Association between mixed exposure to endocrine-disrupting chemicals and cognitive function in elderly Americans.

OBJECTIVES: Studies exploring the relationship between mixed exposure to endocrine-disrupting chemicals (EDCs) and cognition are limited, with even more scarce studies conducted in the elderly. The aim of this study was to investigate the association between mixed exposure to five categories of EDCs and cognition in elderly Americans. STUDY DESIGN: Cross-sectional study. METHODS: 727 participants from the 2011-2014 National Health and Nutrition Examination Survey were incorporated into this study, and the levels of 47 EDC metabolites were measured. Cognitive function was assessed using immediate recall test (IRT), delayed recall test (DRT), animal fluency test (AFT), and digit symbol substitution test (DSST), and all the cognitive test scores were standardized. The individual and combined effects of EDC metabolites on the cognitive function in older adults were assessed using three analytical methods. RESULTS: The results showed that exposure to perfluorononanoic acid, polychlorinated biphenyl (PCB) 199, and PCB 206 was associated with the z-scores on the cognitive tests. Negative associations between mixed exposure to EDCs and the AFT and Global z-scores and a positive relationship with the DRT z-score were found in the WQS regression. The BKMR results revealed a positive trend between the mixture of EDCs and the DRT z-score. However, compared to the median, exposure to mixtures in the 45th percentile and below was associated with a decreased DRT z-score. CONCLUSIONS: Mixed exposure to EDCs may adversely affect the global cognitive function in elderly individuals. Necessary measures are needed to restrict EDCs use to protect the cognitive health of older adults.

Improving the effectiveness of lifestyle interventions for gestational diabetes prevention: a meta-analysis and meta-regression.

BACKGROUND: Diet and exercise during pregnancy have been used to prevent gestational diabetes mellitus (GDM) with some success. OBJECTIVE: To examine the effectiveness of lifestyle intervention on GDM prevention and to identify key effectiveness moderators to improve the prevention strategy. SEARCH STRATEGY: Pubmed, Scopus, Cochrane, and cross-references were searched. SELECTION CRITERIA: Randomised controlled trials (RCTs) evaluating lifestyle interventions during pregnancy for GDM prevention. DATA COLLECTION AND ANALYSIS: Two independent reviewers extracted data. A random-effects model was used to analyse the relative risk (RR) and 95% confidence interval (95% CI). Meta-regressions and subgroup analyses were used to investigate important moderators of effectiveness. MAIN RESULTS: Forty-seven RCTs involving 15 745 participants showed that diet and exercise during pregnancy were preventive of GDM (RR 0.77, 95% CI 0.69-0.87). Four key aspects were identified to improve the preventive effect: targeting the high-risk population; an early initiation of the intervention; the correct intensity and frequency of exercise; and gestational weight gain management. Although 24 RCTs targeted women who were overweight or obese, body mass index (BMI) failed to predict the effectiveness of an intervention. Instead, interventions are most effective in high-incidence populations rather than simply in women who are overweight or obese. Furthermore, exercise of moderate intensity for 50-60 minutes twice a week could lead to an approximately 24% reduction in GDM. CONCLUSION: The best strategy to prevent GDM is to target the high-risk population predicted by risk evaluation models and to control the gestational weight gain of women through intensified diet and exercise modifications early in their pregnancy. TWEETABLE ABSTRACT: Four key effectiveness moderators of lifestyle interventions for GDM prevention.

Epithelial-mesenchymal transition biomarkers and support vector machine guided model in preoperatively predicting regional lymph node metastasis for rectal cancer.

BACKGROUND: Current imaging modalities are inadequate in preoperatively predicting regional lymph node metastasis (RLNM) status in rectal cancer (RC). Here, we designed support vector machine (SVM) model to address this issue by integrating epithelial-mesenchymal-transition (EMT)-related biomarkers along with clinicopathological variables. METHODS: Using tissue microarrays and immunohistochemistry, the EMT-related biomarkers expression was measured in 193 RC patients. Of which, 74 patients were assigned to the training set to select the robust variables for designing SVM model. The SVM model predictive value was validated in the testing set (119 patients). RESULTS: In training set, eight variables, including six EMT-related biomarkers and two clinicopathological variables, were selected to devise SVM model. In testing set, we identified 63 patients with high risk to RLNM and 56 patients with low risk. The sensitivity, specificity and overall accuracy of SVM in predicting RLNM were 68.3%, 81.1% and 72.3%, respectively. Importantly, multivariate logistic regression analysis showed that SVM model was indeed an independent predictor of RLNM status (odds ratio, 11.536; 95% confidence interval, 4.113-32.361; P<0.0001). CONCLUSION: Our SVM-based model displayed moderately strong predictive power in defining the RLNM status in RC patients, providing an important approach to select RLNM high-risk subgroup for neoadjuvant chemoradiotherapy.