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弥合差距:关于骨质疏松症残疾、脂肪因子以及人工智能在绝经后女性中的作用的叙述性综述

Bridging the Gap: A narrative review of osteoporosis disability, adipokines, and the role of AI in postmenopausal women.

作者信息

Tariq Saba, Jabbar Sohail, Ahmad Awais, Tariq Sundus

机构信息

Saba Tariq, Department of Pharmacology & Therapeutics, University Medical and Dental College, The University of Faisalabad, Post-doctoral Fellow, University of Birmingham, England, UK.

Sohail Jabbar, Department of Computer Science, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia.

出版信息

Pak J Med Sci. 2024 Aug;40(7):1572-1577. doi: 10.12669/pjms.40.7.9072.

Abstract

Osteoporosis is a global health concern characterized by reduced bone density and compromised bone quality, resulting in an increased risk of fractures, particularly in postmenopausal women. The assessment of bone mineral density (BMD) plays a pivotal role in diagnosing osteoporosis, as it accounts for approximately 70% of overall bone strength. The World Health Organization (WHO) has endorsed BMD measurement as a reliable method for diagnosing this condition. In Pakistan, the incidence of bone fractures is on the rise, largely attributable to an aging population and a range of contributing factors. Understanding the global and local prevalence of osteoporosis, its impact on morbidity and mortality, and the contributing factors is vital for developing effective preventive and therapeutic strategies. The role of adipokines, including chemerin, vaspin, and omentin-1, in bone metabolism is an emerging area of investigation. These adipokines play diverse roles in physiology, ranging from inflammation and metabolic regulation to cardiovascular health. Understanding their potential impact on bone health is a topic of ongoing research. The intricate relationship between bone density, bone quality, and overall bone strength is central to understanding the diagnosis and management of osteoporosis. Current innovation in machine learning and predictive model can bring revolution in the field of bone health and osteoporosis. Early identification of people with osteoporosis or risk of fracture through machine learning can prevent disability and improve the quality of life.

摘要

骨质疏松症是一个全球性的健康问题,其特征是骨密度降低和骨质受损,导致骨折风险增加,尤其是在绝经后女性中。骨密度(BMD)评估在骨质疏松症诊断中起着关键作用,因为它约占骨骼整体强度的70%。世界卫生组织(WHO)认可骨密度测量是诊断这种疾病的可靠方法。在巴基斯坦,骨折发生率正在上升,这在很大程度上归因于人口老龄化和一系列促成因素。了解骨质疏松症的全球和本地患病率、其对发病率和死亡率的影响以及促成因素,对于制定有效的预防和治疗策略至关重要。包括chemerin、vaspin和omentin-1在内的脂肪因子在骨代谢中的作用是一个新兴的研究领域。这些脂肪因子在生理学中发挥着多种作用,从炎症和代谢调节到心血管健康。了解它们对骨骼健康的潜在影响是一个正在进行研究的课题。骨密度、骨质和骨骼整体强度之间的复杂关系是理解骨质疏松症诊断和管理的核心。机器学习和预测模型方面的当前创新可以给骨骼健康和骨质疏松症领域带来变革。通过机器学习早期识别骨质疏松症患者或骨折风险人群可以预防残疾并提高生活质量。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0976/11255809/54349a4384cb/PJMS-40-1572-g001.jpg

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