Effects of glenoid inclination and acromion index on humeral head translation and glenoid articular cartilage strain

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Background

Previous clinical studies have reported associations between glenoid inclination (GI), the acromion index (AI), and the critical shoulder angle (CSA) on the one hand and the occurrence of glenohumeral osteoarthritis and supraspinatus tendon tears on the other hand. The objective of this work was to analyze the correlations and relative importance of these different anatomic parameters.

Methods

Using a musculoskeletal shoulder model developed from magnetic resonance imaging scans of 1 healthy volunteer, we varied independently GI from 0° to 15° and AI from 0.5 to 0.8. The corresponding CSA varied from 20.9° to 44.1°. We then evaluated humeral head translation and critical strain volume in the glenoid articular cartilage at 60° of abduction in the scapular plane. These values were correlated with GI, AI, and CSA.

Results

Humeral head translation was positively correlated with GI (R = 0.828, P < .0001), AI (R = 0.539, P < .0001), and CSA (R = 0.964, P < .0001). Glenoid articular cartilage strain was also positively correlated with GI (R = 0.489, P = .0004) but negatively with AI (R = −0.860, P < .0001) and CSA (R = −0.285, P < .0473).

Conclusions

The biomechanical shoulder model is consistent with clinical observations. The prediction strength of CSA is confirmed for humeral head translation and thus presumably for rotator cuff tendon tears, whereas the AI seems more appropriate to evaluate the risk of glenohumeral osteoarthritis caused by excessive articular cartilage strain. As a next step, we should corroborate these theoretical findings with clinical data.

Section snippets

Materials and methods

We used a generic numerical model of the shoulder to vary independently the lateral extension of the acromion and GI (Figure 2, Figure 2).19, 36 The model was developed from magnetic resonance imaging (MRI) scans of a 27-year-old healthy male volunteer showing no signs of glenohumeral, acromioclavicular, or sternoclavicular joint disorders. MRI scans were obtained using a specific protocol consisting of two 3-dimensional T1-weighted sequences on a 3-T MRI scanner (Trio; Siemens Healthcare,

Results

In comparison with the reference, the relative humeral head inferior-superior translation was positively correlated with both GI and AI (Fig. 3). The correlation with GI was very strong, whereas the correlation with AI was moderate (Table I). However, the relative inferior-superior translation was statistically significantly (P = .0005) higher for large AI (+10.5%) than for small AI (−12.0%). On average, the effect of GI was 1.6 times larger than the effect of AI. The multiple correlation with

Discussion

Several clinical studies reported a statistical correlation between scapular anatomy and the occurrence of degenerative shoulder diseases. GI and AI were associated with rotator cuff tendon tears and glenohumeral osteoarthritis.17, 27, 29, 31, 39, 41 In this study, we used a biomechanical shoulder model to evaluate the relative effects of these 2 different anatomic parameters on upward translation of the humeral head and glenoid articular cartilage strain. Assuming the hypothesis that humeral

Conclusion

CSA is a combination of GI and AI. It efficiently represents the effects of these 2 parameters on humeral head migration because both GI and AI have the same positive effect. For articular cartilage strain, CSA seems less relevant because GI and AI have opposite effects. Consequently, CSA might be a good indicator of the risk of tendon tear, but AI might be more efficient to predict the risk of osteoarthritis. Although degenerative shoulder diseases are known to be multicausal, anatomic

Disclaimer

This study was supported by the Swiss National Science Foundation (K-349 32K1_122512) and the Center for Biomedical Imaging of Lausanne University Hospital.

The authors, their immediate families, and any research foundations with which they are affiliated have not received any financial payments or other benefits from any commercial entity related to the subject of this article.

Acknowledgments

The authors thank Jean-Baptiste Ledoux for help with MRI data acquisition.

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