Relationship between sarcopenia and depression in older patients with diabetes: An investigation using the Japanese version of SARC‐F

肌萎缩 医学 萧条(经济学) 优势比 糖尿病 逻辑回归 内科学 门诊部 可能性 老年学 物理疗法 内分泌学 宏观经济学 经济
作者
Satoshi Ida,Kazuya Murata,Mari Nakai,Sho Ito,Theodore K. Malmstrom,Yuki Ishihara,Kanako Imataka,Akihiro Uchida,Kou Monguchi,Ryutaro Kaneko,Ryoko Fujiwara,Hiroka Takahashi
出处
期刊:Geriatrics & Gerontology International [Wiley]
卷期号:18 (9): 1318-1322 被引量:15
标识
DOI:10.1111/ggi.13461
摘要

Aim The purpose of the present study was to investigate the relationship between sarcopenia and depression in older patients with diabetes using the Japanese version of SARC‐F. Methods Participants included patients with diabetes aged ≥65 years who were undergoing outpatient treatment at the Ise Red Cross Hospital, Ise, Japan. Depression was measured using the Japanese version of the Patient Health Questionnaire 9, which is a nine‐item questionnaire. Sarcopenia was assessed using the Japanese version of SARC‐F, a self‐administered questionnaire comprising five question items. Multiple logistic regression analysis with depression as the dependent variable and sarcopenia as the explanatory variable was used to calculate the odds ratio for depression in patients with sarcopenia. Results A total of 275 patients (167 men, 108 women) were the study participants. The adjusted odds ratio for depression in male patients with sarcopenia was 5.76 (95% CI 1.83–18.12, P = 0.003). The adjusted odds ratio for depression in female patients with sarcopenia was 2.62 (95% CI 0.68–10.05, P = 0.159). Conclusions A statistically significant relationship was shown between sarcopenia and depression in older male patients with diabetes. We believe that drawing the attention of physicians to sarcopenia prevalence by using the Japanese version of SARC‐F will contribute to the detection of depression in older male patients with diabetes. Geriatr Gerontol Int 2018; 18: 1318–1322 .
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
2秒前
科研通AI5应助sun采纳,获得10
2秒前
shitzu完成签到 ,获得积分10
3秒前
choco发布了新的文献求助10
5秒前
6秒前
李健的小迷弟应助sun采纳,获得10
6秒前
Jzhang应助liyuchen采纳,获得10
6秒前
魏伯安发布了新的文献求助30
6秒前
jjjjjj发布了新的文献求助30
8秒前
9秒前
伯赏诗霜发布了新的文献求助10
9秒前
糟糕的鹏飞完成签到 ,获得积分10
10秒前
10秒前
欢呼凡旋完成签到,获得积分10
11秒前
韩邹光完成签到,获得积分10
13秒前
xg发布了新的文献求助10
13秒前
14秒前
dktrrrr完成签到,获得积分10
14秒前
季生完成签到,获得积分10
17秒前
徐徐完成签到,获得积分10
17秒前
18秒前
18秒前
haku完成签到,获得积分10
20秒前
可爱的函函应助laodie采纳,获得10
22秒前
Singularity应助忆楠采纳,获得10
23秒前
24秒前
请叫我风吹麦浪应助PengHu采纳,获得30
25秒前
jjjjjj完成签到,获得积分10
25秒前
凝子老师发布了新的文献求助10
27秒前
27秒前
橙子fy16_发布了新的文献求助10
29秒前
cookie完成签到,获得积分10
29秒前
柒柒的小熊完成签到,获得积分10
30秒前
30秒前
Hello应助萌之痴痴采纳,获得10
31秒前
hahaer完成签到,获得积分10
33秒前
领导范儿应助失眠虔纹采纳,获得10
34秒前
35秒前
Owen应助凝子老师采纳,获得10
38秒前
38秒前
高分求助中
Continuum Thermodynamics and Material Modelling 3000
Production Logging: Theoretical and Interpretive Elements 2700
Ensartinib (Ensacove) for Non-Small Cell Lung Cancer 1000
Unseen Mendieta: The Unpublished Works of Ana Mendieta 1000
Bacterial collagenases and their clinical applications 800
El viaje de una vida: Memorias de María Lecea 800
Luis Lacasa - Sobre esto y aquello 700
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 有机化学 生物化学 物理 纳米技术 计算机科学 内科学 化学工程 复合材料 基因 遗传学 物理化学 催化作用 量子力学 光电子学 冶金
热门帖子
关注 科研通微信公众号,转发送积分 3527998
求助须知:如何正确求助?哪些是违规求助? 3108225
关于积分的说明 9288086
捐赠科研通 2805889
什么是DOI,文献DOI怎么找? 1540195
邀请新用户注册赠送积分活动 716950
科研通“疑难数据库(出版商)”最低求助积分说明 709849