Background This study examines the association between socioeconomic and lifestyle factors and the prevalence of hypertension among elderly individuals in rural Southwest China. Methods A cross-sectional survey of 4833 consenting adults aged ≥ 60 years in rural regions of Yunnan Province, China, was conducted in 2017. Data on individual socioeconomic status, sleep quality, physical activity level, and family history of hypertension were collected with a standardized questionnaire. Blood pressure, fasting blood glucose, height, weight, and waist circumference were also measured. An individual socioeconomic position (SEP) index was constructed using principal component analysis. Structural equation modelling (SEM) was applied to analyse the association between socioeconomic and lifestyle factors and the prevalence of hypertension. Results The overall prevalence of hypertension was 50.6% in the study population. Body fat distribution, including measures of obesity and central obesity, had the greatest total effect on hypertension (0.21), followed by family history of hypertension (0.14), biological sex (0.08), sleep quality (− 0.07), SEP (− 0.06), physical inactivity (0.06), and diabetes (0.06). Body fat distribution, SEP, and family history of hypertension had both direct and indirect effects on hypertension, whereas physical inactivity, diabetes, and sleep quality were directly associated with the prevalence of hypertension. Biological sex was indirectly associated with the prevalence of hypertension. Conclusions SEP, body fat distribution, physical inactivity, diabetes, and sleep quality critically influence the prevalence of hypertension. Future interventions to prevent and control hypertension should give increased attention to individuals with low SEP and should focus on controlling diabetes and obesity, increasing physical activity levels, and improving quality of sleep among older adults aged ≥ 60 years in rural Southwest China.

The genetic polymorphisms of 20 autosomal short tandem repeat (STR) loci included in the PowerPlex® 21 kit were evaluated from 2068 unrelated, healthy individuals from the Chinese Han population of Yunnan Province in southwest China. All of the loci reached Hardy-Weinberg equilibrium. These loci were examined to determine allele frequencies and forensic statistical parameters. The genetic relationships among the Yunnan Han and other Chinese populations were also estimated. The combined discrimination power and probability of excluding paternity of the 20 STR loci were 0.99999999999999999999999126 and 0.999999975, respectively. In addition, mutation rates from 4363 parentage cases (2215 trios and 2148 duos) were investigated in this study. A total of 164 mutations were observed in 6578 meioses from the 20 loci. The highest mutation rate was observed in D12S391 (0.30%), and the lowest mutation rates were observed in D13S317 (0.03%) and TPOX (0.03%). The average mutation rate for the 20 loci was estimated to be 1.246 × 10 per meiosis. The mutations were primarily single-step and paternal mutations.

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Background: This study examines the association between socioeconomic and lifestyle factors and the prevalence of hypertension among the elderly in rural southwest China.Methods: A cross-sectional survey of 4,833 consenting adults aged ≥60 years in rural regions of Yunnan Province, China was conducted in 2017. Data on individual socioeconomic status, sleep quality, physical activity level, and family history of hypertension were collected with a standardized questionnaire. Blood pressure, fasting blood glucose, height, weight, and waist circumference were also measured. An individual socioeconomic position (SEP) index was constructed using principal component analysis. Structure equation modeling (SEM) was applied to analyze the association between socioeconomic and lifestyle factors and the prevalence of hypertension. Results: The overall prevalence of hypertension was 50.6% in the study population. The following associated factors had statistically significant effect on hypertension: body composition, including measures of obesity and central obesity, had the greatest total effect on hypertension (0.21), followed by family history of hypertension (0.14), gender (0.08), sleep quality (-0.07), SEP (-0.06), physical inactivity (0.06), and diabetes (0.06). Body composition, SEP, and family history of hypertension had both direct and indirect effects on hypertension, whereas those of physical inactivity, diabetes, and sleep quality were directly associated with prevalence of hypertension. Gender was indirectly associated with prevalence of hypertension. Conclusion: Individual SEP, body composition, physical inactivity, diabetes, and sleep quality critically influence the prevalence of hypertension. Future interventions to prevent and control hypertension should give increased attention to individuals with low SEP and should focus on controlling diabetes and obesity, increasing physical activity levels, and improving quality of sleep among older adults aged ≥60 years in rural southwest China.