Hummel Jana, Engelke Klaus, Freitag-Wolf Sandra, Yilmas Eren, Bartenschlager Stefan, Sigurdsson Sigurdur, Gudnason Vilmundur, Glüer Claus-C, Chaudry Oliver
Department of Medicine 3, Friedrich-Alexander-University Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany.
Institute of Medical Informatics and Statistics, Kiel University, Kiel, Germany.
Front Endocrinol (Lausanne). 2025 Mar 26;16:1566424. doi: 10.3389/fendo.2025.1566424. eCollection 2025.
Vertebral fractures (VFs) significantly increase risk of subsequent fractures. Areal bone mineral density (BMD) assessed by DXA and volumetric BMD by QCT, are strong predictors of VF. Nevertheless, risk prediction should be further improved. This study used data from the Age, Gene/Environment Susceptibility Reykjavik (AGES-Reykjavik) cohort to evaluate whether trabecular texture and paraspinal muscle assessments improve the prediction of the first incident VF.
CT scans of the L1 and L2 vertebrae of 843 elderly subjects; including 167 subjects with incident, VFs occurring within a 5-year period and 676 controls without fractures. Image analysis included measurement of BMD, cortical thickness and of parameters characterizing trabecular architecture and the autochthonous muscles. Fifty variables were used as predictors, including a BMD, a trabecular texture and a muscle subset. Each included age, BMI and corresponding parameters of the QCT analysis. The number of variables in each subset was reduced using stepwise logistic regression to create multivariable fracture prediction models. Model accuracy was assessed using the likelihood ratio test (LRT) and the area under the curve (AUC) criteria. Bootstrap analyses were performed to assess the stability of the model selection process.
96 women and 78 men with prior VF were excluded. Of 50 initial predictors, 17 were significant for women and 11 for men. Bone and texture models showed significantly better fracture prediction in women (p<0.001) and men (p<0.01) than the combination of age and BMI. The muscle model showed better fracture prediction in men only (p<0.03). Compared to the BMD model alone, LRT showed a significantly improved VF prediction of the combinations of BMD with texture (women and men) (p<0.05) or with muscle models (men only) (p=0.03) but no significant increases in AUC values (AUC women: Age&BMI: 0.57, BMD: 0.69, combined model: 0.69; AUC men: Age&BMI: 0.63, BMD: 0.71, combined models 0.73-0.77).
Trabecular texture and muscle parameters significantly improved prediction of first VF over age and BMI, but improvements were small compared to BMD, which remained the primary predictor for both sexes. Although muscle measures showed some predictive power, particularly in men, their clinical significance was marginal. Integral BMD should remain the focus for fracture risk assessment in clinical practice.
椎体骨折(VFs)会显著增加后续骨折的风险。通过双能X线吸收法(DXA)评估的面积骨密度(BMD)和通过定量CT(QCT)评估的体积骨密度是椎体骨折的有力预测指标。然而,风险预测仍需进一步改进。本研究使用来自雷克雅未克年龄、基因/环境易感性(AGES-雷克雅未克)队列的数据,以评估小梁结构和椎旁肌肉评估是否能改善首次发生椎体骨折的预测。
对843名老年受试者的L1和L2椎体进行CT扫描;其中包括167名在5年内发生首次椎体骨折的受试者以及676名未发生骨折的对照者。图像分析包括骨密度、皮质厚度以及表征小梁结构和椎旁肌肉的参数测量。50个变量被用作预测指标,包括一个骨密度、一个小梁结构和一个肌肉子集。每个子集都包括年龄、体重指数(BMI)以及QCT分析的相应参数。使用逐步逻辑回归减少每个子集中的变量数量,以创建多变量骨折预测模型。使用似然比检验(LRT)和曲线下面积(AUC)标准评估模型准确性。进行自助法分析以评估模型选择过程的稳定性。
排除了96名有椎体骨折史的女性和78名男性。在50个初始预测指标中,17个对女性有显著意义,11个对男性有显著意义。骨密度和结构模型在女性(p<0.001)和男性(p<0.01)中显示出比年龄和BMI组合显著更好的骨折预测效果。肌肉模型仅在男性中显示出更好的骨折预测效果(p<0.03)。与单独的骨密度模型相比,LRT显示骨密度与结构(女性和男性)(p<0.05)或与肌肉模型(仅男性)(p = 0.03)组合的椎体骨折预测有显著改善,但AUC值没有显著增加(女性AUC:年龄&BMI:0.57,骨密度:0.69,联合模型:0.69;男性AUC:年龄&BMI:0.63,骨密度:0.71,联合模型0.73 - 0.77)。
小梁结构和肌肉参数在预测首次椎体骨折方面比年龄和BMI有显著改善,但与骨密度相比改善较小,骨密度仍然是两性的主要预测指标。尽管肌肉测量显示出一定的预测能力,特别是在男性中,但其临床意义不大。在临床实践中,整体骨密度应仍然是骨折风险评估的重点。