Department of Nutritional Sciences, School of Life Course and Population Sciences, Faculty of Life Sciences and Medicine, King's College London, London, WC2R 2LS, UK.
Department of Twin Research and Genetic Epidemiology, School of Life Course and Population Sciences, Faculty of Life Sciences and Medicine, King's College London, London, WC2R 2LS, UK.
Eur J Nutr. 2024 Nov 28;64(1):29. doi: 10.1007/s00394-024-03511-x.
Diet is an important modifiable lifestyle factor for human health, and plant-rich dietary patterns are associated with lower risk of non-communicable diseases in numerous studies. However, objective assessment of plant-rich dietary exposure in nutritional epidemiology remains challenging.
This study aimed to develop and evaluate metabolic signatures of the most widely used plant-rich dietary patterns using a targeted metabolomics method comprising 108 plant food metabolites.
A total of 218 healthy participants were included, aged 51.5 ± 17.7 years, with 24 h urine samples measured using ultra-high-performance liquid chromatography-mass spectrometry. The validation dataset employed three sample types to test the robustness of the signature, including 24 h urine (n = 88), plasma (n = 195), and spot urine (n = 198). Adherence to the plant-rich diet was assessed using a priori plant-rich dietary patterns calculated using Food Frequency Questionnaires. A combination of metabolites evaluating the adherence to a specific diet was identified as metabolic signature. We applied linear regression analysis to select the metabolites significantly associated with dietary patterns (adjusting energy intake), and ridge regression to estimate penalized weights of each candidate metabolite. The correlation between metabolic signature and the dietary pattern was assessed by Spearman analysis (FDR < 0.05).
The metabolic signatures consisting of 42, 22, 35, 15, 33, and 33 predictive metabolites across different subclasses were found to be associated with adherence to Amended Mediterranean Score (A-MED), Original MED (O-MED), Dietary Approaches to Stop Hypertension (DASH), Mediterranean-DASH Intervention for Neurodegenerative Delay (MIND), healthy Plant-based Diet Index (hPDI) and unhealthy PDI (uDPI), respectively. The overlapping and distinct predictive metabolites across six dietary patterns predominantly consisted of phenolic acids (n = 38), including 14 cinnamic acids, 14 hydroxybenzoic acids, seven phenylacetic acids, and three hippuric acids. Six metabolites were included in all signatures, including two lignans: enterolactone-glucuronide, enterolactone-sulfate, and four phenolic acids: cinnamic acid, cinnamic acid-4'-sulfate, 2'-hydroxycinnamic acid, and 4-methoxybenzoic acid-3-sulfate. The established signatures were robustly correlated with dietary patterns in the validation datasets (r = 0.13-0.40, FDR < 0.05).
We developed and evaluated a set of metabolic signatures that reflected the adherence to plant-rich dietary patterns, suggesting the potential of these signatures to serve as an objective assessment of free-living eating habits.
饮食是影响人类健康的重要可改变生活方式因素，大量研究表明富含植物的饮食模式与较低的非传染性疾病风险相关。然而，营养流行病学中对富含植物的饮食暴露进行客观评估仍然具有挑战性。
本研究旨在使用靶向代谢组学方法（包含 108 种植物源性食物代谢物）开发和评估最广泛使用的富含植物饮食模式的代谢特征。
共纳入 218 名健康参与者，年龄为 51.5±17.7 岁，使用超高效液相色谱-质谱法测量 24 小时尿液样本。验证数据集采用三种样本类型来测试特征的稳健性，包括 24 小时尿液（n=88）、血浆（n=195）和点尿（n=198）。采用基于食物频率问卷的先验富含植物饮食模式来评估富含植物的饮食。将评估特定饮食依从性的一组代谢物组合作为代谢特征。我们应用线性回归分析来选择与饮食模式显著相关的代谢物（调整能量摄入），并应用岭回归来估计每个候选代谢物的惩罚权重。通过 Spearman 分析（FDR<0.05）评估代谢特征与饮食模式之间的相关性。
发现与修正地中海饮食评分（A-MED）、原始 MED（O-MED）、高血压饮食防治法（DASH）、地中海-高血压饮食防治法神经退行性延迟（MIND）、健康植物性饮食指数（hPDI）和不健康 PDI（uPDI）的依从性相关的代谢特征分别由 42、22、35、15、33 和 33 种不同亚类的预测代谢物组成。六个饮食模式之间重叠和独特的预测代谢物主要由酚酸（n=38）组成，包括 14 种肉桂酸、14 种羟基苯甲酸、7 种苯乙酸和 3 种马尿酸。六个代谢物存在于所有特征中，包括两种木脂素：肠内酯-葡糖苷酸、肠内酯-硫酸盐，以及四种酚酸：肉桂酸、肉桂酸-4'-硫酸盐、2'-羟基肉桂酸和 4-甲氧基苯甲酸-3-硫酸盐。在验证数据集中，建立的特征与饮食模式之间具有稳健的相关性（r=0.13-0.40，FDR<0.05）。
我们开发并评估了一组反映富含植物的饮食模式依从性的代谢特征，这表明这些特征有可能作为自由生活饮食习惯的客观评估。
