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Abstract：
OBJECTIVE: To identify bone marrow lesion (BML) trajectories over 4 years and their demographic and structural predictors in middle-aged and older adults with or at increased risk of knee osteoarthritis (OA).
METHODS: A total of 614 participants (mean age 61 years, 62% female) from the Osteoarthritis Initiative cohort (OAI) were included. BMLs in 15 anatomical locations of the knee were measured annually from baseline to 4 years using the Magnetic Resonance Imaging Osteoarthritis Knee Score (MOAKS) method. BML trajectories were determined using latent class mixed models (LCMMs). Multinomial logistic regression was used to examine baseline characteristics that predicted BML trajectories.
RESULTS: Three distinct BML trajectories were identified: \"Mild-stable BMLs\" (25.9%), \"Moderate-stable BMLs\" (66.4%), and \"Rapid-rise BMLs\" (7.7%). Compared to the \"Mild-stable BMLs\" trajectory, current smokers were more likely to be in the \"Moderate-stable BMLs\" (odds ratio [OR] 2.089, P < 0.001) and \"Rapid-rise\" (OR 2.462, P < 0.001) trajectories. Moreover, female sex and meniscal tears were associated with an increased risk of being in the \"Rapid-rise BMLs\" trajectory (OR 2.023 to 2.504, P < 0.05). Participants who had higher education levels and drank more alcohol were more likely to be in the \"Rapid-rise BMLs\" trajectory (OR 1.624 to 3.178, P < 0.05) and less likely to be in the \"Moderate-stable BMLs\" trajectory (OR 0.668 to 0.674, P < 0.05).
CONCLUSIONS: During the 4-year follow-up, most participants had relatively stable BMLs, few had enlarged BMLs, and no trajectory of decreased BMLs was identified. Sociodemographic factors, lifestyle, and knee structural pathology play roles in predicting distinct BML trajectories.
METHODS: A total of 614 participants (mean age 61 years, 62% female) from the Osteoarthritis Initiative cohort (OAI) were included. BMLs in 15 anatomical locations of the knee were measured annually from baseline to 4 years using the Magnetic Resonance Imaging Osteoarthritis Knee Score (MOAKS) method. BML trajectories were determined using latent class mixed models (LCMMs). Multinomial logistic regression was used to examine baseline characteristics that predicted BML trajectories.
RESULTS: Three distinct BML trajectories were identified: \"Mild-stable BMLs\" (25.9%), \"Moderate-stable BMLs\" (66.4%), and \"Rapid-rise BMLs\" (7.7%). Compared to the \"Mild-stable BMLs\" trajectory, current smokers were more likely to be in the \"Moderate-stable BMLs\" (odds ratio [OR] 2.089, P < 0.001) and \"Rapid-rise\" (OR 2.462, P < 0.001) trajectories. Moreover, female sex and meniscal tears were associated with an increased risk of being in the \"Rapid-rise BMLs\" trajectory (OR 2.023 to 2.504, P < 0.05). Participants who had higher education levels and drank more alcohol were more likely to be in the \"Rapid-rise BMLs\" trajectory (OR 1.624 to 3.178, P < 0.05) and less likely to be in the \"Moderate-stable BMLs\" trajectory (OR 0.668 to 0.674, P < 0.05).
CONCLUSIONS: During the 4-year follow-up, most participants had relatively stable BMLs, few had enlarged BMLs, and no trajectory of decreased BMLs was identified. Sociodemographic factors, lifestyle, and knee structural pathology play roles in predicting distinct BML trajectories.
摘要：
目的：在患有膝骨关节炎(OA)或其风险增加的中老年人中，确定4年以上骨髓病变(BML)的轨迹及其人口统计学和结构预测因素。
方法：共有614名参与者（平均年龄61岁，62%的女性)来自骨关节炎倡议队列(OAI)。从基线到4年，每年使用磁共振成像骨关节炎膝关节评分（MOAKS）方法测量膝关节15个解剖位置的BML。使用潜在类别混合模型(LCMMs)确定BML轨迹。多项逻辑回归用于检查预测BML轨迹的基线特征。
结果：确定了三个不同的BML轨迹：“轻度稳定的BML”（25.9％），“中等稳定的BML”（66.4%），和“快速上升的BML”（7.7%）。与“温和稳定的BML”轨迹相比，当前吸烟者更有可能处于"中等稳定的BML"(比值比[OR]2.089,P<0.001)和"快速上升"(OR2.462,P<0.001)轨迹中.此外，女性和半月板撕裂与处于“快速上升BMLs”轨迹的风险增加相关(OR2.023~2.504,P<0.05)。受教育程度较高且饮酒较多的参与者更有可能处于“快速上升的BML”轨迹（OR1.624至3.178，P<0.05），而处于“中等稳定的BML”轨迹（OR0.668至0.674，P<0.05）的可能性较小。
结论：在4年的随访期间，大多数参与者有相对稳定的BML，很少有人增大了BML，没有发现BMLs减少的轨迹。社会人口因素，生活方式,和膝关节结构病理学在预测不同的BML轨迹中起作用。
方法：共有614名参与者（平均年龄61岁，62%的女性)来自骨关节炎倡议队列(OAI)。从基线到4年，每年使用磁共振成像骨关节炎膝关节评分（MOAKS）方法测量膝关节15个解剖位置的BML。使用潜在类别混合模型(LCMMs)确定BML轨迹。多项逻辑回归用于检查预测BML轨迹的基线特征。
结果：确定了三个不同的BML轨迹：“轻度稳定的BML”（25.9％），“中等稳定的BML”（66.4%），和“快速上升的BML”（7.7%）。与“温和稳定的BML”轨迹相比，当前吸烟者更有可能处于"中等稳定的BML"(比值比[OR]2.089,P<0.001)和"快速上升"(OR2.462,P<0.001)轨迹中.此外，女性和半月板撕裂与处于“快速上升BMLs”轨迹的风险增加相关(OR2.023~2.504,P<0.05)。受教育程度较高且饮酒较多的参与者更有可能处于“快速上升的BML”轨迹（OR1.624至3.178，P<0.05），而处于“中等稳定的BML”轨迹（OR0.668至0.674，P<0.05）的可能性较小。
结论：在4年的随访期间，大多数参与者有相对稳定的BML，很少有人增大了BML，没有发现BMLs减少的轨迹。社会人口因素，生活方式,和膝关节结构病理学在预测不同的BML轨迹中起作用。
