Objective: To investigate and thoroughly understand the physical examination results of retired employees from a certain unit in Beijing, analyze their bone mineral density (BMD), and identify risk factors that may indicate osteoporosis. This provides a reference for the individualized prevention, identification, and control of osteoporosis among retired employees. Methods: The bone mineral density and potential factors of 148 retired employees from a unit in 2023 were analyzed and categorized into osteoporosis and non-osteoporosis groups. Key factors from the physical examinations of the two groups were compared. Spearman’s correlation analysis was used to determine the correlation between key factors and osteoporosis. Significant key factors were included in a regression analysis. A multivariate binary logistics regression was employed to identify risk factors indicative of osteoporosis. Results: Correlation analysis revealed that gender, age, and ECG ST-segment length were significantly associated with osteoporosis. Regression analysis showed that for each additional year of age, the likelihood of developing osteoporosis increased by 1.058 times; females were 2.865 times more likely to develop osteoporosis compared to males; the longer the ECG ST-segment, the higher the likelihood of osteoporosis. Conclusion: Gender, age, and ECG ST-segment length are significantly associated with osteoporosis. These indicators can provide reference points for early identification, early intervention, and reducing the incidence of osteoporosis in clinical settings.
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