ObjectivesTo systematically evaluate the impact of Tai Chi on the fall and balance function of middle-aged and the elderly.MethodsPubMed, EMbase, Web of Science, The Cochrane Library, CNKI and WanFang Data databases were electronically searched to collect randomized controlled trials (RCTs) on the influence of Tai Chi on the balance function and fall of middle-aged and the elderly from inception to August, 2019. Two reviewers independently screened literature, extracted data and assessed the risk of bias of included studies. Meta-analysis was then performed by RevMan 5.3 software.ResultsA total of 20 RCTs involving 3 842 patients were included. The results of meta-analysis showed that the Tai Chi group was superior to the control group in the improvement of the fall rate (RR=0.82, 95%CI 0.75 to 0.90, P<0.000 01), single-leg standing test (MD=5.76, 95%CI 0.62 to 10.90, P=0.03), Berg balance scale (MD=1.04, 95%CI 0.23 to 1.85, P=0.01), timed up and go (MD=−0.71, 95%CI −0.88 to −0.54, P<0.000 01), chair stand test (MD=0.87, 95%CI 0.05 to 1.70, P=0.04) and other indicators, and the difference was statistically significant.ConclusionsThe current evidence shows that Tai Chi can improve the balance function of middle-aged and the elderly, reduce the fall rate, and have a certain preventive effect on falls. Due to limited quality and quantity of the included studies, more high quality studies are required to verify the above conclusion.
ObjectiveTo analyze the effects of alcohol consumption on oral flora of middle-aged and elderly men from the core area of southwestern China, and explore the relationship between excessive-alcohol-consumption-related flora and alcohol-related cancer.MethodsFrom March to June 2018, saliva samples of target subjects were collected for 16S ribosomal RNA gene sequencing, and a questionnaire survey which took drinking history of each participant as the target variable was conducted. According to the amount of alcohol consumed, the subjects were divided into non-drinking group, moderate-drinking group, and excessive-drinking group. The microbial analysis of α diversity, analysis of group difference of oral flora abundance, bacterial function prediction, and receiver operating characteristic (ROC) curve model prediction were carried out.ResultsA total of 59 subjects were included. There were 23 cases (39.0%) in the non-drinking group, 23 cases (39.0%) in the moderate-drinking group, and 13 cases (22.0%) in the excessive-drinking group. The average age was (61.90±8.85) years. Excessive drinking increased the abundance of oral flora (P<0.05), and could change the abundance of specific genus such as Peptostreptococcus and TM7［G-6］ (P<0.05) and regulate cancer-related pathways (P<0.05). ROC analysis found that a panel of three genus oral bacteria such as TM7［G-6］ might effectively distinguish the non-drinking group from the excessive-drinking group (area under curve=0.915).ConclusionsGenus of Peptostreptococcus and TM7_［G-6］ are the potential oral flora biomarkers for the excessive-drinking of target subjects. Some excessive drinking-related flora are closely related to oral cancer.
ObjectiveTo establish a hypertension prediction model for middle-aged and elderly people in China and to use the basic public health service database for performance validation. MethodsThe literature related to hypertension was retrieved from the internet. Using meta-analysis to assess the effect value of influencing factors. Statistically significant factors, which were also combined in the database, were extracted as the predictors of the models. The predictors’ effect values were logarithmarithm-transformed as the parameters of the Logit function model and the risk score model. Participants who were never diagnosed with hypertension at the physical examination of health service project of Hongguang Town Health Center in Pidu District of Chengdu from January 1, 2017, to January 1, 2022, were considered as the external validation group. ResultsA total of 15 original studies were involved in the meta-analysis and 11 statistically significant influencing factors for hypertension were identified, including age, female, systolic blood pressure, diastolic blood pressure, BMI, central obesity, triglyceride, smoking, drinking, history of diabetes and family history of hypertension. Of 4997 qualified participants, 684 individuals were identified with hypertension during the five-years follow-up. External validation indicated an AUC of 0.571 for the Logit function model and an AUC of 0.657 for the risk score model. ConclusionIn this study, we developed two different prediction models based on the results of meta-analysis. National basic public health service database is used to verify the models. The risk score model has a better prediction performance, which may help quickly stratify the risk class of the community crowd and strengthen the primary-level assistance system.