Type 2 diabetes: is obesity for diabetic retinopathy good or bad? A cross-sectional study.

IF 3.9 2区医学 Q2 NUTRITION & DIETETICS Nutrition & Metabolism Pub Date : 2024-08-19 DOI:10.1186/s12986-024-00842-8

Zheyuan Chen, Xuejing Zhong, Ruiyu Lin, Shuling Liu, Hui Cao, Hangju Chen, Baozhen Cao, Mei Tu, Wen Wei

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Abstract

Background: The relationship between obesity and diabetic retinopathy (DR) remains controversial, and the relationship between sarcopenic obesity and DR is still unclear. The purpose of this study is to investigate the relationship between obesity, sarcopenic obesity, and DR in patients with type 2 diabetes mellitus (T2DM).

Methods: A cross-sectional study was conducted on patients with T2DM. Obesity was assessed by body mass index (BMI), fat mass index (FMI), android fat mass, gynoid fat mass, and visceral adipose tissue (VAT) mass. Sarcopenia was defined according to the criteria of Consensus of the Asian Working Group for Sarcopenia (AWGS 2019). Sarcopenic obesity was defined as the coexistence of sarcopenia and obesity. The association between obesity, sarcopenic obesity, and DR was examined using univariable and multivariable logistic regression models.

Results: A total of 367 patients with T2DM (mean age 58.3 years; 57.6% male) were involved in this study. The prevalence of DR was 28.3%. In total patients, significant adverse relationships between obesity and DR were observed when obesity was assessed by BMI (adjusted odds ratio [aOR] 0.54, 95% confidence interval [CI] 0.31 to 0.96, p = 0.036), FMI (aOR 0.49, 95% CI 0.28 to 0.85, p = 0.012), android fat mass (aOR 0.51, 95% CI 0.29 to 0.89, p = 0.019), gynoid fat mass (aOR 0.52, 95% CI 0.30 to 0.91, p = 0.021) or VAT mass (aOR 0.45, 95% CI 0.25 to 0.78, p = 0.005). In patients with T2DM and obesity, the prevalence of sarcopenic obesity was 14.8% (n = 23) when obesity was assessed by BMI, 30.6% (n = 56) when assessed by FMI, 27.9% (n = 51) when assessed by android fat mass, 28.4% (n = 52) when assessed by gynoid fat mass, and 30.6% (n = 56) when assessed by VAT mass. Sarcopenic obesity was associated with DR when obesity was assessed by BMI (aOR 2.61, 95% CI 1.07 to 6.37, p = 0.035), android fat mass (aOR 3.27, 95% CI 1.37 to 7.80, p = 0.007), or VAT mass (aOR 2.50, 95% CI 1.06 to 5.92, p = 0.037).

Conclusions: Patients with T2DM showed a substantial inverse relationship between DR and obesity, and sarcopenic obesity was considerably favorably associated with DR. Detection of sarcopenia in patients with T2DM, especially in obese T2DM, is essential to guide clinical intervention in DR.

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2 型糖尿病：肥胖对糖尿病视网膜病变是利还是弊？一项横断面研究。

背景：肥胖与糖尿病视网膜病变（DR）之间的关系仍存在争议，而肌肉疏松性肥胖与DR之间的关系仍不清楚。本研究旨在探讨 2 型糖尿病（T2DM）患者的肥胖、肌肉疏松性肥胖与 DR 之间的关系：方法：对 2 型糖尿病患者进行横断面研究。肥胖通过体重指数（BMI）、脂肪量指数（FMI）、甲状腺脂肪量、雌激素脂肪量和内脏脂肪组织（VAT）量进行评估。肌少症是根据亚洲肌少症工作组共识（AWGS 2019）标准定义的。肌少症肥胖症是指同时存在肌少症和肥胖症。使用单变量和多变量逻辑回归模型研究了肥胖、肌肉疏松性肥胖和DR之间的关系：共有 367 名 T2DM 患者（平均年龄 58.3 岁；57.6% 为男性）参与了此次研究。DR的发病率为28.3%。在所有患者中，通过体重指数（调整后的几率比 [aOR] 0.54，95% 置信区间 [CI] 0.31 至 0.96，P = 0.036）、FMI（aOR 0.49，95% CI 0.28 至 0.85，p = 0.012）、甲状腺脂肪量（aOR 0.51，95% CI 0.29 至 0.89，p = 0.019）、妇科脂肪量（aOR 0.52，95% CI 0.30 至 0.91，p = 0.021）或 VAT 量（aOR 0.45，95% CI 0.25 至 0.78，p = 0.005）。在 T2DM 和肥胖症患者中，用体重指数评估肥胖症时，肌肉疏松性肥胖的发生率为 14.8%（n = 23）；用 FMI 评估时，发生率为 30.6%（n = 56）；用甲状腺脂肪量评估时，发生率为 27.9%（n = 51）；用雌激素脂肪量评估时，发生率为 28.4%（n = 52）；用 VAT 量评估时，发生率为 30.6%（n = 56）。当肥胖以体重指数（aOR 2.61，95% CI 1.07 至 6.37，p = 0.035）、雌激素脂肪量（aOR 3.27，95% CI 1.37 至 7.80，p = 0.007）或 VAT 量（aOR 2.50，95% CI 1.06 至 5.92，p = 0.037）评估时，肌松性肥胖与 DR 相关：T2DM患者的DR与肥胖之间存在显著的反向关系，而肌肉疏松性肥胖与DR之间存在显著的正相关关系。检测 T2DM 患者的肌少症，尤其是肥胖 T2DM 患者的肌少症，对于指导 DR 的临床干预至关重要。

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来源期刊

Nutrition & Metabolism 医学-营养学

CiteScore

8.40

自引率

0.00%

发文量

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.