解读油酸在糖尿病视网膜病变中的作用:单不饱和脂肪酸的实证分析。

IF 3.9 2区 医学 Q2 NUTRITION & DIETETICS
Ziyi Wang, Hui Wang, Yuxin Chen, Yang Chen, Xinlv Zhang, Anthony Diwon, Guomiao Zhang, Qichao Sheng, Huiqin Mei, Yixi Xu, Xiaoyu Zhang, Qingyang Mao, Chao Zheng, Guangyun Mao
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引用次数: 0

摘要

目的:现有文献表明,油酸(OA)是饮食和血浆中最普遍的单不饱和脂肪酸(MUFA),以其对胰岛素抵抗和炎症的有益影响而闻名。然而,其在糖尿病视网膜病变(DR)中的作用尚不清楚。本研究旨在阐明OA与DR之间的关系,并探讨其在DR检测中的潜力。方法:我们于2017年8月至2018年6月在中国开展了一项两中心、倾向评分匹配的病例对照研究,包括69例诊断为DR的2型糖尿病(T2D)患者(病例)和69例匹配的未诊断为DR的T2D患者(对照组)。采用弹性网络回归(ENET)、加权分位数回归(WQS)和贝叶斯核机回归(BKMR)研究了7种不同的MUFAs对DR的影响,重点研究了关键的脂质生物标志物。通过计算AUC来评估这些生物标志物的诊断效用。结果:MUFAs与DR之间呈显著负相关,其中OA被ENET、WQS和BKMR鉴定为关键。与最低分位数相比,OA第二分位数受试者的调整OR和95% CI为0.25(0.09,0.69),而OA第三分位数受试者的调整OR和95% CI为0.11(0.04,0.30)。这些结果在亚组和敏感性分析中是一致的。单独OA的AUC (95% CI)为0.72(0.63,0.81),合并其他协变量时增加到0.77(0.69,0.85)。结论:我们的研究结果揭示了血浆OA水平与DR风险之间存在显著的负相关关系,这表明OA可以作为识别2型糖尿病患者是否患有DR的有价值的生物标志物。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deciphering the role of oleic acid in diabetic retinopathy: an empirical analysis of monounsaturated fatty acids.

Aims: The existing literature indicates that oleic acid (OA) is the most prevalent monounsaturated fatty acid (MUFA) in both diet and plasma, known for its beneficial impact on insulin resistance and inflammation. However, its role in diabetic retinopathy (DR) remains unclear. This study aims to elucidate the association between OA and DR and explore its potential in DR detection.

Methods: We conducted a two-center, propensity score-matched case-control study, including 69 type 2 diabetes (T2D) patients with diagnosed DR (cases) and 69 matched T2D individuals without DR (control), in China from August 2017 to June 2018. Multiple logistic regression models analyzed the association between MUFAs and DR. The impact of 7 distinct MUFAs on DR was examined using elastic net regression (ENET), weighted quantile regression (WQS), and Bayesian kernel machine regression (BKMR), focusing on key lipid biomarkers. The diagnostic utility of these biomarkers was assessed by calculating the AUC.

Results: A significant negative correlation was found between MUFAs and DR, with OA identified as pivotal by ENET, WQS, and BKMR. The adjusted OR and 95% CI for DR were 0.25 (0.09, 0.69) for subjects in the 2nd tertile of OA and 0.11 (0.04, 0.30) for the 3rd tertile, compared to the lowest tertile. These results were consistent across subgroup and sensitivity analyses. The AUC (95% CI) for OA alone was 0.72 (0.63, 0.81), increasing to 0.77 (0.69, 0.85) when combined with other covariates.

Conclusions: Our findings reveal a robust inverse relationship between plasma OA levels and DR risk, suggesting that OA could serve as a valuable biomarker for identifying type 2 diabetic patients with DR.

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来源期刊
Nutrition & Metabolism
Nutrition & Metabolism 医学-营养学
CiteScore
8.40
自引率
0.00%
发文量
78
审稿时长
4-8 weeks
期刊介绍: Nutrition & Metabolism publishes studies with a clear focus on nutrition and metabolism with applications ranging from nutrition needs, exercise physiology, clinical and population studies, as well as the underlying mechanisms in these aspects. The areas of interest for Nutrition & Metabolism encompass studies in molecular nutrition in the context of obesity, diabetes, lipedemias, metabolic syndrome and exercise physiology. Manuscripts related to molecular, cellular and human metabolism, nutrient sensing and nutrient–gene interactions are also in interest, as are submissions that have employed new and innovative strategies like metabolomics/lipidomics or other omic-based biomarkers to predict nutritional status and metabolic diseases. Key areas we wish to encourage submissions from include: -how diet and specific nutrients interact with genes, proteins or metabolites to influence metabolic phenotypes and disease outcomes; -the role of epigenetic factors and the microbiome in the pathogenesis of metabolic diseases and their influence on metabolic responses to diet and food components; -how diet and other environmental factors affect epigenetics and microbiota; the extent to which genetic and nongenetic factors modify personal metabolic responses to diet and food compositions and the mechanisms involved; -how specific biologic networks and nutrient sensing mechanisms attribute to metabolic variability.
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