## 3 Role of Musculoskeletal Models in Disease Prevention and Patient Stratification  
The use of MSK modelling and simulations in cross sectional studies (i.e., single time point) can provide valuable information into disease states and biomarkers (e.g., joint contact forces) that cannot be (easily) measured _in vivo_. For example, MSK modelling and simulation methods have been applied to patients who have undergone knee ligament reconstructions [55] who are at an elevated risk of osteoarthritis (OA) development. This was previously thought to be driven by higher joint contact forces following reconstructive surgery, it was shown, utilising MSK modelling and simulations, that these patients exhibit lower joint contact forces. This suggests that lower knee joint contact forces, instead of higher, play an important role in OA development. Likewise, others [56] were unable to indicate significant differences in knee joint contact forces in subjects with focal cartilage defects, following traumatic injury. Based on this insight, the magnitude of joint contact forces may be used as a biomarker to monitor disease development in this specific population. These biomarkers can then inform targeted rehabilitation, be used as model-based outcome measures, and as early warning signs or detectors for developing diseases which do not present in typical clinical tests or static medical imaging modalities. Likewise, the use of MSK modelling and simulations enables researchers to determine differences in the role of altered mechanical joint environment in OA at different lower-limb joints (i.e., knee vs. hip OA). For example, during gait, joint contact forces in the involved joint is elevated in knee OA [57], whereas elevated joint (i.e., hip) contact forces are not present in hip OA patients [58; 59].  
Although MSK modelling and simulations are highly valuable in investigating MSK diseases, a potentially more powerful use is in preventing and slowing the progression of different MSK conditions. In this respect, MSK models can be employed within longitudinal trials to study changes in joint contact loading over time [60] (Figure 4). These longitudinal studies allow researchers/clinicians to identify patients who do and do not develop/progress with respect to a specific MSK condition. Examining parameters such as joint moments and in particular joint contact forces would allow researchers/clinicians to identify potentially modifiable parameters to slow disease progression. These biomarkers can then be targeted in rehabilitation such as gait retraining to reduce the likelihood of disease development and limit disease progression. While the above-mentioned longitudinal monitoring has been carried out in typically small cohorts, the type of analysis mentioned above (i.e., biomarker identification) is yet to become mainstream. One of the major limitations is the absence of longitudinal movement analysis data in large longitudinal studies. Including 3D motion capture data in the follow-up of OA patients, together with, medical imaging and various questionnaires which assess subjective disease state, would allow untangling the role of mechanical joint loading in disease progression. With increased computational speed and adequate data collection, these longitudinal studies will become more common in the future as simulation methodologies are more accepted by the medical community.