2-Furoylglycine as a Candidate Biomarker of Coffee Consumption

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2-Furoylglycine as a candidate biomarker of coffee consumption Silke S Heinzmann, Elaine Holmes, Sunil Kochhar, Jeremy Kirk Nicholson, and Philippe Schmitt-Kopplin J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jafc.5b03040 • Publication Date (Web): 11 Sep 2015 Downloaded from http://pubs.acs.org on September 22, 2015

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Journal of Agricultural and Food Chemistry

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2-Furoylglycine as a candidate biomarker of coffee

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consumption

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Silke S Heinzmann1,*, Elaine Holmes2, Sunil Kochhar3, Jeremy K Nicholson2, Philippe

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Schmitt-Kopplin1,4

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1 Helmholtz Zentrum München, Research Unit Analytical BioGeoChemistry, 85764

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Neuherberg, Germany

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2 Biomolecular Medicine, Section of Computational and Systems Medicine, Department of

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Surgery and Cancer, Faculty of Medicine, Imperial College London, South Kensington,

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London SW7 2AZ, UK

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3 Nestlé Research Center, Nestec, Vers-chez-les-Blancs, 1000 Lausanne 26, Switzerland

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4 Technische Universität München, Chair of Analytical Food Chemistry, 85354 Freising,

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Germany

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* Corresponding author: Silke S. Heinzmann, Helmholtz Zentrum München, Research Unit

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BioGeoChemistry, 85764 Neuherberg, Germany. E-mail: silke.heinzmann@helmholtz-

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muenchen.de. Phone: +49 89 3187 3308. Fax: +49 89 3187 2705

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Table of Contents categories: 1) Metabolomics Applied to Agriculture and Food; 2) Food and

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Beverage Chemistry/Biochemistry; 3) Analytical Methods

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1 ACS Paragon Plus Environment


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Abstract

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Specific and sensitive food biomarkers are necessary to support dietary intake assessment and

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link nutritional habits to potential impact on human health. A multistep nutritional

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intervention study was conducted to suggest novel biomarkers for coffee consumption.

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1

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furoylglycine (2-FG) as a novel putative biomarker for coffee consumption. We relatively

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quantified 2-FG in the urine of coffee drinkers and investigated its origin, metabolism and

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excretion kinetics. When searching for its potential precursors, we found different furan

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derivatives in coffee products, which are known to get metabolized to 2-FG. Maximal urinary

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excretion of 2-FG occurred 2 hours after consumption (p=0.0002) and returned to baseline

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after 24 hours (p=0.74). The biomarker was not excreted after consumption of coffee

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substitutes such as tea and chicory coffee and might therefore be a promising acute biomarker

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for the detection of coffee consumption in human urine.

H NMR metabolic profiling combined with multivariate data analysis resolved 2-

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Keywords: coffee biomarker, urine, metabolomics, NMR spectroscopy, 2-furoylglycine,

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nutritional intervention

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Introduction

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Coffee is an important part of the diet and serves as social, sensory and stimulatory beverage.

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Its consumption varies greatly between cultures and countries (1). After water, it is the most

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consumed beverage worldwide (2) and has therefore received extensive attention with regard

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to evaluation of its beneficial and adverse health effects. These investigations have focused

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either on the effect of coffee as a whole, or on ingredient-specific effects, e.g. from caffeine.

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The vasopressor effect of caffeine occur directly with exposure, however no long-term

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impact of caffeine on proposed hypertension has been confirmed (3), suggesting tolerance

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towards caffeine and rapid return to baseline blood pressure. Physiological effects attributed

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to caffeine encompass increased alertness (4, 5), ergogenic effects on exercise (6, 7) and

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thermogeneic effects on energy metabolism (8-10). Investigations into the inverse association

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of developing neurodegenerative diseases, such as Alzheimer’s and Parkinson’s disease in

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relation to coffee drinking habits also primarily discuss the active ingredients caffeine as the

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main bioactive compound (11-16). Effects on liver, e.g. onset of liver cirrhosis (17, 18) and

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reduced liver cancer risk (19) are reported within a wider bioactive frame of coffee

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ingredients and not limited to caffeine alone. Beneficial effects of coffee on other types of

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cancer are controversially discussed, since other lifestyle factors might bias the observed

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effects on reduced risk of e.g. colon cancer (20). While no associations were found for

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cardiovascular diseases (21, 22), both stroke risk (23) and diabetes type 2 risk have been

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inversely associated with both caffeinated and decaffeinated coffee (24, 25). In summary,

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overall mortality is not increased with frequent coffee consumption and a moderate inverse

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association could be found for CVD mortality (26, 27).

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Questions arise towards which bioactive ingredients, besides caffeine, might be responsible

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for the aforementioned effects. Coffee is rich in flavonoids, chlorogenic acid and melanoidins



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with antioxidative properties on the one hand (28, 29) and hypercholesteremic and

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homocysteine raising effects on the other hand (30). Simple heterocyclic rings such as pyrrole

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and furans have antioxidant properties but also have hepatotoxic and hepatocarcinogenic

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effects in high doses (31, 32). The composition of coffee with regard to these bioactive

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ingredients depends largely on the variety, type of roasting and brewing method (33). For

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example, beans roasted at high temperatures contain increased furan levels (34); on the other

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hand coffee filters largely remove the diterpenes cafestol and kahweol components of ground

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coffee (30).

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Food frequency questionnaires are a great source for determining dietary habits and other

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food questionnaires such as dietary recalls help to collect more specific data on consumed

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foods and beverages on the day (35). However, such questionnaires may be susceptible to

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errors in underreporting of less healthy and overestimation of health beneficial foods. It is

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therefore of great value to have food biomarkers available, that may accurately allow for

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quantification or qualitative investigation of consumed foods via measurement of such

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compounds in urine or plasma. To monitor intake frequency in individuals, it is extremely

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helpful to have a non-invasive biomarker in hand that is specific to coffee and sensitive

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enough for detection with straight forward analytical chemistry tool (36). Several biomarkers

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for coffee have been proposed including caffeine, polyphenols and trigonelline (37-41).

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Ingestion of caffeine results in excretion of caffeine and other xanthine derivatives (37),

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chlorogenic acid leads to excretion of different quinic acids and hippuric acids (38-40) and

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trigonelline and its roasting product N-methylpyridinium are also directly excreted in urine

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(41). However, most detected markers to date are not specific to coffee consumption. For

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example, caffeine can be found in other stimulatory beverages and the excretion of

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polyphenol-derived markers is not specific to coffee, since fruits, vegetables and tea are also


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rich in polyphenols (38, 42). Furthermore, metabolism of polyphenols is dependent on the gut

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microbiota, and therefore subject to high inter-individual variation in humans (43-45).

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Non-targeted metabolic profiling has in the past been shown to deliver novel food

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biomarkers, without prior knowledge of ingredients or processing and metabolism steps (46-

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48). Here, we conducted a multi-step approach for sequentially elucidating novel biomarkers

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for coffee consumption and established their temporal excretion profiles using 1H NMR

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based metabolic profiling of a coffee intervention study.

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Materials and Methods

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Dietary Intervention Study

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A nutritional intervention study was designed to detect urinary biomarkers of different foods

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consumption by using a non-targeted metabolic profiling approach. The study involved 8

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volunteers (7 women and 1 man; age range: 28–45 y) who met the following inclusion

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criteria: healthy, aged 18–45 y, nonsmokers and body mass index (BMI; in kg/m2) 18–25,

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absence of regular drug intake and regular food supplement intake, and no antibiotic use

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within the previous 3 months. Participants consumed a standardized breakfast, lunch, and

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dinner that comprised whole-grain bread and cheese (breakfast), a ham sandwich (lunch), and

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different dinners on each day (dinner) from the run-in day (day 0) until lunch on day 7.

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Participants were allowed to drink coffee between 8 am and 12 (i.e. for breakfast and as

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desired, until 12 am). Urine was collected 4 times/d (first morning urine, before lunch, before

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dinner, and at bed time), from the morning of day 1 until the evening of day 6. Urine

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specimens were collected into sterile tubes (Sterilin, Aberbargoed, UK) and kept in the fridge

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for a maximum of 12 hours before storage at -40°C until analysis. The study was approved by



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Imperial College London Research Ethics Committee and is registered at clinicaltrials.gov as

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NCT01102049.

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Coffee Study

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A food study was undertaken to characterize the excretion profile of 2-furoylglycine after

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coffee consumption. Five volunteers (3 women and 2 men; age range: 24–34 y) who met the

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inclusion criteria of being self-assessed as healthy, aged 18–45 y, nonsmokers, a BMI of 18–

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30 kg/m2 and an absence of regular drug intake and regular food supplement intake were

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recruited. The number of volunteers was based on power calculations with values from the

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previous dietary intervention study with α = 0.05 and power of the test 0.80. Other than

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coffee consumption, participants were not restricted in their choice of foods for the duration

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of the study. Day 0 served as a run-in day, where no coffee consumption was allowed; a

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coffee challenge (1 espresso with or without milk) was administered on day 1 at 10 am and

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spot urine specimens were collected on day 1 (i.e., baseline samples, 6 times/d, collected at 8

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am, 10 am, 12 am, 4 pm, and 8 pm and at bed time); and at 8 am and 10 am on day 2 (study

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finish). Urine specimens were collected in sterile tubes (Sterilin) and kept in the fridge before

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they were stored at -80 °C until analysis.

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Standard Spiking experiment

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The chemical standard of 2-furoylglycine was purchased from Sigma Aldrich, was dissolved

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in NMR buffer and analyzed with 1H NMR spectroscopy. Furthermore, a representative urine

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sample from time point “12 am” was analyzed with the same spectroscopic settings as the

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standard. Lastly, a 1H NMR spectrum of the same urine, spiked with 2-furoylglycine in

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appropriate physiological amounts was acquired and the resulting spectrum compared with

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the original spectrum.


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Coffee analysis

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Three different kinds of coffee were analyzed, espresso, instant espresso and chicory coffee.

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Coffees were prepared as per normal consumption, i.e. 12.5 g beans power per 50 mL

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extraction water, 15 g instant espresso powder per 50 mL water and 8 g chicory coffee

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powder per 200 mL water, and an aliquot of 50 µL was mixed with 200 µL NMR buffer (90

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% D2O 500 mM PO4 buffer with 0.1 % TSP, pH 7.4), centrifuged and transferred to 3 mm

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outer diameter NMR Bruker Match tubes (Hilgenberg GmbH, Malsfeld, Germany).

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NMR spectroscopic analysis

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Urine analysis

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An aliquot of each urine sample was analyzed by 1H NMR analysis. Urine and NMR buffer

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were mixed 2:1, centrifuged and 550uL of the supernatant transferred to 5 mm (dietary

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intervention study) or 120 uL to 3 mm (coffee study) outer diameter NMR Bruker Match

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tubes. Urine specimens of the dietary intervention study were acquired on a Bruker 600 MHz

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spectrometer (Bruker Biospin, Rheinstetten, Germany) that operated at 600.13 MHz. The

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spectrometer used a standard one-dimensional pulse-sequence [recycle delay (RD)-90°-t1-

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90°-mixing time (tm)-90°-acquire] free-induction decay (FID) with water-suppression

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irradiation during a RD of 2 s with tm set to 100 ms and a 90° pulse set to 10 µs. Spectra

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were acquired with 128 scans into 32 K data points with a spectral width of 12,000 Hz. Urine

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specimens from the coffee study were analyzed on a Bruker 800 MHz spectrometer operating

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at 800.35 MHz with a quadrupole inverse cryogenic probe. A standard 1-dimensional (1D)

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pulse sequence [recycle delay (RD)-90°-t1-90°-tm-90°-acquire FID] was acquired, with water

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suppression irradiation during RD of 2 s, mixing time (tm) set on 200 ms and a 90° pulse set

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to 10.13 µs, collecting 512 scans into 64 K data points with a spectral width of 12 ppm. All



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spectra were manually phased, baseline corrected and calibrated to TSP (δ 0.00) with

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TopSpin 3.2 (Bruker BioSpin, Rheinstetten, Germany). Data were imported to Matlab

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(Mathworks, Massachusetts, USA) and further processed i.e. water region removed (δ 4.70 –

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5.21). The spectra were normalized by using a probabilistic quotient normalization algorithm

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(49) to account for urinary dilution effects and aligned by using a recursive segment-wise

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peak-alignment algorithm (50). Relative quantification of metabolites of interest was done by

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integration of the area under the curve (AUC), as outlined in Heinzmann et al. (46) and

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expressed as relative measure to creatinine concentration.

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Coffee analysis

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Coffee samples were analyzed with a Bruker 800 MHz spectrometer equipped with a

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quadrupole inverse cryogenic probe, wherewith 1H NMR spectra and 2D spectra (TOCSY

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and HSQC) where acquired. For 2D NMR spectra, phase-sensitive sensitivity-improved 2D

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TOCSY with WATERGATE (3-9-19) and using DIPSI-2, were acquired. For each spectrum,

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19228 × 1024 data points were collected using 32 scans per increment, an acquisition time of

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1 s, and 16 dummy scans. The spectral widths were set to 12 and 12 ppm in the F2 and F1

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dimensions, respectively. For the 2D 1H-13C HSQC spectra, phase-sensitive ge-2D-HSQC

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using PEP and adiabatic pulses for inversion and refocusing with gradients were used. For

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each 2D spectrum, 4096 × 1024 data points were collected using 32 scans per increment, an

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acquisition time of 0.25 s, and 16 dummy scans. The spectral widths were set to 12 and 230

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ppm in the proton and carbon dimensions, respectively. Processing of the spectra was carried

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out in TopSpin 3.2. FIDs were multiplied by an exponential decaying function corresponding

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to a line broadening of 0.3 Hz before Fourier transformation. All spectra were manually

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phased, baseline corrected and calibrated to TSP (δ 0.00).

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Multivariate analysis

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All timed urine specimens of the dietary intervention study that were collected at lunchtime

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were defined as “coffee samples”, all others (first morning urine, before dinner and before

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bed) as “no coffee samples”. Volunteers participated in the dietary intervention trial for six

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days; therefore 24 urine samples per participant were collected. Pairwise orthogonal partial

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least squares discriminant analysis (OPLS-DA) (51) was carried out with Matlab software.

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The OPLS-DA loading plots were created with an in-house Matlab script according to the

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method of Cloarec et al. (52).

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Results and Discussion

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Discovery Phase: Non-targeted 1H NMR metabolic profiling of urine from a 6-day dietary

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intervention study

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To discover novel biomarkers of food consumption, we performed a human dietary

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intervention trial (n = 8), where participants consumed a standardized breakfast and lunch,

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and varying dinner each day (for more details see also Heinzmann et al. (46)). As part of the

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dietary protocol, participants were allowed coffee for breakfast and until 12 am each day (SI

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Figure 1). Six participants chose coffee, one participant consumed chicory coffee and one

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deviated from the protocol and drank Earl Grey tea every day. Urinary metabolite profiles

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were acquired to enable comparison of metabolites excreted after coffee consumption. OPLS-

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DA analysis of the urines from coffee-drinking volunteers revealed putative biomarker(s)

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with chemical shifts at δ 8.79 (d), δ 7.20, δ 6.65 (dd), δ 4.40 (s). 2D HSQC NMR suggested

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these shifts derived from two different metabolites (see SI Figure 2), namely 2-furoylglycine

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(2-FG) and N-methylpyridinium (NMP). Confirmation of the chemical structures was carried

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out by standard spiking experiments of the chemical standards 2-FG and NMP into urine

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samples (Figure 2 and SI Figure 3). 1H NMR shifts of 2-FG standard were δ 6.65 (dd, H-C4

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of furoyl), δ 7.20 (dd, H-C3 of furoyl), δ 7.71 (d, H-C5 of furoyl), δ 3.93 (s, CH2 of glycine).

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The standard matched the prediction from the OPLS-DA analysis, with the exception of the

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two latter signals, which were overlapped with other metabolites and did not resolve in the

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OPLS-DA loadings plot. NMP has previously been linked to coffee consumption by Lang et

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al. (41). Urinary excretion of the glycine conjugate of derivatized furan, 2-FG, has not, to our

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knowledge, been reported to be associated with coffee consumption. In serum, however, it

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has been previously detected (53). Integration of the H-C3 of furoyl signal confirmed high

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excretion of 2-FG in coffee consumers especially in the urine samples at lunch time. Neither


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chicory coffee nor Earl Grey tea consumption resulted in excretion of 2-FG with urine

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(Figure 3), and inspection of urine spectra post consumption of these coffee alternatives did

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not show 1H NMR-detectable quantities of 2-FG (SI Figure 4). Interestingly, hippurate,

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which is discussed as a potential biomarker for coffee consumption (37), correlated

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negatively with coffee consumption. The coffee consumption period was closely aligned with

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breakfast, which was not rich in polyphenols, while dinner (i.e. part of the „non-coffee

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sample“ group) was always rich in fruit and vegetables (46) and polyphenols can be

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metabolized to hippurate (45, 54).

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Dietary sourcing phase: Possible sources of 2-furoylglycine in coffee

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To investigate the origin of 2-FG in participant’s urine samples after coffee consumption, we

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interrogated existing literature on furan derivates in different coffees, their concentration and

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possible degradation pathways in humans after consumption (Figure 4). Indeed, furan

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metabolites arise through roasting of coffee beans via the Maillard reaction (55) and highest

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concentrations can be found in roasted ground coffee (6900 µg/kg) and considerably lower

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amounts in instant coffee (569 µg/kg) (56-59). The amount of furan varies, depending on

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coffee bean species and roast degree (33). Other sources of furan exist, but provide much

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lower quantities, e.g. baby foods with up to 29 µg/kg and soups and sauces (< 24 µg/kg) and

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0-4 µg/kg in tea (55, 60, 61) and are therefore not expected to interfere with 2-FG excretion.

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Dependent on the chosen coffee brewing procedure, about 2-50% of the bean-derived furans

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get extracted into coffee (59). Moon et al. have identified furans in coffee and found 7

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different furan derivatives such as furfuryl alcohol and furfural (34). After consumption,

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furans undergo conversion to furfural and 2-furoic acid, then conjugation with glycine to 2-

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FG, which can then be excreted via the kidney (62-64), see Figure 4. Remarkably, while the



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roasting process can potentially produce furan and various 2-furan metabolites, the human

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metabolism reduces such diversity to a single metabolite being excreted, namely 2-FG.

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From our 1H NMR spectral analysis, we detected furan derivatives such as 2-furoic acid and

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furfuryl alcohol in relatively high quantities in espresso (Figure 5 and SI Figure 6). Instant

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espresso also contained 2-furoic acid, and much lower quantities were found in chicory

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coffee. This overview of furan derivates is in consensus with suggested furan concentrations

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in existing literature (33, 34, 57-59). Furfuryl alcohol was not found in instant espresso or

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chicory coffee. Trigonelline and its roasting product NMP could be found in espresso and

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instant espresso but not in chicory coffee. An additional furan derivate, 5-

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hydroxymethylfurfural, appeared in instant espresso and in high amounts in chicory coffee,

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and only little amounts in espresso. This metabolite is proposed to get metabolized to 5-

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hydroxymethylfuroic acid and 2,5-furandicarboxylfuroic acid and their glycine adducts (65)

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and is therefore not expected to influence 2-FG excretion in the chicory coffee or instant

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espresso consumer.

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Confirmation Phase: Excretion kinetics of 2-furoylglycine after coffee consumption

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In order to confirm the putative biomarker and investigate the mode of excretion, we carried

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out an additional coffee intervention study where participants avoided coffee for 24 hours and

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then collected several timed spot urine samples over 24 hours after a single dose of espresso.

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Excretion of 2-FG was quantified by the ratio of area under the curve integration of 2-FG (δ

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6.640-6.653) and creatinine (δ 4.0405-4.0475). All participants showed a maximal excretion

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of 2-FG after 2 hours (p=0.0002) and a rapid decline to near baseline after 24 hours (p=0.74),

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as shown in Figure 6. In more detail, the mean value of 2-FG/creatinine ratio was 0.0038 and

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0.0041 2 and 0 hrs pre coffee consumption, where this value is a result of baseline and other

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peak background integration. At 2 hours post consumption the value increased to 0.031,


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dropped rapidly to 0.011 4 hours post consumption and further declined to 0.0044 after 24

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hours. The inter-individual variation was minimal and all participants showed a similar

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trajectory (Figure 6). Deviation of the excretion curve, e.g. volunteer A did not decline to

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baseline after 24 hours, was largely attributed to peak overlap in the spectral region of 2-FG.

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Overall, the excretion can be described as first order excretion kinetics, as evidenced by a fast

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accumulation of the metabolite in urine and decline to baseline within 24 hours. It can be

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discussed however, that more sensitive analytical chemistry measurements such as UPLC-

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MS might allow detection of 2-FG for a longer period (66). This has been shown by Lang et

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al. (41), where NMP could be detected up to 5 days post coffee consumption. For

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comparison, NMP showed similar excretion kinetics to 2-FG (SI Figure 5).

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In summary, we introduced a novel biomarker for coffee consumption, by combining non-

294

targeted 1H NMR metabolic profiling in a nutritional intervention study, investigation of

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possible dietary sources of the biomarker and finally validation of the putative marker in a

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second food intervention study. In contrast to previously identified biomarkers such as

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caffeine and chlorogenic acid degradation products we identified a biomarker that is

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dependent on the roasting procedure of the coffee bean, and therefore not influenced by

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consumption of tea, fruit and vegetables or caffeine containing energy drinks, which makes it

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highly specific to coffee. As a next step, the biomarker could now be further validated in a

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larger cohort, where coffee drinking behavior, different coffee types and varying amounts of

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ingested coffee are recorded and matching urine samples are available for quantification of 2-

303

FG to assess its quantitative rather than qualitative value. A combination of data from a well-

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monitored coffee drinking cohort as assessed by urinary 2-FG quantification with e.g. CVD

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risk factors such as blood pressure, blood lipid parameters and BMI should deliver valuable

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information on potential health benefit of coffee consumption on disease risk and disease



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progression. For example, urinary metabolic profiling data from the INTERMAP study

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highlighted the here assigned coffee biomarker to be inversely associated with BMI (67).

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Taking advantage of the fact that different coffee biomarkers derive from different

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ingredients of coffee, a simultaneous analysis of several coffee biomarkers should pave the

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way to design a biomarker pattern for sensitive and highly specific detection of coffee

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consumption in individuals and allow meaningful conclusion on the influence of coffee

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consumption in health and disease.

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Abbreviations Used

316

NMR, nuclear magnetic resonance

317

TSP, trimethylsilyl-tetradeuteropropionic acid

318

FID, free induction decay

319

HSQC, heteronuclear single quantum coherence

320

TOCSY, total correlation spectroscopy

321

DIPSI, decoupling in the presence of scalar interactions

322

OPLS-DA, orthogonal partial least squares discriminant analysis

323

PEP, preservation of equivalent pathways

324

WATERGATE, water suppression by gradient tailored excitation

325

2-FG, 2-furoylglycine

326

NMP, N-methylpyridinium

327

AUC, area under the curve

328 329

Acknowledgement

330

The authors would like to thank Roman Lang and Thomas Hofmann from Technische

331

Universität München for the kind donation of the chemical standard N-methylpyridinium.

332 333

The authors declare no competing financial interest.

334 335

Supporting Information Description

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Figure 1: Study design for the 6-day dietary intervention study with n=8 volunteers.

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Figure 2: Two-dimensional HSQC spectrum of spiked urine with assignments for 2-FG and

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NMP.



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Figure 3: 1H NMR urine profiles of a sample obtained after coffee consumption with standard

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addition of 2-furoylglycine and N-methylpyridinium.

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Figure 4: Urine spectral region of 2-FG (6.63 – 6.67 ppm) in all volunteers.

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Figure 5: (A) Excretion kinetics of 2-furoylglycine and (B) of N-methylpyridinium after

343

coffee consumption (n=5) over 26 hours.

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Figure 6: Two-dimensional NMR spectroscopy (1H-1H-TOCSY) of (A) espresso coffee, (B)

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instant espresso and (C) chiory coffee.

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40. Ito, H.; Gonthier, M. P.; Manach, C.; Morand, C.; Mennen, L.; Remesy, C.; Scalbert, A., Polyphenol levels in human urine after intake of six different polyphenol-rich beverages. Br. J. Nutr. 2005, 94, 500-509. 41. Lang, R.; Wahl, A.; Stark, T.; Hofmann, T., Urinary N-methylpyridinium and trigonelline as candidate dietary biomarkers of coffee consumption. Mol. Nutr. Food Res. 2011, 55, 1613-1623. 42. Mennen, L. I.; Sapinho, D.; Ito, H.; Bertrais, S.; Galan, P.; Hercberg, S.; Scalbert, A., Urinary flavonoids and phenolic acids as biomarkers of intake for polyphenol-rich foods. Br. J. Nutr. 2006, 96, 191-198. 43. Hervert-Hernandez, D.; Goni, I., Dietary Polyphenols and Human Gut Microbiota: a Review. Food Rev. Int. 2011, 27, 154-169. 44. Walker, A. W.; Ince, J.; Duncan, S. H.; Webster, L. M.; Holtrop, G.; Ze, X.; Brown, D.; Stares, M. D.; Scott, P.; Bergerat, A.; Louis, P.; McIntosh, F.; Johnstone, A. M.; Lobley, G. E.; Parkhill, J.; Flint, H. J., Dominant and diet-responsive groups of bacteria within the human colonic microbiota. Isme Journal 2011, 5, 220-230. 45. Heinzmann, S. S.; Merrifield, C. A.; Rezzi, S.; Kochhar, S.; Lindon, J. C.; Holmes, E.; Nicholson, J. K., Stability and Robustness of Human Metabolic Phenotypes in Response to Sequential Food Challenges. Journal of Proteome Research 2011. 46. Heinzmann, S. S.; Brown, I. J.; Chan, Q.; Bictash, M.; Dumas, M.-E.; Kochhar, S.; Stamler, J.; Holmes, E.; Elliott, P.; Nicholson, J. K., Metabolic profiling strategy for discovery of nutritional biomarkers: proline betaine as a marker of citrus consumption. Am. J. Clin. Nutr. 2010, 92, 436-443. 47. O'Sullivan, A.; Gibney, M. J.; Brennan, L., Dietary intake patterns are reflected in metabolomic profiles: potential role in dietary assessment studies. Am. J. Clin. Nutr. 2011, 93, 314321. 48. Lloyd, A. J.; Fave, G.; Beckmann, M.; Lin, W.; Tailliart, K.; Xie, L.; Mathers, J. C.; Draper, J., Use of mass spectrometry fingerprinting to identify urinary metabolites after consumption of specific foods. Am. J. Clin. Nutr. 2011, 94, 981-991. 49. Dieterle, F.; Ross, A.; Schlotterbeck, G.; Senn, H., Probabilistic quotient normalization as robust method to account for dilution of complex biological mixtures. Application in H-1 NMR metabonomics. AnaCh 2006, 78, 4281-4290. 50. Veselkov, K. A.; Lindon, J. C.; Ebbels, T. M. D.; Crockford, D.; Volynkin, V. V.; Holmes, E.; Davies, D. B.; Nicholson, J. K., Recursive Segment-Wise Peak Alignment of Biological 1H NMR Spectra for Improved Metabolic Biomarker Recovery. AnaCh 2009, 81, 56-66. 51. Trygg, J.; Wold, S., PLS regression on wavelet compressed NIR spectra. Chemometrics Intellig. Lab. Syst. 1998, 42, 209-220. 52. Cloarec, O.; Dumas, M. E.; Craig, A.; Barton, R. H.; Trygg, J.; Hudson, J.; Blancher, C.; Gauguier, D.; Lindon, J. C.; Holmes, E.; Nicholson, J., Statistical total correlation spectroscopy: An exploratory approach for latent biomarker identification from metabolic H-1 NMR data sets. AnaCh 2005, 77, 1282-1289. 53. Guertin, K. A.; Loftfield, E.; Boca, S. M.; Sampson, J. N.; Moore, S. C.; Xiao, Q.; Huang, W.-Y.; Xiong, X.; Freedman, N. D.; Cross, A. J.; Sinha, R., Serum biomarkers of habitual coffee consumption may provide insight into the mechanism underlying the association between coffee consumption and colorectal cancer. Am. J. Clin. Nutr. 2015, 101, 1000-1011. 54. van Duijnhoven, F. J. B.; Bueno-De-Mesquita, H. B.; Ferrari, P.; Jenab, M.; Boshuizen, H. C.; Ros, M. M.; Casagrande, C.; Tjonneland, A.; Olsen, A.; Overvad, K.; Thorlacius-Ussing, O.; ClavelChapelon, F.; Boutron-Ruault, M. C.; Morois, S.; Kaaks, R.; Linseisen, J.; Boeing, H.; Noothlings, U.; Trichopoulou, A.; Trichopoulos, D.; Misirli, G.; Palli, D.; Sieri, S.; Panico, S.; Tumino, R.; Vineis, P.; Peeters, P. H. M.; van Gils, C. H.; Ocke, M. C.; Lund, E.; Engeset, D.; Skeie, G.; Suarez, L. R.; Gonzalez, C. A.; Sanchez, M. J.; Dorronsoro, M.; Navarro, C.; Barricarte, A.; Berglund, G.; Manjer, J.; Hallmans, G.; Palmqvist, R.; Bingham, S. A.; Khaw, K. T.; Key, T. J.; Allen, N. E.; Boffetta, P.; Slimani, N.; Rinaldi, S.; Gallo, V.; Norat, T.; Riboli, E., Fruit, vegetables, and colorectal cancer risk: the European Prospective Investigation into Cancer and Nutrition. Am. J. Clin. Nutr. 2009, 89, 1441-1452. 19 ACS Paragon Plus Environment


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Figure Captions

529 530

Figure 1: OPLS-DA scores and loadings plots of urine specimen after coffee challenge (12

531

am) vs all other urine collection (8 am, 8 pm, bedtime). (A) The scores plot shows a clear

532

separation of coffee vs no coffee groups with a goodness of fit (R2Y) of 0.58 and predictive

533

value (Q2Y) of 0.22. (B) Loadings plot of the coffee challenge indicates putative biomarkers

534

with chemical shifts at a δ 8.79 (d), b δ 7.20, c δ 6.65 (dd), d δ 4.40 (s), with highest

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correlation coefficient for δ 6.65 (dd).

536 537

Figure 2: Standard addition experiment of 2-furoylglycine to a urine sample. Black: urine

538

sample collected after coffee consumption, green: spiking of 2-furoylglycine to the same

539

urine sample for confirmation of the assumed chemical structure of the biomarker. Chemical

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shifts are δ 7.71 (d), δ 7.20 (dd), δ 6.65 (dd), δ 3.93 (s).

541 542

Figure 3: Boxplot and individual measurement points (purple) of urinary 2-furoylglycine

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excretion in volunteers at the 4 different time points. Boxplots are divided into blue: “coffee

544

consumption”, green: “alternative consumption of Earl Grey tea”, magenta: “alternative

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consumption of Chicory coffee”.

546 547

Figure 4: Summary of origin, extraction, metabolism and excretion of furan metabolites and

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2-furoylglycine. Abbreviation: 1, furan; 2, furfural; 3, 2-furoic acid; 4, furfuryl alcohol; 5,

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furfural diacetate; 6, 2-furoylglycine.

550 551

Figure 5: 1H-NMR spectrum of (A) espresso coffee, (B) instant espresso, (C) chicory coffee

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with main peaks labelled. Spectra of (B) and (C) are enlarged by x 6 and x 24 respectively.



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553

(D) Two-dimensional NMR spectroscopy (1H-1H-TOCSY) of the aromatic area (δ 9.3 – 6.0)

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of espresso coffee; 1, trigonelline; 2, N-methylpyridinium; 3, formate; 4, caffeine; 5,

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chlorogenic acids; 6, fumarate; 7, furfurylalcohol; 8, 2-furoic acid; 9, 5-

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hydroxymethylfurfural (5-HMF); 10, unknown compound (signals linked in TOCSY).

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Figure 6: Excretion kinetics of 2-furoylglycine after coffee consumption (n=5) over 26

559

hours. Coffee was consumed at 10 am. 2-FG was relatively quantified by integration of the

560

spectral region δ 6.640 – 6.654, including the doublet of doublets at δ 6.647 in relation to

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creatinine (δ 4.056 – 4.072). The five individuals showed similar trajectory in 2-FG excretion

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with maximal excretion of the biomarker at 2 hours post coffee consumption and little inter-

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individual variation.

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2-Furoylglycine as a Candidate Biomarker of Coffee Consumption

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