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New Nordic diet versus average Danish diet: a randomized controlled trial revealed healthy long-term effects of the new Nordic diet by GC-MS blood plasma metabolomics Bekzod Khakimov, Sanne Kellebjerg Poulsen, Francesco Savorani, Evrim Acar , Gözde Gürdeniz, Thomas Meinert Larsen, Arne Astrup, Lars O. Dragsted, and Soeren Balling Engelsen J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.6b00109 • Publication Date (Web): 05 May 2016 Downloaded from http://pubs.acs.org on May 12, 2016

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New Nordic diet versus average Danish diet: a randomized controlled trial revealed healthy long-term effects of the new Nordic diet by GC-MS blood plasma metabolomics

Bekzod Khakimov*,1, Sanne Kellebjerg Poulsen2, Francesco Savorani1, Evrim Acar1, Gözde Gürdeniz2, Thomas M. Larsen2, Arne Astrup2, Lars O. Dragsted2, Søren Balling Engelsen*,1

1

Department of Food Science, Faculty of Science, University of Copenhagen

2

Department of Nutrition Exercise and Sports, Faculty of Science, University of Copenhagen

*Corresponding authors Dr. Bekzod Khakimov, Email: [email protected], Tel.: +45 35332974, Fax: +45 35333245, Address: Rolighedsvej 26, 1958 Frederiksberg C, Denmark. Prof. Søren Balling Engelsen, Email: [email protected], Tel.: +45 35333205, Fax: +45 35333245, Address: Rolighedsvej 26, 1958 Frederiksberg C, Denmark.

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ABSTRACT A previous study has shown effects of the New Nordic Diet (NND) to stimulate weight loss and lower systolic and diastolic blood pressure in obese Danish women and men in a randomized, controlled dietary intervention study.

This work demonstrates long-term

metabolic effects of the NND as compared to an Average Danish Diet (ADD) in blood plasma and reveals associations between metabolic changes and health beneficial effects of the NND including weight loss. A total of 145 individuals completed the intervention and blood samples were taken along with clinical examinations before the intervention started (week 0), and after 12 and 26 weeks. The plasma metabolome was measured using GC-MS and the final metabolite table contained 144 variables. Significant and novel metabolic effects of the diet, resulting weight loss, gender, and intervention study season were revealed using PLS-DA and ASCA. Several metabolites reflecting specific differences in the diets, especially intake of plant foods and seafood, and in energy metabolism related to ketone bodies and gluconeogenesis, formed the predominant metabolite pattern discriminating the intervention groups. Among NND subjects higher levels of vaccenic acid and 3-hydroxybutanoic acid were related to a higher weight loss, while higher concentrations of salicylic, lactic and Naspartic acids, and 1,5-anhydro-D-sorbitol were related to a lower weight loss. Specific gender- and seasonal differences were also observed. The study strongly indicates that healthy diets high in fish, vegetables, fruit, and wholegrain facilitated weight loss and improved insulin sensitivity by increasing ketosis and gluconeogenesis in the fasting state.

Keywords: weight loss, health benefit, biomarker, diet, metabolomics, PARAFAC2, ASCA, PLS-DA


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INTRODUCTION Blood plasma constitutes up to 55% of the human total blood volume and holds blood cells and lipids in suspension playing a key role transporting metabolites and excretory products in the body. Plasma mostly contains water (up to 95%), proteins, carbohydrates, hormones, clotting factors and several hundreds of small metabolites that dynamically change during metabolism 1. The level of several plasma components such as sugars and hormones reflect human health and are often used for diagnostic purposes in medicine. Likewise, the relative levels of small blood plasma metabolites represent a metabolic status that is largely determined by health, diet, and other life style and individual factors. Recent advances in ‘omics’ technology have opened new horizons using blood plasma for understanding e.g. glucose homeostasis

2

or metabolic perturbations during diabetes 3, and even forecasting

chronic disease 4. Moreover, the plasma metabolome also contains rich information about the effects of ingestion of multiple food components as well as signatures of whole diets 5. Diet is an important factor that influences human health and life expectancy, and may significantly vary according to culture as well as socio-economic factors

5d, 6

. In 2003, a

sustainable, healthy Nordic food cuisine of high organoleptic quality was formulated in a Manifest 7. A few years later a project was launched in Denmark called OPUS (defined as Optimal well-being, development and health for Danish children through a healthy New Nordic Diet) and focused on investigating possibilities to develop a healthy New Nordic Diet (NND) based on regional foods that is attractive to the public and environmentally friendly 8. This

trial

was

registered

at

www.clinicaltrials.gov

as

NCT01195610

(https://clinicaltrials.gov/ct2/show/NCT01195610). The New Nordic Diet was developed based on existing scientific knowledge within health and nutrition 9 and characterized by high content of vegetables, fruits, whole grains, nuts, fish and various seafood products 9. A randomized controlled dietary intervention study, SHOPUS (SHop in OPUS), was conducted



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within the OPUS project that included a comparison of the health effects of the NND and an Average Danish Diet (ADD) diet

10

. The intervention study lasted for 6 months where

participants were given special foods for NND and ADD diets, free of charge, in a special shop organized by the University of Copenhagen. The study illustrated beneficial health effects of the NND in adults with increased waist circumference

10

and a similar ‘Healthy

Nordic Diet’ decreased inflammatory gene expression in subcutaneous adipose tissue in individuals with features of the metabolic syndrome

11

. An untargeted metabolomics study

performed on urine samples from the SHOPUS study proved the presence of significant metabolic differences between NND and ADD diets and identified food related biomarkers that were used to estimate compliance to each dietary pattern 12. The present study investigates the use of blood plasma GC-MS metabolomics for discovering metabolic differences between individuals who followed NND and ADD diets during the 6 months of dietary intervention. In addition to diet, effects related to the season of the year where participants started and ended the intervention study (later called season), participants’ gender and the weight loss within NND participants were also investigated. Blood plasma GC-MS metabolomics involved a newly developed methodology for derivatization of a broader spectrum of metabolites using trimethylsilyl cyanide (TMSCN)

13

as well as an

efficient multi-way decomposition method, namely PARAFAC2 for processing the GC-MS data

14

. To the best of our knowledge, this is the first application of PARAFAC2 to process

GC-MS metabolomics data from human blood plasma. Ultimately the extracted metabolite table was analyzed in context to the experimental design by employing ANOVASimultaneous Component Analysis (ASCA)

15

and partial least squares discriminant analysis

(PLS-DA) 16.


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EXPERIMENTAL SECTION Study design A 6-month non-blinded, parallel, randomized, controlled dietary intervention study was carried out to investigate the health effect of the NND compared to the ADD, as previously reported in detail 10. The intervention started with one week of run-in time when participants became familiar with the study shop, in which all foods were handed out free of charge throughout the intervention. During the last three days of the run-in time all participants were provided with specific ADD foods in pre-specific amounts. This ensured energy balance and served as a standardization period. The intervention study was carried out between October 2010 and July 2011, with 147 centrally obese Danish men and women. Clinical examinations and blood samples were collected at three time points, T0 (week 0, before starting intervention), T1 (after week 12) and T2 (after week 26) (Figure 1). Participants were randomly assigned to either the NND or the ADD diet in a 3:2 ratio and 145 participants who completed the intervention (89 NND and 56 ADD) were included in this study. In terms of dietary intake the main difference between ADD and NND were macronutrient composition and intake of foods from fifteen pre-specified food groups identified as central for the NND 9. Detailed information on the percentage of consumers and average amount of consumption of the most explanatory foods for NND and ADD is published elsewhere 12. These pre-specified food groups reflect a seasonal variation and, as a consequence, dietary intake within the NND differs during the four temperate seasons of the year. All participants in the current study started the intervention during the autumn or winter season; the majority completed week 12 examinations (T1) during winter season and week 26 examinations (T2) were mostly conducted during spring, although some participants had their last examination in the winter or summer season. Hence, blood samples representing the same time point of intervention



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were collected during different seasons of the year and may have represented different foods as part of the NND. In contrast the ADD diet was relatively insensitive to seasonal changes. Blood plasma metabolite extraction Ethylenediaminetetraacetic acid (EDTA) was used as an anticoagulant; blood plasma was separated from freshly collected samples and kept in -80°C until analysis. Prior to metabolite extraction, the plasma samples were thawed at room temperature and vigorously vortexed for 20 seconds. In order to obtain clear plasma, insoluble particles were removed by centrifugation at 16,000 g at room temperature for 3 minutes. A pooled control sample was obtained by mixing 50 µl of plasma from each sample. Plasma (60 µl) was transferred into 0.6 ml Eppendorf tubes followed by addition of 180 µl of ice cooled acetonitrile, immediately vortexed for 10 seconds and further mixed at the frequency 23 Hz for 3 minutes. In order to remove proteins, the plasma extracts were centrifuged at 20,000 g for 10 minutes at 4°C and 50 µl of clear supernatant was completely dried in 200 µl glass inserts using SpeedVac vacuum centrifugation (Labogene, Lynge, Denmark) at 30°C for 2 hours, at 1500 rpm. Metabolite extractions were performed in batches of 20 randomly selected samples at a time. GC-MS analysis After addition of 2 ppm Internal Standard (IS), palmitic acid methyl ester, samples were derivatized in two steps: (1) addition of 10 µl of 20 mg ml-1 solution of methoxyamine hydrochloride in pyridine and agitated at 40°C for 90 minutes at 750 rpm; (2) addition of 10 µl trimethylsilyl cyanide (TMSCN)

13

and agitated at 40°C for 40 minutes at 750 rpm.

Immediately after derivatization, 1 µl of sample was injected into a cooled injection system (CIS port) using splitless mode and the septum purge flow and purge flow to split vent (at 2.5 min after injection) were set to 25 and 15 ml min-1, respectively. Initial temperature of CIS was 40°C, heated at 12 °C sec-1 after 30 s of equilibrium time, and kept for 5 min after


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reaching 320°C. Then, the CIS port gradually cooled until 250°C at 5°C s-1 where after the temperature was kept constant during the run. The GC-MS consisted of an Agilent 7890A GC and an Agilent 5975C series MSD (Agilent Technologies, Glostrup, Denmark). GC separation was performed on a Phenomenex ZB 5MSi 5% Phe column (30 m x 250 µm x 0.25 µm) (Phenomenex ApS, Værløse, Denmark). A hydrogen generator (Precision Hydrogen Trace 500, Peak Scientific Instruments Ltd, UK) was used to supply a carrier gas, hydrogen, at the constant column flow rate of 1.2 ml min-1. The initial temperature of the GC oven was set to 60°C. The post run time at 60°C was set to 5 min. All steps involving sample derivatization and injection were automated using a DualRait MultiPurpose Sampler (MPS) (Gerstel, GmbH & Co. KG, Mülheim an der Ruhr, Germany). A deactivated glass wool packed liner of CIS was exchanged every 55 injections. Thus, the whole GC-MS data acquisition was performed within 8 linear exchange batches where all three samples (T0, T1 and T2) from the same individual were kept in the same batch. Samples within a single batch were randomized prior to derivatization and GC-MS analysis. One blank sample that contained only derivatization reagents and two control samples, a pooled sample and an alkane mixture sample (all even C10-C40 alkanes at 50 mg L-1 in hexane) was injected between each 5 or 10 samples. Blank samples were used for eliminating reagent derived peaks, and pooled sample and alkane mixture samples assisted to monitor GC-MS stability over batches. Mass spectra were recorded in the range of 50-500 m/z with a scanning frequency of 3.2 scans sec-1, and the MS detector and ion source was switched off during the first 7.6 min of solvent delay time. The transfer line, ion source and quadrupole temperatures were set to 290°C, 230°C and 150°C, respectively. The mass spectrometer was tuned according to manufacturer’s recommendation by using perfluorotributylamine (PFTBA). The MPS autosampler and GC-MS was controlled from ChemStation software (ver: E.02.02.1431, Agilent). Data pre-processing



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In order to extract relative concentrations of peaks detected by GC-MS, the raw data was processed by PARAFAC2, as previously described in Khakimov et al.

14a

. Prior to the

PARAFAC2 modeling, the raw GC-MS data was arranged in a three-way array as, 4205ELUTION

TIME POINTS

× 450MASS

SPECTRA

× 441SAMPLES, and divided into 116 smaller data

intervals in elution time dimension and each interval was modeled individually. This insured the necessary reduced complexity of the data to facilitate easy and faster PARAFAC2 model development and validation. For each data interval, PARAFAC2 models with one to ten components, depending on the complexity of the data, were developed. Then all these models were evaluated to find a single PARAFAC2 model per data interval with an optimal number of components. Several parameters of PARAFAC2 models were taken into account for selecting an optimal number of components, including the explained variance, core consistency, residuals, and comparison of elution time and mass spectral profiles resolved by PARAFAC2 against the raw data. PARAFAC2 concentration profiles, which represent relative concentrations of detected peaks, were extracted from validated models and used to construct a final metabolite table. In order to minimize non-sample related variations the final metabolite table was normalized in two steps: (1) normalization based on the area of the IS, (2) experimental variation derived from any GC-MS linear change between batches was removed by subtracting the mean of a variable within a batch from each of the corresponding variables. Metabolite identification PARAFAC2-based deconvoluted mass spectra of each resolved peak were extracted and compared against NIST11 (NIST Version 2.0, NIST, USA). Retention indices (RI) were calculated using the Van den Dool and Kratz equation 17 and from retention times of C10-C40 all-even alkanes that were analyzed using the same GC-MS method. Metabolites were identified either at level 1 using authentic standards or at level 2 with an identification


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criterion of EI-MS match ≥ 80 (%) and RI match (±30). Furthermore, tentative identification (level 3) was performed using retention time and spectral similarity (EI-MS match ≥ 65 (%)) of observed peaks

18

. The second identification criterion was that EI-MS matched with

derivatized metabolites when labile protons being trimethylsilylated (TMS) or metabolites with aldehyde groups and labile protons being methoximated and trimethylsilylated (MEOXTMS). Data analysis An effect of diet on the blood plasma metabolome was evaluated using two experimental data points per participant collected at week 12 (T1) and week 26 (T2). According to the experimental design (Figure 1), along with diet, effects derived from gender and season and their interactions may contribute to a significant variation. Subsequently this variation may confound and/or interact with diet-related metabolomic changes. Therefore we performed ANOVA-Simultaneous Component Analysis (ASCA), for decomposing the data matrix into several effect matrices (e.g., Xdiet, Xgender, Xseason) using the study design. ASCA can be regarded as an ANOVA that allows partitioning the sources of variance derived from main and interaction effects using all variables simultaneously. However, for interactions between effects, ASCA results are only valid if a study design is fully balanced, meaning that there is an equal number of samples in all levels among investigated factors (equal block size). In this study three main effects were evaluated, diet, gender and season, and also the most interesting two-factor interaction effects, diet × season and diet × gender (Figure 2). Prior to the investigation of the interactions between effects, the basically unbalanced study design was balanced at the cost of sample size reduction. Some samples were left out in random order (to get equal number of samples in each block e.g. NND and ADD) and this procedure was repeated 2,000 times generating 2,000 balanced datasets. In order to evaluate specifically the diet × season and diet × gender interactions these datasets were further subjected to an ASCA



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permutation test (2,000 permutations) 19 (Figure 2A,B). This procedure allowed evaluating the significance of interactions and their variance contributions. In order to evaluate significance of the only three main effects, diet, gender and season, without reduction in sample size, we performed ASCA permutation test by using a complete (unbalanced) dataset since interpretation of main effects remain valid even though the data is not balanced (Figure 2C). In a similar manner, the PLS-DA based classification models were developed for diet, effects of gender (male vs female) and season (winter vs spring) using the metabolomics data corresponding to the week 12 (T1) and week 26 (T2) of the intervention period and time before the intervention period, week 0 (T0), was excluded from the analysis. Discrimination of individuals who followed NND and lost ≥ 6% of their initial body weight (later referred as high Weight Loss ↑(WL)) from those who lost ≤ 2% or gained weight (later referred as low Weight Loss ↓(WL)) was performed using a delta metabolomics data set. The delta metabolomics data set, ∆X, was obtained as follows: ∆X1=XT1 – XT0, ∆X2=XT2 – XT0, and ∆X=[∆X1; ∆X2], meaning that the two resulting delta matrices, having the same number of variables and a number of samples, were concatenated resulting a new ∆X matrix. The cutoff value of 6% for the high Weight Loss ↑(WL) subjects was determined based on the initial average body weight (89.7 ± 16.4 kg) of the NND subjects and the average change (-4.74 ± 0.48 kg) after the intervention

10

. This showed that in average the NND subjects lost a

significant body weight of approximately 6%. The cutoff value for the low or no Weight Loss ↓(WL) subjects was determined as 2% of the initial body weight since this variation is close to the average daily body weight variation. The average Weight Loss among ADD subjects was -1.52 ± 0.45 kg, while the average initial body weight was 90.3 ± 18.2 kg which corresponds to approximately 1.7 % of the initial body weight. Thus, the ADD subjects were not included in the Weight Loss study. A total of 80 NND samples (corresponding to 58


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subjects), 40 high Weight Loss samples (corresponding to 31 subjects) and 40 low Weight Loss samples (corresponding to 27 subjects) were included in the PLS-DA modelling. PLS-DA model optimization and validation All PLS-DA models were optimized and their performance was assessed in a similar fashion as the double cross validation procedure described in Szymanska et al.

20

. The statistical

significance of optimized final models was evaluated with permutation testing using two diagnostic statistics, the Area Under the Receiver Operating Characteristics Curve (AUC) and the misclassification rate 21. The final PLS-DA models were obtained in two steps, (1) model optimization, which involved assessment of the model complexity (number of latent variables) and variable selection and (2) model assessment using the virgin test set samples. The initial dataset was randomly divided into a virgin test set and a calibration set, in 1:3 or 1:4 ratio, keeping both time points, T1 and T2, corresponding to the same individual present either in the test set or in the calibration set. Further PLS-DA model optimization was performed on the calibration set while the virgin test set remained intact until final model assessment. The calibration set samples were further subdivided into a validation and a training set, in a 1:4 ratio, in the same way as mentioned above, so that both T1 and T2 samples from the same individual were always assigned to the same set. In the next step, the training set was employed to evaluate the model complexity (number of latent variables (LV)) using cross validation and the most informative variables were selected based on Variable Importance for Projection (VIP) scores

22

and regression coefficients as follows: As long as

variable selection kept improving the performance of the model on the validation set in terms of misclassification rate, the variable set was reduced, the number of components was updated, a new model was built, and the threshold for VIP scores was increased. When reducing the variable set no longer improved the performance on the validation set, the final set of variables was recorded. Based on the optimized model using the training set, the



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validation set was predicted using an optimal number of LVs and selected variables as informative variables. This model optimization procedure was repeated for 100 training and validation set pairs generated using only samples in the calibration set, and an optimal number of LVs was selected from the model having an average performance based on AUC and misclassification rate. In order to simplify model interpretation and improve classification power, variables that were selected at least in 80% of the training and the validation set pairs were selected for the final model assessment. The final PLS-DA models were assessed by predicting the classes of the virgin test set samples using the optimal number of LVs and the selected variables found during the model optimization step. In order to double check the test set validation results, a PLS-DA permutation test was performed by randomizing class categorical variables; models were then assessed by diagnostic statistics, AUC and misclassification rate. In addition to PLS-DA analysis, a univariate data analysis, i.e., a oneway ANOVA test, was applied to investigate metabolites that have independently changed due to one or more design factors (diet, season, gender and weight loss) included in the study. Such a univariate analysis may allow revealing effects that may otherwise be masked in multivariate analysis due to the comparatively large number of uninformative variables 23. Multivariate data analysis was performed using MATLAB version R2015a (8.5.0.197613) (The MathWorks, Inc. USA) and the PLSToolbox version 7.9.4 (Eigenvector Research, Inc., USA). RESULTS A total of 376 components were deconvoluted from PARAFAC2 models of the raw GC-MS data. After removal of the PARAFAC2 components that corresponded to the baseline, column bleed and reagent derived peaks, tails of neighboring peaks, and other non-related regions, a final set of 144 pure peaks were obtained and used for the data analysis. Each of the 144


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peaks was characterized by its unique EI-MS and RI and 69 of them were identified at level 2 according to the Metabolomics Standards Initiatives (MSI)

18

. These metabolites

corresponded to 17 amino acids, 12 fatty acids, 11 sugars, 9 organic acids, 7 sugar alcohols, 3 phenolics and 10 other metabolites including 2 indole derivatives, ibuprofen, uric acid and cholesterol (Table S-1). Furthermore, 5 metabolites were identified at level 1 using authentic standards, 17 metabolites were tentatively identified, level 3 of MSI, based on spectral similarity (EI-MS match ≥ 65 (%)). The final metabolite table, consisting of 435 samples (145 individuals at T0, T1 and T2) and 144 variables, was analyzed by a Principal Component Analysis (PCA). The Hotelling T2 and Q Residuals of the corresponding PCA model showed 6 samples as being outliers and hindered an overall variance present in the data. Thus these samples were removed and further data analysis was performed on 429 samples. In order to evaluate any effect of diet, the baseline point before the intervention (T0) was removed and a PCA model was developed using only T1 and T2 samples (286 samples). However, the PCA modeling was not sufficient to discriminate individuals who followed ADD or NND diets (data not shown) which may be explained by the presence of strong effect modification from foods common to both diets as well as season and gender. Thus the dataset, containing T1 and T2 samples, was further analyzed by ASCA for evaluating the significance- and variance distribution of three main effects, diet, season and gender and their interaction terms using all variables simultaneously (Figure 2). Although season had three levels, winter, spring and summer in T1 and T2 samples, we have considered only winter and spring samples since they made up 93% of the samples (Figure 1). The ASCA permutation test performed on the balanced datasets, generated from unbalanced design as described in the methods section, allowed evaluation of two-factor interaction effects, diet × season (Figure 2A) and diet × gender (Figure 2B), as well as main effects, diet, gender and season. Although variance contribution of all main effects were as small as 1-2.5%, they were statistically significant and



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depicted notably different sum of squares of the effect matrices in permutation test, except when diet effect was on the border of being significant due to a large reduction in sample size during design balancing (Figure 2B). The ASCA permutation test also proved that neither diet × season nor diet × gender interaction effects were significant, with p-values of 0.84 and 0.46, respectively. An unbalanced ASCA permutation test was performed in order to evaluate the main effects without reduction in sample size. The ASCA showed significant differences for diet, season and sex, although the effect of diet was considerably smaller than that of season and gender. It can be speculated that a large amount of unexplained variation in the residual matrix, E, may be derived from individual differences of participants as well as random effects generated during the data acquisition and/or data pre-processing. In addition, a one-way ANOVA test was performed for each metabolite in order to investigate effects of diet, season and gender. It is worth mentioning that the majority of metabolites that were found to be significantly affected by any of the investigated class variables in the univariate one-way ANOVA test were also found as markers in subsequent Partial Least Squares Discriminant Analysis (PLSDA) models (Table 1 and Table S-1). In the following sections, PLS-DA will illustrate more detailed effects of diet as well as season and gender as well as weight loss among NND individuals, and the most relevant classifier metabolites will be discussed. Discrimination of NND and ADD diets A PLS-DA model for discriminating individuals who followed NND and ADD diet was optimized and assessed as described in methods section. After variable selection, the final PLS-DA model optimized using training dataset (2 LVs, AUC of 0.82 and Error of 20%) included a set of 33 variables that were selected as the most important discriminant variables. A total of 14 out of 33 selected classifiers were also found to be significant in one-way


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ANOVA test (Table S-1). The model was then assessed to predict the diet classes of the virgin test set samples and illustrated high classification power with AUC of 0.80 and misclassification error of 21% (Figure 3). The permutation test showed different diagnostic statistics for the selected model compared to the permuted models (Figure S-1). A total of 21 out of the 33 selected classifier metabolites for the diet effect were identified. Based on the PLS-DA loadings plot, the relative concentration of lactic acid, oxalic acid, alanine, threonine, diethyl phthalate, 2,6-diisopropylnaphthalene (2,6-DIPN), citric acid and cholesterol were higher in ADD samples as compared to the NND samples (later referred as markers for ADD) (Figure 3B). In contrast, the relative concentrations of 3-hydroxybutanoic acid, erythritol, 2-hydroxybenzoic acid, aspartic acid, 2,3,4-trihydroxybutanoic acid, xylitol, N-acetylaspartic acid, 2,5-dimethoxyphenylpropionic acid and palmitoleic acid were higher in the NND samples (later referred as markers for NND) (Figure 3B). These findings were in agreement with the delta mean differences of metabolites between ADD and NND samples calculated from the raw data (Table 1). In order to evaluate the diet effect independent of seasonal and gender related variations, two additional PLS-DA models were developed including only samples collected in winter (Figure S-2) or samples corresponding to female individuals only (Figure S-3). The classification performance of the PLS-DA models including only winter or female samples were similar to the global PLS-DA model (Figure 3) with AUC of 0.83 and 0.81, respectively. The misclassification rate for predicting diet classes of test set samples was 22% for both models. A total of 20 metabolites were selected for developing the PLS-DA model, including winter samples, based on VIP scores and 14 of them were also previously selected in the global PLS-DA model (Figure 3). The PLS-DA model which included only female samples detected 23 metabolites as important variables after variable selection and 15 of them overlapped with the metabolites selected by the global PLS-DA model (Figure 3).



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Effects of season and gender The majority of samples in the intervention period of week 12 (T1) were collected in winter season, while week 26 (T2) samples were collected in the spring (Figure 1). In order to study the effect of season, a PLS-DA model for season classification was developed and optimized as described in methods section. Variable selection, model optimization and assessment were performed using training and validation sample sets. An optimized PLS-DA model developed using training samples (2 LVs, AUC of 0.94 and Error of 14%) included 21 metabolites, and this model was able to predict the season classes of the virgin test set samples with high accuracy (AUC of 0.93 and Error of 16%) (Figure 4A and Figure S-4). Moreover, the permutation test performed by permuting the original season classes showed that the PLS-DA model developed using the original class categorical variables possessed different model diagnostic statistics (AUC of 0.94 and Error of 9%) compared to the permuted models (Figure S-4). Out of 21 selected metabolites during PLS-DA model development, 20 of them were also found to be significant in a one-way ANOVA test (Table S-1). Major metabolites that were in higher concentrations in spring samples were terephthalic acid, maltose, palmitic acid, 3-indolepropionic acid, tryptophan, stearic acid and sucrose whereas winter samples contained more of 2-aminoheptanedioic acid and four unknown metabolites. A PLS-DA based discrimination of female and male individuals was also performed in a similar way as for the seasons. An optimized final model (2 LVs, AUC of 0.85 and Error of 15%) was developed using training and validation samples and after variable selection, 21 metabolites were included. This model was further tested in order to evaluate its prediction power using virgin test samples and showed high predictive power with an AUC of 0.84 and a misclassification error of 17% (Figure S-4B and Figure S-5). A permutation test performed to evaluate statistical significance of the obtained model diagnostics showed a significantly higher AUC (0.85) and lower misclassification error (18%) for the original class model


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compared to the permuted models (Figure S-5). A set of 14 out of the 21 selected metabolites during the PLS-DA model development overlapped with those detected by one-way ANOVA (Table S-1). Based on the loadings plot, almost all of the selected classifier metabolites were in higher concentrations in male samples except palmitoleic acid which was found to be the only metabolite that dominate in female samples (Figure S-5). Weight loss in NND Weight loss effects on the plasma metabolome were evaluated by PLS-DA discrimination of individuals who followed NND diet and lost ≥ 6% of their initial body weight compared to those who lost ≤ 2% or gained weight during the intervention study. The PLS-DA model was developed and optimized using the delta metabolomics data as described in methods section. The final model (1 LV, AUC of 0.84 and Error of 19%) was obtained using training and validation samples and 22 metabolites were selected as informative variables during the PLSDA model optimization. The predictive power of the final model was evaluated by discriminating the virgin test set samples and showed accurate prediction with an AUC of 0.83 and a misclassification error of 23% (Figure 5). In order to double check the obtained PLS-DA model, a permutation test was performed which also depicted significant differences in model diagnostics between the original class PLS-DA model and all permuted class PLSDA models (Figure S-6). One-way ANOVA tests showed that 20 metabolites were different among NND individuals who lost > 6% weight and those who did not lose weight. Among these metabolites 12 were also selected as weight loss markers in PLS-DA model optimization. These overlapping metabolites included 3-hydroxybutanoic acid, vaccenic acid, 4,5-dihydroxy-1,2-dithiane, pyrophosphate, N-aspartic acid and 1,5-anhydro-D-sorbitol. The regression coefficients and loadings plot of the PLS-DA model suggest that the majority of the selected metabolites were



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in higher concentrations in individuals who did not lose weight (↓WL), including lactic acid, 2-hydroxybenzoic acid, 4,5-dihydroxy-1,2-dithiane, pyrophosphate, N-aspartic acid, 1,5anhydro-D-sorbitol and 3-(2,5-dimethoxyphenyl)propionic acid (later referred to as markers for low ↓(WL)). However, plasma from individuals who lost more weight contained higher relative concentrations of five metabolites including 3-hydroxybutanoic acid, 3indolepropionic acid, vaccenic acid, pseudouridine and sucrose (later referred as markers for high ↑(WL)). DISCUSSION Since multivariate statistics reveals discriminative metabolite patterns without indicating any specific importance of the individual features in the pattern, the overlap of many features with those significant by the univariate analyses underlines that many of the metabolites may in fact be reasonable to evaluate for their individual biological importance. The untargeted approach to explore several aspects of a complex dietary intervention by semi-quantitative GC-MS analysis allowed us to identify a number of metabolite patterns and individual metabolites that represent differences in diet, diet-related weight loss, gender, and season based on multivariate and univariate models. Previous research on urine samples from the same dietary intervention study using LC-QTOF also indicated that the two diets, NND and ADD, could be separated based on PLS-DA analysis of the collected data 12. Blood plasma, as opposed to urine, represents homeostasis and fasting morning samples represent a time point that should be >8 hours from the last meal. The discriminant metabolites are therefore expected to reflect more permanent differences in metabolism between the diet groups, but may also include compounds with long half-lives from recent meals or from the diets or their seasonality at large. New Nordic Diet versus Average Danish Diet


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The optimized PLS-DA classification model for diet, developed using 75% of the total number of samples, was able to predict classes of the remaining test set samples (25% of the samples) with 21 % of error. Considering possible individual variations present within diet groups, potentially non-compliant individuals and possible experimental errors derived from sampling and GC-MS metabolomics analysis, the obtained misclassification error for the test set samples might be expected and is therefore acceptable. A similar metabolomics study performed on urine samples of the same NND and ADD individuals yielded a PLS-DA diet classification model with a similar error level of 19% for the test set samples

12

. This

classification model was optimized using the urine LC-MS dataset with more than four thousand variables, while in the present study only 144 variables were included. Repeated PLS-DA classification modelling including only one season (winter) or gender (female) resulted in a slightly better classification performance for diet, although the majority of the selected variables overlapped with those selected in the global PLS-DA model. Moreover, more than half of the metabolites, selected as being different between NND and ADD samples (found from one-way ANOVA test) were in agreement with classifier metabolites selected during the PLS-DA model optimization. These results illustrate the robustness of the PLS-DA classification model for the diet and allow us to interpret some of the metabolites responsible for the discrimination. The global PLS-DA model for the diet shows that misclassification mainly occurred amongst the NND individuals (Figure 3), while the majority of ADD samples were correctly classified. This result is in agreement with the previous urine LC-MS metabolomics study 12. Most of the misclassification observed by GCMS did not reflect direct lack of compliance evidenced by metabolites from foods that are not part of the NND. We therefore assume that the relatively higher misclassification amongst NND individuals observed in the present study is more closely related to the complexity of



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the diet, seasonality and variations of foods offered to volunteers within the food groups included in the diet. The main metabolites identified as markers for the New Nordic Diet are shown in Table 1. Although there may be several explanations for some of the metabolites, they may be largely grouped into metabolites reflecting different food intake and metabolites reflecting energy metabolism. Metabolites reflecting different food intake Glycine, threonine and oxalate. Plasma content of glycine was higher in NND compared to ADD and at the same time threonine, the main precursor for glycine synthesis was found to be reduced in NND plasma. Glycine has many functions and may be pseudo-essential in the sense that sufficient endogenous formation is important for maintenance of normal functions, including glutathione synthesis 24. Glycine can also be formed from glyoxylate in a reaction with glutamate. Glyoxylate is also the main endogenous precursor of oxalate so an increased flux towards glycine could potentially explain also the relative decrease in oxalate. High contents of oxalate are found in some foods from the NND, including spinach, parsley, rhubarb and cabbage 25. However, oxalate is also higher in legumes, including soybean 26, and soy intake may have been higher in the ADD diet because it is richer in industrially processed foods. Fasting plasma levels of oxalate, which has an elimination half life through the kidney of around 90 minutes 27 would be expected to reflect endogenous metabolism rather than food intake. We favour therefore an explanation reflecting an increased glycine synthesis in the NND group. This might be due to the higher exposure to isothiocyanates from cruciferous vegetables since these compounds utilize glutathione for detoxification into mercapturates. Although glycine is released during mercapturate formation, a higher excretion of mercapturates has been reported in the urine in the NND group from this study


12

and may

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demand a higher level of glycine to satisfy the needs for the flux from glutathione to mercapturates. Sugar

alcohols.

Several

sugar

alcohols

and

acids

including

erythritol,

2,3,4-

trihydroxybutanoic acid, and xylitol were observed as higher with NND. Erythritol and xylitol are widespread in plant foods and since artificial sweeteners were not abundant in the diets the higher intake of plant foods in the NND group is probably reflected here. Erythritol is a four-carbon sugar alcohol which occurs naturally in fruits, vegetables, and fermented foods, but it is also formed endogenously in the human body. It is rapidly absorbed in the gastrointestinal tract and excreted in the urine without undergoing any metabolic change

28

.

Erythritol is considered as a safe sweetener as it, unlike other sweeteners, does not cause adverse effects like tooth decay, effects on lactation, or raise in the blood sugar level. We speculate that the enriched fruit and vegetable content in the NND diet results in long-term elevation of the erythritol level in the blood plasma. To the best of our knowledge this study demonstrates, for the first time, an influence of a diet high in fruit an vegetables on the erythritol level of the human blood plasma. Xylitol is another sugar alcohol which is obtained only through the diet. It occurs naturally in many fruits (e.g., berries, plums), vegetables (cauliflower, mushrooms) as well as grains (oat) and is considered the sweetest polyol and often used as an added sweetener. We assume that the higher amount of xylitol in plasma of NND individuals is related to the consumption of more fruits and vegetables. Despite being as sweet as table sugar, it has 33% less calories than sucrose. Since it is absorbed at a slower rate than glucose it does not contribute to increasing the blood sugar levels 29. Threonic acid may be a degradation product of ascorbate 30 or of N-acetyl-glycosamine 31 that are common human plasma metabolites. Threonate may also result from oxidation of erythrose, a common sugar from grains and mushrooms through a pathway recently shown to be active in humans 32. The higher intake of grains or the higher ascorbate intakes with NND



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may explain this finding; however, our finding cannot be ruled out as an artifact of plasma ascorbate degradation since samples were not stabilized to protect ascorbate. 2-Hydroxybenzoic acid (salicylic acid) is another compound which is abundant in food plants 33

. Salicylic acid is a phenolic acid derived from the metabolism of salicin and it is

widespread in plants, where it plays a key role in growth, development, photosynthesis and defence against pathogens

34

. Salicylic acid is also the main metabolite of a commonly used

pain killer and anti-inflammatory drug 35, but with the reported antiinflammatory and weight loss actions of the NND it would be unlikely to observe an increased intake of any pain killer in the NND group

10

. The content of salicylic acid and its salts (salicylates) is particularly

high in some vegetables and fruits including cucumber, broccoli, eggplant, seaweed, radish, green beans, mushrooms, avocado, spinach, tomato, orange, lemon, pineapple, berries, grape and apricot 33. Salicylic acid has a half-life in excess of 6 hours so fasting plasma levels can build up to some extent with an increased daily intake

36

. This may explain a long-term

increase of blood plasma salicylic acid content in NND individuals compared to the ADD individuals whose diet was less rich in those vegetables and fruits. N-Acetylaspartic acid (NAA) is synthesized in the neurons from aspartic acid and acetyl coenzyme A and it is the second most abundant molecule in the brain after glutamate. The concentration of NAA in the brain is important for brain function and it may be increased by eicosapentaenoids

37

. We speculate that higher intakes of n-3 fatty acids in the NND group

may be responsible for higher production of NAA. Some studies associate increased NAA in brain with creativity and better memory

38

. It has been shown that NAA is endogenously

synthesized in the brain by direct acylation of aspartic acid 39. However, some of the everyday foodstuff also possess high concentration of NAA including coffee beans and vegetables 40. Thus it is possible that the higher concentration of NAA in the blood plasma from the NND


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group might also partially be related to the higher consumption of vegetables. However, the relative contributions of diet and brain-derived synthesis to plasma NAA is not known. Palmitoleic acid is a monounsaturated long chain fatty acid that is a constituent of human adipose tissue triglycerides. It occurs in all tissues but with a relatively higher amount in the liver tissues. In humans it occurs both as endogenously synthesized from palmitic acid, and as an exogenous fatty acid from the diet. The main dietary sources of palmitoleic acid are animal oils and vegetable oils. It is found as the most abundant unsaturated fatty acid in fish oil samples 41, but the highest concentration of this fatty acid is found in sea buckthorn oil 42. Sea buchthorn berries have a high fat content and were abundant in NND so this might be a likely reason for a metabolite pattern with an increased level of palmitoleic acid reflecting that diet group. The difference between the plasma level of palmitoleic acid being higher in the NND group is in contrast with our previously published data where the ADD group had a significantly higher level of whole fatty acids, however the latter measurements were performed on whole blood

43

. It is therefore important to see the present result as part of a

metabolite pattern in the blood plasma since the absolute difference between the whole blood fatty acids in the two groups was found to be the opposite by univariate analysis. 2,6-Diisopropylnaphtalene (2,6-DIPN) stands out from the other patterns. It is an antisprouting agent used for potatoes. However, the higher level observed in ADD does not reflect a higher intake of conventional potatoes in this group since this intake was actually almost three times higher in the NND group where only a small fraction of all potatoes provided were organic. This is in contrast to fruit, vegetables and cereals where a large fraction consumed by the volunteers in the NND group was organic. However, consumption of industrial potato products such as frozen potatoes and potato chips was 50% higher among ADD group than in the NND group, according to records from our study shop. 2,6-DIPN is classified as a growth regulator biopesticide and is commonly used for sprout control of



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industrial potatoes before storage

44

. It is thus likely that the consequent higher level of the

2,6-DIPN among ADD subjects is due to the higher average consumption of industrial potato based products in this group. The observation of an increased 3-(2,5-dimethoxyphenyl)propionic excretion after NND is a novel finding. The compound is currently only known as a laboratory chemical, however it is closely related to several hydrocinnamic acid derived human metabolites of plant polyphenols. It can thus be anticipated that this compound derives from one of the more unusual foods in the NND, however no direct food source of this compound has been described so far. Metabolites reflecting patterns of altered energy metabolism 3-hydroxybutanoic acid is one of the three main ketone bodies synthesized from fatty acids in the liver and used in the brain as an energy source replacing glucose 45. This process, called ketosis, is an alternative energy production to glycolysis and the concentration of ketone bodies in the blood increases when the body’s metabolism switches to ketosis and consumes body fat. Ketosis also increased the use of glucogenic substrates, including lactate, alanine, and threonine for gluconeogenesis. Apart from overnight sleep, elevated ketosis may occur during low-carbohydrate diets, fasting and prolonged exercise

46

. Another study has shown

that the concentration of 3-hydroxybutanoic acid and its oxidation rate in blood plasma is much lower in obese women compared to lean women suggesting a lower production and use of ketone bodies in obese individuals 47. This aligns well to our finding that individuals who lost more weight followed NND diet and had a higher concentration of 3-hydroxybutanoic acid than individuals whose weight loss was not significant.

An increased level of 3-

hydroxybutanoic acid in urine was also observed after intervention with a Mediterranean diet


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compared with a low-fat diet 48 indicating that is is a good general marker of healthy diets in fasting plasma samples. Lactic acid is constantly produced in muscle from pyruvate and its concentration in muscle increases during exercise

49

. In the liver it is used for gluconeogenesis by reversing this

process to produce pyruvate. Thus a difference in lactate in the fasting state between NND and ADD groups might be interpreted as a higher rate of gluconeogenesis in the NND group that indeed presents a significant lower amount of this metabolite. Previous studies have shown that the relative level of lactic acid is increased in type-2 diabetic patients with high BMI again indicating that reduced hepatic gluconeogenesis might lead to increased lactate levels. We interpret the difference in lactate between the two groups as part of a metabolite pattern indicating higher gluconeogenesis in the NND group. Alanine is another substrate used in hepatic gluconeogenesis and it is showing the same pattern as lactate indicating that increased gluconeogenesis in NND could be a likely explanation for the differences between the ADD and NND groups. Interestingly, an elevated level of alanine has been associated with increased blood pressure during hypertension 50. In the present study this is also in agreement with the increased blood pressure observed in ADD compared to NND individuals. Threonine is an essential α-amino acid in humans that is obtained exclusively from dietary sources. As for alanine a greater consumption of meat products rich in threonine in the ADD diet group compared to NND may be one of the reasons for an elevated level of threonine in the ADD blood plasma, however its use for glucose and glycine production in the NND subjects is probably the main drivers of the observed difference. An NMR metabolomics study revealed an increased level of threonine in urine of mice fed a high-fat diet

51

. In

another study using human serum, increased serum threonine levels in type 2 diabetic patients



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with high BMI was found as one of the factors discriminating them from non-diabetic persons with a low BMI 52. Citric acid is an organic acid produced in the citric acid cycle or consumed with the diet. Citric acid is found in high concentrations in citrus fruits which were allowed only in ADD diet and forbidden in NND diet. It is also used as a natural food preservative added to soft drinks and the consumption of soft drinks was much higher in ADD diet than in NND diet. Thus, the increased level of citric acid in blood plasma of ADD individuals could be explained by the greater consumption of citrus fruits and soft drinks and would be in agreement with the previous urine metabolomics study performed on the same ADD and NND individuals where limonene and other citrus-related metabolites were detected in higher concentrations in ADD subjects 12. However, plasma citrate has a halflife time of only around 36 minutes

53

hence fasting plasma citrate is unlikely to reflect recent dietary intake.

Decreased plasma citrate in NND may also be related to some of the other observations in the current study, e.g. a result of increased gluconeogenesis; thus an increased production of pyruvate to form oxaloacetate may also utilize citrate. However, citrate is well known to reflect glucose regulation and is directly enhancing the rate-limiting fructose-1,6bisphosphatase so a lower citrate level may thereby switch off gluconeogenesis. Although the exact relationship of citrate with the effects of NND would demand a different study design, thus we favor an overall explanation of the pattern observed as a reflection of increased gluconeogenesis in the NND group. This may explain the lack of a change in plasma glucose despite an improved insulin regulation among the more obese subjects in the study 10. Some of the newly generated glucose may also be further metabolised into glycogen for storage under the formation of pyrophosphate, a metabolite also found to be more abundant in the plasma of the NND group compared with ADD.


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Aspartic acid is a non-essential amino acid synthesised from glutamic acid and a high content of it is found in various foods including asparagus, fish and seaweeds

54

that are more

abundant in the NND diet. We suppose that a higher intake may be reflected in a higher plasma level, however aspartate is in fast equilibrium with oxaloacetate so it is more likely reflecting an increase in this metabolite, which is the starting point from the citric acid cycle for gluconeogenesis. The relatively higher concentration of cholesterol observed in blood plasma of ADD individuals compared to the NND individuals might be associated with high consumption of animal products such as milk, cheese, meat, butter and eggs. A decrease in very low density lipoprotein (VLDL) cholesterol levels was previously reported in NND individuals compared with ADD but total lipoprotein cholesterol was not decreased

10

just like we did not find a

significant decrease in this study by ANOVA. A lowered plasma cholesterol level has been found in mice fed with fish oil 55. Indeed the NND diet is rich in fish so this might be one of the reasons that a metabolite pattern including a lower level of cholesterol is characteristic for NND individuals compared to ADD. Metabolites related to weight loss in NND individuals The plasma concentrations of several metabolites changed in NND individuals who lost at least 6% or more of their initial body weight compared to those who did not lose significant body weight. The metabolite patterns reflected by weight change may also be derived into those directly associated with the weight loss process and those that are more likely a reflection of the more extensive dietary change among those who successfully lost weight. Metabolites mainly reflecting dietary change Vaccenic acid is a trans-fatty acid which is consumed with the diet but also produced in humans endogenously. Dairy products and sea buckthorn contain relatively high



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concentrations of vaccenic acid

56

. Previous observational studies and studies with rodents

have found correlations between the levels of conjugated linoleic acid isomers including also their precursor vaccenic acid and weight loss 57. In another rodent study the diet with vaccenic acid supplementation resulted in a 6% reduction in total body fat, stimulated adipose tissue redistribution and decreased adipocyte size by 44% in obese rats compared to control rats 58. Thus, we hypothesize that the elevated level of the vaccenic acid in NND individuals with high weight loss might be associated with the dietary characteristics of the NND and in particular with intakes of dairy products and certain berries. This is consistent with recent meta-analyses showing shorter-term benefits of dairy products on weight loss 59. Metabolite patterns reflecting the weight loss process Lactate and pyrophosphate. As we argued for the metabolite pattern with lactic acid representing a differential rate of gluconeogenesis, this is also likely to be the case within the NND group where a larger weight loss was also associated with better insulin sensitivity but failed to decrease the fasting glucose level. This pattern also included pyrophosphate as a potential marker of glycogen production and this metabolite was further enhanced with an increased weight loss. 3-hydroxybutanoic acid is found in this study to be a significant marker as such for weight loss within the NND group as well as a marker for NND. An associations between an increased level of plasma 3-hydroxybutanoic acid level and food deprivation and low calorie diet has previously been described

60

. Furthermore, a metabolomics study performed in a

controlled feeding trial was able to develop a weight loss prediction model using both clinical and metabolomics data and revealed a direct correlation between the increased level of plasma 3-hydroxybutanoic acid and weight loss in obese adults 61. In addition, an increased level of 3-hydroxybutanoic acid in blood of moderately obese healthy females after exercise was


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proposed to be used for predicting weight loss after three-months program of physical activity 62

. Another study shows significant weight loss in rats induced by long-term infusion of 3-

hydroxybutanoic acid in the ventricle of the rat brain 63. Elevated concentration of plasma 3hydroxybutanoic acid was associated with a reduced level of plasma fatty acids in genetically modified mice that were resistant to the diet-induced obesity

64

. These studies suggest the

presence of a possible relationship between weight loss (or resistance to weight gain) and an increased level of 3-hydroxybutanoic acid in the blood. While 2-hydroxybenzoate (salicylate) is generally increased with the NND, it is decreased within the group of NND subjects with significant weight loss. It would be tempting to speculate that this differential change within the NND group is caused by a reduced intake of pain killers with weight loss. However, the opposite effect, i.e. a higher consumption of salicylate-containing Nordic foods in those with a higher weight loss, would have been expected from the contrast in this metabolite between NND and ADD. In the presence of two factors, diet and medication, that can potentially change plasma salicylate in opposite directions it is difficult to predict or explain the final overall change. 1,5-anhydro-D-sorbitol (or 1,5-anhydroglucitol) is a compound found in many foods but it is also endogenously produced. It is not further metabolized and is excreted by the kidneys. It has a steady-state plasma concentration which is inversely related to plasma glucose because excretion is increased in hyperglycemia 65. A decrease of this compound with weight loss is a direct indication of better glycemic control 66. It is generally accepted as a better marker than plasma glucose or HbA1c for average short-term changes in plasma glucose so the lack of significantly lower values for these other markers may not be surprising. A higher sucrose plasma concentration among those losing more weight seems somewhat counter-intuitive. In an abstract reporting a controlled study of sucrose intake, the plasma



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sucrose – as opposed to sucrose in urine - was not observed as a marker of sucrose intake whereas erythronic acid was a marker

67

. The latter metabolite was not observed here as a

marker of weight loss so the higher sucrose level in fasting samples may be related to some other phenomenon than dietary sucrose intake. It can thus be speculated that dietary sucrose from fruit and vegetables may be slowly released in the lower gut as cell walls are degraded and that plasma fasting sucrose may therefore be a reflection of a relatively higher intake of the tougher vegetables such as cabbages, carrots, apple and leek in those individuals with the highest weight loss. Indeed, the dietary records shows that the average consumption of fruits and vegetables that are considered to be ‘tough’ is 3-fold higher among individuals who followed the NND diet (490 g/day) compared to those consuming the ADD diet (165 g/day). In total 98% of the vegetables consumed by the NND group were tough vegetables whereas only 66% of the vegetables consumed by the ADD group were tough. 3-Indolepropionic acid is a microbial degradation product of tryptophan found in human plasma 68. Tryptophan can result in several microbial degradation products of which some are associated with kidney toxicity while others such as 3-indolepropionic acid may be associated with decreased oxidative stress and protection of neuronal tissue 69. Healthy diets tend to shift microbial metabolism towards higher 3-indolepropionic acid formation as also seen here for the NND. The observation of a lower level of this compound as part of the pattern characteristic of weight loss within the NND group is unexpected, but may reflect lower intake of tryptophan as part of lower total food intake in subjects having a larger weight loss. However, it might also reflect the fact that tryptophan may be used both as a glucogenic and a ketogenic substrate so that lower tryptophan simply reflects an increase in both ketogenesis and gluconeogenesis with weight loss. Pseudouridine is a common modified base in RNA and an increased level of it in plasma was originally proposed as a marker of cancer development 70. However, it has later been observed


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also to be increased in other conditions of tissue remodeling. Changes in the distribution of fat and lean mass in patients has been found previously to be accompanied by a large increase in pseudouridine 71. Clearly several tissues, including adipose are being remodeled under weight loss and we speculate that this is why the increase is seen here more prominently in the first period of the study when the weight loss was the largest. Effects related to season and gender Seasonal effects may largely reflect seasonal differences in the foods offered in the trial supermarket. The actual changes observed, concern mainly unknown metabolites, probably reflecting that most of the human metabolites from specific food components are still not known. The few changes observed with identified metabolites, i.e. increases in 2aminoheptanedioic acid,

and decreases in maltose, sucrose, tryptophan and of 3-

indolepropionic-, palmitic- and stearic acid give only a few clues. The cause of an increase in the spring season in 2-aminoheptanedioic acid is uncertain. The compound has only been described as a constituent of carob and carob flour is a well-known thickener and stabilizer in many foods, including ice-cream. Accordingly, a possible explanation might be that increasing temperatures increase intake of ice-cream; however the decrease in saturated fats, palmitic and stearic acid does not support this interpretation unless it is assumed that winter foods in general are richer in animal products and saturated fats. The decrease in tryptophan explains the lower amount of its microbial metabolite, 3-indolepropionic acid, and may point to a lower intake of some protein-rich foods, possibly meat. Reduced fasting plasma levels of sucrose in the spring may be due to lower liberation of these compounds in the large intestine from digestion of plant foods due either to a decreased intake of the tougher “winter” plant foods such as cabbages or to more efficient gut microbial carbohydrate degradation. So the few indications that we have from the seasonal changes would indicate lower intakes of



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tougher vegetables and meats with saturated fats and a possibly higher intake of emulsified foods such as ice-creams during the transition from winter to spring. Metabolites differing between males and females were all, except for palmitoleic acid, higher among the males. This is largely in accordance with the classical study on metabolites affected by gender and ethnic group

72

, which also shows palmitoleic acid among the few

metabolites observed to be higher in women (1). Other fatty acids were also reported to be higher in females and while we can confirm this direction, other fatty acids did not reach statistical significance in our dataset. Plasma amino acid and their metabolites, including microbial degradation products, were also previously reported to be higher in men than in women (1). This confirms our findings related to threonine, valine, phenylalanine as well as 2,3-dihydroxybutanoic acid (from threonine) and uric acid. Metabolites related to energy metabolism were also generally higher in males thereby explaining higher levels of citric acid. 5-hydroxytryptophol is a common serotonin metabolite. While foods may be the main source of serotonin degradation products, endogenous synthesis may also contribute and the rate of synthesis in men has been shown to be substantially higher than in women (2). Our finding here may reflect this difference but previous studies on gender differences in the metabolome have not highlighted this observation. Since alcohol intake is a known confounder that may increase the formation of 5-hydroxytryptophol we cannot rule out that gender-specific life style differences may alternatively explain our findings (3). Higher cholesterol among men may be a reflection of the selection criteria for this study where high (stable) cholesterol levels were among the inclusion criteria. Any gender differences in this study cannot therefore be interpreted as a general difference between men and women.


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CONCLUSIONS A completely untargeted GC-MS metabolomics study using a newly developed protocol has been conducted using plasma samples from an intervention study concerning the effects of a change from an average Western (Danish) diet to a more sustainable healthy diet based on local seasonal foods, the New Nordic diet. A total of 144 metabolites were detected in the human blood plasma out of which 69 were identified at level 2. A significant difference in the blood metabolome between the ADD and NND individuals was revealed, although it is smaller in magnitude compared to the variations derived from season and gender. Our findings related to gender mainly corroborate previous findings by others whereas season may be mainly reflected by metabolites from typical seasonal foods. The metabolites found to be at higher levels in the NND individuals include two groups; those reflecting directly the altered NND diet with a higher intake of fish, vegetables including crucifers as well as wholegrain; other metabolites reflected mainly the impact of NND on energy metabolism to increase gluconeogenesis and ketosis thereby improving insulin sensitivity. These metabolites are altogether interpreted as metabolites with health beneficial effects. The obtained classification model shows that it is possible to discriminate between NND followers who lost weight from those who did not. The fasting plasma of individuals who lost more weight contained higher relative concentrations of five metabolites reflecting even stronger effects on energy metabolism as well as certain food-related metabolites possibly reflecting higher intakes of sea buckthorn berries and dairy products. Our study strongly indicates that healthy diets high in fish, vegetables, fruit, and wholegrain help to improve insulin sensitivity by increasing ketosis and gluconeogenesis in the fasting



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state and that some of the foods and metabolic processes may further explain the reduced blood pressure and cholesterol thereby generally improving metabolic health.

ACKNOWLEDGEMENTS The study was conducted as part of the OPUS project which is supported by a grant from the Nordea Foundation, Denmark. OPUS is an acronym of the Danish title of the project 'Optimal wellbeing, development and health for Danish children through a healthy New Nordic Diet'. The Faculty of Science (University of Copenhagen) is acknowledged for support to the eliteresearch area “Metabolomics and bioactive compounds” and to the strategic interdisciplinary research project UNIK-Food, Fitness and Pharma. All participants are thanked for their participation in the study. CONFLICT OF INTEREST DISCLOSURE All authors of the paper declare no competing financial interest. SOURCE OF FINANCIAL SUPPORT This study is part of the OPUS project “Optimal well-being, development and health for Danish children through a healthy New Nordic Diet.” OPUS is supported by a grant from the Nordea Foundation, Denmark TRIAL REGISTRATION This

trial

was

registered

at

www.clinicaltrials.gov

(https://clinicaltrials.gov/ct2/show/NCT01195610).


as

NCT01195610

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AUTHOR CONTRIBUTION SKP, TML, LOD, AA, SBE designed the research. BK conducted the research including blood plasma metabolite extraction, GC-MS data acquisition, data processing and statistical analysis. EA developed PLS-DA based variable selection method. BK, LOD, SBE interpreted the results, wrote the paper and had primary responsibility for final content. All authors contributed to the revision and final approval of the paper.

ABBREVIATIONS ADD (Average Danish Diet), ASCA (ANOVA-Simultaneous Component Analysis), Area Under the Receiver Operating Characteristics Curve (AUC), Body Mass Index (BMI), NND (New Nordic Diet), EDTA (Ethylenediaminetetraacetic acid), GC-MS (Gas ChromatographyMass Spectrometry), LV (Latent Variable), LC-QTOF (Liquid Chromatography-Quadrupole Time-Of-Flight), NAA (N-Acetylaspartic acid), PARAFAC2 (PARAllel FACtor Analysis2), PLS-DA (Partial Least Squares-Discriminant Analysis), Principal Component Analysis (PCA), RNA (Ribonucleic acid), TMSCN (Trimethylsilyl cyanide), Trimethylsilyl (TMS), 2,6-DIPN (2,6-diisopropylnaphthalene), VIP (Variable Importance for Projection), WL (Weight Lose) SUPPORTING INFORMATION Table S-1 lists all variables measured by GC-MS analysis of blood plasma samples from whole study. Figures S-1-6 demonstrate prediction power of the PLS-DA models.



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ASSOCIATED CONTENT Table S-1. A list of 144 peaks detected from GC-MS metabolomics data obtained from blood plasma samples. PARAFAC2 deconvoluted EI-MS mass spectra of each peak were used for metabolite identification employing metabolite libraries, NIST11, Fiehn GC-MS library from LECO and Golm Metabolome Database (GMD). Figure S-1. Area Under the Curve (AUC) of Receiver Operating Characteristic (ROC) curve of the PLS-DA model and Permutation Test results performed for classification of the NND and ADD diets. Figure S-2. Permutation test, Area Under the Curve (AUC) of Receiver Operating Characteristic (ROC) curves, scores, loadings plots of the PLS-DA model developed for classification of the NND and ADD diets including winter season samples. Figure S-3. Permutation test, Area Under the Curve (AUC) of Receiver Operating Characteristic (ROC) curves, scores, loadings plots of the PLS-DA model developed for classification of the NND and ADD diets including female samples. Figure S-4. Permutation test, scores, loadings plots of the PLS-DA model developed for classification of the intervetion seasons, winter versus spring. Figure S-5. Permutation test, scores, loadings plots of the PLS-DA model developed for classification of the participants gender. Figure S-6. Permutation test and Area Under the Curve (AUC) of Receiver Operating Characteristic (ROC) curves of the PLS-DA model developed for classification of the NND subjects who lost more than 6% of their pre-intervention body mass from those who lost less than 2% of their initial weight.


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60. López-Soldado, I.; Betancor-Fernández, A.; Herrera, E., Differential metabolic response to 48 h food deprivation at different periods of pregnancy in the rat. J. Physiol. Biochem. 2002, 58 (2), 75-85. 61. Piccolo, B.; Grapov, D.; Garvey, W. T.; Harper, M.-E.; Fiehn, O.; Adams, S.; Newman, J., Impact of a human missense UCP3 polymorphism on the plasma metabolomic profile: support for a mitochondrial fuel-partitioning role for UCP3 (248.4). The FASEB Journal 2014, 28 (1 Supplement). 62. Matoulek, M.; Svobodova, S.; Vetrovska, R.; Stranska, Z.; Svacina, S., Post-Exercise Changes of Beta Hydroxybutyrate as a Predictor of Weight Changes. Physiol. Res. 2014, 63, S321S325. 63. Davis, J. D.; Wirtshafter, D.; Asin, K. E.; Brief, D., Sustained intracerebroventricular infusion of brain fuels body-weight and food intake in rats. Science 1981, 212 (4490), 81-83. 64. Liu, X. T.; Rossmeisl, M.; McClaine, J.; Kozak, L. P., Paradoxical resistance to dietinduced obesity in UCP1-deficient mice (vol 111, pp 399, 2003). Journal of Clinical Investigation 2003, 111 (5), 759-759. 65. Skupien, J.; Gorczynska-Kosiorz, S.; Klupa, T.; Wanic, K.; Button, E. A.; Sieradzki, J.; Malecki, M. T., Clinical application of 1,5-anhydroglucitol measurements in patients with hepatocyte nuclear factor-1 alpha maturity-onset diabetes of the young. Diabetes Care 2008, 31 (8), 1496-1501. 66. (a) McGill, J. R. B.; Cole, T. G.; Nowatzke, W.; Houghton, S.; Ammirati, E. B.; Gautille, T.; Sarno, M. J., Circulating 1,5-anhydroglucitol levels in adults patients with diabetes reflect longitudinal changes of glycemia. Diabetes Care 2004, 27 (8), 1859-1865; (b) Dworacka, M.; Winiarska, H.; Szymanska, M.; Kuczynski, S.; Szczawinska, K.; Wierusz-Wysocka, B., 1,5-anhydro-D-glucitol: a novel marker of glucose excursions. International Journal of Clinical Practice 2002, 40-44. 67. Joosen, A. M.; Clarke, M. M.; Lodge, J. K.; Mugridge, O.; Frost, G.; Engel, B.; Taillart, K.; Lloyd, A. J.; Beckmann, M.; Draper, J., Development of a biomarker for sucrose intake based on metabolomics. Proceedings of the Nutrition Society 2012, 71 (OCE2), E188-E188. 68. Yokoyama, M. T.; Carlson, J. R., Microbial metabolites of tryptophan in the intestinaltract with special reference to skatole. American Journal of Clinical Nutrition 1979, 32 (1), 173-178. 69. Hwang, I. K.; Yoo, K. Y.; Li, H.; Park, O. K.; Lee, C. H.; Choi, J. H.; Jeong, Y. G.; Lee, Y. L.; Kim, Y. M.; Kwon, Y. G.; Won, M. H., Indole-3-Propionic Acid Attenuates Neuronal Damage and Oxidative Stress in the Ischemic Hippocampus. Journal of Neuroscience Research 2009, 87 (9), 21262137. 70. (a) Russo, T.; Colonna, A.; Salvatore, F.; Cimino, F.; Bridges, S.; Gurgo, C., Serum pseudouridine as a biochemical biomarker in the development of AKR mouse lymphoma. Cancer Research 1984, 44 (6), 2567-2570; (b) Seidel, A.; Brunner, S.; Seidel, P.; Fritz, G. I.; Herbarth, O., Modified nucleosides: an accurate tumour marker for clinical diagnosis of cancer, early detection and therapy control. British Journal of Cancer 2006, 94 (11), 1726-1733. 71. Broekhuizen, R.; Grimble, R. F.; Howell, W. M.; Shale, D. J.; Creutzberg, E. C.; Wouters, E. F.; Schols, A. M., Pulmonary cachexia, systemic inflammatory profile, and the interleukin 1β −511 single nucleotide polymorphism. The American Journal of Clinical Nutrition 2005, 82 (5), 1059-1064. 72. Lawton, B. A.; Rose, S. B.; Elley, C. R.; Dowell, A. C.; Fenton, A.; Moyes, S. A., Exercise on prescription for women aged 40-74 recruited through primary care: two year randomised controlled trial. Br. Med. J. 2008, 337, 7.



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FIGURE LEGENDS Figure 1. Overview of the intervention study design. A total of 145 volunteers (45 male and 100 female) followed either New Nordic Diet (NND) or Average Danish Diet (ADD) over 26 weeks between October 2010 and July 2011. One week run-in time served as a standardization period of the participants before starting the intervention in Week 0 (Time 0). Clinical examinations and blood sample collection took place at three time points, week 0 (Time 0), week 12 (Time 1), and week 26 (Time 2). Samples were collected in the morning at least after 8 hours of fasting from last meal. Yellow, grey, green and blue color codes correspond to the number of individuals who have been examined in autumn, winter, spring and summer seaons, respectively. Figure 2. ASCA based decomposition of three main effects: (1) diet, ADD vs NND, (2) season, Winter (Win) vs Spring (Spr), and (3) gender, Male (M) vs Female (F) and two factor interaction effects as well as the overall variance contribution of effects across 144 metabolites. The null hypothesis (H0) was checked for each main and interaction effects using sum of squares of effect matrices and p-values were assessed by permutation test (2,000 permutations). In order to investigate interaction effects, the native unbalanced design (samples are not equally distributed between design blocks, levels of each factor) was balanced in a manner which ensured that each design block contained an equal number of samples by randomly removing samples from blocks containing more samples. A total of 2,000 fully balanced data sets were generated and each of them were subjected to permutation test. Thus, the shown p-values for main and two factor interaction effects (A) and (B) the mean p-values are calculated from the permutation tests of 2,000 balanced data sets. In order to evaluate the main effects alone without balancing (no reduction in sample size), since in the case of non-balanced design the variance distribution and p-values remain valid for main effect only and not for interactions, the native unbalanced design was also directly used (C).


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Figure 3. LV1 scores (A) and loadings (B) plot of the 2 Latent Variable (LV) PLS-DA model developed for discriminating individuals who followed Average Danish Diet (ADD) against those who followed the New Nordic Diet (NND). The model was optimized using training dataset containing 75% of samples and validated using an independent test dataset constituting 25% of the total number of samples. It is worth to mention that both sample time points, week 12 (T1) and week 26 (T2), of the same individual were included in either training or in test set samples. Variables shown on the loadings plot are numbered in the same order as in Table 1. Detailed PLS-DA model optimization and validation procedures are described in the methods section. * AUC: Area Under the Curve of Receiver Operating Characteristic (ROC); Error: misclassification rate of test set samples. ROC curves and PLSDA permutation test results are shown in the Figure S-1. Figure 4. (A) Area Under the Curve (AUC) of Receiver Operating Characteristic (ROC) curve of the PLS-DA model (2LVs, 16% Error) developed for discriminating winter (162 samples) and spring (106 samples) time points, regardless of the diet. (B) ROC curve of the PLS-DA model (2 LVs, 17% Error) developed for discriminating females (198 samples) from males (88 samples). The models have been validated using an independent test set samples constituting 25% of the total number of samples. Discriminating metabolites are listed in Table 1. Detailed PLS-DA model optimization and validation procedures are described in the methods section. Scores and loadings plot of both models are demonstrated in Figures S-4 and 5. Figure 5. The effect of New Nordic Diet on ∆ Weight Loss (%). (A) Percentage weight loss of NND samples calculated from body weight measured before and after 12 and 26 weeks of the intervention. Scores (B) and loadings (C) plot of the PLS-DA model developed for discriminating NND individuals who lost ≥ 6% of their pre-intervention time body weight from those who gained or lost less than 2% of their initial body weight. Total of 40 high



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Weight Loss samples (corresponding to 31 subjects) and 40 low Weight Loss samples (corresponding to 27 subjects) were included in the PLS-DA modeling. The model was validated using an independent test set samples constituting 30% of the total number of samples. Detailed PLS-DA model optimization and validation procedures are described in the methods section. Variables shown on the loadings plot are numbered in the same order as in Table 1. ROC curves and PLS-DA permutation test results are shown in the Figures S-6.


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Table 1. List of discriminating variables selected in PLS-DA variable selection for classifying diet, NND vs ADD, and high weight loss (↑WL) (loss of ≥6% of pre-intervention time body weight) vs low weight loss (↓WL) (≤2% of pre-intervention time body weight) among subjects who followed NND. Metabolites selected as markers are highlighted with grey background. Relative differences of metabolites between NND and ADD individuals at the intervention study times T1 and T2 were calculated from the raw data as follows: T1DIET = (((mean(NNDT1) – mean(ADDT1)) / mean(ADDT1)) × 100) and T2DIET = (((mean(NNDT2) – mean(ADDT2)) / mean(ADDT2)) × 100). The percentage differences of metabolite levels between two diets are represented with up (↑) and down (↓) arrows. Up (↑) arrow indicate positive change meaning that relative concentration of a metabolite in NND individuals on average was higher than in ADD individuals. Down (↓) arrow indicates exactly the opposite meaning that relative amount of a metabolite was greater in ADD than in NND. Relative differences of metabolites between high weight loss (↑WL) and low weight loss (↓WL) NND individuals were calculated as follows: T1↑WL = (((mean(↑WLT1) – mean(↑WLT0)) / mean(↑WLT0)) × 100) and T1↓WL = (((mean(↓WLT2) – mean(↓WLT0)) / mean(↓WLT0)) × 100) followed by T1WL = T1↑WL - T1↓WL. T2WL has been calculated in the same way as T1WL. The percentage differences of metabolite levels between high weight loss (↑WL) and low weight loss (↓WL) are represented with up (↑) and down (↓) arrows. Up (↑) arrow indicate positive change meaning relative concentration of a metabolite in high weight loss (↑WL) individuals on average was higher than in the low weight loss (↓WL) individuals. Down (↓) arrows indicate the opposite meaning that a metabolite level was greater in ↓WL than in ↑WL. Bigger bold arrows illustrate metabolites where relative differences were ≥20% and smaller arrows

New Nordic Diet versus Average Danish Diet: A ... - ACS Publications

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