Metabolic syndrome affects the long‐term survival of patients with non‐endometrioid carcinoma of the uterine corpus

The effect of metabolic syndrome on postoperative complications and long-term survival of patients with colorectal cancer

Background

Metabolic syndrome (MetS) is associated with poor prognosis in many cancers. However, the relationship between metabolic syndrome and overall survival (OS) in patients with colorectal cancer (CRC) remains unclear. We aimed to comprehensively analyze whether MetS could affect postoperative complications and long-term survival in patients with CRC.

Methods

We included patients who underwent CRC resection at our center between January 2016 and December 2018. Bias was reduced through propensity score matching analysis. Patients with CRC were divided into the MetS and non-MetS groups based on whether they had MetS. Univariate and multivariate analyses were used to identify risk factors affecting OS.

Results

We included 268 patients; among them, 120 were included for further analysis after propensity score matching. There were no significant between-group differences in the clinicopathological features after matching. Compared with the non-MetS group, the MetS group had a shorter OS (P = 0.027); however, there was no significant between-group difference in postoperative complications. Multivariate analysis revealed that MetS (hazard ratio [HR] = 1.997, P = 0.042), tumor-node-metastasis stage (HR = 2.422, P = 0.003), and intestinal obstruction (HR = 2.761, P = 0.010) were independent risk factors for OS.

Conclusions

MetS affects the long-term survival of patients with CRC without affecting postoperative complications.

Metabolic risk score as a predictor in a nomogram for assessing myometrial invasion for endometrial cancer

The purpose of the present study was to investigate the predictive value of metabolic syndrome in evaluating myometrial invasion (MI) in patients with endometrial cancer (EC). The study retrospectively included patients with EC who were diagnosed between January 2006 and December 2020 at the Department of Gynecology of Nanjing First Hospital (Nanjing, China). The metabolic risk score (MRS) was calculated using multiple metabolic indicators. Univariate and multivariate logistic regression analyses were performed to determine significant predictive factors for MI. A nomogram was then constructed based on the independent risk factors identified. A calibration curve, a receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used to evaluate the effectiveness of the nomogram. A total of 549 patients were randomly assigned to a training or validation cohort, with a 2:1 ratio. Data was then gathered on significant predictors of MI in the training cohort, including MRS [odds ratio (OR), 1.06; 95% confidence interval (CI), 1.01-1.11; P=0.023], histological type (OR, 1.98; 95% CI, 1.11-3.53; P=0.023), lymph node metastasis (OR, 3.15; 95% CI, 1.61-6.15; P<0.001) and tumor grade (grade 2: OR, 1.71; 95% CI, 1.23-2.39; P=0.002; Grade 3: OR, 2.10; 95% CI, 1.53-2.88; P<0.001). Multivariate analysis indicated that MRS was an independent risk factor for MI in both cohorts. A nomogram was generated to predict a patient's probability of MI based on the four independent risk factors. ROC curve analysis showed that, compared with the clinical model (model 1), the combined model with MRS (model 2) significantly improved the diagnostic accuracy of MI in patients with EC (area under the curve in model 1 vs. model 2: 0.737 vs. 0.828 in the training cohort and 0.713 vs. 0.759 in the validation cohort). Calibration plots showed that the training and validation cohorts were well calibrated. DCA showed that a net benefit is obtained from the application of the nomogram. Overall, the present study developed and validated a MRS-based nomogram predicting MI in patients with EC preoperatively. The establishment of this model may promote the use of precision medicine and targeted therapy in EC and has the potential to improve the prognosis of patients affected by EC.

Development and Validation of a Nomogram Based on Metabolic Risk Score for Assessing Lymphovascular Space Invasion in Patients with Endometrial Cancer

OBJECTIVE

This study assessed the predictive value of the metabolic risk score (MRS) for lymphovascular space invasion (LVSI) in endometrial cancer (EC) patients.

METHODS

We included 1076 patients who were diagnosed with EC between January 2006 and December 2020 in Peking University People's Hospital. All patients were randomly divided into the training and validation cohorts in a ratio of 2:1. Data on clinicopathological indicators were collected. Univariable and multivariable logistic regression analysis was used to define candidate factors for LVSI. A backward stepwise selection was then used to select variables for inclusion in a nomogram. The performance of the nomogram was evaluated by discrimination, calibration, and clinical usefulness.

RESULTS

Independent predictors of LVSI included differentiation grades (G2: OR = 1.800, 95% CI: 1.050-3.070, p = 0.032) (G3: OR = 3.49, 95% CI: 1.870-6.520, p < 0.001), histology (OR = 2.723, 95% CI: 1.370-5.415, p = 0.004), MI (OR = 4.286, 95% CI: 2.663-6.896, p < 0.001), and MRS (OR = 1.124, 95% CI: 1.067-1.185, p < 0.001) in the training cohort. A nomogram was established to predict a patient's probability of developing LVSI based on these factors. The ROC curve analysis showed that an MRS-based nomogram significantly improved the efficiency of diagnosing LVSI compared with the nomogram based on clinicopathological factors (p = 0.0376 and p = 0.0386 in the training and validation cohort, respectively). Subsequently, the calibration plot showed a favorable consistency in both groups. Moreover, we conducted a decision curve analysis, showing the great clinical benefit obtained from the application of our nomogram. However, our study faced several limitations. Further external validation and a larger sample size are needed in future studies.

CONCLUSION

MRS-based nomograms are useful for predicting LVSI in patients with EC and may facilitate better clinical decision-making.

Conditional estimates for uterine serous cancer: Tools for survivorship counseling and planning

OBJECTIVES

Develop conditional survival and risk-assessment estimates for uterine serous carcinoma (USC) overall and stratified by stage as tools for annual survivorship counseling and care planning.

METHODS

Patients in the National Cancer Data Base diagnosed between 2004 and 2014 with stage I-IV USC were eligible. Individuals missing stage or survival data or with multiple malignancies were excluded. Five-year conditional survival was estimated using the stage-stratified Kaplan-Meier method annually during follow-up. A standardized mortality ratio (SMR) estimated the proportion of observed to expected deaths in the U.S. adjusted for year, age, and race. The relationships between prognostic factors and survival were studied using multivariate Cox modeling at diagnosis and conditioned on surviving 5-years.

RESULTS

There were 14,575 participants, including 43% with stage I, 8% with stage II, 29% with stage III, and 20% with stage IV USC. Five-year survival at diagnosis vs. after surviving 5-years was 52% vs. 75% overall, 77% vs. 81% for stage I, 57% vs. 72% for stage II, 40% vs. 66% for stage III, and 17% vs. 60% for stage IV USC, respectively (P < 0.0001). Incremental improvements in 5-year conditional survival and reductions in SMR tracked with annual follow-up and higher stage. The adjusted risk of death at diagnosis vs. after surviving 5-years was 1.15 vs. 1.40 per 5-year increase of age, 1.26 vs. 1.68 for Medicaid insurance, 3.92 vs. 2.48 for stage III disease, and 6.65 vs. 2.79 for stage IV disease, respectively (P < 0.0001).

CONCLUSION

In USC, the evolution of conditional survival permits annual reassessments of prognosis to tailor survivorship counseling and care planning.

Correlation of Metabolic Factors with Endometrial Atypical Hyperplasia and Endometrial Cancer: Development and Assessment of a New Predictive Nomogram

Purpose

This study aimed to investigate the association of metabolic factors with endometrial atypical hyperplasia and endometrial cancer, and to develop a nomogram model to predict the risk of developing endometrial cancer.

Patients and Methods

We collected data of patients with endometrial atypical hyperplasia and endometrial cancer as the case group and then selected patients with simple hyperplasia or polypoid hyperplasia of the endometrium during the same period as the control group using the age-matched method. Laboratory results of metabolic factors were retrieved from the clinical data of the two groups of patients. Multivariable logistic regression analysis was used to determine the risk factors associated with endometrial malignant hyperplasia and to develop a nomogram prediction model of risk factors associated with endometrial malignant hyperplasia. Discrimination, calibration, and clinical usefulness of the prediction model were assessed using the C-index, calibration plot, and decision curve analysis.

Results

Predictors included in the column line graph model were age, hypertension, diabetes, BMI, uric acid, and hyperlipidemia. We calculated the C-index of the model and performed bootstrap validation. Our nomogram model had good discriminatory power and was well calibrated. Decision curve analysis was also used to guide the practical application of this column line graph model.

Conclusion

The development of endometrial malignant hyperplasia is significantly associated with metabolic factors: BMI > 25, hyperuricemia, and hyperlipidemia are the main risk factors. Hypertension, hyperglycemia and elevated CA199 were also associated with the development of endometrial malignant hyperplasia. The nomogram prediction model based on physical examination and laboratory testing developed in this study can be used as a rapid method for predicting the risk of endometrial malignancy development and screening for risk factors in a population of women with metabolism-related high-risk factors.

Prospective cohort study of metabolic syndrome and endometrial cancer survival

OBJECTIVE

Comorbidities are known to increase endometrial cancer risk, but the separate and combined impact of these risk factors on endometrial cancer survival remains unclear. This study aimed to determine the associations between metabolic syndrome and its components with disease-free survival, overall survival, endometrial cancer-specific survival and recurrence among endometrial cancer survivors.

METHODS

Cases from a population-based case-control study who were diagnosed with primary endometrial cancer between 2002 and 2006 in Alberta, Canada were followed until death or March 20, 2019. Baseline in-person interviews, direct anthropometric measurements and fasting blood samples were used to assess metabolic syndrome (presence of ≥3 of the following: waist circumference ≥ 88 cm, fasting blood glucose ≥100 mg/dL, triglycerides ≥150 mg/dL, high-density lipoprotein cholesterol <50 mg/dL and self-reported hypertension). Cox proportional hazards regression and Fine and Gray competing risk models were used to estimate multivariate-adjusted hazard ratios (95% CI) for these associations.

RESULTS

Among 540 endometrial cancer survivors, 325 had metabolic syndrome at diagnosis and 132 had a recurrence and/or died during the median 14.2 years of follow-up (range: 0.3-16.5 years). In multivariable analyses, being diagnosed with metabolic syndrome (HR = 1.98, 95% CI = 1.07-3.67) and having an elevated waist circumference (≥88 cm; HR = 2.12, 95% CI = 1.18-3.80; HR_{per 5 cm} = 1.21, 95% CI = 1.07-1.36) were associated with worse overall survival. Additionally, increasing waist circumference (per 5 cm) was also associated worse with disease-free survival (HR_{per 5 cm} = 1.11, 95% CI = 1.00-1.24).

CONCLUSION

The metabolic syndrome, in particular central adiposity, were associated with worse overall and disease-free survival in endometrial cancer survivors.