR was developed to predict 1-, 3-, and 5-year survival probabilities.

Results: A total of 1438 patients were included in this study. Patients with a high ACR (≥38.5) had significantly worse outcomes compared with those having a low ACR (<38.5). Specifically, patients with a low ACR had significantly poorer PFS (43.6% vs 63.3%, p<0.001) and OS (46.2% vs 66.2%, p<0.001) than those with a high ACR. The multivariate Cox proportional hazards regression model indicated that ACR was an independent prognostic factor for both PFS (HR = 0.635, 95% CI: 0.532–0.758, p < 0.001) and OS (HR = 0.615, 95% CI: 0.512–0.738, p < 0.001). Logistic regression analysis showed that low ACR was an independent risk factor for disease recurrence (OR = 0.588, 95% CI: 0.439–0.787, p < 0.001) and sarcopenia (OR = 0.674, 95% CI: 0.496–0.917, p < 0.001). The ACR-based nomogram demonstrated good predictive accuracy, with a concordance index of 0.731 for OS and 0.723 for PFS.

Conclusions: The serum ACR is a promising prognostic biomarker for predicting poor outcomes in CRC patients.The ACR-based nomogram can serve as a valuable tool for individualized prognosis assessment and treatment planning in clinical practice.

Albumin-creatinine ratio

Recurrence

Sarcopenia

Colorectal cancer

Prognosis

Colorectal cancer (CRC) is one of the common malignant tumors worldwide, posing a serious threat to human health. In recent years, its incidence rate has ranked among the top in various cancers and is on the rise^{1, 2}. In China, the situation of CRC incidence is also not optimistic, bringing a heavy burden to patients and their families³. Symptoms in patients with early-stage CRC may be inconspicuous. As the disease progresses, symptoms such as blood in the stool, abdominal pain, and changes in bowel habits occur, severely affecting the quality of life of patients. Patients with advanced CRC often experience metastasis, which greatly increases the difficulty of treatment and leads to a high mortality rate^{4, 5}. Therefore, accurately assessing the prognosis of CRC patients is of crucial significance for formulating personalized treatment plans, improving patient survival rates, and enhancing the quality of life.

Currently, tumor-specific factors are the most commonly used indicators for assessing the prognosis of CRC in clinical practice, including tumor staging, pathological type, tumor markers, etc. However, these traditional indicators have certain limitations. Some require invasive procedures, some are costly to detect, and none of them can comprehensively and accurately predict the prognosis of patients^6-8. Moreover, due to the heterogeneity of tumors, even patients with the same pathological stage may have significantly different prognoses^{9, 10}. Patient - related factors are also important determinants of the prognosis of CRC patients, including nutritional status, physical performance, and skeletal muscle mass. In recent years, an increasing number of studies have focused on the fact that cost - effective blood characteristics reflecting the host status may be effectively used to predict the clinical outcomes of CRC patients^11-13. The serum albumin - creatinine ratio (ACR) as a potential novel biomarker has gradually attracted the attention of researchers^14-16. Serum albumin is an important indicator reflecting the body's nutritional status and immune function. A low albumin level often indicates poor nutritional status and low immune function in patients, which is associated with the poor prognosis of various diseases^17-19. Serum creatinine is a derivative of skeletal muscle protein creatine phosphate and is mainly affected by the metabolism of muscle tissue in the body. A decrease in serum creatinine levels may suggest a reduction in muscle mass, and a decrease in muscle mass is an important factor affecting the poor prognosis of cancer patients^20-22. The ACR formed by combining the two may comprehensively reflect the patient's nutritional and skeletal muscle status and has potential value for the prognosis assessment of CRC patients.

Currently, there are few studies on the relationship between ACR and the prognosis of CRC. This study aims to deeply explore the impact of the serum ACR on the progression - free survival (PFS) and overall survival (OS) of CRC patients. Meanwhile, it explores whether ACR can serve as an effective predictive marker for tumor recurrence and sarcopenia, with the hope of providing more effective prognostic assessment indicators and a basis for treatment decision - making in clinical practice.

Study population

This study selected CRC patients who underwent surgical treatment at the Department of Colorectal and Anal Surgery, the First Affiliated Hospital of Guangxi Medical University, from January 2012 to December 2015 as the research subjects. The inclusion criteria were as follows: histopathologically confirmed colorectal cancer; complete serum albumin and creatinine test data, as well as other relevant clinical information; patients aged 18 years and above. The exclusion criteria were: receiving neoadjuvant radiotherapy and chemotherapy before surgery; coexisting with other malignant tumors; having severe renal insufficiency or immunodeficiency; suffering from acute or chronic inflammatory diseases. This study strictly adhered to the provisions of the Declaration of Helsinki during the research process and was approved by the Ethics Committee of the First Affiliated Hospital of Guangxi Medical University. Since a retrospective study design was adopted, informed consent was not required.

Data Collection

Preoperative clinical data of patients were collected through the hospital's electronic medical record system, covering aspects such as basic patient information, tumor - related information, laboratory test results, and treatment information. Basic patient information included gender, age, height, weight, and the presence of hypertension, diabetes, etc. Tumor - related information encompassed TNM staging, the presence of nerve or blood vessel invasion, tumor size, and tumor differentiation degree. Laboratory test data included tumor markers, serum albumin, serum creatinine, and other indicators. Treatment information mainly recorded whether patients received radiotherapy, chemotherapy, or other treatment methods after surgery. All blood samples were collected within 7 days before the patient's surgery for the detection of relevant indicators.

Serum ACR Detection Method

The bromocresol green method was used for serum albumin detection. The principle is that in a buffer solution with a pH of 4.2, the tryptophan residues in albumin molecules bind to bromocresol green to form a blue - green complex. The intensity of the color is proportional to the albumin content. The absorbance is measured by colorimetry, and then the serum albumin concentration is calculated. An automatic biochemical analyzer was used for detection. Serum creatinine was detected using the picric acid method (Jaffe method). Under alkaline conditions, creatinine reacts with picric acid to form an orange - red picric acid - creatinine complex. The absorbance is measured by colorimetry, and the absorbance is positively correlated with the creatinine content, so as to determine the serum creatinine concentration. The same automatic biochemical analyzer was used for detection. The calculation method of the serum ACR was: serum albumin concentration (g/L) / serum creatinine concentration (mg/dL). To ensure the accuracy and reliability of the test results, the testing instruments were calibrated and maintained regularly, and standard samples were used for quality control to ensure that the testing process was carried out under strict quality monitoring. The neutrophil - to - lymphocyte ratio (NLR) is defined as the neutrophil count divided by the lymphocyte count, the platelet - to - lymphocyte ratio (PLR) is defined as the platelet count divided by the lymphocyte count, and the prognostic nutritional index (PNI) is defined as serum albumin (g/L) + 5×total peripheral blood lymphocytes (10⁹/L).

Sarcopenia

The updated consensus on the diagnosis and treatment of sarcopenia in 2019 by the Asian Working Group for Sarcopenia (AWGS) was widely used as the diagnostic criteria for sarcopenia in the Asian population. That is, when the skeletal muscle mass index (SMI) is less than 6.92 Kg/㎡ in men and less than 5.13 Kg/㎡ in women, it is considered to have sarcopenia. The appendicular skeletal muscle mass (ASM) was estimated according to the anthropometric equation, and the calculation equation was as follows: 0.193×weight (kg)+0.107×height (cm)-4.157×gender (male = 1, female = 2)-0.037×age (years)-2.631. The skeletal muscle index (SMI) was defined as ASM divided by the square of height (㎡), that is, SMI = ASM/Ht² (Kg/㎡).

Follow - up

The follow - up methods for patients after surgery included telephone follow - up and outpatient review. Follow - up was conducted once every 3 months in the first 2 years after surgery, once every 6 months in the subsequent 3 years, and once a year after 5 years. The follow - up endpoint was the patient's death or the last follow - up time (January 2023). The follow - up content mainly included inquiring about the patient's symptoms and signs and conducting relevant examinations, such as tumor marker detection, imaging examinations, etc., to assess the disease recurrence and survival status of the patient. The primary endpoint indicators of the study were OS and PFS. OS was calculated from the date of surgery to the patient's death or the last follow - up date; PFS was defined as the time from the date of surgery to local recurrence or distant metastasis of the disease.

Statistical analysis

Continuous variables were expressed as mean ± standard deviation (SD) or median (interquartile range), and categorical variables were expressed as number of cases (percentage). The chi - square test or Fisher's exact test was used for the comparison of categorical variables, and the t - test was used for the comparison of continuous variables. The optimal cut - off values for ACR were determined using the ROC curve. Restricted Cubic Splines (RCS) were used to explore the associations between ACR and PFS/OS. The Kaplan - Meier method was used to draw survival curves, and the log - rank test was used to compare the OS and PFS of patients in different ACR groups to analyze the relationship between ACR and patient prognosis. The Cox proportional hazards regression model was used to determine the independent association between ACR and OS and PFS in CRC patients. Univariate and multivariate analyses were performed using the Cox proportional hazards regression model to screen independent prognostic factors affecting OS and PFS. Based on the variables with statistical significance in the Cox regression model, a prediction model was constructed using R software, and a nomogram was generated. The discrimination ability and calibration of the prediction model were evaluated through internal validation curves, the concordance index (C - index), and the receiver operating characteristic (ROC) curve. Decision curve analysis (DCA) was used to intuitively compare the benefits of the prediction model and traditional tumor staging in clinical applications. A p < 0.05 was considered as the standard for statistical significance. Statistical analysis was performed using the R statistical software package (version 4.0.2).

Patient Baseline Characteristics

A total of 1,438 CRC patients were included in this study, and their baseline characteristics are presented in Table S1. The mean age of the patients was 58.14 ± 13.13 years. Male patients accounted for 62.7% (902 cases), while female patients accounted for 36.3% (536 cases). There were 704 (49.0%) colon cancer patients and 734 (51.0%) rectal cancer patients. According to the 8th edition of the AJCC cancer staging system, 763 patients (53.1%) were in stages I - II, and 675 patients (46.9%) were in stages III - IV. The serum ACR exhibited a wide distribution range among the patients, with a minimum value of 0.02, a maximum value of 244.20, a mean value of 8.37 ± 13.80, and a median value of 4.58. Additionally, the serum ACR also had a broad distribution, with a minimum of 6.02, a maximum of 120.86, a mean of 45.78 ± 13.81, and a median of 44.56. By plotting the ROC curve, the optimal cut - off value of ACR was determined to be 38.5 (Figure S1). Based on this, the patients were divided into a low - ACR group (ACR < 38.5, n = 902) and a high - ACR group (ACR ≥ 38.5, n = 536). Further analysis of the ACR differences among patients with different characteristics revealed that ACR was significantly correlated with the patients' gender, age, tumor location, tumor size, CEA level, and vascular invasion (all p < 0.05). The mean ACR value of deceased patients was significantly lower than that of living patients (53.8% vs 33.8%, P < 0.001). The mean ACR value of patients with recurrence was lower than that of non - recurrence patients (33.5% vs 24.9, P = 0.001). Moreover, compared with high - ACR patients, low - ACR patients had a 4 - day longer hospital stay (p < 0.001) and an additional hospital cost of 2,488.77 yuan (p < 0.001).

Comparison of composite nutritional markers

To compare the predictive abilities of ACR and other composite nutritional markers for the prognosis of CRC patients, ROC curves were plotted, and the areas under the curves (AUCs) were calculated. For 3 - year PFS, the AUC of ACR was higher than those of the NLR, PLR, and PNI (0.568 vs 0.545 vs 0.534 vs 0.557, respectively). Similarly, for 5 - year PFS, the AUC of ACR was also higher than those of NLR, PLR, and PNI (0.554 vs 0.547 vs 0.541 vs 0.559). Compared with NLR (3 - year OS, 0.565; 5 - year OS, 0.552), PLR (3 - year OS, 0.555; 5 - year OS, 0.545), and PNI (3 - year OS, 0.566; 5 - year OS, 0.560), ACR demonstrated better prognostic predictive efficacy (3 - year OS, 0.570; 5 - year OS, 0.560) (Figure S2).

Kaplan - Meier Survival Curves of Serum ACR and Prognosis

Kaplan - Meier survival analysis showed that the 5 - year PFS of patients in the high - ACR group was significantly lower than that in the low - ACR group (43.6% vs 63.3%, p < 0.001), and the 5 - year OS was also significantly lower than that in the low - ACR group (46.2% vs 73.8%, p < 0.001), as shown in Figure 1. In the subgroup analysis, for patients with TNM stages I - II, the 5 - year PFS of the low - ACR group was 57.8%, which was lower than 81.9% in the high - ACR group (p < 0.001) (Figure 2A); the 5 - year OS of the low - ACR group was 60.6%, lower than 84.9% in the high - ACR group (p < 0.001) (Figure 2B). For patients with TNM stages III - IV, the 5 - year PFS of the low - ACR group was 39.3%, lower than 56.6% in the high - ACR group (p = 0.010) (Figure 2C); the 5 - year OS of the low - ACR group was 41.8%, lower than 59.4% in the high - ACR group (p = 0.010) (Figure 2D). In the colon cancer subgroup, the 5 - year PFS of the low - ACR group was 45.1%, lower than 65.9% in the high - ACR group (p < 0.001) (Figure S3A); the 5 - year OS of the low - ACR group was 47.8%, lower than 67.4% in the high - ACR group (p < 0.001) (Figure S3B). In the rectal cancer subgroup, the 5 - year PFS of the low - ACR group was 41.9%, lower than 61.1% in the high - ACR group (p < 0.001) (Figure S4A); the 5 - year OS of the low - ACR group was 44.3%, lower than 65.1% in the high - ACR group (p < 0.001) (Figure S4B). In the CEA - normal subgroup, the 5 - year PFS of the low - ACR group was 48.8%, lower than 72.3% in the high - ACR group (p < 0.001) (Figure S5A); the 5 - year OS of the low - ACR group was 52.4%, lower than 74.8% in the high - ACR group (p < 0.001) (Figure S5B). In the CEA - elevated subgroup, the 5 - year PFS of the low - ACR group was 37.7%, lower than 48.9% in the high - ACR group (p = 0.086) (Figure S5C); the 5 - year OS of the low - ACR group was 39.1%, lower than 52.4% in the high - ACR group (p = 0.026) (Figure S5D).

Association Between ACR and Survival Outcomes

RCS analysis revealed a non - linear relationship between ACR and PFS/OS in CRC patients. As the ACR increased, the hazard ratio (HR) gradually increased. This trend was consistent across different calibration models (Figure S6). For every 1 - SD increase in ACR, the risk of PFS and OS in CRC patients was reduced by 14.0% (HR=0.860, 95% CI: 0.785–0.942, p = 0.001) and 17.8% (HR=0.822, 95% CI: 0.747–0.904, p < 0.001), respectively. Multivariate Cox regression analysis identified high ACR as an independent predictor of poor PFS (HR = 0.635, 95% CI: 0.532–0.758, p < 0.001) and OS (HR = 0.615, 95% CI: 0.512–0.738, p < 0.001). Trend test showed that a quartile analysis of ACR found that patients in the second, third, and fourth quartiles had adverse PFS rates that were 0.617, 0.556, and 0.652 times lower, respectively, than those in the first quartile (Table 1). Similarly, as the ACR increased, the HR for OS also gradually declined. The second quartile (Q2, 0.605), the third quartile (Q3, 0.555), and the fourth quartile (Q4, 0.591) led to an increased risk of adverse OS for patients (Table 2). For PFS, a multivariable forest plot revealed that ACR was an independent risk factor for the majority of patient subgroups (Figure 3A). Similarly, in most subgroups, patients with a high ACR had a relatively worse prognosis than those with a low ACR (Figure 3B).

Association Between ACR and Recurrence

Patients with CRC in the low ACR group had a higher recurrence rate than those in the high ACR group (33.5% vs 24.9%, p = 0.001). As presented in Table 3, in the adjusted model, there was a significant association between serum ACR as a continuous variable and recurrence (Per SD increment: odds ratios (ORs)=0.850, 95%CI: 0.740 - 0.980, p = 0.023). Multivariable logistic regression analysis indicated that low ACR was an independent risk factor influencing disease recurrence (OR=0.588, 95% CI: 0.439 - 0.787, p < 0.001). When serum ACR was analyzed as quartiles in the adjusted model, compared with the first quartile (Q1), participants in the second quartile (Q2, OR=0.598, 95%CI: 0.415 - 0.862, p = 0.006), the third quartile (Q3, OR=0.566, 95%CI: 0.387 - 0.827, p = 0.003), and the fourth quartile (Q4, OR=0.547, 95%CI: 0.70 - 0.809, p = 0.006) had a significantly higher risk of recurrence.

Association Between ACR and Sarcopenia

The incidence of sarcopenia was higher in CRC patients in the low ACR group than in the high ACR group (25.5% vs 15.6%, p < 0.001). Logistic regression analysis revealed that for every 1 - SD increase in ACR, the risk of sarcopenia in CRC patients was reduced by 33.7% (OR = 0.760, 95%CI = 0.650 - 0.880, p < 0.001). Low ACR was an independent risk factor affecting sarcopenia (OR=0.674. 95% CI: 0.496 - 0.917, p < 0.001). When ACR was divided into quartiles, with the increase of ACR, the prognosis of patients gradually improved. The ORs of sarcopenia were 0.853 (0.588 - 1.239), 0.528 (0.350 - 0.797), and 0.524 (0.339 - 0.810) respectively (Table 4).

Establishment of ACR - based Prediction Nomograms

In the univariate analysis, factors such as age, T stage, N stage, M stage, perineural invasion, vascular invasion, differentiation, tumor size, CEA, and ACR were closely associated with the prognosis of CRC patients. The factors with significance in the univariate analysis were incorporated into the Cox proportional - hazards regression model for multivariate analysis. The results showed that only T stage, N stage, M stage, vascular invasion, CEA, and ACR were independent prognostic factors affecting the PFS of CRC patients (Table S2). In the multivariate OS analysis, only T stage, N stage, M stage, vascular invasion, differentiation, CEA, and ACR were independent prognostic factors affecting CRC patients (Table S3). Based on the factors with statistical significance in the multivariate Cox regression analysis, a prognostic prediction model was constructed using R software, and ACR - based prediction nomograms were generated, as shown in Figure S7 and Figure S8. This nomogram assigns scores to each factor, enabling the intuitive prediction of the 1 - year, 3 - year, and 5 - year PFS and OS probabilities of patients. The prediction model was evaluated through internal validation curves, the C - index, and the ROC curve. The C - index of the PFS nomogram was 0.723, and the AUCs for 1 - year, 3 - year, and 5 - year were 0.795, 0.781, and 0.767 respectively (Figure S9A). The C - index of the OS nomogram was 0.731, and the AUCs for 1 - year, 3 - year, and 5 - year were 0.752, 0.778, and 0.768 respectively (Figure S9B). The calibration curves indicated that the predicted 1 - year, 3 - year, and 5 - year PFS and OS probabilities by the nomogram had a good consistency with the actually observed probabilities, as shown in Figure S10. The DCA demonstrated that within the 1 - 5 - year prediction interval, the ACR - based prognostic prediction model had significantly greater benefits in clinical applications than the traditional TNM staging, as shown in Figure S11. Patients were classified into high - scoring and low - scoring groups based on the median scores of the nomogram. The results showed that the high - scoring group had significantly worse PFS/OS compared to the low - scoring group (Figure S12).

To further validate the effectiveness of the model, patients were randomly divided into a validation cohort A (n = 902) and a validation cohort B (n = 536) at a ratio of 7:3 (Table S4). In validation cohort A, ACR could effectively stratify the PFS and OS of patients. The 5 - year PFS and OS of patients in the low ACR group were significantly lower than those in the high ACR group (PFS: 46.0% vs 62.4%, p = 0.001; OS: 48.8% vs 65.6%, p < 0.001) (Figure S13). In validation cohort B, it was also observed that the 5 - year PFS and OS of patients in the low ACR group were lower than those in the high ACR group (PFS: 37.8% vs 65.4%, p < 0.001; OS: 40.0% vs 67.5%, p < 0.001) (Figure S14). The C - indices of the PFS and OS nomograms in validation cohort A were 0.711 and 0.725 respectively, and those in validation cohort B were 0.750 and 0.748 respectively, all indicating good prediction accuracy. The ROC curves and calibration curves of the validation cohorts further confirmed the reliability of the model, with all AUCs > 0.75 (Figure S15). The calibration curves showed a good consistency between the predicted probabilities and the actual probabilities (Figure S16). The DCA results indicated that within the 1 - 5 - year prediction interval, the ACR - based prognostic prediction model had significantly greater benefits in clinical applications than the traditional TNM staging (Figure S17). When patients in both validation cohorts were stratified into high - scoring and low - scoring groups based on the nomogram scores, the high - scoring group consistently had worse PFS and OS outcomes, further validating the predictive power of the ACR - based nomogram (Figure S18).

In this study, we conducted a retrospective analysis of 1,438 CRC patients to explore the prognostic value of serum ACR for the prognosis of CRC patients and sarcopenia. Our results clearly demonstrate that a low ACR is strongly associated with poorer OS and PFS compared to a high ACR. Specifically, the 5 - year OS and PFS were significantly lower in the high - ACR group. The OS rates were 45.2% in the high - ACR group and 68.3% in the low - ACR group, while the PFS were 40.5% and 62.1% respectively. This difference was also evident in various subgroups. For instance, in patients with TNM stages I - II and III - IV, in colon cancer and rectal cancer patients, as well as in subgroups with normal and elevated CEA levels, patients in the low - ACR group consistently had a worse survival status. Multivariate Cox regression analysis further confirmed that ACR is an independent prognostic factor for both OS and PFS. These findings highlight the potential of ACR as a robust and easily accessible biomarker for predicting adverse outcomes in CRC patients.

ACR also has a close association with recurrence and sarcopenia. The recurrence rate of patients in the low - ACR group was significantly higher than that in the high - ACR group. Moreover, ACR, as a continuous variable, was significantly associated with recurrence. Multivariable logistic regression analysis indicated that low ACR is an independent risk factor for disease recurrence. Regarding sarcopenia, the incidence was higher in the low - ACR group. For every 1 - SD increase in ACR, the risk of sarcopenia in CRC patients decreased by 24%. Low ACR was also an independent risk factor for sarcopenia. These results further emphasize the importance of ACR in identifying high - risk patients. These findings suggest that ACR can serve as a valuable biomarker for identifying patients who may benefit from more aggressive treatment strategies or targeted nutritional interventions. For example, patients with low ACR levels may be candidates for rehabilitation programs aimed at improving nutritional status and muscle mass before surgery or chemotherapy. During the treatment process, by monitoring changes in ACR, doctors can promptly understand the patient's physical condition and treatment response, determine the effectiveness of the treatment, and decide whether to adjust the treatment plan. For patients with a high risk of recurrence, doctors can implement more intensive follow - up measures to detect early signs of recurrence and initiate timely treatment, thereby improving the patient's survival rate. The nonlinear relationship between ACR and survival outcomes, as revealed by RCS, indicates that the prognostic impact of ACR is not linear but follows a threshold effect. This finding has important clinical implications, suggesting that there may be a critical ACR value. Beyond this value, the risk of adverse outcomes increases significantly. Identifying this threshold can help clinicians stratify patients more effectively and customize interventions accordingly.

In this study, the close association between ACR and the prognosis of CRC patients is well - founded. Its underlying mechanism is closely related to the physiological significance of serum albumin and serum creatinine, as well as the comprehensive state of the body reflected by their combination. ACR, which integrates serum albumin and creatinine levels, provides a comprehensive reflection of a patient's nutritional status and skeletal muscle mass. Serum albumin, a key indicator of nutritional status and systemic inflammation, has been widely recognized as a predictor of poor outcomes in cancer patients^{17, 23}. Low serum albumin levels are often associated with malnutrition, chronic inflammation, and impaired immune function, all of which contribute to worse clinical outcomes. On the other hand, serum creatinine, a by - product of muscle metabolism, can be used as a surrogate for skeletal muscle mass. Low serum creatinine levels may indicate sarcopenia, a condition characterized by the loss of muscle mass and strength. Sarcopenia has been increasingly recognized as a critical factor in cancer prognosis^{24, 25}. By combining these two parameters, ACR offers a more comprehensive assessment of a patient's overall health status, making it a valuable tool for predicting survival outcomes in CRC.

To further enhance the clinical applicability of our findings, we developed an ACR - based nomogram, which demonstrated good predictive accuracy. This nomogram provides clinicians with a practical tool for individualized prognosis assessment, enabling more informed treatment decisions. The DCA further validated the clinical utility of the ACR - based nomogram. It showed that the ACR - based nomogram offers greater net benefit compared to traditional TNM staging in predicting 1 - year, 3 - year, and 5 - year survival probabilities. This suggests that incorporating ACR into existing prognostic models could significantly improve their predictive power. Using the ACR - based nomogram, doctors can intuitively predict the 1 - year, 3 - year, and 5 - year PFS and OS probabilities of patients. Then, they can develop personalized treatment plans according to the specific conditions of patients. For patients with a poor prognosis, doctors can strengthen monitoring and interventions, such as formulating more aggressive chemotherapy plans in advance and providing nutritional support, to improve the patient's prognosis. For patients with a good prognosis, doctors can appropriately reduce excessive treatment, reducing the patient's economic burden and treatment side effects and improving the patient's quality of life.

Despite the important achievements in exploring the prognostic value of the serum ACR for CRC patients in this study, it inevitably has some limitations. The retrospective design may introduce selection bias and unmeasured confounders. The single - center cohort limits the generalizability of the results. ACR was only measured once preoperatively, potentially missing dynamic changes. Sarcopenia was assessed using anthropometric equations instead of imaging, which may reduce the accuracy of the assessment. Additionally, the ACR - based nomogram requires external validation in prospective, multicenter studies to confirm its clinical utility. Despite these limitations, ACR shows promise as a prognostic tool. However, further research is needed to address these constraints and optimize its application in CRC management.

The serum ACR is a promising and easily measurable prognostic biomarker for predicting poor outcomes in CRC patients. Our study highlights the significant association between high ACR levels and reduced OS and PFS, independent of traditional prognostic factors such as TNM staging. The ACR - based nomogram developed in this study provides a practical and reliable tool for individualized prognosis assessment and treatment planning in clinical practice. By incorporating ACR into existing prognostic models, clinicians can better stratify patients and identify those at higher risk of adverse outcomes, enabling more personalized and effective treatment strategies.

Acknowledgments

The authors thank the members for their substantial work on data collection and patient follow-up.

Authors’ contributions

Jialiang Gan, and Lishuang Wei, and Hailun Xie carried out the design of this study, analyses of statistics and draft the manuscript. Taiqi Chen, Nuo Xu, Siyu Lin, Yibo Chen, Shuyao Wang, Hailun Xie, Lishuang Wei, and Shuangyi Tang carried out collection of the statistics and prepared the manuscript. All authors read and approved the final manuscript.

Funding

This study was supported by the Young Elite Scientist Sponsorship Program by Cast (YESS20220687), the Youth Science Foundation Project of Guangxi Medical University (GXMUYSF 202548), and the 18th batch of Special Funding from the China Postdoctoral Science Foundation (2025T180639).

Ethics Committee Approval:

This study followed the Helsinki declaration. All participants signed an informed consent form and this study was approved by the Institutional Review Board of the hospital (Registration number: NO.2022-KY-(043)).

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Competing interests

The authors have declared that no competing interest exists.

Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer statistics, 2022. CA Cancer J Clin. Jan 2022;72(1):7-33. doi:10.3322/caac.21708
Sung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. May 2021;71(3):209-249. doi:10.3322/caac.21660
Zheng R, Zhang S, Zeng H, et al. Cancer incidence and mortality in China, 2016. J Natl Cancer Cent. Mar 2022;2(1):1-9. doi:10.1016/j.jncc.2022.02.002
Kittrongsiri K, Wanitsuwan W, Prechawittayakul P, Sangroongruangsri S, Cairns J, Chaikledkaew U. Survival analysis of colorectal cancer patients in a Thai hospital-based cancer registry. Expert Rev Gastroenterol Hepatol. Apr 2020;14(4):291-300. doi:10.1080/17474124.2020.1740087
Mangone L, Marinelli F, Bisceglia I, Braghiroli MB, Damato A, Pinto C. Five-year relative survival by stage of breast and colon cancers in northern Italy. Front Oncol. 2022;12:982461. doi:10.3389/fonc.2022.982461
Argilés G, Tabernero J, Labianca R, et al. Localised colon cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol. Oct 2020;31(10):1291-1305. doi:10.1016/j.annonc.2020.06.022
Goldstein MJ, Mitchell EP. Carcinoembryonic antigen in the staging and follow-up of patients with colorectal cancer. Cancer Invest. 2005;23(4):338-51. doi:10.1081/cnv-58878
Shen X, Xiang M, Tang J, et al. Evaluation of peripheral blood inflammation indexes as prognostic markers for colorectal cancer metastasis. Sci Rep. Sep 3 2024;14(1):20489. doi:10.1038/s41598-024-68150-y
Edge SB, Compton CC. The American Joint Committee on Cancer: the 7th edition of the AJCC cancer staging manual and the future of TNM. Annals of surgical oncology. 2010;17(6):1471-1474.
Li J, Mei S, Zhou S, Zhao F, Liu Q. Perineural invasion is a prognostic factor in stage II colorectal cancer but not a treatment indicator for traditional chemotherapy: a retrospective cohort study. J Gastrointest Oncol. Apr 2022;13(2):710-721. doi:10.21037/jgo-22-277
Xie H, Wei L, Yuan G, Liu M, Tang S, Gan J. Prognostic Value of Prognostic Nutritional Index in Patients With Colorectal Cancer Undergoing Surgical Treatment. Front Nutr. 2022;9:794489. doi:10.3389/fnut.2022.794489
Xie H, Wei L, Wang Q, Tang S, Gan J. Apolipoprotein A-I levels in the survival of patients with colorectal cancer: a retrospective study. Front Endocrinol (Lausanne). 2024;15:1318416. doi:10.3389/fendo.2024.1318416
Moro-Valdezate D, Martín-Arévalo J, Cózar-Lozano C, et al. Prognostic value of routine blood biomarkers in 3-year survival of resectable colorectal cancer patients: a prognostic nomogram for clinical practice. Int J Colorectal Dis. Mar 5 2025;40(1):58. doi:10.1007/s00384-025-04848-3
Willegger M, Posch F, Schieder S, et al. Serum creatinine and albumin predict sarcoma-specific survival in patients with myofibroblastic and fibroblastic sarcomas. J Orthop Res. Dec 2017;35(12):2815-2824. doi:10.1002/jor.23598
Panotopoulos J, Posch F, Funovics PT, et al. Elevated serum creatinine and low albumin are associated with poor outcomes in patients with liposarcoma. J Orthop Res. Mar 2016;34(3):533-8. doi:10.1002/jor.23002
Kong S, Yu S, He W, et al. Serum Albumin-to-Creatinine Ratio: A Novel Predictor of Pulmonary Infection in Patients with ST-Segment Elevation Myocardial Infarction Undergoing Percutaneous Coronary Intervention. J Atheroscler Thromb. Dec 1 2024;31(12):1680-1691. doi:10.5551/jat.64717
Nazha B, Moussaly E, Zaarour M, Weerasinghe C, Azab B. Hypoalbuminemia in colorectal cancer prognosis: Nutritional marker or inflammatory surrogate? World J Gastrointest Surg. Dec 27 2015;7(12):370-7. doi:10.4240/wjgs.v7.i12.370
Evans DC, Corkins MR, Malone A, et al. The Use of Visceral Proteins as Nutrition Markers: An ASPEN Position Paper. Nutr Clin Pract. Feb 2021;36(1):22-28. doi:10.1002/ncp.10588
Chiang JM, Chang CJ, Jiang SF, et al. Pre-operative serum albumin level substantially predicts post-operative morbidity and mortality among patients with colorectal cancer who undergo elective colectomy. Eur J Cancer Care (Engl). Mar 2017;26(2)doi:10.1111/ecc.12403
Jung CY, Kim HW, Han SH, Yoo TH, Kang SW, Park JT. Creatinine-cystatin C ratio and mortality in cancer patients: a retrospective cohort study. J Cachexia Sarcopenia Muscle. Aug 2022;13(4):2064-2072. doi:10.1002/jcsm.13006
Thongprayoon C, Cheungpasitporn W, Kittanamongkolchai W, Harrison AM, Kashani K. Prognostic Importance of Low Admission Serum Creatinine Concentration for Mortality in Hospitalized Patients. Am J Med. May 2017;130(5):545-554.e1. doi:10.1016/j.amjmed.2016.11.020
Gao S, Xie H, Wei L, et al. Serum creatinine/cystatin C ratio as a prognostic indicator for patients with colorectal cancer. Front Oncol. 2023;13:1155520. doi:10.3389/fonc.2023.1155520
Gupta D, Lis CG. Pretreatment serum albumin as a predictor of cancer survival: a systematic review of the epidemiological literature. Nutr J. Dec 22 2010;9:69. doi:10.1186/1475-2891-9-69
Sun J, Yang H, Cai W, et al. Serum creatinine/cystatin C ratio as a surrogate marker for sarcopenia in patients with gastric cancer. BMC Gastroenterol. Jan 19 2022;22(1):26. doi:10.1186/s12876-022-02093-4
Kishimoto K, Hasegawa D, Uemura S, et al. Association between muscle mass evaluated by computed tomography and the serum creatinine-cystatin C ratio in children with cancer: A cross-sectional study. Nutrition. Jul-Aug 2022;99-100:111679. doi:10.1016/j.nut.2022.111679

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Serum Albumin-Creatinine Ratio Predicts Sarcopenia and Poor Outcomes in Patients with Colorectal Cancer

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