Rong Xie, Li-Guo Zhu, Zi-Kai Jin, Tian-Xiao Feng, Ke Zhao, Da Wang, Ling-Hui Li, Xu Wei
{"title":"绝经后骨质疏松合并血脂异常患者临床特点及影响因素分析","authors":"Rong Xie, Li-Guo Zhu, Zi-Kai Jin, Tian-Xiao Feng, Ke Zhao, Da Wang, Ling-Hui Li, Xu Wei","doi":"10.12200/j.issn.1003-0034.20250217","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>To explore the co-morbid influencing factors of postmenopausal osteoporosis(PMOP) and dyslipidemia, and to provide evidence-based basis for clinical co-morbidity management.</p><p><strong>Methods: </strong>Based on the 2017 to 2018 Beijing community cross-sectional survey data, PMOP patients were included and divided into the dyslipidemia group and the uncomplicated dyslipidemia group according to whether they were comorbid with dyslipidemia. Demographic characteristics, living habits and disease history were collected through questionnaires, and bone mineral density and bone metabolism biomarkers (osteocalcin, blood calcium, serum typeⅠprocollagen N-terminal prepeptide, etc.) were detected on site. Co-morbidity risk factors were analyzed using binary logistic regression.</p><p><strong>Results: </strong>Three hundred and twenty patients with PMOP were included, including the comorbid group (75 patients) and the uncomplicated group (245 patients). The results showed that history of cardiovascular disease [<i>OR</i>=1.801, 95%<i>CI</i>(1.003, 3.236), <i>P</i>=0.049], history of cerebrovascular disease [<i>OR</i>=2.923, 95%<i>CI</i>(1.460, 5.854), <i>P</i>=0.002], frying and cooking methods[<i>OR</i>=5.388, 95%<i>CI</i>(1.632, 17.793), <i>P</i>=0.006], OST results[<i>OR</i>=0.910, 95%<i>CI</i>(0.843, 0.983), <i>P</i>=0.016], and blood Ca results [<i>OR</i>=60.249, 95%<i>CI</i>(1.862, 1 949.926), <i>P</i>=0.021] were the influencing factors of PMOP complicated with dyslipidemia.</p><p><strong>Conclusion: </strong>Focus should be placed on the influencing factors of PMOP and dyslipidemia co-morbidities, with emphasis on multidimensional assessment, combining lifestyle interventions with bone metabolism marker monitoring to optimize co-morbidity management.</p>","PeriodicalId":23964,"journal":{"name":"Zhongguo gu shang = China journal of orthopaedics and traumatology","volume":"38 5","pages":"487-93"},"PeriodicalIF":0.0000,"publicationDate":"2025-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"[Analysis of clinical characteristics and influencing factors of patients with postmenopausal osteoporosis combined with dyslipidemia].\",\"authors\":\"Rong Xie, Li-Guo Zhu, Zi-Kai Jin, Tian-Xiao Feng, Ke Zhao, Da Wang, Ling-Hui Li, Xu Wei\",\"doi\":\"10.12200/j.issn.1003-0034.20250217\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objective: </strong>To explore the co-morbid influencing factors of postmenopausal osteoporosis(PMOP) and dyslipidemia, and to provide evidence-based basis for clinical co-morbidity management.</p><p><strong>Methods: </strong>Based on the 2017 to 2018 Beijing community cross-sectional survey data, PMOP patients were included and divided into the dyslipidemia group and the uncomplicated dyslipidemia group according to whether they were comorbid with dyslipidemia. Demographic characteristics, living habits and disease history were collected through questionnaires, and bone mineral density and bone metabolism biomarkers (osteocalcin, blood calcium, serum typeⅠprocollagen N-terminal prepeptide, etc.) were detected on site. Co-morbidity risk factors were analyzed using binary logistic regression.</p><p><strong>Results: </strong>Three hundred and twenty patients with PMOP were included, including the comorbid group (75 patients) and the uncomplicated group (245 patients). The results showed that history of cardiovascular disease [<i>OR</i>=1.801, 95%<i>CI</i>(1.003, 3.236), <i>P</i>=0.049], history of cerebrovascular disease [<i>OR</i>=2.923, 95%<i>CI</i>(1.460, 5.854), <i>P</i>=0.002], frying and cooking methods[<i>OR</i>=5.388, 95%<i>CI</i>(1.632, 17.793), <i>P</i>=0.006], OST results[<i>OR</i>=0.910, 95%<i>CI</i>(0.843, 0.983), <i>P</i>=0.016], and blood Ca results [<i>OR</i>=60.249, 95%<i>CI</i>(1.862, 1 949.926), <i>P</i>=0.021] were the influencing factors of PMOP complicated with dyslipidemia.</p><p><strong>Conclusion: </strong>Focus should be placed on the influencing factors of PMOP and dyslipidemia co-morbidities, with emphasis on multidimensional assessment, combining lifestyle interventions with bone metabolism marker monitoring to optimize co-morbidity management.</p>\",\"PeriodicalId\":23964,\"journal\":{\"name\":\"Zhongguo gu shang = China journal of orthopaedics and traumatology\",\"volume\":\"38 5\",\"pages\":\"487-93\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-05-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Zhongguo gu shang = China journal of orthopaedics and traumatology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.12200/j.issn.1003-0034.20250217\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"Medicine\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Zhongguo gu shang = China journal of orthopaedics and traumatology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.12200/j.issn.1003-0034.20250217","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Medicine","Score":null,"Total":0}
[Analysis of clinical characteristics and influencing factors of patients with postmenopausal osteoporosis combined with dyslipidemia].
Objective: To explore the co-morbid influencing factors of postmenopausal osteoporosis(PMOP) and dyslipidemia, and to provide evidence-based basis for clinical co-morbidity management.
Methods: Based on the 2017 to 2018 Beijing community cross-sectional survey data, PMOP patients were included and divided into the dyslipidemia group and the uncomplicated dyslipidemia group according to whether they were comorbid with dyslipidemia. Demographic characteristics, living habits and disease history were collected through questionnaires, and bone mineral density and bone metabolism biomarkers (osteocalcin, blood calcium, serum typeⅠprocollagen N-terminal prepeptide, etc.) were detected on site. Co-morbidity risk factors were analyzed using binary logistic regression.
Results: Three hundred and twenty patients with PMOP were included, including the comorbid group (75 patients) and the uncomplicated group (245 patients). The results showed that history of cardiovascular disease [OR=1.801, 95%CI(1.003, 3.236), P=0.049], history of cerebrovascular disease [OR=2.923, 95%CI(1.460, 5.854), P=0.002], frying and cooking methods[OR=5.388, 95%CI(1.632, 17.793), P=0.006], OST results[OR=0.910, 95%CI(0.843, 0.983), P=0.016], and blood Ca results [OR=60.249, 95%CI(1.862, 1 949.926), P=0.021] were the influencing factors of PMOP complicated with dyslipidemia.
Conclusion: Focus should be placed on the influencing factors of PMOP and dyslipidemia co-morbidities, with emphasis on multidimensional assessment, combining lifestyle interventions with bone metabolism marker monitoring to optimize co-morbidity management.