1
|
Martiniakova M, Biro R, Penzes N, Sarocka A, Kovacova V, Mondockova V, Omelka R. Links among Obesity, Type 2 Diabetes Mellitus, and Osteoporosis: Bone as a Target. Int J Mol Sci 2024; 25:4827. [PMID: 38732046 PMCID: PMC11084398 DOI: 10.3390/ijms25094827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2024] [Revised: 04/25/2024] [Accepted: 04/27/2024] [Indexed: 05/13/2024] Open
Abstract
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.
Collapse
Affiliation(s)
- Monika Martiniakova
- Department of Zoology and Anthropology, Faculty of Natural Sciences and Informatics, Constantine the Philosopher University in Nitra, 949 01 Nitra, Slovakia; (R.B.); (V.K.)
| | - Roman Biro
- Department of Zoology and Anthropology, Faculty of Natural Sciences and Informatics, Constantine the Philosopher University in Nitra, 949 01 Nitra, Slovakia; (R.B.); (V.K.)
| | - Noemi Penzes
- Department of Botany and Genetics, Faculty of Natural Sciences and Informatics, Constantine the Philosopher University in Nitra, 949 01 Nitra, Slovakia; (N.P.); (A.S.); (V.M.); (R.O.)
| | - Anna Sarocka
- Department of Botany and Genetics, Faculty of Natural Sciences and Informatics, Constantine the Philosopher University in Nitra, 949 01 Nitra, Slovakia; (N.P.); (A.S.); (V.M.); (R.O.)
| | - Veronika Kovacova
- Department of Zoology and Anthropology, Faculty of Natural Sciences and Informatics, Constantine the Philosopher University in Nitra, 949 01 Nitra, Slovakia; (R.B.); (V.K.)
| | - Vladimira Mondockova
- Department of Botany and Genetics, Faculty of Natural Sciences and Informatics, Constantine the Philosopher University in Nitra, 949 01 Nitra, Slovakia; (N.P.); (A.S.); (V.M.); (R.O.)
| | - Radoslav Omelka
- Department of Botany and Genetics, Faculty of Natural Sciences and Informatics, Constantine the Philosopher University in Nitra, 949 01 Nitra, Slovakia; (N.P.); (A.S.); (V.M.); (R.O.)
| |
Collapse
|
2
|
Engelke K, Chaudry O, Gast L, Eldib MAB, Wang L, Laredo JD, Schett G, Nagel AM. Magnetic resonance imaging techniques for the quantitative analysis of skeletal muscle: State of the art. J Orthop Translat 2023; 42:57-72. [PMID: 37654433 PMCID: PMC10465967 DOI: 10.1016/j.jot.2023.07.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 07/04/2023] [Accepted: 07/19/2023] [Indexed: 09/02/2023] Open
Abstract
Background Magnetic resonance imaging (MRI) is the dominant 3D imaging modality to quantify muscle properties in skeletal muscle disorders, in inherited and acquired muscle diseases, and in sarcopenia, in cachexia and frailty. Methods This review covers T1 weighted and Dixon sequences, introduces T2 mapping, diffusion tensor imaging (DTI) and non-proton MRI. Technical concepts, strengths, limitations and translational aspects of these techniques are discussed in detail. Examples of clinical applications are outlined. For comparison 31P-and 13C-MR Spectroscopy are also addressed. Results MRI technology provides a rich toolset to assess muscle deterioration. In addition to classical measures such as muscle atrophy using T1 weighted imaging and fat infiltration using Dixon sequences, parameters characterizing inflammation from T2 maps, tissue sodium using non-proton MRI techniques or concentration or fiber architecture using diffusion tensor imaging may be useful for an even earlier diagnosis of the impairment of muscle quality. Conclusion Quantitative MRI provides new options for muscle research and clinical applications. Current limitations that also impair its more widespread use in clinical trials are lack of standardization, ambiguity of image segmentation and analysis approaches, a multitude of outcome parameters without a clear strategy which ones to use and the lack of normal data.
Collapse
Affiliation(s)
- Klaus Engelke
- Department of Medicine III, Friedrich-Alexander University of Erlangen-Nürnberg, University Hospital Erlangen, Ulmenweg 18, 91054, Erlangen, Germany
- Institute of Medical Physics (IMP), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Henkestr. 91, 91052, Erlangen, Germany
- Clario Inc, Germany
| | - Oliver Chaudry
- Department of Medicine III, Friedrich-Alexander University of Erlangen-Nürnberg, University Hospital Erlangen, Ulmenweg 18, 91054, Erlangen, Germany
| | - Lena Gast
- Institute of Radiology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), University Hospital Erlangen, Maximiliansplatz 3, 91054, Erlangen, Germany
| | | | - Ling Wang
- Department of Radiology, Beijing Jishuitan Hospital, Beijing, China
| | - Jean-Denis Laredo
- Service d’Imagerie Médicale, Institut Mutualiste Montsouris & B3OA, UMR CNRS 7052, Inserm U1271 Université de Paris-Cité, Paris, France
| | - Georg Schett
- Department of Medicine III, Friedrich-Alexander University of Erlangen-Nürnberg, University Hospital Erlangen, Ulmenweg 18, 91054, Erlangen, Germany
| | - Armin M. Nagel
- Institute of Radiology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), University Hospital Erlangen, Maximiliansplatz 3, 91054, Erlangen, Germany
- Division of Medical Physics in Radiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany
| |
Collapse
|
3
|
Ehmig J, Engel G, Lotz J, Lehmann W, Taheri S, Schilling AF, Seif Amir Hosseini A, Panahi B. MR-Imaging in Osteoarthritis: Current Standard of Practice and Future Outlook. Diagnostics (Basel) 2023; 13:2586. [PMID: 37568949 PMCID: PMC10417111 DOI: 10.3390/diagnostics13152586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 07/30/2023] [Accepted: 08/01/2023] [Indexed: 08/13/2023] Open
Abstract
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.
Collapse
Affiliation(s)
- Jonathan Ehmig
- Institute of Diagnostic and Interventional Radiology, University Medical Center Göttingen, 37075 Göttingen, Germany; (J.E.); (G.E.)
| | - Günther Engel
- Institute of Diagnostic and Interventional Radiology, University Medical Center Göttingen, 37075 Göttingen, Germany; (J.E.); (G.E.)
| | - Joachim Lotz
- Institute of Diagnostic and Interventional Radiology, University Medical Center Göttingen, 37075 Göttingen, Germany; (J.E.); (G.E.)
| | - Wolfgang Lehmann
- Clinic of Trauma, Orthopedics and Reconstructive Surgery, Georg-August-University of Göttingen, 37075 Göttingen, Germany
| | - Shahed Taheri
- Clinic of Trauma, Orthopedics and Reconstructive Surgery, Georg-August-University of Göttingen, 37075 Göttingen, Germany
| | - Arndt F. Schilling
- Clinic of Trauma, Orthopedics and Reconstructive Surgery, Georg-August-University of Göttingen, 37075 Göttingen, Germany
| | - Ali Seif Amir Hosseini
- Institute of Diagnostic and Interventional Radiology, University Medical Center Göttingen, 37075 Göttingen, Germany; (J.E.); (G.E.)
| | - Babak Panahi
- Institute of Diagnostic and Interventional Radiology, University Medical Center Göttingen, 37075 Göttingen, Germany; (J.E.); (G.E.)
| |
Collapse
|
4
|
Eck BL, Yang M, Elias JJ, Winalski CS, Altahawi F, Subhas N, Li X. Quantitative MRI for Evaluation of Musculoskeletal Disease: Cartilage and Muscle Composition, Joint Inflammation, and Biomechanics in Osteoarthritis. Invest Radiol 2023; 58:60-75. [PMID: 36165880 PMCID: PMC10198374 DOI: 10.1097/rli.0000000000000909] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
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.
Collapse
Affiliation(s)
- Brendan L. Eck
- Program of Advanced Musculoskeletal Imaging, Cleveland Clinic, Cleveland, OH, USA
- Imaging Instute, Cleveland Clinic, Cleveland, OH, USA
| | - Mingrui Yang
- Program of Advanced Musculoskeletal Imaging, Cleveland Clinic, Cleveland, OH, USA
- Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | - John J. Elias
- Program of Advanced Musculoskeletal Imaging, Cleveland Clinic, Cleveland, OH, USA
- Department of Research, Cleveland Clinic Akron General, Akron, OH, USA
| | - Carl S. Winalski
- Program of Advanced Musculoskeletal Imaging, Cleveland Clinic, Cleveland, OH, USA
- Imaging Instute, Cleveland Clinic, Cleveland, OH, USA
- Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Faysal Altahawi
- Program of Advanced Musculoskeletal Imaging, Cleveland Clinic, Cleveland, OH, USA
- Imaging Instute, Cleveland Clinic, Cleveland, OH, USA
| | - Naveen Subhas
- Program of Advanced Musculoskeletal Imaging, Cleveland Clinic, Cleveland, OH, USA
- Imaging Instute, Cleveland Clinic, Cleveland, OH, USA
| | - Xiaojuan Li
- Program of Advanced Musculoskeletal Imaging, Cleveland Clinic, Cleveland, OH, USA
- Imaging Instute, Cleveland Clinic, Cleveland, OH, USA
- Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| |
Collapse
|
5
|
Gili T, Di Carlo G, Capuani S, Auconi P, Caldarelli G, Polimeni A. Complexity and data mining in dental research: A network medicine perspective on interceptive orthodontics. Orthod Craniofac Res 2021; 24 Suppl 2:16-25. [PMID: 34519158 PMCID: PMC9292769 DOI: 10.1111/ocr.12520] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2021] [Revised: 06/23/2021] [Indexed: 12/19/2022]
Abstract
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.
Collapse
Affiliation(s)
- Tommaso Gili
- Networks UnitIMT School for Advanced Studies LuccaLuccaItaly
- CNR‐ISC Unità SapienzaRomeItaly
| | - Gabriele Di Carlo
- Department of Oral and Maxillo‐Facial SciencesSapienza University of RomeRomeItaly
| | | | | | - Guido Caldarelli
- CNR‐ISC Unità SapienzaRomeItaly
- Department of Molecular Sciences and NanosystemsCa’Foscari University of VeniceVenezia MestreItaly
| | - Antonella Polimeni
- Department of Oral and Maxillo‐Facial SciencesSapienza University of RomeRomeItaly
| |
Collapse
|