2型糖尿病合并骨质疏松症的骨折风险评估和治疗研究进展
Research Progress in Fracture Risk Assessment and Treatment for T2DM Complicated with Osteoporosis
DOI: 10.12677/acm.2025.1582310, PDF, HTML, XML,   
作者: 刘诗怡, 李桂琼*:重庆医科大学第二附属医院全科医学科,重庆
关键词: 2型糖尿病骨质疏松症骨折风险评估药物治疗Type 2 Diabetes Mellitus Osteoporosis Fracture Risk Assessment Drug Therapy
摘要: 2型糖尿病(type 2 diabetes mellitus, T2DM)患者的骨密度通常保持正常或增加,但他们的骨折风险却比非糖尿病患者更高。这使得T2DM患者成为一个高度易感的群体,需要特别关注骨骼健康管理。然而,传统的骨折风险评估方法,包括双能X线吸收法和骨折风险评估工具低估了T2DM患者的骨折风险。随着骨折风险评估方法的发展和新型降糖药物治疗地位的显著提高,尽管有证据表明T2DM合并骨质疏松症(osteoporosis, OP)与骨折风险有关,但目前缺乏专门针对T2DM患者的随机对照试验来确定最佳的骨折风险评估和治疗方法。因此,本文旨在对T2DM合并OP患者骨折风险评估、新型降糖药物及抗骨质疏松药物治疗的研究现状作一综述,以提供新的理论依据。
Abstract: The bone mineral density of people with type 2 diabetes mellitus (T2DM) usually remains normal or increases, but their risk of fractures is higher than those without diabetes. This makes people with T2DM a highly susceptible group that requires particular attention to skeletal health management. However, traditional methods for assessing fracture risk, including dual-energy X-ray absorptiometry and fracture risk assessment tools, underestimate the fracture risk in people with T2DM. As fracture risk assessment methods evolve and novel hypoglycemic treatments gain prominence, despite evidence linking T2DM and osteoporosis to higher fracture risk, there is a lack of randomized controlled trials for people with T2DM to identify optimal fracture risk assessment and treatment strategies. Therefore, the purpose of this paper is to review the current research on fracture risk assessment, novel hypoglycemic drugs and, anti-osteoporosis drug treatments in T2DM people with OP, in order to provide a new theoretical basis.
文章引用:刘诗怡, 李桂琼. 2型糖尿病合并骨质疏松症的骨折风险评估和治疗研究进展[J]. 临床医学进展, 2025, 15(8): 869-879. https://doi.org/10.12677/acm.2025.1582310

1. 引言

2型糖尿病(type 2 diabetes mellitus, T2DM)是一种全球性的慢性代谢性疾病,随着人口老龄化的加剧,T2DM的患病率逐年上升,T2DM已成为我国公共卫生的重要问题[1]。多项流行病学研究表明,T2DM患者骨折风险显著高于非糖尿病人群[2] [3]。一项大型前瞻性研究发现,T2DM患者髋部骨折的风险是非糖尿病人群的1.5倍[4]。骨质疏松性骨折(osteoporotic fracture, OF)是骨质疏松症(osteoporosis, OP)的一种严重并发症及终点事件,其特征是骨组织微结构破坏和骨密度下降,导致脆性骨折的风险增加,尤其是在50岁及以上的男性和女性中[5]。OP是一种常见的老年性疾病,近年来,越来越多的研究表明,T2DM患者OP和OF风险显著增加,T2DM与OP之间的相互作用可能归因于胰岛素抵抗、钙和磷代谢的变化以及激素变化等[6] [7]。目前,通过可用的风险评估和治疗方法,许多OF是可以预防的。因此,我们回顾了有关T2DM合并OP患者骨骼评估的研究现况,并总结了新型降糖药物在维护T2DM合并OP患者骨骼健康方面的安全性与疗效的证据。

2. T2DM患者合并OP的骨折风险评估方法和工具

2.1. 双能X线吸收法(Dual-Energy X-Ray Absorptiometry, DXA)

DXA作为诊断OP的金标准,其测定的骨密度(bone mineral density, BMD)是评估骨折风险的常用指标[8]。但在T2DM患者中,其BMD通常处于正常或偏高,仅依赖这一指标通常会低估骨折风险[7] [9]。这种矛盾现象表明,T2DM可能通过其他机制导致骨折风险增加,如骨结构减弱、骨转换减少导致骨丢失增加、骨面积较小、代谢控制不良或跌倒风险增加[10] [11]。DXA不能提供关于骨3D微观结构和皮质孔隙度的信息,也不能区分松质骨和皮质骨的BMD [12],并且不同制造商的非标准化BMD机器测量结果会因部位不同而有差异。因此,通过DXA测量BMD来评估T2DM合并OP患者骨折风险的效能十分有限。

2.2. 骨转换标志物(Bone Turnover Markers, BTMs)

BTMs是反映骨细胞活动的可靠生物标志物,比BMD更能早期反映骨代谢变化,用于评估骨代谢并监测OP的管理。这些标志物主要由骨破坏过程中的副产物组成,如骨吸收标志物:I型胶原交联羧基末端肽(C-terminal telopeptide of type I collagen, CTX)和I型胶原交联氨基末端肽(N-terminal telopeptide of type I collagen, NTX)等;骨形成标志物:I型原胶原氨基端前肽(procollagen type I N-terminal propeptide, P1NP)、骨钙素和骨源性碱性磷酸酶等。研究表明,T2DM患者的BTMs通常低于非T2DM患者,反映了骨形成和骨吸收的减少[13]。尽管BTMs在监测骨转换方面可靠且有用,但有病例队列研究显示,它们不能用作T2DM骨折风险的预测指标[14]

2.3. 骨小梁评分(Trabecular Bone Score, TBS)

TBS是通过专门软件从腰椎DXA图像提取的灰度纹理指数,用于评估椎体骨小梁微结构[9]。张翠平等[15]研究表明,TBS在T2DM患者中是一个重要的骨折风险预测工具,可以独立于BMD提供额外的信息,与单独使用骨折风险评估工具(fracture risk assessment tool, FRAX)相比,使用TBS调整后的FRAX可以提高T2DM骨折风险预测[16]。根据SILVA等[17]研究,TBS可分为三类:正常的微结构:≥1.35、部分退化的微结构:1.20~1.35、退化的微结构:≤1.20。研究发现,T2DM患者的TBS临界值为1.279,属于部分退化的骨小梁微结构类别,这解释了即使T2DM患者的骨含量正常,骨质量的恶化也可能导致骨质疏松和椎体骨折[18]

2.4. 其他成像技术

除了上述所提到的方法外,其他一些成像技术作为传统BMD测量的潜在补充也可用于预测T2DM患者发生骨折的风险:定量计算机断层扫描(quantitative computed tomography, QCT)、高分辨率外周定量计算机断层扫描(high-resolution peripheral quantitative computed tomography, HR-pQCT)、定量超声(quantitative ultrasound, QUS)、射频超声多光谱(radiofrequency echographic multi spectrometry, REMS)等。

QCT和HR-pQCT使用CT三维体积数据进行分析,能够测量真实的体积骨密度(volumetric bone mineral density, vBMD),并分别评估皮质骨和松质骨。QCT得到了国际临床骨密度学会的认可,并在其共识中描述了QCT在骨质疏松症方面的应用。多国协作研究表明,HR-pQCT参数在骨折预测中能提高预测准确性,但增量改善幅度较小[19]。目前,使用QCT和HR-pQCT评估T2DM患者的骨折风险的临床研究较少,QIU等[20]研究表明,基于QCT结合临床特征对骨质异常的T2DM具有良好的预测价值。有限元分析(finite element analysis, FEA)模型多年来一直被应用于设计和制造过程,并至今仍为许多工程应用的标准参考。在医学领域,基于QCT的FEM不仅能够测量vBMD和骨微结构,还能生成多种生物力学参数,准确预测不同部位的骨强度,并反映骨的几何形状、结构及各种机械特性[21]。这些信息对于评估T2DM患者的骨折风险尤为重要,但该方法在临床实践中尚未广泛应用。研究指出,QCT的空间分辨率不足以可视化和量化松质骨微结构和皮质孔隙度,虽然可以实现更高的空间分辨率,但不建议这样做,因为这会导致辐射剂量增加,而QCT的辐射剂量已经相对较高[22]

QUS具有便携、低成本和无电离辐射等特点,国际临床骨密度学会的一项立场声明表明,足跟QUS能够独立且与DXA一样有效地预测脆性骨折[23]。但QUS在糖尿病患者中的应用较少,且结果不一致。此外,QUS技术存在一些重要的局限性,特别是QUS不能用于诊断分类,因为世界卫生组织的标准是基于DXA测量的BMD建立的,不能与FRAX一起使用[24]。REMS技术通过分析未经滤波的原始超声信号来评估BMD和骨质量,是继QUS在超声方面的新技术,有望成为评估糖尿病患者骨骼状态的一种创新方法。目前REMS关于T2DM的研究较少,只有Caffarelli等[25]的研究评估了REMS技术较DXA更利于T2DM患者骨质疏松症的诊断。

2.5. 骨折风险评估工具(Fracture Risk Assessment Tool, FRAX)

FRAX于2008年由世界卫生组织合作中心在英国谢菲尔德大学开发,FRAX通过结合临床风险因素和BMD来计算未来10年发生主要骨质疏松性骨折和髋部骨折的概率,经过多个前瞻性人群研究的数据验证,已成为全球最广泛使用和验证的风险评估工具之一[26]。T2DM在FRAX工具中的处理方式存在局限性,一方面在FRAX工具的构建中,糖尿病并不是一个主要的输入变量;另一方面FRAX假设次要原因对骨折风险的影响主要是通过降低BMD来实现的[27]。为了提高FRAX对T2DM的性能,对FRAX工具输入进行了以下四种调整的评估:1. 将股骨颈T评分降低0.5 SD;2. 将患者的年龄增加10年;3. 将“类风湿关节炎”作为合并症替代T2DM;4. 添加骨小梁评分调整。尽管每种调整都改善了T2DM患者的骨折风险预测,但没有一种方法在所有情况下(如:不同类型的骨折、不同糖尿病病程或不同的糖尿病亚组中)都是最优的[16]。在2023年初,英国谢菲尔德大学的研究人员发布了FRAXplus的测试版,其输入更多详细的参数,包括:最近发生的骨质疏松性骨折的时间和部位、糖皮质激素的剂量、T2DM的持续时间、TBS、过去一年内的跌倒次数、髋轴长度以及髋部和脊柱BMD之间的差异[28]。在T2DM患者中,较长的糖尿病持续时间和胰岛素使用与更高的髋部骨折风险相关[29],FRAXplus纳入的T2DM的持续时间尤其重要。尽管FRAXplus目前仍处于测试阶段,但其改进的功能有望在未来为临床决策提供更可靠的支持。

2.6. 其他临床风险评估工具

除了FRAX工具外,现已开发出多种骨折风险评估工具。Garvan骨折风险工具由澳大利亚Garvan医学研究所开发用于估计人群在5年和10年的OF和髋部骨折风险,主要依赖于BMD来预测骨折风险[30]。与FRAX工具类似,由于T2DM患者的BMD通常较高或正常,Garvan骨折风险计算器在预测T2DM患者的骨折风险时通常会低估实际风险[4] [9] [31]。一项队列研究在对患有糖尿病的女性的股骨颈T评分降低0.3后重新计算,结果发现,这种调整在很大程度上降低了Garvan对OF和髋部骨折风险的低估[31]。QFracture骨折风险评估工具是由英国研究人员开发的用于估计30至99岁人群在1到10年内的主要骨质疏松性骨折和髋部骨折风险,也是唯一一个除了正在测试的FRAXplus之外将糖尿病作为变量纳入骨折风险预测的计算工具,但存在不将BMD作为输入变量、忽略死亡率作为竞争风险、仅针对英国人群校准等缺点[32]。目前还没有专门评估QFracture在T2DM患者OF预测性能的研究。

2.7. 微压痕(Microindentation)和骨组织形态计量学(Bone Histomorphometry)

微压痕是一种近年来用于直接评估骨骼在组织水平上的机械性能的新技术。目前有两种微压痕方法:循环参考点微压痕和冲击微压痕,分别对应两种设备:Biodent设备和OsteoProbe设备,前者主要用于实验室测试,后者常用于临床评估。冲击微压痕是一种经皮、微侵入的技术,患者耐受性良好,其通过一个特定的冲击力使胫骨前中部皮质骨表面产生凹痕,测量骨组织对机械挑战的抵抗能力,这种抵抗能力以骨材料强度指数(bone material strength index, BMSi)表示[33]。研究表明,T2DM患者的BMSi较低,根据FRAX估算的主要骨质疏松性骨折和髋部骨折风险增加与较低的BMSi评分显著相关,且这种关联独立于骨密度[34]。此外,长期高血糖导致晚期糖基化终末产物的积累,抑制了成骨细胞增殖和分化,并且损害骨基质,进而增加骨折风险,有研究表明,无论是否在T2DM患者中,BMSi与皮肤晚期糖基化终末产物呈显著负相关[35] [36],这表明冲击微压痕测量的BMSi能够反映骨质材料特性的变化。然而,由于该技术尚未广泛应用于临床,且目前仅适用于胫骨,是否能反映全身骨骼的材料特性还需进一步研究。骨组织形态计量学通过分析骨组织切片的二维显微图像,获取骨结构的静态和动态参数,包括骨厚度、体积、表面积、骨形成和吸收速率等[37]。虽然这是评估骨代谢状态的金标准,但由于其侵入性和操作复杂,临床应用受限。在糖尿病患者中的研究有限,研究表示尽管血糖控制良好,T2DM患者骨转换相关的基因表达下降,晚期糖基化终末产物含量增加且骨强度降低[10]

2.8. 人工智能(Artificial Intelligence, AI)

与传统的预测工具相比,AI算法在骨折风险评估中取得了更优越的效率。AI已被用作影像学解读的辅助技术和初始筛查工具。人工智能辅助的机会性CT筛查技术通过应用深度学习神经网络,能够高效预测BMD,同时减少患者不必要的辐射风险。临床研究指出,在无需增加额外的费用和放射暴露基础上,AI 辅助机会性CT在60岁以上人群和T2DM患者中,可以作为一种有效的骨质疏松筛查工具[38]。机器学习(machine learning, ML)模型越来越多地用于以高精度识别髋部骨折风险[39]。ML在识别图像方面具有更强的能力,可以帮助缺乏经验的临床医生进行高度准确的诊断。目前,大多数研究重点集中于ML在预测骨质疏松症指标(如BMD)或图像中的作用[40]。然而,ML在预测骨质疏松性骨折方面的准确性和效率尚未得到充分研究。总体而言,ML和AI等新型骨折风险工具有待开发,新工具和算法需要在T2DM群体中进行外部验证,有望进一步提高风险预测的有效性和可靠性。

3. T2DM合并OP的新型降糖药物治疗

长期使用噻唑烷二酮类对骨代谢有负面影响现已达成共识。多项研究表明,磺脲类药物对骨代谢和BMD的影响是中性的[41]。尽管如此,仍有许多研究指出,磺脲类药物导致低血糖的风险较高,增加了跌倒事件的发生率,跌倒是老年人受伤、残疾和死亡的主要原因之一,也是导致骨折的主要原因[42]。二甲双胍是治疗T2DM最常用的药物,其可以抑制骨髓中间充质干细胞向脂肪细胞分化,从而降低骨折风险[43]。一项我国大连的回顾性研究发现,甘精胰岛素有助于提高T2DM患者的BMD,利于骨代谢[44]。但LEE等[45]研究发现,胰岛素通过增加低血糖和跌倒的风险间接使T2DM老年男性骨折风险增加。与使用口服降糖药的患者相比,接受胰岛素治疗的T2DM患者的骨折风险更高[29]。与传统的降糖药物相比,新型降糖药物越来越多地用于T2DM患者心血管和肾脏的保护,有些在减重中应用越来越广泛。这引发了我们对T2DM患者降糖治疗在OP和OF安全性和疗效上的担忧,为此,我们对以下三类新型降糖药物进行阐述。

3.1. 钠-葡萄糖协同转运蛋白2抑制剂(Sodium-Glucose Transport Protein 2 Inhibitors, SGLT2抑制剂)

SGLT2抑制剂通过促进尿液中葡萄糖的排泄来降低血糖水平,并改善心血管和糖尿病肾病的结局,但同时增加尿钙排泄,导致钙磷稳态失衡,增加骨折风险。研究表明,卡格列净会破坏骨微结构,减少股骨和椎骨的骨强度,降低总髋部BMD [46]。CANVAS研究报道卡格列净可能比安慰剂增加骨折风险,但CREDENCE研究显示没有证据表明观察到的骨折风险与卡格列净治疗有关[47] [48]。一项随机III期研究表明,使用卡格列净的T2DM患者骨折事件显著增加,具体原因可能是在CANVAS研究中受试者年龄较大,肾小球滤过率较低,使用利尿剂的频率较高,直立性低血压和姿势性头晕等不良事件导致跌倒风险增加有关[47]。但是在T2DM小鼠模型中,卡格列净增加了BMD,并改善了T2DM小鼠的骨微结构,此外,在高糖环境中,卡格列净在5 µM浓度下促进了成骨细胞分化[49]。TAN等[50]研究表明,恩格列净联合对症治疗在T2DM合并OP患者中显著改善血糖代谢、磷钙代谢和BMD,并降低骨折发生率,具有积极的临床效果。不同研究对于SGLT2抑制剂对骨折风险的影响存在不一致的结果,其中卡格列净的争议较大。一项大规模的国际元分析发现,有骨折史相比没有骨折史的人群,发生任何临床骨折的风险增加了约88% [51],针对既往有骨折史的T2DM合并OP患者,使用SGLT2抑制剂需要权衡利弊,特别是卡格列净,并且应考虑进行BMD和骨折风险评估。

3.2. 胰高血糖素样肽-1受体激动剂(Glucagon-Like Peptide-1 Receptor Agonists, GLP-1受体激动剂)

从利拉鲁肽、度拉糖肽到司美格鲁肽,再到葡萄糖依赖性胰岛素释放多肽(GIP)和胰高血糖素样肽-1 (GLP-1)受体双重激动剂替尔泊肽陆续在中国上市,GLP-1受体激动剂在肥胖和T2DM患者治疗和管理中取得了新的进展。此外,GLP-1 受体激动剂经证实对心血管有益,据最新III期SOUL研究主要结果公布,口服司美格鲁肽显著降低T2DM合并心血管疾病和/或慢性肾脏病患者的心血管风险14% [52]。对于骨骼的影响,此前,多项荟萃分析表明,尽管一些细胞和动物研究表明GLP-1受体激动剂对骨细胞、骨转换和BMD有积极影响,但人类研究表示对此结果要么为阴性,要么有限且相互矛盾[53] [54]。最新一项荟萃分析显示,GLP-1受体激动剂不仅增加了腰椎和股骨颈的BMD,还抑制骨吸收和促进骨形成来保护骨健康[55]。另一项丹麦为期10年观察性研究表明,GLP-1受体激动剂在T2DM患者治疗中,骨折发生率总体呈下降趋势,特别是在70~79岁这个年龄组中,T2DM患者的骨折发生率中位数下降了26.4% [56]。综上所述,GLP-1受体激动剂在治疗T2DM合并OP患者时呈现中性或积极作用,除了控制血糖外,GLP-1受体激动剂在体重管理和肥胖相关骨折预防方面的潜力值得关注,特别是在伴有多个骨折风险因素和代谢控制不良的T2D合并OP患者中。

3.3. 二肽基肽酶-4抑制剂(Dipeptidyl Peptidase IV Inhibitors, DPP-4抑制剂)

GIP、GLP-1以及许多其他调节肽都是广泛存在的酶DPP-4的底物,由于GIP和GLP-1对胰岛素分泌和血糖稳态有显著的好处,DPP-4抑制剂用于治疗T2DM。在骨骼影响方面,DPP-4抑制剂对体重和血糖的控制较GLP-1受体激动剂弱,但其能改善体重减轻和降低低血糖风险[57],从而降低骨折风险。同时,DPP-4抑制剂通过增加内源性GLP-1水平参与胰腺–内分泌–骨轴,降低骨吸收、促进骨形成,减少OP和骨折的发生率[58]。然而,研究表明某些DPP-4抑制剂如:西格列汀、维格列汀、沙格列汀可能增加骨折风险[59]。总体而言,DPP-4抑制剂在使用中的好处大于对骨折风险的不利影响,临床上可以考虑其作为治疗T2DM合并OP患者的选择之一。

4. T2DM合并OP的抗骨质疏松药物治疗

抑制骨吸收药物包括:双膦酸盐、地舒单抗、选择性雌激素受体调节剂(雷洛昔芬、他莫昔芬)。无论是在减少骨折风险、增加BMD还是降低骨转换标志物方面,双膦酸盐治疗在T2DM和非糖尿病患者中的效果相似[60],而地舒单抗显著增加了BMD并降低了患有OP和T2DM的女性椎体骨折的风险[61]。雷洛昔芬还能够降低低密度脂蛋白胆固醇水平,并减少骨转换标志物(NTX、骨源性碱性磷酸酶)的水平,还显著降低了同型半胱氨酸水平,这有助于改善骨质量,同时对血糖代谢没有不良影响[62]。然而,他莫昔芬可能会增加糖尿病风险和降低胰岛素敏感性[63]。促进骨形成药物如:特立帕肽和阿巴洛帕肽,都是有效的OP治疗药物,阿巴洛帕肽在T2DM患者中表现出与非T2DM患者相似的有效性,显著改善了髋部、股骨颈和腰椎的BMD [64],减少了髋部和椎体骨折的风险。罗莫佐单抗通过抑制骨硬化蛋白,可以增加成骨细胞活性,促进骨形成,它还能降低破骨细胞的活性,减少骨吸收[65]。目前,关于罗莫佐单抗在T2DM患者中的有效性和安全性尚无具体证据。低血清维生素D水平与老年群体发生T2DM的风险增加显著相关,机制可能包括维生素D对胰岛素分泌的调节、减少胰岛素抵抗、改善葡萄糖转运、减少慢性炎症等[66]。活性维生素D类似物艾地骨化醇在预防T2DM方面有显著效果,对于糖尿病前期基础胰岛素分泌不足的人群有预防作用,并且研究也观察到BMD的增加[67]

5. T2DM合并OP患者的管理

国际骨质疏松基金会建议,所有50岁及以上的T2DM患者应接受骨密度(DXA)筛查,在资源受限的情况下,可优先为50~60岁之间且有临床骨折风险因素的患者提供DXA检查[68]。对于有髋部或椎体骨折史的患者,建议启动骨质疏松治疗(二级预防) [68]。当FRAX评分超过特定的干预阈值和/或T值 ≤ 2.0时,建议进行预防性治疗(一级预防) [68]。长期高血糖会增加骨折风险,因此应严格控制血糖水平。T2DM合并视网膜病变的患者,跌倒风险较高,应采取措施改善平衡能力,避免跌倒。无禁忌条件下,保证足够的钙和维生素D摄入,以支持骨健康[69]。进行适当的负重运动如:步行、慢跑,有助于增强骨密度和改善平衡能力,降低骨折风险。

6. 总结与展望

虽然T2DM患者的BMD正常或升高,但骨折风险仍高,这给临床上如何管理T2DM合并OP群体的骨折风险带来挑战。现有的骨折风险评估方法和工具有一定的预测效能,但缺乏特异性,寻找更精确的骨折风险预测技术仍处于探索阶段。ML和AI算法正在加速风险预测的发展,当前的成像技术与先进的图像处理(FEA或AI)相结合,有潜力在BMD之外增加对骨折风险评估的价值。未来的研究应集中在提高这些技术的准确性、可操作性和更加个性化的个体化方法中,以更好地指导临床决策。对于T2DM合并OP管理,除规律的骨骼健康筛查、一级和二级预防干预、保证钙和维生素D摄入、适当的负重运动外,随着新型降糖药物广泛使用,在治疗T2DM合并OP时,需要综合考虑新型降糖药物对这些患者骨骼健康方面的安全性与疗效。GLP-1受体激动剂、DPP-4抑制剂对骨结局有中性或积极影响。但SGLT2抑制剂由于骨折风险结果尚存在不一致,应谨慎使用,特别是在有既往骨折病史的患者中。当前的抗骨质疏松药物,如:双膦酸盐、地舒单抗、雷洛昔芬、特立帕肽、阿巴洛帕肽在T2DM患者中似乎是安全有效的。此外,其他一些药物,如维生素D和活性维生素D在预防骨质疏松有积极作用,还有血糖益处。未来研究需综合分析新型降糖药与抗骨质疏松药对骨骼健康的影响,以优化T2DM患者的个体化治疗策略。

NOTES

*通讯作者。

参考文献

[1] Xu, Y., Lu, J., Li, M., Wang, T., Wang, K., Cao, Q., et al. (2024) Diabetes in China Part 1: Epidemiology and Risk Factors. The Lancet Public Health, 9, e1089-e1097.
https://doi.org/10.1016/s2468-2667(24)00250-0
[2] Tebé, C., Martínez-Laguna, D., Carbonell-Abella, C., Reyes, C., Moreno, V., Diez-Perez, A., et al. (2019) The Association between Type 2 Diabetes Mellitus, Hip Fracture, and Post-Hip Fracture Mortality: A Multi-State Cohort Analysis. Osteoporosis International, 30, 2407-2415.
https://doi.org/10.1007/s00198-019-05122-3
[3] Koromani, F., Oei, L., Shevroja, E., Trajanoska, K., Schoufour, J., Muka, T., et al. (2019) Vertebral Fractures in Individuals with Type 2 Diabetes: More than Skeletal Complications Alone. Diabetes Care, 43, 137-144.
https://doi.org/10.2337/dc19-0925
[4] Dahl, J., Gulseth, H.L., Forsén, L., Hoff, M., Forsmo, S., Åsvold, B.O., et al. (2021) Risk of Hip and Forearm Fracture in Subjects with Type 2 Diabetes Mellitus and Latent Autoimmune Diabetes of Adults. the HUNT Study, Norway. Bone, 153, Article 116110.
https://doi.org/10.1016/j.bone.2021.116110
[5] Geusens, P., van den Bergh, J., Roux, C., Chapurlat, R., Center, J., Bliuc, D., et al. (2024) The Fracture Phenotypes in Women and Men of 50 Years and Older with a Recent Clinical Fracture. Current Osteoporosis Reports, 22, 611-620.
https://doi.org/10.1007/s11914-024-00885-z
[6] Khosla, S., Samakkarnthai, P., Monroe, D.G. and Farr, J.N. (2021) Update on the Pathogenesis and Treatment of Skeletal Fragility in Type 2 Diabetes Mellitus. Nature Reviews Endocrinology, 17, 685-697.
https://doi.org/10.1038/s41574-021-00555-5
[7] Zoulakis, M., Johansson, L., Litsne, H., Axelsson, K. and Lorentzon, M. (2024) Type 2 Diabetes and Fracture Risk in Older Women. JAMA Network Open, 7, e2425106.
https://doi.org/10.1001/jamanetworkopen.2024.25106
[8] Walker, M.D. and Shane, E. (2023) Postmenopausal Osteoporosis. New England Journal of Medicine, 389, 1979-1991.
https://doi.org/10.1056/nejmcp2307353
[9] 刘建民, 朱大龙, 母义明, 等. 糖尿病患者骨折风险管理中国专家共识[J]. 中华骨质疏松和骨矿盐疾病杂志, 2019, 12(4): 319-335.
[10] Leanza, G., Cannata, F., Faraj, M., Pedone, C., Viola, V., Tramontana, F., et al. (2024) Bone Canonical Wnt Signaling Is Downregulated in Type 2 Diabetes and Associates with Higher Advanced Glycation End-Products (AGEs) Content and Reduced Bone Strength. eLife, 12, RP90437.
[11] Napoli, N., Chandran, M., Pierroz, D.D., Abrahamsen, B., Schwartz, A.V. and Ferrari, S.L. (2016) Mechanisms of Diabetes Mellitus-Induced Bone Fragility. Nature Reviews Endocrinology, 13, 208-219.
https://doi.org/10.1038/nrendo.2016.153
[12] Sangondimath, G., Sen, R.K. and T., F.R. (2023) DEXA and Imaging in Osteoporosis. Indian Journal of Orthopaedics, 57, 82-93.
https://doi.org/10.1007/s43465-023-01059-2
[13] Starup-Linde, J. and Vestergaard, P. (2016) Biochemical Bone Turnover Markers in Diabetes Mellitus—A Systematic Review. Bone, 82, 69-78.
https://doi.org/10.1016/j.bone.2015.02.019
[14] Napoli, N., Conte, C., Eastell, R., Ewing, S.K., Bauer, D.C., Strotmeyer, E.S., et al. (2020) Bone Turnover Markers Do Not Predict Fracture Risk in Type 2 Diabetes. Journal of Bone and Mineral Research, 35, 2363-2371.
https://doi.org/10.1002/jbmr.4140
[15] 张翠平, 陈琳, 徐碧林, 等. 骨小梁评分在2型糖尿病患者中评价骨质量的应用[J]. 中国骨质疏松杂志, 2020, 26(7): 1028-1033.
[16] Leslie, W.D., Johansson, H., McCloskey, E.V., Harvey, N.C., Kanis, J.A. and Hans, D. (2018) Comparison of Methods for Improving Fracture Risk Assessment in Diabetes: The Manitoba BMD Registry. Journal of Bone and Mineral Research, 33, 1923-1930.
https://doi.org/10.1002/jbmr.3538
[17] Silva, B.C., Boutroy, S., Zhang, C., McMahon, D.J., Zhou, B., Wang, J., et al. (2013) Trabecular Bone Score (TBS)—A Novel Method to Evaluate Bone Microarchitectural Texture in Patients with Primary Hyperparathyroidism. The Journal of Clinical Endocrinology & Metabolism, 98, 1963-1970.
https://doi.org/10.1210/jc.2012-4255
[18] Lin, Y.-C., Wu, J., Kuo, S.-F., Cheung, Y.-C., Sung, C.-M., Fan, C.-M., et al. (2020) Vertebral Fractures in Type 2 Diabetes Patients: Utility of Trabecular Bone Score and Relationship with Serum Bone Turnover Biomarkers. Journal of Clinical Densitometry, 23, 37-43.
https://doi.org/10.1016/j.jocd.2019.01.003
[19] Cheung, W.-H., Hung, V.W.-Y., Cheuk, K.-Y., Chau, W.-W., Tsoi, K.K.-F., Wong, R.M.-Y., et al. (2021) Best Performance Parameters of HR-pQCT to Predict Fragility Fracture: Systematic Review and Meta-Analysis. Journal of Bone and Mineral Research, 36, 2381-2398.
https://doi.org/10.1002/jbmr.4449
[20] Qiu, H., Yang, H., Yang, Z., Yao, Q., Duan, S., Qin, J., et al. (2022) The Value of Radiomics to Predict Abnormal Bone Mass in Type 2 Diabetes Mellitus Patients Based on CT Imaging for Paravertebral Muscles. Frontiers in Endocrinology, 13, Article 963246.
https://doi.org/10.3389/fendo.2022.963246
[21] Chen, W., Mao, M., Fang, J., Xie, Y. and Rui, Y. (2022) Fracture Risk Assessment in Diabetes Mellitus. Frontiers in Endocrinology, 13, Article 961761.
https://doi.org/10.3389/fendo.2022.961761
[22] Carballido-Gamio, J. (2022) Imaging Techniques to Study Diabetic Bone Disease. Current Opinion in Endocrinology, Diabetes & Obesity, 29, 350-360.
https://doi.org/10.1097/med.0000000000000749
[23] Krieg, M.A., Barkmann, R., Gonnelli, S., Stewart, A., Bauer, D.C., Del Rio Barquero, L., et al. (2008) Quantitative Ultrasound in the Management of Osteoporosis: The 2007 ISCD Official Positions. Journal of Clinical Densitometry, 11, 163-187.
https://doi.org/10.1016/j.jocd.2007.12.011
[24] Gonnelli, S., Al Refaie, A., Baldassini, L., De Vita, M. and Caffarelli, C. (2022) Ultrasound-Based Techniques in Diabetic Bone Disease: State of the Art and Future Perspectives. Indian Journal of Endocrinology and Metabolism, 26, 518-523.
https://doi.org/10.4103/ijem.ijem_347_22
[25] Caffarelli, C., Tomai Pitinca, M.D., Al Refaie, A., Ceccarelli, E. and Gonnelli, S. (2021) Ability of Radiofrequency Echographic Multispectrometry to Identify Osteoporosis Status in Elderly Women with Type 2 Diabetes. Aging Clinical and Experimental Research, 34, 121-127.
https://doi.org/10.1007/s40520-021-01889-w
[26] Schini, M., Johansson, H., Harvey, N.C., Lorentzon, M., Kanis, J.A. and McCloskey, E.V. (2023) An Overview of the Use of the Fracture Risk Assessment Tool (FRAX) in Osteoporosis. Journal of Endocrinological Investigation, 47, 501-511.
https://doi.org/10.1007/s40618-023-02219-9
[27] Vandenput, L., Johansson, H., McCloskey, E.V., Liu, E., Åkesson, K.E., Anderson, F.A., et al. (2022) Update of the Fracture Risk Prediction Tool FRAX: A Systematic Review of Potential Cohorts and Analysis Plan. Osteoporosis International, 33, 2103-2136.
https://doi.org/10.1007/s00198-022-06435-6
[28] Zerikly, R. and Demetriou, E.W. (2024) Use of Fracture Risk Assessment Tool in Clinical Practice and Fracture Risk Assessment Tool Future Directions. Womens Health, 20, 1-6.
https://doi.org/10.1177/17455057241231387
[29] Vilaca, T., Schini, M., Harnan, S., Sutton, A., Poku, E., Allen, I.E., et al. (2020) The Risk of Hip and Non-Vertebral Fractures in Type 1 and Type 2 Diabetes: A Systematic Review and Meta-Analysis Update. Bone, 137, Article 115457.
https://doi.org/10.1016/j.bone.2020.115457
[30] 董玉洁, 刘冀. 骨质疏松性骨折风险评估工具: FRAX、QFracture、Garvan的应用和比较[J]. 临床医学进展, 2021, 11(1): 143-149.
[31] Agarwal, A., Leslie, W.D., Nguyen, T.V., Morin, S.N., Lix, L.M. and Eisman, J.A. (2022) Performance of the Garvan Fracture Risk Calculator in Individuals with Diabetes: A Registry-Based Cohort Study. Calcified Tissue International, 110, 658-665.
https://doi.org/10.1007/s00223-021-00941-1
[32] Sheu, A., Greenfield, J.R., White, C.P. and Center, J.R. (2022) Assessment and Treatment of Osteoporosis and Fractures in Type 2 Diabetes. Trends in Endocrinology & Metabolism, 33, 333-344.
https://doi.org/10.1016/j.tem.2022.02.006
[33] Schoeb, M., Avci, T.M., Winter, E.M. and Appelman-Dijkstra, N.M. (2023) Safety Outcomes of Impact Microindentation: A Prospective Observational Study in the Netherlands. JBMR Plus, 7, e10799.
https://doi.org/10.1002/jbm4.10799
[34] Rufus-Membere, P., Anderson, K.B., Holloway-Kew, K.L., Kotowicz, M.A., Diez-Perez, A. and Pasco, J.A. (2025) Associations between Bone Material Strength Index and FRAX Scores. Journal of Bone and Mineral Metabolism, 43, 230-236.
https://doi.org/10.1007/s00774-024-01575-7
[35] Samakkarnthai, P., Sfeir, J.G., Atkinson, E.J., Achenbach, S.J., Wennberg, P.W., Dyck, P.J., et al. (2020) Determinants of Bone Material Strength and Cortical Porosity in Patients with Type 2 Diabetes Mellitus. The Journal of Clinical Endocrinology & Metabolism, 105, e3718-e3729.
https://doi.org/10.1210/clinem/dgaa388
[36] Lekkala, S., Sacher, S.E., Taylor, E.A., Williams, R.M., Moseley, K.F. and Donnelly, E. (2020) Increased Advanced Glycation Endproducts, Stiffness, and Hardness in Iliac Crest Bone from Postmenopausal Women with Type 2 Diabetes Mellitus on Insulin. Journal of Bone and Mineral Research, 38, 261-277.
https://doi.org/10.1002/jbmr.4757
[37] van Lenthe, G.H., Mueller, T.L., Wirth, A.J. and Müller, R. (2008) Quantification of Bone Structural Parameters and Mechanical Competence at the Distal Radius. Journal of Orthopaedic Trauma, 22, S66-S72.
https://doi.org/10.1097/bot.0b013e31815e9fe1
[38] 谢雨芯, 周素伊, 梅好, 等. 人工智能辅助机会性CT与双能X线骨密度检测在2型糖尿病和非糖尿病患者骨量评估中的比较研究[J]. 重庆医学, 2024, 53(24): 3700-3705.
[39] Kruse, C., Eiken, P. and Vestergaard, P. (2017) Machine Learning Principles Can Improve Hip Fracture Prediction. Calcified Tissue International, 100, 348-360.
https://doi.org/10.1007/s00223-017-0238-7
[40] Wu, Y., Chao, J., Bao, M. and Zhang, N. (2023) Predictive Value of Machine Learning on Fracture Risk in Osteoporosis: A Systematic Review and Meta-Analysis. BMJ Open, 13, e071430.
https://doi.org/10.1136/bmjopen-2022-071430
[41] Paschou, S.Α., Dede, A.D., Anagnostis, P.G., Vryonidou, A., Morganstein, D. and Goulis, D.G. (2017) Type 2 Diabetes and Osteoporosis: A Guide to Optimal Management. The Journal of Clinical Endocrinology & Metabolism, 102, 3621-3634.
https://doi.org/10.1210/jc.2017-00042
[42] Tao, Y., E, M., Shi, J. and Zhang, Z. (2021) Sulfonylureas Use and Fractures Risk in Elderly Patients with Type 2 Diabetes Mellitus: A Meta-Analysis Study. Aging Clinical and Experimental Research, 33, 2133-2139.
https://doi.org/10.1007/s40520-020-01736-4
[43] Ma, T., Tian, X., Zhang, B., Li, M., Wang, Y., Yang, C., et al. (2022) Low-Dose Metformin Targets the Lysosomal AMPK Pathway through PEN2. Nature, 603, 159-165.
https://doi.org/10.1038/s41586-022-04431-8
[44] Liu, D., Bai, J.-J., Yao, J.-J., Wang, Y.-B., Chen, T., Xing, Q., et al. (2021) Association of Insulin Glargine Treatment with Bone Mineral Density in Patients with Type 2 Diabetes Mellitus. Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy, 14, 1909-1917.
https://doi.org/10.2147/dmso.s302627
[45] Lee, R.H., Sloane, R., Pieper, C., Lyles, K.W., Adler, R.A., Van Houtven, C., et al. (2019) Glycemic Control and Insulin Treatment Alter Fracture Risk in Older Men with Type 2 Diabetes Mellitus. Journal of Bone and Mineral Research, 34, 2045-2051.
https://doi.org/10.1002/jbmr.3826
[46] Ye, Y., Zhao, C., Liang, J., Yang, Y., Yu, M. and Qu, X. (2019) Effect of Sodium-Glucose Co-Transporter 2 Inhibitors on Bone Metabolism and Fracture Risk. Frontiers in Pharmacology, 9, Article 1517.
https://doi.org/10.3389/fphar.2018.01517
[47] Watts, N.B., Bilezikian, J.P., Usiskin, K., Edwards, R., Desai, M., Law, G., et al. (2016) Effects of Canagliflozin on Fracture Risk in Patients with Type 2 Diabetes Mellitus. The Journal of Clinical Endocrinology & Metabolism, 101, 157-166.
https://doi.org/10.1210/jc.2015-3167
[48] Perkovic, V., Jardine, M.J., Neal, B., Bompoint, S., Heerspink, H.J.L., Charytan, D.M., et al. (2019) Canagliflozin and Renal Outcomes in Type 2 Diabetes and Nephropathy. New England Journal of Medicine, 380, 2295-2306.
https://doi.org/10.1056/nejmoa1811744
[49] Song, P., Chen, T., Rui, S., Duan, X., Deng, B., Armstrong, D.G., et al. (2022) Canagliflozin Promotes Osteoblastic MC3T3-E1 Differentiation via AMPK/RUNX2 and Improves Bone Microarchitecture in Type 2 Diabetic Mice. Frontiers in Endocrinology, 13, Article 1081039.
https://doi.org/10.3389/fendo.2022.1081039
[50] Tan, J., Guo, A., Zhang, K., Jiang, Y. and Liu, H. (2024) The Effect of Empagliflozin (Sodium-Glucose Cotransporter-2 Inhibitor) on Osteoporosis and Glycemic Parameters in Patients with Type 2 Diabetes: A Quasi-Experimental Study. BMC Musculoskeletal Disorders, 25, Article No. 793.
https://doi.org/10.1186/s12891-024-07900-5
[51] Kanis, J.A., Johansson, H., McCloskey, E.V., Liu, E., Åkesson, K.E., Anderson, F.A., et al. (2023) Previous Fracture and Subsequent Fracture Risk: A Meta-Analysis to Update FRAX. Osteoporosis International, 34, 2027-2045.
https://doi.org/10.1007/s00198-023-06870-z
[52] McGuire, D.K., Marx, N., Mulvagh, S.L., Deanfield, J.E., Inzucchi, S.E., Pop-Busui, R., et al. (2025) Oral Semaglutide and Cardiovascular Outcomes in High-Risk Type 2 Diabetes. New England Journal of Medicine, 392, 2001-2012.
https://doi.org/10.1056/nejmoa2501006
[53] Daniilopoulou, I., Vlachou, E., Lambrou, G.I., Ntikoudi, A., Dokoutsidou, E., Fasoi, G., et al. (2022) The Impact of GLP1 Agonists on Bone Metabolism: A Systematic Review. Medicina, 58, Article 224.
https://doi.org/10.3390/medicina58020224
[54] Viggers, R., Rasmussen, N.H.H. and Vestergaard, P. (2023) Effects of Incretin Therapy on Skeletal Health in Type 2 Diabetes—A Systematic Review. JBMR Plus, 7, e10817.
https://doi.org/10.1002/jbm4.10817
[55] Li, X., Li, Y. and Lei, C. (2024) Effects of Glucagon-Like Peptide-1 Receptor Agonists on Bone Metabolism in Type 2 Diabetes Mellitus: A Systematic Review and Meta-Analysis. International Journal of Endocrinology, 2024, Article 1785321.
https://doi.org/10.1155/2024/1785321
[56] Nasser, M.I., Kvist, A.V., Vestergaard, P., Eastell, R., Burden, A.M. and Frost, M. (2023) Sex and Age Group-Specific Fracture Incidence Rates Trends for Type 1 and 2 Diabetes Mellitus. JBMR Plus, 7, e10836.
https://doi.org/10.1002/jbm4.10836
[57] 王亚雯, 刘靖芳. DPP-4抑制剂对2型糖尿病患者骨代谢的影响及其分子机制[J]. 中国新药与临床杂志, 2023, 42(11): 695-700.
[58] Pechmann, L.M., Pinheiro, F.I., Andrade, V.F.C. and Moreira, C.A. (2024) The Multiple Actions of Dipeptidyl Peptidase 4 (DPP-4) and Its Pharmacological Inhibition on Bone Metabolism: A Review. Diabetology & Metabolic Syndrome, 16, Article No. 175.
https://doi.org/10.1186/s13098-024-01412-x
[59] Zhang, Y.-S., Zheng, Y.-D., Yuan, Y., Chen, S.-C. and Xie, B.-C. (2021) Effects of Anti-Diabetic Drugs on Fracture Risk: A Systematic Review and Network Meta-Analysis. Frontiers in Endocrinology, 12, Article 735824.
https://doi.org/10.3389/fendo.2021.735824
[60] Eastell, R., Vittinghoff, E., Lui, L.Y., Ewing, S.K., Schwartz, A.V., Bauer, D.C., et al. (2022) Diabetes Mellitus and the Benefit of Antiresorptive Therapy on Fracture Risk. Journal of Bone and Mineral Research, 37, 2121-2131.
https://doi.org/10.1002/jbmr.4697
[61] Ferrari, S., Eastell, R., Napoli, N., Schwartz, A., Hofbauer, L.C., Chines, A., et al. (2020) Denosumab in Postmenopausal Women with Osteoporosis and Diabetes: Subgroup Analysis of FREEDOM and FREEDOM Extension. Bone, 134, Article 115268.
https://doi.org/10.1016/j.bone.2020.115268
[62] Mori, H., Okada, Y., Kishikawa, H., Inokuchi, N., Sugimoto, H. and Tanaka, Y. (2013) Effects of Raloxifene on Lipid and Bone Metabolism in Postmenopausal Women with Type 2 Diabetes. Journal of Bone and Mineral Metabolism, 31, 89-95.
https://doi.org/10.1007/s00774-012-0379-8
[63] Jordt, N., Kjærgaard, K.A., Thomsen, R.W., Borgquist, S. and Cronin-Fenton, D. (2023) Breast Cancer and Incidence of Type 2 Diabetes Mellitus: A Systematic Review and Meta-Analysis. Breast Cancer Research and Treatment, 202, 11-22.
https://doi.org/10.1007/s10549-023-07043-6
[64] Dhaliwal, R., Hans, D., Hattersley, G., Mitlak, B., Fitzpatrick, L.A., Wang, Y., et al. (2020) Abaloparatide in Postmenopausal Women with Osteoporosis and Type 2 Diabetes: A Post Hoc Analysis of the ACTIVE Study. JBMR Plus, 4, e10346.
https://doi.org/10.1002/jbm4.10346
[65] Cosman, F., Crittenden, D.B., Adachi, J.D., Binkley, N., Czerwinski, E., Ferrari, S., et al. (2016) Romosozumab Treatment in Postmenopausal Women with Osteoporosis. New England Journal of Medicine, 375, 1532-1543.
https://doi.org/10.1056/nejmoa1607948
[66] Dominguez, L.J., Veronese, N., Marrone, E., Di Palermo, C., Iommi, C., Ruggirello, R., et al. (2024) Vitamin D and Risk of Incident Type 2 Diabetes in Older Adults: An Updated Systematic Review and Meta-Analysis. Nutrients, 16, Article 1561.
https://doi.org/10.3390/nu16111561
[67] Kawahara, T., Suzuki, G., Mizuno, S., Inazu, T., Kasagi, F., Kawahara, C., et al. (2022) Effect of Active Vitamin D Treatment on Development of Type 2 Diabetes: DPVD Randomised Controlled Trial in Japanese Population. British Medical Journal, 377, e066222.
https://doi.org/10.1136/bmj-2021-066222
[68] Ferrari, S.L., Abrahamsen, B., Napoli, N., Akesson, K., Chandran, M., Eastell, R., et al. (2018) Diagnosis and Management of Bone Fragility in Diabetes: An Emerging Challenge. Osteoporosis International, 29, 2585-2596.
https://doi.org/10.1007/s00198-018-4650-2
[69] Chandran, M., Mitchell, P.J., Amphansap, T., Bhadada, S.K., Chadha, M., Chan, D.-C., et al. (2021) Development of the Asia Pacific Consortium on Osteoporosis (APCO) Framework: Clinical Standards of Care for the Screening, Diagnosis, and Management of Osteoporosis in the Asia-Pacific Region. Osteoporosis International, 32, 1249-1275.
https://doi.org/10.1007/s00198-020-05742-0