一种新的骨质疏松预测模型的开发和验证:从血清素到脂溶性维生素。

IF 4.7 2区 医学 Q2 CELL & TISSUE ENGINEERING
Jinpeng Wang, Lianfeng Shan, Jing Hang, Hongyang Li, Yan Meng, Wenhai Cao, Chunjian Gu, Jinna Dai, Lin Tao
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引用次数: 0

摘要

目的:我们旨在建立并验证一种基于血清素、脂溶性维生素和骨转换标志物的骨质疏松症预测模型,以提高骨质疏松症的预测准确性。方法:招募55 ~ 65岁绝经后妇女,根据DXA分为三组(正常、骨质减少和骨质疏松)。本研究共纳入109名参与者,分为健康组(39/109,35.8%)、骨质减少组(35/109,32.1%)和骨质疏松组(35/109,32.1%)。测量了参与者血清中血清素、脂溶性维生素和骨转换标志物的浓度。采用逐步判别分析来确定骨质疏松症的有效预测因子。基于Bayes和Fisher的判别函数建立预测模型,并通过留一交叉验证进行验证。采用受试者工作特征(ROC)面(VUS)检验下的正态体积和经验体积来评价预测模型中各变量的预测效果。结果:采用雌激素(E2)、总前胶原1型氨基末端前肽(TP1NP)、甲状旁腺激素(PTH)、BMI、维生素K、血清素、骨钙素(OSTEOC)、维生素A、维生素D3等显著变量建立预测模型。正常、骨质减少和骨质疏松的训练准确率分别为74.4%(29/39)、80.0%(28/35)和85.7%(30/35),总训练准确率为79.8%(87/109)。内部验证结果表明,该方法性能优良,检测准确率为72.5%(72/109)。在这些变量中,血清素和维生素K在骨质疏松症的预测中发挥重要作用。结论:我们成功建立并验证了一种基于血清血清素、脂溶性维生素和骨转换标志物的骨质疏松症预测模型。此外,在这项研究中,血清素和脂溶性维生素之间的互动交流被观察到对骨骼健康至关重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Development and validation of a novel prediction model for osteoporosis : from serotonin to fat-soluble vitamins.

Aims: We aimed to develop and validate a novel prediction model for osteoporosis based on serotonin, fat-soluble vitamins, and bone turnover markers to improve prediction accuracy of osteoporosis.

Methods: Postmenopausal women aged 55 to 65 years were recruited and divided into three groups based on DXA (normal, osteopenia, and osteoporosis). A total of 109 participants were included in this study and split into healthy (39/109, 35.8%), osteopenia (35/109, 32.1%), and osteoporosis groups (35/109, 32.1%). Serum concentrations of serotonin, fat-soluble vitamins, and bone turnover markers of participants were measured. Stepwise discriminant analysis was performed to identify efficient predictors for osteoporosis. The prediction model was developed based on Bayes and Fisher's discriminant functions, and validated via leave-one-out cross-validation. Normal and empirical volume under the receiver operating characteristic (ROC) surface (VUS) tests were used to evaluate predictive effects of variables in the prediction model.

Results: Significant variables including oestrogen (E2), total procollagen type 1 amino-terminal propeptide (TP1NP), parathyroid hormone (PTH), BMI, vitamin K, serotonin, osteocalcin (OSTEOC), vitamin A, and vitamin D3 were used for the development of the prediction model. The training accuracy for normal, osteopenia, and osteoporosis is 74.4% (29/39), 80.0% (28/35), and 85.7% (30/35), respectively, while the total training accuracy is 79.8% (87/109). The internal validation showed excellent performance with 72.5% testing accuracy (72/109). Among these variables, serotonin and vitamin K exert important roles in the prediction of osteoporosis.

Conclusion: We successfully developed and validated a novel prediction model for osteoporosis based on serum concentrations of serotonin, fat-soluble vitamins, and bone turnover markers. In addition, interactive communication between serotonin and fat-soluble vitamins was observed to be critical for bone health in this study.

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来源期刊
Bone & Joint Research
Bone & Joint Research CELL & TISSUE ENGINEERING-ORTHOPEDICS
CiteScore
7.40
自引率
23.90%
发文量
156
审稿时长
12 weeks
期刊介绍: The gold open access journal for the musculoskeletal sciences. Included in PubMed and available in PubMed Central.
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