Using Untargeted Metabolomics to Identify Urinary Biomarkers of Onion Intake
试验速览
- 阶段
- 不适用
- 状态
- 进行中(未招募)
- 入组人数
- 16
- 试验地点
- 1
- 主要终点
- Dose-dependency and change in concentration of potential biomarkers identified in Primary Outcome 1
研究概览
简要总结
Fruit and vegetable (FV) intake has been reported as a modifiable risk factor of globally pervasive chronic diseases. Traditionally, the measurement of dietary intake has been conducted via self-report methods such as food diaries, food frequency questionnaires, and dietary recall. These methods are inherently subject to sources of error and biases. The objective measurement of diet-specific urinary biomarkers has been proposed as an alternate assessment method. A dose-dependent biomarker or biomarker panel for total FV intake has been investigated but not successfully established. In a recent publication as part of this PhD research, the researchers outlined a concise panel of 7 FVs that are predictive of total FV intake in a UK population. Recent studies have implemented an untargeted metabolomic approach to identify novel biomarkers of some of the 7 FVs identified in our prior research, but not with onion intake. The aim of this study is to detect, quantify and identify dose-dependent biomarker(s) of onion intake in a UK population using untargeted metabolomics. Phase 1 will be an acute randomised crossover intervention study, involving the consumption of a standardised portion of cooked onions (test) or couscous (control). Urine samples over the 24-hour period post-consumption will be collected. Phase 2 will be a dose-dependent crossover intervention study, where participants are supplied with supplementary onion portions (low, medium, high) to be consumed with their habitual evening meals. Within each supplementation period, participants will consume the same quantity of onions across the 4 days and collect a midstream first void urine samples on the fifth day. Trial order will be randomised, and a washout period of 3 days will be implemented between supplementation periods. 14 participants will be recruited for both phases of data collection. Urine samples will be analysed by high-performance liquid-chromatography with quadrupole time-of-flight mass spectrometry (LC-QTOF-MS) to identify potential biomarkers.
研究设计
- 研究类型
- Interventional
- 分配方式
- Randomized
- 干预模型
- Crossover
- 主要目的
- Other
- 盲法
- None
入排标准
- 年龄范围
- 18 Years 至 —(Adult, Older Adult)
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •Non-pregnant/lactating
- •BMI between 18.5 and 30 kg/m^2
- •Non-smokers.
排除标准
- •Any diagnosed health condition (chronic or infectious diseases)
- •Consumption of medications/nutritional supplements
- •Any allergies/intolerances to onions/couscous.
结局指标
主要结局
Dose-dependency and change in concentration of potential biomarkers identified in Primary Outcome 1
时间窗: First morning void urine samples following three periods of 4-day onion supplementation.
First morning void urine samples will be obtained after three separate 4-day periods of supplementing evening meals with 40g, 80g and 160g of onions. The validity of candidate biomarkers from the first phase of the study, Primary Outcome 1, shall be assessed by LC-QTOF-MS - quantifying urinary concentrations following the consumption of multiple doses of onions across a range of servings.
Untargeted metabolomics for onion biomarker identification using LC-QTOF-MS.
时间窗: 24-hours postprandial period following onion or couscous consumption.
Urine samples will be analysed to putatively identify biomarkers of onion intake by comparison with control samples using high-performance liquid chromatography coupled to a quadrupole time-of-flight mass spectrometer (LC-QTOF-MS). LC-QTOF-MS analysis allows the simultaneous high-resolution measurement of a broad range of metabolites, hence the untargeted nature of the analysis. Multivariate statistical analysis and Partial least squares Discriminant Analysis included in the mass spectrometry software will be used to analyse LC-QTOF-MS results to identify features that best discriminate between test and control conditions.
次要结局
未报告次要终点
研究者
Elliot Owen
PhD Student
Manchester Metropolitan University
