Usefulness of albumin-globulin ratio as a clinical prognostic factor in patients with thyroid cancer treated with radioiodine

The efficacy of albumin-globulin ratio to predict prognosis in cancer patients

The goal of this systematic review was to identify all of the research within the last 10 years that investigated both the Albumin-Globulin Ratio (AGR) and outcomes of solid tumor cancer patients via quantitative prognostic variables. Multiple scientific databases were researched for journal articles that included keywords relating AGR to prognosis. Once isolated from the databases, the articles were de-duplicated and manually screened based on standardized inclusion/exclusion criteria in a blind format via Rayyan. The collective data were sorted by cancer type, corrected for population size, and used to calculate the average cut-off values for the most popular prognostic variables. In total, 18 independent types of cancer have been evaluated to see if AGR is a prognostic indicator based on multivariate analyses. The average cut-off value for AGR in overall survival was 1.356, while the average cut-off value for AGR in progression free survival was 1.292. AGR was found to be significantly associated with at least one prognostic variable in every type of cancer evaluated based on multivariate analyses. The ease of access and affordability of AGR makes it an invaluable tool applicable to nearly all patients. Overall, AGR is a proven prognostic variable that should always be considered in the evaluation of a solid tumor cancer patient's prognosis. Further research needs to be conducted studying the potential prognostic effect in more types of solid tumors.

Early warning signs of thyroid autoantibodies seroconversion: A retrospective cohort study

BACKGROUND

Serum anti-thyroid peroxidase antibody (anti-TPO) and anti-thyroglobulin antibody (anti-Tg) levels are key indicators for the diagnosis of autoimmune diseases, especially autoimmune thyroiditis. Before the thyroid autoantibodies turn from negative to positive, it is unknown whether any clinical indicators in the body play a warning role.

PURPOSE

To establish an early prediction model of seroconversion to positive thyroid autoantibodies.

METHODS

This retrospective cohort study collected information based on clinical laboratory data. A logistic regression model was used to analyse the risk factors associated with a change in thyroid autoantibodies to an abnormal status. A machine-learning approach was employed to establish an early warning model, and a nomogram was used for model performance assessment and visualisation. Receiver operating characteristic (ROC) curves, calibration curves, and decision curve analyses were used for internal and external validation.

RESULTS

Logistic regression analysis revealed that albumin to globulin ratio, triglyceride levels, and Glutamic acid levels among liver function and some metabolism-related indicators, high density lipoprotein C among metabolism-related indicators, and cystatin C among renal function indicators were all risk factors for thyroid antibody conversion (P<0.05). In addition, several indicators in the blood count correlated with thyroid conversion (P<0.05). Changes in the ratio of free thyroxine to free triiodothyronine were a risk factor for positive thyroid antibody conversion (OR_fT4/fT3=1.763; 95% confidence interval 1.554-2.000). The area under the curve (AUC) of the early warning model based on the positive impact of clinical laboratory indicators, age, and sex was 0.85, which was validated by both internal (AUC 0.8515) and external (AUC 0.8378) validation.

CONCLUSIONS

The early warning model of anti-TPO and anti-Tg conversion combined with some clinical laboratory indicators in routine physical examination has a stable warning efficiency.

Blood Profiles in the Prediction of Radioiodine Refractory Papillary Thyroid Cancer: A Case-Control Study

Purpose

Although most patients with papillary thyroid cancer can be cured by surgery and I-131 ablation, a small proportion will progress to radioactive iodine refractory (RAIR) thyroid cancer. The prediction of RAIR in its early stages can improve patient prognosis. The aim of this article is to evaluate the blood biomarkers in patients with RAIR and to establish a prediction model.

Patients and Methods

Data collected from patients with thyroid cancer that were enrolled from Jan. 2017 to Dec. 2021 were screened. RAIR was defined based on the criteria in the 2015 American Thyroid Association guidelines. The blood biomarkers from the study participants at three admissions timepoints (surgery and first and secondary I-131 ablations) were compared using both parametric and nonparametric tests to identify predictive factors for RAIR. Binary logistic regression analysis was used to construct a prediction model using parameters associated with surgical procedure decision. The model was then assessed with receiver operating characteristic curves.

Results

Thirty-six patients were included in the data analysis. Sixteen blood variables, including the low density lipoprotein-cholesterol-total cholesterol ratio, neutrophils, thyroglobulins, thyroglobulin antibody, thyroid peroxidase antibody, anion gap, etc., were revealed to be predictors for RAIR. The prediction model, which incorporated two parameters, reached an area under the curve of 0.861 (p<0.001).

Conclusion

Conventional blood biomarkers can be used in the prediction of early-stage RAIR. In addition, a prediction model incorporating multiple biomarkers can improve the predictive accuracy.

Prognostic value of pretreatment serum albumin−globulin ratio in urothelial carcinoma: A systematic review and meta-analysis

Objective

To evaluate whether pretreatment albumin−globulin ratio (AGR) can be used as a biomarker for predicting the prognosis of patients with urothelial carcinoma (UC).

Methods

We systematically searched PubMed, Web of Science, China National Knowledge Infrastructure (CNKI), Google Scholar and Cochrane Library; the search time was up to May 2022. Stata 16.0 was used for data processing and statistical analysis.

Results

We identified 12 studies with 5,727 patients from 317 unique citations during the meta-analysis. Our results suggested that a low AGR before treatment was significantly associated with poor overall survival (OS) [hazard ratio (HR) = 1.99, 95% confidence interval (CI) = 1.45-2.75, P < 0.001], cancer-specific survival (CSS) [HR=2.01, 95% CI = 1.50-2.69, P < 0.001] and recurrence-free survival (RFS) [HR=1.39, 95% CI = 1.12-1.72, P = 0.002]. Furthermore, we defined different subgroups according to ethnicity, cancer type, cut-off value, sample size and stage. Similar prognostic outcomes for OS and CSS were observed in most subgroups. However, for subgroup of stage, the low pretreatment AGR only predicted the poor survival of patients with non-metastatic UC.

Conclusion

Our meta-analysis revealed that the AGR before treatment could be used as a predictive biomarker to indicate the prognosis of UC patients during clinical practice, especially in patients with non-metastatic UC.