Background: Studies have shown the positive impact of perceived social support on cognitive function among older adults in rural areas. However, existing studies often overlook the impact of different support sources. This study aimed to explore the relationship between the diverse sources of perceived social support and cognitive function.Methods: Participants were drawn from the Guizhou Rural Older Adults’ Health Study (HSRO) in China. We included 791 participants who participated in a baseline survey in 2019 and a 3-year follow-up survey. Perceived social support was investigated from the six main sources (friend, relative, children, spouse, sibling, and neighbor). Hierarchical linear regression models were used to observe the effects of diverse sources of perceived social support and their combinations on cognitive function.Results: Cognitive function was positively associated with perceived support from children, friends, and neighbors. A positive association was found between cognitive function and increases in each additional source [β = 0.75 (95%CI: 0.51, 0.98), p < 0.001]. Older adults who perceived support from both children and friends showed better cognitive function [β = 2.53 (95%CI: 1.35, 3.72), p < 0.001]. The perception of support from spouse, siblings, and relatives did not show a statistically significant association with cognitive function among older adults in rural areas.Conclusion: This study found that the association between different sources of perceived social support and cognitive function was varied. This study provides scientific evidence that personalized support strategies may benefit in promoting cognitive health in rural older adults.

For a diallelic genetic marker locus, tests like the parental-asymmetry test (PAT) are simple and powerful for detecting parent-of-origin effects. However, these approaches are applicable only to qualitative traits and thus are currently not suitable for quantitative traits. In this paper, the authors propose a novel class of PAT-type parent-of-origin effects tests for quantitative traits in families with both parents and an arbitrary number of children, which is denoted by Q-PAT(c) for some constant c. The authors further develop Q-1-PAT(c) for detection of parent-of-origin effects when information is available on only 1 parent in each family. The authors suggest the Q-C-PAT(c) test for combining families with data on both parental genotypes and families with data on only 1 parental genotype. Simulation studies show that the proposed tests control the empirical type I error rates well under the null hypothesis of no parent-of-origin effects. Power comparison also demonstrates that the proposed methods are more powerful than the existing likelihood ratio test. Although normality is commonly assumed in methods for studying quantitative traits, the tests proposed in this paper do not make any assumption about the distribution of the quantitative trait.

Purpose: To explore clinical and non-clinical characteristics affecting the prognosis of patients with differentiated thyroid cancer with distant metastasis (DTCDM) and establish an accurate overall survival (OS) prognostic model.Patients and methods: Study subjects and related information were obtained from the National Cancer Institute’s surveillance, epidemiology, and results database (SEER). Kaplan‐Meier analysis, log-rank test, and univariate and multivariate Cox analysis were used to screen for factors influencing the OS of patients with DTCDM. Nine variables were introduced to build a machine learning (ML) model, receiver operating characteristic (ROC) was used to evaluate the recognition ability of the model, calibration plots were used to obtain prediction accuracy, and decision curve analysis (DCA) was used to estimate clinical benefit.Results: After applying the inclusion and exclusion criteria, a total of 3,060 patients with DTCDM were included in the survival analysis from 2004 to 2017. A machine learning prediction model was developed with nine variables: age at diagnosis, gender, race, tumor size, histology, regional lymph node metastasis, primary site surgery, radiotherapy, and chemotherapy. After excluding patients who survived <120 months, variables were sub-coded and machine learning was used to model OS prognosis in patients with DTCDM. Patients 6–50 years of age had the highest scores in the model. Other variables with high scores included small tumor size, male sex, and age 51–76. The AUC and calibration curves confirm that the XGBoost model has good performance. DCA shows that our model can be used to support clinical decision-making in a 10-years overall survival model.Conclusion: An artificial intelligence model was constructed using the XGBoost algorithms to predict the 10-years overall survival rate of patients with DTCDM. After model validation and evaluation, the model had good discriminative ability and high clinical value. This model could serve as a clinical tool to help inform treatment decisions for patients with DTCDM.

Background: Poststroke cognitive impairment is a severe and common clinical complication that constitutes a substantial global health burden. We aimed to evaluate the association of 3 cardiac biomarkers in combination with poststroke cognitive impairment and their prognostic significance. Methods and Results: This prospective study included 566 patients with ischemic stroke. Cardiac biomarkers, including sST2 (soluble suppression of tumorigenicity‐2 receptor), GDF‐15 (growth differentiation factor‐15), and NT‐proBNP (N‐terminal pro‐B‐type natriuretic peptide), were measured. Cognitive impairment was defined as a Mini‐Mental State Examination score of <27 or a Montreal Cognitive Assessment score of <25 at 3 months after ischemic stroke. Odds of cognitive impairment 3 months after ischemic stroke increased with the number of elevated cardiac biomarkers (sST2, GDF‐15, and NT‐proBNP; P _trend <0.001). The multivariable adjusted odds ratios (95% CIs) of cognitive impairment defined by the Mini‐Mental State Examination and Montreal Cognitive Assessment were 2.45 (1.48–4.07) and 1.86 (1.10–3.14) for the participants with ≥2 elevated cardiac biomarkers, respectively, compared with those without any elevated cardiac biomarker. Additionally, higher cardiac biomarker scores were associated with an increased risk of cognitive impairment ( P _trend <0.05). Simultaneously adding all 3 cardiac biomarkers to the basic model with traditional risk factors significantly improved the risk prediction of Mini‐Mental State Examination‐defined cognitive impairment (net reclassification improvement=34.99%, P <0.001; integrated discrimination index=2.67%, P <0.001). Similar findings were observed using the Montreal Cognitive Assessment scores. Conclusions: An increased number of elevated novel cardiac biomarkers were associated with an increased odds of poststroke cognitive impairment, suggesting that a combination of these cardiac biomarkers may improve the risk prediction of cognitive impairment. Registration: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT01840072.

Background: Studies that address the changing characteristics of diseases are of great importance for preventing and controlling the occurrence and development of diseases and for improving health. However, studies of the epidemiological characteristics of noncommunicable diseases (NCDs) and malignant tumors (MTs) of the residents in Guiyang, China, are lacking. Objective: The aim of this study was to evaluate the prevalences of NCDs and MTs in residents of Guiyang, Guizhou Province, China, and analyze differences among ages, genders, and regions. Methods: A multistage stratified cluster sampling method was used. Based on the inclusion and exclusion criteria, 81,517 individuals were selected for the study. Of these, 77,381 (94.9%) participants completed the study. Structured questionnaires were used to collect information on demographic characteristics, NCDs, and MTs. The chi-square test (with 95% confidence intervals) was used to analyze differences in disease prevalence among genders, ages, and geographical regions. Results: The major chronic NCDs of Guiyang residents are obesity, hypertension, and diabetes. MTs in women are mostly breast cancer, cervical cancer, and endometrial cancer, whereas in men, MTs are mainly lung cancer, rectal cancer, and gastric cancer. The prevalences of hypertension and diabetes in women are higher than in men, but the prevalences of lung cancer and gastric cancer in men are higher than in women. The epidemiological characteristics of individuals in different life stages are dissimilar. In terms of regional distribution, the prevalences of the above diseases in the Baiyun and Yunyan districts of Guiyang are relatively high. Conclusions: Several NCDs (obesity, hypertension, and diabetes) and MTs (women: breast cancer, cervical cancer, and endometrial cancer; men: lung cancer, rectal cancer, and gastric cancer) should be the focus for the prevention and control of chronic diseases in the future. In particular, the Baiyun and Yunyan districts of Guiyang are the important regions to emphasize.

Background: With over 2 million new cases annually, stroke is associated with the higher amount of disability-adjusted life-years lost than any other disease in China; however, the relationship between sleep time and stroke has not been concluded yet. Aim of this study was to analyze the relationship between sleep duration and stroke history in middle-aged and elderly people in Guiyang, China. Methods: This study was a cross-sectional survey carried out in 40– 99-years-old permanent residents of Guiyang. Yunyan, Wudang, and Baiyun districts and Xiuwen County were selected by stratified multilevel sampling for a face-to-face survey. Demographics, history of stroke, and self-reported sleep behavior data were collected, and multivariable logistic regression models were used to gradually adjust possible confounding factors. Results: A total of 5065 participants were included, of them 126 (2.5%) had a history of stroke. Short sleep (< 7 h) was observed in 11.0%, sufficient sleep (7– 9 h) in 69.4%, and long sleep (> 9 h) in 19.6%. Sleep duration and stroke prevalence showed a U-shaped distribution. When taking the sleep duration of 7– 9 h as a reference, sleep duration > 9 h was associated with stroke (all P < 0.05) in the univariable model (OR = 2.68, 95% CI: 1.83– 3.93) and in the multivariable models 1 (OR = 2.35, 95% CI: 1.59– 3.47), 2 (OR = 2.27, 95% CI: 1.53– 3.37), 3 (OR = 2.25, 95% CI: 1.51– 3.33), and 4 (OR = 2.11, 95% CI: 1.39– 3.19). There were no significant differences between the < 7 and 7– 9 h groups (P > 0.05). Conclusion: Thus, long sleep duration (> 9 h) is independently associated with history of stroke in middle-aged and elderly people in Guiyang.