[1]曾韵杰,刘树华,李小韵,等.基于铜死亡标志物铁氧化还原蛋白1和脂酰转移酶1的绝经后骨质疏松症风险预测模型构建[J].中医正骨,2026,38(01):23-28,34.
　ZENG Yunjie,LIU Shuhua,LI Xiaoyun,et al.Construction of a risk prediction model for postmenopausal osteoporosis based on cuproptosis markers ferredoxin 1 and lipoyltransferase 1[J].The Journal of Traditional Chinese Orthopedics and Traumatology,2026,38(01):23-28,34.
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基于铜死亡标志物铁氧化还原蛋白1和脂酰转移酶1的绝经后骨质疏松症风险预测模型构建()

《中医正骨》[ISSN:1001-6015/CN:41-1162/R]

卷:
第38卷
期数:
2026年01期
页码:
23-28,34
栏目:
骨质疏松症
出版日期:
2026-01-20

文章信息/Info

Title:
Construction of a risk prediction model for postmenopausal osteoporosis based on cuproptosis markers ferredoxin 1 and lipoyltransferase 1
作者:
曾韵杰1; 刘树华1; 李小韵1; 陈桐莹1; 黄宏兴2; 杨贻富1; 杨东升1; 付赛1; 万雷2
1.广州中医药大学第三临床医学院,广东 广州 510006; 2.广州中医药大学第三附属医院,广东 广州 510378
Author(s):
ZENG Yunjie1; LIU Shuhua1; LI Xiaoyun1; CHEN Tongying1; HUANG Hongxing2; YANG Yifu1; YANG Dongsheng1; FU Sai1; WAN Lei2
1.The Third Clinical Medical College of Guangzhou University of Chinese Medicine,Guangzhou 510006,Guangdong,China; 2.The Third Affiliated Hospital of Guangzhou University of Chinese Medicine,Guangzhou 510378,Guangdong,China
关键词:
骨质疏松; 绝经后;  细胞死亡;  铁氧化还原蛋白1;  脂酰转移酶1;  列线图表;  风险;  预测;  随机森林;  ROC曲线
Keywords:
osteoporosis; postmenopausal;  cell death;  ferredoxin 1;  lipoyltransferase 1;  nomograms;  risk;  forecasting;  random forest;  ROC curve
摘要:
目的:基于铜死亡标志物铁氧化还原蛋白1(ferredoxin 1,FDX1)和脂酰转移酶1(lipoyltransferase 1,LIPT1)构建绝经后骨质疏松症(postmenopausal osteoporosis,PMOP)风险预测模型。方法:选择2023年10月至2024年4月就诊的绝经后女性为研究对象。采用双能X射线吸收法检测受试者L1～L4骨密度; 采集外周静脉血,采用电化学发光免疫分析法检测血清Ⅰ型胶原交联羧基末端肽β特殊序列(β-isomerized carboxy-terminal telopeptide collagen typeⅠ,β-CTX)、Ⅰ型前胶原氨基末端肽(typeⅠprocollagen amino-terminal peptide,PⅠNP)和骨钙素(osteocalcin,OC)水平,采用ELISA技术检测血清FDX1、LIPT1水平。根据腰椎骨密度检测结果,将受试者分为骨质疏松组(腰椎骨密度T值≤-2.5)和非骨质疏松组(腰椎骨密度T值>-2.5)。对比2组血清FDX1、LIPT1、β-CTX、PⅠNP、OC水平及骨密度6个PMOP风险预测模型相关变量的组间差异; 采用Spearman相关分析分析血清FDX1、LIPT1水平与骨密度及血清β-CTX、PⅠNP、OC水平的相关性; 采用随机森林算法和受试者操作特征(receiver operating characteristic,ROC)曲线评估上述变量预测PMOP风险的重要性和能力,基于上述变量构建PMOP风险预测模型。结果:①PMOP风险预测模型相关变量对比结果。共纳入90例受试者,骨质疏松组61例、非骨质疏松组29例。2组血清FDX1、LIPT1、β-CTX、PⅠNP、OC水平及骨密度的组间差异均有统计学意义(Z=-6.741,P=0.000; Z=-7.420,P=0.000; Z=-3.333,P=0.001; Z=-2.456,P=0.014; Z=-2.931,P=0.003; t=12.355,P=0.000)。②相关性分析结果。血清LIPT1水平与骨密度呈正相关(rs=0.579,P=0.000),与血清β-CTX水平呈负相关(rs=-0.257,P=0.015),与血清PⅠNP、OC水平不存在相关性(rs=-0.140,P=0.188; rs=-0.192,P=0.070); 血清FDX1水平与骨密度呈负相关(rs=-0.466,P=0.000),与血清β-CTX水平呈正相关(rs=0.239,P=0.024),与血清PⅠNP、OC水平不存在相关性(rs=0.155,P=0.144; rs=0.198,P=0.062); 血清LIPT1水平与血清FDX1水平呈负相关(rs=-0.798,P=0.000)。③相关变量预测PMOP风险的重要性和能力评估结果。随机森林分析结果显示,血清LIPT1水平、骨密度、血清FDX1水平是预测PMOP风险重要性排序前3的变量。ROC曲线分析结果显示,骨密度及血清FDX1、LIPT1、β-CTX、PⅠNP、OC水平均对PMOP具有一定的诊断价值(AUC=0.996,AUC=0.941,AUC=0.985,AUC=0.718,AUC=0.661,AUC=0.692),其中骨密度和血清FDX1、LIPT1水平的诊断价值更高。④PMOP风险预测模型构建结果。基于骨密度及血清FDX1、LIPT1、β-CTX、PⅠNP、OC水平构建PMOP风险列线图预测模型。校准曲线图显示校准曲线与理想对角线高度重合; 决策曲线图显示,在阈值概率范围内,该模型进行决策所获得的临床净获益高于对所有患者进行干预和不对任何患者进行干预这两种极端策略; 临床影响曲线图中实际事件数曲线紧贴高危患者数曲线。结论:铜死亡标志物FDX1、LIPT1与PMOP的发病关系密切; 联合铜死亡标志物FDX1、LIPT1及骨密度、骨转换标志物(β-CTX、PⅠNP、OC)构建的PMOP风险预测模型具有良好的诊断效能和准确度。
Abstract:
Objective:To construct a risk prediction model for postmenopausal osteoporosis(PMOP)based on the cuproptosis markers ferredoxin 1(FDX1)and lipoyltransferase 1(LIPT1).Methods:Postmenopausal women who visited our hospital between October 2023 and April 2024 were selected as the study subjects.Bone mineral density(BMD)of L1-L4 was measured using dual-energy X-ray absorptiometry; peripheral venous blood was collected,and serum levels of β-isomerized carboxy-terminal telopeptide collagen typeⅠ(β-CTX),typeⅠprocollagen amino-terminal peptide(PⅠNP),and osteocalcin(OC)were measured using electrochemiluminescence immunoassay,while serum levels of FDX1 and LIPT1 were detected using ELISA.Based on lumbar spine BMD T-scores,the subjects were divided into an osteoporosis group(T-scores≤-2.5)and a non-osteoporosis group(T-scores>-2.5).The differences in serum FDX1,LIPT1,β-CTX,PⅠNP,OC levels,and BMD—the six variables related to the PMOP risk prediction model—were compared between the two groups.Spearman correlation analysis was used to analyze the correlation between serum FDX1 and LIPT1 levels and BMD as well as serum β-CTX,PⅠNP,and OC levels.Random forest algorithm and receiver operating characteristic(ROC)curves were used to evaluate the importance and capability of the above variables in predicting PMOP risk,and a PMOP risk prediction model was constructed based on these variables.Results:①Comparison results of variables related to the PMOP risk prediction model.A total of 90 subjects were included,including 61 cases in the osteoporosis group and 29 cases in the non-osteoporosis group.The differences in serum FDX1,LIPT1,β-CTX,PⅠNP,OC levels and BMD between the two groups were statistically significant(Z=-6.741,P=0.000; Z=-7.420,P=0.000; Z=-3.333,P=0.001; Z=-2.456,P=0.014; Z=-2.931,P=0.003; t=12.355,P=0.000).②Results of correlation analysis.Serum LIPT1 level was positively correlated with BMD(rs=0.579,P=0.000),negatively correlated with serum β-CTX level(rs=-0.257,P=0.015),and not correlated with serum PⅠNP and OC levels(rs=-0.140,P=0.188; rs=-0.192,P=0.070).Serum FDX1 level was negatively correlated with BMD(rs=-0.466,P=0.000),positively correlated with serum β-CTX level(rs=0.239,P=0.024),and not correlated with serum PⅠNP and OC levels(rs=0.155,P=0.144; rs=0.198,P=0.062).Serum LIPT1 level was negatively correlated with serum FDX1 level(rs=-0.798,P=0.000).③Results of importance and capability evaluation of relevant variables in predicting PMOP risk.Random forest analysis results showed that serum LIPT1 level,BMD,and serum FDX1 level were the top 3 variables in terms of importance for predicting PMOP risk.ROC curve analysis results showed that BMD and serum FDX1,LIPT1,β-CTX,PⅠNP,and OC levels all had certain diagnostic value for PMOP(AUC=0.996,AUC=0.941,AUC=0.985,AUC=0.718,AUC=0.661,AUC=0.692),among which the diagnostic value of BMD and serum FDX1 and LIPT1 levels was higher.④Results of PMOP risk prediction model construction.A PMOP risk nomogram prediction model was constructed based on BMD and serum FDX1,LIPT1,β-CTX,PⅠNP,and OC levels.The calibration curve showed that the calibration curve highly overlapped with the ideal diagonal.The decision curve showed that within the threshold probability range,the clinical net benefit obtained by decision-making using this model was higher than the two extreme strategies of intervening on all patients or not intervening on any patient.In the clinical impact curve,the actual number of events curve closely followed the high-risk number curve.Conclusion:Cuproptosis markers FDX1 and LIPT1 are closely related to the pathogenesis of PMOP.The PMOP risk prediction model constructed by combining cuproptosis markers FDX1,LIPT1 with BMD and bone turnover markers(β-CTX,PⅠNP,OC)demonstrates good diagnostic efficacy and accuracy.

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备注/Memo

备注/Memo:
基金项目:国家自然科学基金项目(82174395); 广东省自然科学基金项目(2022A1515012067); 广东省普通高校特色创新项目(自然科学)(2022KTSCX026); 广东省科技计划项目(2024A1111120018)
通信作者:万雷 E-mail:gzwl802@163.com
(收稿日期:2024-12-03 本文编辑:李晓乐)
更新日期/Last Update: 2026-01-20