RDW与ALB比值在预测骨质疏松症中的价值
The predictive value for osteoporosis-based on RDW to ALB ratio
  
DOI:10.3969/j.issn.1006-7108.2026.01.007
中文关键词:  骨质疏松  红细胞分布宽度  白蛋白  机器学习
英文关键词:osteoporosis  red blood cell distribution width  albumin  machine learning
基金项目:新疆维吾尔自治区自然科学基金(2022D01C635);“天山英才”培养计划医药卫生高层次人才项目(TSYC202301B065)
作者单位
黄俊1 李淼1 魏静航1 程新春2* 1.新疆医科大学研究生学院新疆 乌鲁木齐 830001 2.新疆维吾尔自治区人民医院老年医学中心新疆 乌鲁木齐 830001 
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中文摘要:
      目的 探讨在新疆维吾尔自治区人民医院就诊的各年龄段患者中,红细胞分布宽度(red blood cell distribution width,RDW)与白蛋白(albumin,ALB)比值RAR与骨质疏松症(osteoporosis,OP)之间的关联性,并评估RAR作为预测骨质疏松症指标的价值。方法 选取2020年10月26日至2025年1月10日接受骨质疏松筛查的患者数据。研究纳入了常见的人口统计学特征、常规血细胞分析和生化分析指标,选择了数据完整度达到30%以上的变量进行分析。通过单因素和多因素逻辑回归分析探索各个变量与骨质疏松症结局的关系,以识别可能影响结果的因素。进一步,利用多因素逻辑回归模型和机器学习技术,在控制其他协变量的情况下,验证RAR与OP之间的统计学关联。最终,采用限制立方样条(restricted cubic spline,RCS)曲线确定RAR与OP关系的最佳截断点,以揭示其临床意义。结果 研究表明年龄、性别、饮酒习惯、体质量指数(BMI)、白细胞计数、嗜酸性粒细胞比例、中性粒细胞比例、AST/ALT比率、碱性磷酸酶(ALP)、高密度脂蛋白胆固醇(HDL-C)以及RAR均与骨质疏松症显著相关。机器学习的结果也支持了上述发现,证实了这些因素对预测骨质疏松症的重要性。结论 RAR与骨质疏松风险呈正相关,当RAR≥3.18时,骨质疏松发生风险增加。提示RAR可作为一种简便、经济的辅助指标,用于骨质疏松的早期风险评估。
英文摘要:
      Objective To investigate the association between the red blood cell distribution width to albumin ratio (RAR) and osteoporosis (OP) in different age groups at the Xinjiang Uygur Autonomous Region People's Hospital, and to evaluate the predictive value of RAR for OP. Methods Patients who underwent osteoporosis screening were selected from October 26, 2020 to January 10, 2025. Covariates included common demographic characteristics, routine hematological parameters, and standard biochemical indices, with variables included in the analysis if their data completion rate exceeded 30%. Univariable and multivariable logistic regression analyses were performed to explore the relationships between various factors and osteoporosis outcomes, identifying potential influencing variables for further investigation. Multivariable logistic regression models and machine learning algorithms were employed to confirm the statistical significance of RAR in predicting OP after adjusting for other covariates. Finally, restricted cubic spline (RCS) curves were used to determine the cutoff point of RAR and assess its clinical significance. Results In this study, age, sex, alcohol consumption, BMI, white blood cell count, eosinophil percentage, neutrophil percentage, AST/ALT ratio, ALP, HDL-C, and RAR were significantly associated with osteoporosis. Similar findings were observed in the machine learning models. Conclusion RAR was positively correlated with the risk of osteoporosis, with an increased risk of osteoporosis when the RAR valve over 3.18. It is suggested that RAR can be used as a simple and economical and assistant indicator for early risk assessment of osteoporosis.
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