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下面我们将为大家提供四期连载内容，本文为第二期。本次介绍2016年关于膝关节骨性关节炎预测的最新综述，包括软骨细胞外基质和力学损伤，以及力学模型VS统计模型。 软骨细胞外基质和力学损伤 软骨细胞外基质包含数十种胶原蛋白、蛋白聚糖和糖蛋白，这些成分沉浸在名为组织液的内膜液体中（图1）。调节组织生物力学功能属性的两个最重要的大分子可以说是II型胶原蛋白和蛋白聚糖（聚合物）。软骨硬度受到的压力来自于带负电荷的蛋白聚糖之间的斥力和组织中液体流出困难。从组织中流出的组织液通过混合模式润滑的方式有助于降低软骨摩擦性。此外，胶原蛋白有助于抵抗负荷和蛋白聚糖的自身降解，缺少胶原蛋白会使软骨膨胀，并加速软骨蛋白聚糖降解。
图1 显示由软骨细胞生成的蛋白聚糖携带强负电荷。由此产生的斥力（电荷和渗透，小红箭头所示）使软骨呈现膨胀趋势。软骨胶原网络（固定于底层骨）使软骨具有抗张强度和膨胀约束力，并能释放蛋白聚糖至关节间隙。因此，胶原蛋白通常处在拉伸状态（大红色箭头所示）。图片显示的并非尺寸上的拉伸。
有些临床文献对依据临床症状（如疼痛和功能障碍）还是依据放射学或MRI呈现的结构性变化确认骨性关节炎进行了讨论。我们应用了一个面向功能的方法，认为骨性关节炎是软骨无法维持力学性能的功能障碍性疾病：基础力学（如弹性变形和间质流体压力）发生变化，无法达到保持组织完整性所需水平时，就会出现组织受损。需要注意的是这个功能障碍的诱因可以是软骨组织内部，也可以是外部；事实上，骨性关节炎通常被视为是一种全关节疾病。我们提出的骨性关节炎的功能性确认方法与国际骨关节炎研究协会（OARSI）建议的骨性关节炎病发和病情发展的早期诊断相一致的。
尽管我们都知道软骨细胞能随化学和环境信号调节细胞外基质，但是大量和/或长期的信号变化会加速组织病变。这可以通过多种机制发生（如图2）。异常的持续性强负荷或者组织衰弱都会导致组织过度变形，进而导致软骨细胞凋亡。软骨表面的接触润滑性不足或过度活动都会导致软骨过度磨损（如水管工或越野滑雪者）。另一方面，过少活动或静态载荷会延缓软骨细胞合成蛋白聚糖和胶原蛋白，从而抑制细胞外基质修复。
图2. 说明了两种潜在的骨性关节炎力学损伤路径。值得注意的是，其他力学或非力学路径（图中未显示）也可引发骨性关节炎。(a) 正常健康的软骨可能经历(b)长期过度活动或承受重复性微负荷，这会导致软骨表面磨损，同时循环性载荷给软骨细胞造成了较高压力。(c) 另外，健康软骨可能因承受高负荷（短期）出现裂缝、软骨细胞死亡、细胞因子释放伴随蛋白酶介导的细胞外基质退化和软骨下骨受损。(d) 最终，当超过软骨修复能力时，这两种路径均会使软骨受损。
如果要了解引发组织受损或骨性关节炎的生物力学因素，需要先明白关节载荷和几何形态的差异是如何导致软骨自身所处的力学环境发生改变的。更普遍地来说，因为骨性关节炎较罕见，即使仅从力学角度建立针对软骨平衡的力学模型也是有必要的。
力学模型VS统计模型 力学模型与基础物理，化学和生物学相关，描述了这几方面之间随时间的相互作用和发展过程。该模型不仅涉及力学载荷，还能表现细胞信号传导通路，代谢作用，细胞外基质合成和蛋白水解等过程。与单纯的统计方法不同，一个合理的力学模型可以将实验数据应用得恰到好处。例如，与蛋白结合的胰岛素样生长因子IGF-1和血清、滑液和软骨中存在的蛋白酶之间的相互作用，只有当它们扩散转运至组织，并由组织调节使它们接触时才具有意义。体重指数提供了一个简单例子：我们知道体重指数与骨性关节炎发病危险在统计学上具有相关性，但是否与力学原因有关只有通过个体特异性模型才能确定，这一模型将膝关节几何形状和质量与动量守恒定律方程进行了结合。此外，力学模型使得生物模拟实验可以依据个体“参数”研究疾病过程或提出治疗策略。
上述有关力学模型的看法有些乐观，原因是对于模型结构和参数值认识不足。建模的艺术是对能够深入洞察问题的模型结构的直觉。正如我们在上文提出的，以细胞外基质转换率为重点是骨性关节炎预测的一个合适的切入点。也可选择不同的模型结构（如侧重关节力）。建立骨性关节炎力学模型并没有唯一准确的方法，建模者和待解决的具体问题的不同，模型结构也会不同。
这里建立具有不确定性的模型是有益的，它们对应两种主要类型。假设其中一个模型针对普通人。许多模型参数甚至模型的核心结构，如模型包括的现象，我们只知道其处在一定范围内，称之为人群不确定性。与人群不确定性相比，我们更倾向于以个体不确定性描述特殊个体从正常到病变的过程。无论何时应用不确定性，都需要将随机方法应用到力学模型。
然而，这可能会掩盖掉某些尚未解释的人群差异性，削弱例如生物力学危险因素和骨性关节炎疗效之间的联系。膝关节内侧载荷的间接力学检测，如膝内收力矩，为骨性关节炎病发的预测提供了比体重或膝关节冠状面对齐（单独或联合）更好的方法。据此我们推断如果将更多相关的生物力学因素进行评估，如润滑和组织实变持续时间，可能会减少尚未解释的人群差异性，也会提高预测能力。与体重指数或膝内收力矩相比，这些个体特异性组织力学状态与骨性关节炎发病危险性的数量相关性更强（图3）。如图4所示，一种多项个体特异性建模方法可提供相关性强的参数。
图3. 概括了与更多已知因素合并的软骨力学环境参数。
图4. 整合成像、步态和软骨质量数据到人膝关节软骨多项个体特异性模型。针对每个部分的进一步讨论，请参见主网格拟合，肌电图-提供步态肌力信息，膝软骨应力-应变和软骨多孔弹性模型。我们认为与已有研究中应用的风险因素相比，软骨实变和液体渗出相关的组织水平参数与软骨损伤和缺陷的扩大具有更强的相关性。 CARTILAGE EXTRACELLULAR MATRIX AND MECHANICAL FAILURE The ECM of cartilage includes dozens of collagens, proteoglycans, and glycoproteins, all enmeshed within intratissue water, called the interstitial fluid (Fig. 1). Arguably the two most structurally important macromolecules that regulate the tissue’s biomechanical functional properties are type II collagen and the proteoglycan aggrecan (as aggregate). The stiffness of cartilage under compression comes from both the repulsion between negatively charged aggrecans and the difficulty that the fluid has in squeezing out of the tissue. The interstitial fluid leaving the tissue then helps to give cartilage its famously low frictional properties via so-called mixed-mode lubrication. In addition, collagen helps resist shear loads and the loss of aggrecan itself, which otherwise would swell apart and be rapidly lost from cartilage.
In the clinical literature there is some discussion as to whether to define OA by clinical symptoms, like pain and disability, or by structural changes inferred through radiology or MRI. We take a more function-oriented approach and consider OA as an inability of cartilage to maintain its functional mechanical properties: the tissue has failed when fundamental mechanical variables, such as deformational resilience and interstitial fluid pressure, fall below levels required to maintain tissue integrity. Note that the root cause of this failure may be internal or external to the cartilage tissue; indeed, OA is commonly regarded as a disease of the whole joint. Our functional definition of OA is consistent with that advocated by the Osteoarthritis Research Society International (OARSI) for early identification of OA risk and progression.
Although chondrocytes are known to adjust the ECM in response to chemical and environmental signals, substantial and/or long-term changes in these signals make the tissue more vulnerable to failure. This can occur through various mechanisms (exemplified in Fig. 2). Excessive tissue deformation, from either abnormally large sustained loads or abnormally weak tissue, can cause chondrocyte apoptosis. Insufficient lubrication between contacting cartilage surfaces or excessive activity will lead to excessive cartilage wear (as experienced by plumbers or cross-country skiers, for example). On the other hand, too low activity or static loads are known to inhibit ECM repair by retarding chondrocyte synthesis of aggrecan and collagen.
If we are to understand the biomechanical factors contributing to tissue failure, or OA, we need to start by understanding how observable differences in joint loads and geometry translate to changes in the mechanical environment experienced by the cartilage itself. More generally, since OA is rarely-if ever-the result of mechanics alone, a so-called mechanistic model of cartilage homeostasis is needed.
MECHANISTIC MODELS VS. STATISTICAL MODELS A mechanistic model is one pertaining to the underlying physical, chemical and biological mechanisms, describing how these processes interact and evolve in time. Far from being limited to mechanical loading alone, such a model could also involve cell signaling pathways, metabolic effects, ECM synthesis and proteolysis, and so on. Unlike a purely statistical approach, an appropriate mechanistic model allows experimental data to be placed in its proper context. For example, the interaction of the insulin-like growth factor IGF-1 with the binding proteins and proteases found in serum, synovial fluid and cartilage only makes sense when it is placed in the context of diffusive transport into the tissue and the ability of the tissue to regulate its exposure. BMI furnishes a simpler example: we know it is statistically connected to OA risk, but whether or not this is due to mechanical reasons can only be ascertained in a subject-specific model of cartilage mechanics incorporating knee geometry and the equations of mass and momentum balance. Furthermore, a mechanistic model enables ‘in silico’ experiments to investigate disease processes or reveal treatment strategies based on an individual’s combination of ‘parameters’.
The above somewhat rosy view of mechanistic modeling is undermined by imperfect knowledge of model structure and parameter values. The art of modeling is intuiting a model structure that can give insight into the question being asked. For example, we have argued above that focusing on turnover of the ECM is an appropriate conceptual starting point for questions related to OA prediction. Others might choose a different model structure (e.g., focus on joint forces). There is no single right way to model OA mechanistically, and model structure will vary with the modelers and the specific question being addressed.
It is useful here to treat model uncertainty as belonging to two main types. Imagine a model for a generic person. Many of the model parameters and even the core structure of the model, such as the phenomena it includes, will be only known to within a range; we call this population uncertainty. In contrast to population uncertainty, we refer to individual uncertainty as how a particular individual may vary from this generic person. Whenever uncertainty arises, stochastic approaches need to be coupled to mechanistic models.
Nevertheless, it may be possible to remove some of the unexplained population variability weakening the association between, for example, current biomechanical risk factors and OA outcomes. Indirect mechanical measures of loading of the medial compartment of the knee, such as knee adduction moments, provide a much better prediction for OA progression than body weight or frontal plane knee alignment, either alone or in combination. Extrapolating, we would expect that if even more relevant biomechanical factors were to be evaluated, such as the duration of lubrication and tissue consolidation, unexplained population variability may be reduced and so our predictive ability would increase. These subject-specific tissue mechanical conditions are likely to be a stronger metric to associate with OA risk than, say, BMI or knee adduction moments (Fig. 3). A multiscale subject-specific modeling approach, as depicted in Fig. 4, may be able to provide these stronger metrics. 由MediCool医库软件 王露黔 编译 原文来自：Predicting Knee Osteoarthritis Ann Biomed Eng. 2016 Jan;44(1):222-33. |
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