The L-shaped relationship between composite dietary antioxidant index and sarcopenic obesity in elderly adults: a cross-sectional study

横断面研究 肌萎缩性肥胖 体质指数 肥胖 环境卫生 肌萎缩 医学 索引(排版) 老年学 内科学 计算机科学 病理 万维网
作者
He Wu,Xiyi Chen,Shi Zhu,Jieyu Liu,Ziqi Meng,Chenguo Zheng,Chong-Jun Zhou
出处
期刊:Frontiers in Nutrition [Frontiers Media]
卷期号:11
标识
DOI:10.3389/fnut.2024.1428856
摘要

Background This study aimed to examine the associations of the Composite Dietary Antioxidant Index (CDAI) with sarcopenic obesity (SO) using the National Health and Nutrition Examination Survey (NHANES) database. Methods Data were gathered from NHANES between 2001 and 2004. To examine the relationship between CDAI and the occurrence of SO, multiple logistic regression analyses were performed. Subgroup analyses were performed to demonstrate the stability of the results. Restricted cubic splines were utilized to examine the non-linear correlations. Results A total of 2,333 elderly individuals were included in the study. In the multivariate logistic regression crude model, we revealed an odds ratio (OR) of 0.928 [95% confidence interval (CI), 0.891–0.965, p < 0.001] for the correlation between CDAI and SO. The ORs were 0.626 (95% CI, 0.463–0.842) and 0.487 (95% CI, 0.354–0.667) for CDAI tertiles 2 and 3, respectively ( p for trend <0.001), after full adjustment. The subgroup analysis findings demonstrated a reliable and enduring connection between CDAI and SO across various subgroups. However, the strength of the correlation between CDAI and SO was significantly affected by diabetes ( p for interaction = 0.027). Moreover, restricted cubic spline analysis revealed an L-shaped relationship. Conclusion The present study identified an L-shaped correlation between CDAI and SO in elderly participants’ demographics. The implications of these findings were significant for future studies and the formulation of dietary guidelines.

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
小王子完成签到,获得积分10
1秒前
ATEVYG完成签到 ,获得积分10
2秒前
飞飞猪发布了新的文献求助10
3秒前
andjdd完成签到,获得积分10
3秒前
8秒前
pdf12完成签到,获得积分10
9秒前
qjw发布了新的文献求助10
10秒前
HUHU发布了新的文献求助10
13秒前
13秒前
14秒前
专一的蛋挞完成签到,获得积分10
15秒前
菜菜发布了新的文献求助30
17秒前
18秒前
18秒前
111发布了新的文献求助10
18秒前
红莲墨生发布了新的文献求助10
20秒前
21秒前
niufuking完成签到,获得积分10
22秒前
Jiangpeng Wu完成签到,获得积分10
23秒前
陈年人完成签到 ,获得积分10
23秒前
HUHU完成签到,获得积分10
24秒前
科研猫发布了新的文献求助10
24秒前
可鲁贝洛斯完成签到,获得积分10
28秒前
红莲墨生完成签到,获得积分10
28秒前
顺心小凝完成签到,获得积分10
30秒前
pdf123完成签到,获得积分10
30秒前
清风发布了新的文献求助200
33秒前
科研通AI6.2应助向前采纳,获得30
33秒前
34秒前
chope完成签到,获得积分10
35秒前
39秒前
leoskrrr完成签到,获得积分10
40秒前
Kevin完成签到,获得积分10
42秒前
43秒前
44秒前
Jason完成签到,获得积分10
45秒前
高高的外套完成签到,获得积分10
46秒前
46秒前
coini发布了新的文献求助10
48秒前
51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
PowerCascade: A Synthetic Dataset for Cascading Failure Analysis in Power Systems 2000
Picture this! Including first nations fiction picture books in school library collections 1500
Signals, Systems, and Signal Processing 610
Unlocking Chemical Thinking: Reimagining Chemistry Teaching and Learning 555
Photodetectors: From Ultraviolet to Infrared 500
Cancer Targets: Novel Therapies and Emerging Research Directions (Part 1) 400
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6359636
求助须知:如何正确求助?哪些是违规求助? 8173646
关于积分的说明 17214945
捐赠科研通 5414627
什么是DOI,文献DOI怎么找? 2865583
邀请新用户注册赠送积分活动 1842883
关于科研通互助平台的介绍 1691124