Differences between muscle from osteoporotic and osteoarthritic subjects: in vitro study by diffusion-tensor MRI and histological findings

Links among Obesity, Type 2 Diabetes Mellitus, and Osteoporosis: Bone as a Target

Obesity, type 2 diabetes mellitus (T2DM) and osteoporosis are serious diseases with an ever-increasing incidence that quite often coexist, especially in the elderly. Individuals with obesity and T2DM have impaired bone quality and an elevated risk of fragility fractures, despite higher and/or unchanged bone mineral density (BMD). The effect of obesity on fracture risk is site-specific, with reduced risk for several fractures (e.g., hip, pelvis, and wrist) and increased risk for others (e.g., humerus, ankle, upper leg, elbow, vertebrae, and rib). Patients with T2DM have a greater risk of hip, upper leg, foot, humerus, and total fractures. A chronic pro-inflammatory state, increased risk of falls, secondary complications, and pharmacotherapy can contribute to the pathophysiology of aforementioned fractures. Bisphosphonates and denosumab significantly reduced the risk of vertebral fractures in patients with both obesity and T2DM. Teriparatide significantly lowered non-vertebral fracture risk in T2DM subjects. It is important to recognize elevated fracture risk and osteoporosis in obese and T2DM patients, as they are currently considered low risk and tend to be underdiagnosed and undertreated. The implementation of better diagnostic tools, including trabecular bone score, lumbar spine BMD/body mass index (BMI) ratio, and microRNAs to predict bone fragility, could improve fracture prevention in this patient group.

MR-Imaging in Osteoarthritis: Current Standard of Practice and Future Outlook

Osteoarthritis (OA) is a common degenerative joint disease that affects millions of people worldwide. Magnetic resonance imaging (MRI) has emerged as a powerful tool for the evaluation and monitoring of OA due to its ability to visualize soft tissues and bone with high resolution. This review aims to provide an overview of the current state of MRI in OA, with a special focus on the knee, including protocol recommendations for clinical and research settings. Furthermore, new developments in the field of musculoskeletal MRI are highlighted in this review. These include compositional MRI techniques, such as T2 mapping and T1rho imaging, which can provide additional important information about the biochemical composition of cartilage and other joint tissues. In addition, this review discusses semiquantitative joint assessment based on MRI findings, which is a widely used method for evaluating OA severity and progression in the knee. We analyze the most common scoring methods and discuss potential benefits. Techniques to reduce acquisition times and the potential impact of deep learning in MR imaging for OA are also discussed, as these technological advances may impact clinical routine in the future.

Quantitative MRI for Evaluation of Musculoskeletal Disease: Cartilage and Muscle Composition, Joint Inflammation, and Biomechanics in Osteoarthritis

ABSTRACT

Magnetic resonance imaging (MRI) is a valuable tool for evaluating musculoskeletal disease as it offers a range of image contrasts that are sensitive to underlying tissue biochemical composition and microstructure. Although MRI has the ability to provide high-resolution, information-rich images suitable for musculoskeletal applications, most MRI utilization remains in qualitative evaluation. Quantitative MRI (qMRI) provides additional value beyond qualitative assessment via objective metrics that can support disease characterization, disease progression monitoring, or therapy response. In this review, musculoskeletal qMRI techniques are summarized with a focus on techniques developed for osteoarthritis evaluation. Cartilage compositional MRI methods are described with a detailed discussion on relaxometric mapping (T 2 , T 2 *, T 1ρ ) without contrast agents. Methods to assess inflammation are described, including perfusion imaging, volume and signal changes, contrast-enhanced T 1 mapping, and semiquantitative scoring systems. Quantitative characterization of structure and function by bone shape modeling and joint kinematics are described. Muscle evaluation by qMRI is discussed, including size (area, volume), relaxometric mapping (T 1 , T 2 , T 1ρ ), fat fraction quantification, diffusion imaging, and metabolic assessment by 31 P-MR and creatine chemical exchange saturation transfer. Other notable technologies to support qMRI in musculoskeletal evaluation are described, including magnetic resonance fingerprinting, ultrashort echo time imaging, ultrahigh-field MRI, and hybrid MRI-positron emission tomography. Challenges for adopting and using qMRI in musculoskeletal evaluation are discussed, including the need for metal artifact suppression and qMRI standardization.

Complexity and data mining in dental research: A network medicine perspective on interceptive orthodontics

Procedures and models of computerized data analysis are becoming researchers' and practitioners' thinking partners by transforming the reasoning underlying biomedicine. Complexity theory, Network analysis and Artificial Intelligence are already approaching this discipline, intending to provide support for patient's diagnosis, prognosis and treatments. At the same time, due to the sparsity, noisiness and time-dependency of medical data, such procedures are raising many unprecedented problems related to the mismatch between the human mind's reasoning and the outputs of computational models. Thanks to these computational, non-anthropocentric models, a patient's clinical situation can be elucidated in the orthodontic discipline, and the growth outcome can be approximated. However, to have confidence in these procedures, orthodontists should be warned of the related benefits and risks. Here we want to present how these innovative approaches can derive better patients' characterization, also offering a different point of view about patient's classification, prognosis and treatment.