Dietary patterns and exploratory gut microbiota profiles associated with diabetic retinopathy and cognitive impairment in type 2 diabetes.

IF 3.9 2区 医学 Q2 NUTRITION & DIETETICS
Yuan Sheng, Haiyan Chi, Changling Li, Baogeng Huai, Xuejiao Song, Deshan Liu
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引用次数: 0

Abstract

Background: Diabetic retinopathy (DR) and cognitive impairment are closely related complications of type 2 diabetes mellitus (T2DM). Although dietary patterns and gut microbiota have each been linked to these conditions, their combined associations with co-occurring DR and cognitive impairment remain unclear. This study examined dietary patterns and exploratory gut microbiota profiles in relation to co-occurring DR and cognitive impairment in patients with T2DM.

Methods: In this cross-sectional study, 306 patients with T2DM were classified into four groups: no DR with normal cognition (DMCN), no DR with cognitive impairment (DMCI), DR with normal cognition (DRCN), and DR with cognitive impairment (DRCI). Dietary patterns were derived using principal component analysis. Gut microbiota composition was assessed using 16 S rRNA sequencing in a subset of 108 participants. Multinomial logistic regression was used to examine associations between dietary patterns and group classification, and microbiome analyses included diversity, taxonomic composition, exploratory differential abundance, and diet-microbiota correlations.

Results: Four dietary patterns were identified. In fully adjusted models, DP-I and DP-II were associated with higher odds of DMCI and DRCI, respectively, whereas DP-III was associated with lower odds of both DMCI and DRCN. DP-IV showed no significant association. Gut microbiota analyses showed modest but statistically significant group-related differences in community structure, with partial overlap across groups. Exploratory LEfSe analysis identified group-associated taxa, including higher relative abundances of Bifidobacterium, Streptococcus, and Dubosiella in DMCN and of Pseudomonas, Bilophila, and Sarcina in DRCI. However, these genus-level differences were not significant after covariate-adjusted MaAsLin2 analysis with false discovery rate (FDR) correction. Nominal diet-microbiota correlations were observed but were not statistically robust after FDR correction.

Conclusion: Dietary patterns were associated with clinical group classification based on DR and cognitive impairment in patients with T2DM. Gut microbiota analyses suggested modest, exploratory group-related differences, but diet-microbiota correlations were not statistically robust after FDR correction. These cross-sectional findings should be interpreted as hypothesis-generating and require validation in larger longitudinal studies.

饮食模式和探索性肠道微生物群与2型糖尿病视网膜病变和认知障碍相关
背景:糖尿病视网膜病变(DR)和认知功能障碍是2型糖尿病(T2DM)密切相关的并发症。尽管饮食模式和肠道微生物群都与这些疾病有关,但它们与共同发生的DR和认知障碍的综合关系尚不清楚。本研究考察了饮食模式和探索性肠道菌群与T2DM患者并发DR和认知障碍的关系。方法:本横断面研究将306例T2DM患者分为4组:无认知正常DR (DMCN)、无认知障碍DR (DMCI)、认知正常DR (DRCN)和认知障碍DR (DRCI)。采用主成分分析得出饮食模式。使用16s rRNA测序对108名参与者的肠道微生物群组成进行评估。使用多项逻辑回归来检验饮食模式与类群分类之间的关系,微生物组分析包括多样性、分类组成、探索性差异丰度和饮食-微生物群相关性。结果:确定了四种饮食模式。在完全调整的模型中,DP-I和DP-II分别与DMCI和DRCI的较高几率相关,而DP-III与DMCI和DRCN的较低几率相关。DP-IV无显著相关性。肠道菌群分析显示群落结构存在适度但统计学上显著的组相关差异,组间存在部分重叠。探索性的LEfSe分析发现了类群相关的分类群,包括DMCN中双歧杆菌、链球菌和杜波氏菌的相对丰度较高,DRCI中假单胞菌、嗜杆菌和Sarcina的相对丰度较高。然而,在协变量调整MaAsLin2分析并校正错误发现率(FDR)后,这些属水平差异并不显著。观察到名义饮食与微生物群的相关性,但在FDR校正后统计上并不稳健。结论:饮食模式与T2DM患者基于DR和认知功能障碍的临床分型相关。肠道微生物群分析显示适度的探索性组相关差异,但饮食与微生物群的相关性在FDR校正后没有统计学上的显著性。这些横断面研究结果应被解释为假设产生,需要在更大的纵向研究中验证。
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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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