Clarke Erin D, Gómez-Martín María, Stanford Jordan, Yilmaz Ali, Ustun Ilyas, Wood Lisa, Green Brian, Graham Stewart F, Collins Clare E
School of Health Sciences, College of Health, Medicine and Wellbeing, The University of Newcastle, Callaghan, NSW 2308, Australia.
Hunter Medical Research Institute Food and Nutrition Research Program, Hunter Medical Research Institute, New Lambton Heights, NSW 2305, Australia.
Nutrients. 2024 Dec 17;16(24):4358. doi: 10.3390/nu16244358.
BACKGROUND/OBJECTIVES: Thus far, no studies have examined the relationship between fruit and vegetable (F and V) intake, urinary metabolite quantities, and weight change. Therefore, the aim of the current study was to explore changes in urinary metabolomic profiles during and after a 10-week weight loss intervention where participants were prescribed a high F and V diet (7 servings daily).
Adults with overweight and obesity ( = 34) received medical nutrition therapy counselling to increase their F and V intakes to national targets (7 servings a day). Data collection included weight, dietary intake, and urine samples at baseline at week 2 and week 10. Urinary metabolite profiles were quantified using H NMR spectroscopy. Machine learning statistical approaches were employed to identify novel urine-based metabolite biomarkers associated with high F and V diet patterns at weeks 2 and 10. Metabolic changes appearing in urine in response to diet were quantified using Metabolite Set Enrichment Analysis (MSEA).
Energy intake was significantly lower ( = 0.02) at week 10 compared with baseline. Total F and V intake was significantly higher at week 2 and week 10 ( < 0.05). In total, 123 urinary metabolites were quantified. At week 10, 21 metabolites showed significant changes relative to baseline. Of these, 11 metabolites also significantly changed at week 2. These overlapping metabolites were acetic acid, dimethylamine, choline, fumaric acid, glutamic acid, L-tyrosine, histidine, succinic acid, uracil, histamine, and 2-hydroxyglutarate. Ridge Classifier and Linear Discriminant Analysis provided best prediction accuracy values of 0.96 when metabolite level of baseline was compared to week 10.
Urinary metabolites quantified represent potential candidate biomarkers of high F and V intake, associated with a reduction in energy intake. Further studies are needed to validate these findings in larger population studies.
背景/目的:到目前为止,尚无研究探讨水果和蔬菜(F&V)摄入量、尿液代谢物数量与体重变化之间的关系。因此,本研究的目的是探索在为期10周的体重减轻干预期间及之后尿液代谢组学谱的变化,在此期间参与者被规定摄入高F&V饮食(每日7份)。
超重和肥胖的成年人(n = 34)接受医学营养治疗咨询,以将他们的F&V摄入量提高到国家目标(每天7份)。数据收集包括基线、第2周和第10周时的体重、饮食摄入量和尿液样本。使用核磁共振波谱法定量尿液代谢物谱。采用机器学习统计方法来识别在第2周和第10周时与高F&V饮食模式相关的基于尿液的新型代谢物生物标志物。使用代谢物集富集分析(MSEA)对因饮食而在尿液中出现的代谢变化进行定量。
与基线相比,第10周时能量摄入量显著降低(P = 0.02)。第2周和第10周时F&V总摄入量显著更高(P < 0.05)。总共定量了123种尿液代谢物。在第10周时,21种代谢物相对于基线显示出显著变化。其中,11种代谢物在第2周时也有显著变化。这些重叠的代谢物是乙酸、二甲胺、胆碱、富马酸、谷氨酸、L-酪氨酸、组氨酸、琥珀酸、尿嘧啶、组胺和2-羟基戊二酸。当将基线代谢物水平与第10周进行比较时,岭分类器和线性判别分析提供的最佳预测准确率值为0.96。
定量的尿液代谢物代表了高F&V摄入量的潜在候选生物标志物,与能量摄入减少有关。需要进一步的研究在更大规模的人群研究中验证这些发现。