Associations between measures of adiposity over 10 years and patella cartilage in population-based asymptomatic women

Objective: Osteoarthritis (OA) most commonly affects the patellofemoral compartment of the knee, and is a major cause of pain and disability. Structural changes that evolve prior to the onset of symptoms can be visualised using magnetic resonance imaging (MRI). There is little known information about the role of adiposity on the early structural changes in the patella cartilage in younger, asymptomatic adult females.

Methods: One hundred and sixty asymptomatic women (20–49 years) participating in the Geelong Osteoporosis Study underwent knee MRI (2006–8). Weight and body mass index (BMI) were measured 10 years prior (1994–7, baseline) and at the time of MRI (current), with change over the period calculated (current–baseline). Relationships between measures of adiposity and patella cartilage volume and defects were examined.

Results: After adjustment for age and patella bone volume, there was a reduction of 13 ml (95% confidence interval (95% CI), −25.7, −0.55) in patella cartilage volume for every 1 unit increase in current BMI, and a reduction of 27 ml (95% CI −52.6, −1.5) per BMI unit increase over 10 years (P=0.04 for both). No significant association was observed between baseline BMI and patella cartilage volume (P=0.16). Increased baseline and current weight and BMI were associated with increased prevalence of patella cartilage defects (all P<0.001).

Conclusions: Adiposity and weight gain during midlife are associated with detrimental structural change at the patella in young to middle-aged healthy non-osteoarthritic women. Maintaining a healthy weight and avoiding weight gain in younger asymptomatic women may be important in the prevention of patellofemoral OA.

Dynamically predicting protein functions from semantic associations of proteins

Predicting functions of un-annotated proteins is a significant challenge in the post-genomics era. Among existing computational approaches, exploiting interactions between proteins to predict functions of un-annotated proteins is widely used. However, it remains difficult to extract semantic associations between proteins (i.e. protein associations in terms of protein functionality) from protein interactions and incorporate extracted semantic associations to more effectively predict protein functions. Furthermore, existing approaches and algorithms regard the function prediction as a one-off procedure, ignoring dynamic and mutual associations between proteins. Therefore, deriving and exploiting semantic associations between proteins to dynamically predict functions are a promising and challenging approach for achieving better prediction results. In this paper, we propose an innovative algorithm to incorporate semantic associations between proteins into a dynamic procedure of protein function prediction. The semantic association between two proteins is measured by the semantic similarity of two proteins which is defined by the similarities of functions two proteins possess. To achieve better prediction results, function similarities are also incorporated into the prediction procedure. The algorithm dynamically predicts functions by iteratively selecting functions for the un-annotated protein and updating the similarities between the un-annotated protein and its neighbour annotated proteins until such suitable functions are selected that the similarities no longer change. The experimental results on real protein interaction datasets demonstrated that our method outperformed the similar and non-dynamic function prediction methods. Incorporating semantic associations between proteins into a dynamic procedure of function prediction reflects intrinsic relationships among proteins as well as dynamic features of protein interactions, and therefore, can significantly improve prediction results.