Denosumab and the Risk of Diabetes in Patients Treated for Osteoporosis

德诺苏马布 骨质疏松症 医学 倾向得分匹配 糖尿病 危险系数 混淆 内科学 比例危险模型 兰克尔 肿瘤科 葡萄糖稳态 内分泌学 胰岛素 胰岛素抵抗 受体 置信区间 激活剂(遗传学)
作者
Huei‐Kai Huang,Alice Chuang,Tzu‐Chi Liao,Shih‐Chieh Shao,Peter Pin‐Sung Liu,Yu‐Kang Tu,Edward Chia‐Cheng Lai
出处
期刊:JAMA network open [American Medical Association]
卷期号:7 (2): e2354734-e2354734 被引量:4
标识
DOI:10.1001/jamanetworkopen.2023.54734
摘要

Importance Denosumab, a humanized monoclonal antibody against receptor activator of nuclear factor κB ligand (RANKL), is a widely used antiresorptive medication for osteoporosis treatment. Recent preclinical studies indicate that inhibition of RANKL signaling improves insulin sensitivity, glucose tolerance, and β-cell proliferation, suggesting that denosumab may improve glucose homeostasis; however, whether denosumab reduces the risk of incident diabetes remains unclear. Objective To evaluate whether denosumab use is associated with a lower risk of developing diabetes in patients with osteoporosis. Design, Setting, and Participants This nationwide, propensity score–matched cohort study used administrative data from Taiwan’s National Health Insurance Research Database. Adult patients who received denosumab for osteoporosis therapy in Taiwan between 2012 and 2019 were included. To eliminate the inherent bias from confounding by indication, the patients were categorized into a treatment group (34 255 patients who initiated denosumab treatment and adhered to it) and a comparison group (34 255 patients who initiated denosumab treatment but discontinued it after the initial dose) according to the administration status of the second dose of denosumab. Propensity score matching was performed to balance patient characteristics and to control for confounders. Exposure Treatment with denosumab. Main Outcomes and Measures The primary outcome was incident diabetes requiring treatment with antidiabetic drugs. A Cox proportional hazards model was used to estimate the hazard ratio (HR) for incident diabetes. Data were analyzed from January 1 to November 30, 2023. Results After propensity score matching, 68 510 patients were included (mean [SD] age, 77.7 [9.8] years; 57 762 [84.3%] female). During a mean (SD) follow-up of 1.9 (1.6) years, 2016 patients developed diabetes in the treatment group and 3220 developed diabetes in the comparison group (incidence rate, 35.9 vs 43.6 per 1000 person-years). Compared with the comparison group, denosumab treatment was associated with a lower risk of incident diabetes (HR, 0.84; 95% CI, 0.78-0.90). Several sensitivity analyses also demonstrated similar results of lower diabetes risk associated with denosumab treatment. Conclusions and relevance The results from this cohort study indicating that denosumab treatment was associated with lower risk of incident diabetes may help physicians choose an appropriate antiosteoporosis medication for patients with osteoporosis while also considering the risk of diabetes.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
1秒前
2秒前
2秒前
邱静完成签到,获得积分10
2秒前
李爱国应助落寞的紫夏采纳,获得10
2秒前
丸子鱼发布了新的文献求助10
4秒前
4秒前
思源应助首体卢本伟采纳,获得100
5秒前
CH发布了新的文献求助10
5秒前
邱静发布了新的文献求助10
5秒前
纪云禾发布了新的文献求助10
6秒前
菜鸡5号完成签到,获得积分10
6秒前
hhj发布了新的文献求助10
6秒前
Lingeek应助帝国超级硕士采纳,获得50
7秒前
现代老鼠发布了新的文献求助10
7秒前
9秒前
糊涂的新竹完成签到,获得积分10
9秒前
我不秃头完成签到,获得积分10
9秒前
结实白秋发布了新的文献求助10
10秒前
warden完成签到 ,获得积分10
10秒前
搜集达人应助sensenzou采纳,获得10
10秒前
汤佳乐发布了新的文献求助10
11秒前
YU完成签到 ,获得积分10
11秒前
安详香旋应助像风如你采纳,获得10
12秒前
顺利的飞荷完成签到,获得积分0
12秒前
老的火龙果应助Oguri_Cap采纳,获得10
12秒前
13秒前
万能图书馆应助纸鸢采纳,获得10
14秒前
qwl发布了新的文献求助10
14秒前
Oo发布了新的文献求助10
14秒前
萧雨墨完成签到,获得积分10
14秒前
xn完成签到,获得积分10
16秒前
张力航发布了新的文献求助10
20秒前
Ricardo完成签到,获得积分10
23秒前
脑洞疼应助晴晴qaq采纳,获得10
25秒前
英俊的铭应助乐观书南采纳,获得10
25秒前
Oo完成签到,获得积分10
26秒前
26秒前
26秒前
LPY完成签到 ,获得积分10
26秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Encyclopedia of Cardiovascular Research and Medicine(2e) 820
自動車の空力技術 800
Essentials of Carbohydrate Chemistry and Biochemistry, 4th Edition 800
Organizational Behavior 510
Management and the Arts 510
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 计算机科学 化学工程 工程类 有机化学 物理 复合材料 生物化学 内科学 细胞生物学 基因 遗传学 免疫学 冶金 光电子学 癌症研究
热门帖子
关注 科研通微信公众号,转发送积分 7782164
求助须知:如何正确求助?哪些是违规求助? 9321752
关于积分的说明 20384651
捐赠科研通 7370029
什么是DOI,文献DOI怎么找? 3320304
关于科研通互助平台的介绍 2468061
邀请新用户注册赠送积分活动 2336181