Selenoprotein genetic variants may modify the association between serum selenium and oral cancer risk

Preoperative Serum Selenium Concentrations and Disease-Specific Survival in Patients With Oral Cancer: A Long-Term Follow-Up Study

BACKGROUND

Serum selenium (Se) concentration has been reported to be associated with the incidence of oral cancer. The association between serum Se and long-term survival in oral cancer patients is still unclear.

PURPOSE

The purpose of this study is to measure the association between serum Se and disease-specific survival (DSS).

STUDY DESIGN, SETTING, AND SAMPLE

This was a single-center, prospective cohort study conducted at the First Affiliated Hospital of Fujian Medical University (Fujian Province, China) from September 2011 to December 2018. The inclusion criteria were patients with newly diagnosed primary oral cancer confirmed by histology. The exclusion criteria were patients with recurrent oral cancer or metastatic cancer.

PREDICTOR VARIABLE

The predictor variable is the preoperative serum Se concentration measured using inductively coupled plasma-mass spectrometry.

MAIN OUTCOME VARIABLE(S)

The primary outcome variable is DSS calculated from the date of diagnosis to the date of death due to oral cancer or the end of follow-up, whichever occurred first.

COVARIATES

The covariates were age, sex, occupation, education level, body mass index, surgery therapy, adjuvant therapy, tumor node metastasis stage, and pathological grading.

ANALYSES

Kaplan-Meier survival analysis, Cox proportional hazards regression, and restricted cubic spline regression were utilized. P value < .05 was significant.

RESULTS

The sample was composed of 235 subjects with a median age of 59 years (ranged from 20 to 80 years) and 142 (60.43%) were male. The median follow-up was 54.90 months (interquartile range: 35.47). Se levels were associated with DSS (unadjusted hazard ratio [HR] = 0.70; 95% confidence interval [CI]: 0.54-0.91) suggesting that higher levels of Se are associated with longer or improved DSS. After adjustment of age, sex, occupation, education level, residence, tumor node metastasis stage, pathological grading, surgery therapy, radiotherapy, and chemotherapy, patients with higher serum Se had a better DSS (aHR = 0.67; 95% CI: 0.49-0.92). Of note, we found that the association between serum Se and DSS was observed only in patients with radiotherapy (aHR = 0.49; 95% CI: 0.33-0.73). And the protective effect of radiotherapy on survival was only observed in patients with higher Se concentrations (aHR = 0.36; 95% CI: 0.20-0.63). Additionally, there was a multiplicative interaction between Se and radiotherapy on the prognosis of oral cancer patients (Pinteraction<0.01).

CONCLUSION AND RELEVANCE

Our findings suggest that a high Se concentration might contribute to better DSS among oral cancer patients, and the effect may partly depend on radiotherapy treatment. Given these findings, additional research should focus on the role of Se in DSS among oral cancer patients and the interaction with radiotherapy.

Risk Prediction Models for Oral Cancer: A Systematic Review

In the last 30 years, there has been an increasing incidence of oral cancer worldwide. Earlier detection of oral cancer has been shown to improve survival rates. However, given the relatively low prevalence of this disease, population-wide screening is likely to be inefficient. Risk prediction models could be used to target screening to those at highest risk or to select individuals for preventative interventions. This review (a) systematically identified published models that predict the development of oral cancer and are suitable for use in the general population and (b) described and compared the identified models, focusing on their development, including risk factors, performance and applicability to risk-stratified screening. A search was carried out in November 2022 in the Medline, Embase and Cochrane Library databases to identify primary research papers that report the development or validation of models predicting the risk of developing oral cancer (cancers of the oral cavity or oropharynx). The PROBAST tool was used to evaluate the risk of bias in the identified studies and the applicability of the models they describe. The search identified 11,222 articles, of which 14 studies (describing 23 models), satisfied the eligibility criteria of this review. The most commonly included risk factors were age (n = 20), alcohol consumption (n = 18) and smoking (n = 17). Six of the included models incorporated genetic information and three used biomarkers as predictors. Including information on human papillomavirus status was shown to improve model performance; however, this was only included in a small number of models. Most of the identified models (n = 13) showed good or excellent discrimination (AUROC > 0.7). Only fourteen models had been validated and only two of these validations were carried out in populations distinct from the model development population (external validation). Conclusions: Several risk prediction models have been identified that could be used to identify individuals at the highest risk of oral cancer within the context of screening programmes. However, external validation of these models in the target population is required, and, subsequently, an assessment of the feasibility of implementation with a risk-stratified screening programme for oral cancer.

The role of rare earth elements and dietary intake in tongue cancer: a mediation analysis in southeast China

Objective

The current research aimed to examine how dietary intake and rare earth elements may affect the development of tongue cancer.

Methods

The serum levels of 10 rare earth elements (REEs) in 171 cases and 171 healthy matched controls were measured by inductively coupled plasma mass spectrometry (ICP-MS). The conditional logistic regression was used to examine the relationship between dietary intake, serum levels of 10 REEs, and tongue cancer. Mediation effect and multiplicative interaction analysis were then performed to estimate the potential contribution of REEs in dietary intake associated with tongue cancer.

Results

Compared with the control group, patients with tongue cancer consumed significantly less fish, seafood, fruit, green leafy vegetables, and non-green leafy vegetables, with higher serum praseodymium (Pr), dysprosium (Dy), and lanthanum (La) levels, and lower serum cerium (Ce) and scandium (Sc) levels. The interaction effect was observed between some REEs and food categories. Green vegetables' impact on the risk of tongue cancer is partially attributed to the La and Thorium (Th) elements (P < 0.05, the mediated proportion were 14.933% and 25.280%, respectively). The effect of non-green leafy vegetables for tongue cancer mediated via Pr, Dy, and Th (P < 0.05, the mediated proportion were 0.408%, 12.010%, and 8.969%, respectively), and the Sc components in seafood (P < 0.05, the mediated proportion was 26.120%) is partly responsible for their influence on the risk of tongue cancer.

Conclusion

The correlation between REEs and dietary intakes for tongue cancer is compact but intricate. Some REEs interact with food intake to influence tongue cancer, while others act as a mediator.

The association between essential trace element mixture and cognitive function in Chinese community-dwelling older adults

BACKGROUND

The evidence about the effect of essential trace element (ETE) mixture on cognitive function amongst older adults is limited. This study aims to evaluate the associations of single ETEs and ETE mixture with cognitive function using a representative sample of community-dwelling older adults in China.

METHODS

A total of 3814 older adults were included in the study. Urinary concentrations of selenium (Se), vanadium (V), cobalt (Co), strontium (Sr), and molybdenum (Mo) were detected by inductively coupled plasma mass spectrometry. Cognitive function in older adults was assessed using the Chinese version of the Mini-Mental State Examination (MMSE). Linear regression and Bayesian kernel machine regression (BKMR) were performed to explore the associations of single ETEs and ETE mixture with cognitive function, respectively.

RESULTS

Linear regression showed that urinary levels of Se and V were positively associated with MMSE scores in the adjusted single-element models. BKMR also showed marginally positive associations of Se and V with MMSE scores. Moreover, higher urinary levels of ETE mixture were significantly associated with increased MMSE scores in a dose-response pattern, and Se was the most important contributor within the mixture. Both Se and V demonstrated positive additive effects on the associations of other ETEs with MMSE scores, whereas Co had a negative additive effect.

CONCLUSIONS

V and Se are positively associated with cognitive function, individually and as a mixture. ETE mixture exhibits a linear dose-response association with improved cognitive function, with Se being the most important component within the mixture. Mixture analyses rather than single ETE analyses may provide a real-world perspective on the relationship between ETE mixture and cognitive function. Further cohort studies are needed to clarify the association of multiple ETEs with cognitive function.