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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Nutr.</journal-id>
<journal-title>Frontiers in Nutrition</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Nutr.</abbrev-journal-title>
<issn pub-type="epub">2296-861X</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.3389/fnut.2023.1198531</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Nutrition</subject>
<subj-group>
<subject>Original Research</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>Human blood plasma biomarkers of diet and weight loss among centrally obese subjects in a New Nordic Diet intervention</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname>Trimigno</surname>
<given-names>Alessia</given-names>
</name>
<xref rid="aff1" ref-type="aff"><sup>1</sup></xref>
<uri xlink:href="https://loop.frontiersin.org/people/2254808/overview"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Khakimov</surname>
<given-names>Bekzod</given-names>
</name>
<xref rid="aff1" ref-type="aff"><sup>1</sup></xref>
<uri xlink:href="https://loop.frontiersin.org/people/1620056/overview"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Rasmussen</surname>
<given-names>Morten Arendt</given-names>
</name>
<xref rid="aff1" ref-type="aff"><sup>1</sup></xref>
<xref rid="aff2" ref-type="aff"><sup>2</sup></xref>
<uri xlink:href="https://loop.frontiersin.org/people/661456/overview"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Dragsted</surname>
<given-names>Lars Ove</given-names>
</name>
<xref rid="aff3" ref-type="aff"><sup>3</sup></xref>
<uri xlink:href="https://loop.frontiersin.org/people/687315/overview"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Larsen</surname>
<given-names>Thomas Meinert</given-names>
</name>
<xref rid="aff3" ref-type="aff"><sup>3</sup></xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Astrup</surname>
<given-names>Arne</given-names>
</name>
<xref rid="aff3" ref-type="aff"><sup>3</sup></xref>
<uri xlink:href="https://loop.frontiersin.org/people/1568328/overview"/>
</contrib>
<contrib contrib-type="author" corresp="yes">
<name>
<surname>Engelsen</surname>
<given-names>S&#x00F8;ren Balling</given-names>
</name>
<xref rid="aff1" ref-type="aff"><sup>1</sup></xref>
<xref rid="c001" ref-type="corresp"><sup>&#x002A;</sup></xref>
<uri xlink:href="https://loop.frontiersin.org/people/257830/overview"/>
</contrib>
</contrib-group>
<aff id="aff1"><sup>1</sup><institution>Department of Food Science, Faculty of Science, University of Copenhagen</institution>, <addr-line>Frederiksberg</addr-line>, <country>Denmark</country></aff>
<aff id="aff2"><sup>2</sup><institution>COPSAC (Copenhagen Prospective Studies on Asthma in Childhood), Herlev and Gentofte Hospital, University of Copenhagen</institution>, <addr-line>Copenhagen</addr-line>, <country>Denmark</country></aff>
<aff id="aff3"><sup>3</sup><institution>Department of Nutrition Exercise and Sports, Faculty of Science, University of Copenhagen</institution>, <addr-line>Copenhagen</addr-line>, <country>Denmark</country></aff>
<author-notes>
<fn id="fn0001" fn-type="edited-by"><p>Edited by: Vibeke H. Telle-Hansen, Oslo Metropolitan University, Norway</p></fn>
<fn id="fn0002" fn-type="edited-by"><p>Reviewed by: Elias Carl Olof Bj&#x00F6;rnson, University of Gothenburg, Sweden; Sumei Hu, Beijing Technology and Business University, China</p></fn>
<corresp id="c001">&#x002A;Correspondence: S&#x00F8;ren Balling Engelsen, <email>se@food.ku.dk</email></corresp>
</author-notes>
<pub-date pub-type="epub">
<day>15</day>
<month>06</month>
<year>2023</year>
</pub-date>
<pub-date pub-type="collection">
<year>2023</year>
</pub-date>
<volume>10</volume>
<elocation-id>1198531</elocation-id>
<history>
<date date-type="received">
<day>01</day>
<month>04</month>
<year>2023</year>
</date>
<date date-type="accepted">
<day>15</day>
<month>05</month>
<year>2023</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#x00A9; 2023 Trimigno, Khakimov, Rasmussen, Dragsted, Larsen, Astrup and Engelsen.</copyright-statement>
<copyright-year>2023</copyright-year>
<copyright-holder>Trimigno, Khakimov, Rasmussen, Dragsted, Larsen, Astrup and Engelsen</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/">
<p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</p>
</license>
</permissions>
<abstract>
<sec>
<title>Scope</title>
<p>The New Nordic Diet (NND) has been shown to promote weight loss and lower blood pressure amongst obese people. This study investigates blood plasma metabolite and lipoprotein biomarkers differentiating subjects who followed Average Danish Diet (ADD) or NND. The study also evaluates how the individual response to the diet is reflected in the metabolic differences between NND subjects who lost or maintained their pre-intervention weight.</p>
</sec>
<sec>
<title>Methods</title>
<p>Centrally obese Danes (BMI &#x003E;25) followed NND (90 subjects) or ADD (56 subjects) for 6&#x2009;months. Fasting blood plasma samples, collected at three time-points during the intervention, were screened for metabolites and lipoproteins (LPs) using proton nuclear magnetic resonance spectroscopy. In total, 154 metabolites and 65 lipoproteins were analysed.</p>
</sec>
<sec>
<title>Results</title>
<p>The NND showed a relatively small but significant effect on the plasma metabolome and lipoprotein profiles, with explained variations ranging from 0.6% for lipoproteins to 4.8% for metabolites. A total of 38 metabolites and 11 lipoproteins were found to be affected by the NND. The primary biomarkers differentiating the two diets were found to be HDL-1 cholesterol, apolipoprotein A1, phospholipids, and ketone bodies (3-hydroxybutyric acid, acetone, and acetoacetic acid). The increased levels of ketone bodies detected in the NND group inversely associated with the decrease in diastolic blood pressure of the NND subjects. The study also showed that body weight loss among the NND subjects was weakly associated with plasma levels of citrate.</p>
</sec>
<sec>
<title>Conclusion</title>
<p>The main plasma metabolites associated with NND were acetate, methanol and 3-hydroxybutyrate. The metabolic changes associated with the NND-driven weight loss are mostly pronounced in energy and lipid metabolism.</p>
</sec>
</abstract>
<kwd-group>
<kwd>plasma metabolomics</kwd>
<kwd><sup>1</sup>H NMR</kwd>
<kwd>ketone bodies</kwd>
<kwd>weight loss</kwd>
<kwd>lipoproteins</kwd>
</kwd-group>
<contract-num rid="cn1">4105-00015B</contract-num>
<contract-num rid="cn2">NNF19OC0056246</contract-num>
<contract-sponsor id="cn1">COUNTERSTRIKE project Danish Strategic Research Council/Innovation Foundation Denmark</contract-sponsor>
<contract-sponsor id="cn2">Novo-Nordisk Foundation</contract-sponsor>
<counts>
<fig-count count="7"/>
<table-count count="6"/>
<equation-count count="0"/>
<ref-count count="36"/>
<page-count count="13"/>
<word-count count="7522"/>
</counts>
<custom-meta-wrap>
<custom-meta>
<meta-name>section-at-acceptance</meta-name>
<meta-value>Nutrition and Metabolism</meta-value>
</custom-meta>
</custom-meta-wrap>
</article-meta>
</front>
<body>
<sec id="sec5" sec-type="intro">
<title>Introduction</title>
<p>Human blood plasma contains hundreds of molecules related to metabolism (metabolites) as well as a diversity of lipoproteins, small particles involved in the transport of fats and cholesterol in the aqueous blood streams. Despite a strong push towards homeostasis, the levels of metabolites and lipoproteins in blood change with time depending on multiple factors such as diet, age, health, etc. The importance of diet on human health is well known, but the underlying molecular mechanisms remain elusive. For this reason, blood plasma has been increasingly employed in metabolomics studies to better understand the impact of diet on human metabolism and health (<xref ref-type="bibr" rid="ref1">1</xref>).</p>
<p>In 2004, the new Nordic cuisine was developed by Nordic chefs and promoted as a sustainable, seasonal, and healthy diet. Soon after, in Denmark, the OPUS (optimal well-being, development and health for Danish children through a healthy New Nordic Diet) project was launched (clinical trial NCT01196610 <ext-link xlink:href="https://clinicaltrials.gov/ct2/show/NCT01195610" ext-link-type="uri">https://clinicaltrials.gov/ct2/show/NCT01195610</ext-link>) (<xref ref-type="bibr" rid="ref2">2</xref>). The project had an aim to develop a healthy New Nordic Diet (NND) based on regional production and growth, which could appeal to the public by its taste, healthiness and sustainability, and a low carbon footprint. The NND is characterized by a higher content of organic foods, including whole grains, nuts, berries, fruit and vegetables, fish and seafood, and a lower content of meat. This led to designing a SHOPUS (shop in OPUS) study for better understanding the impact of NND on centrally obese individuals over a period of 6&#x2009;months. The Average Danish Diet (ADD) was used as a control. The recruited subjects could freely choose among products from the assigned diet in a special shop, set up at the University of Copenhagen. Subjects who followed NND displayed a greater weight loss, a larger decrease in blood pressure, a small non-significant drop in LDL cholesterol, and a lower frequency of the metabolic syndrome, when compared to ADD subjects (<xref ref-type="bibr" rid="ref2">2</xref>, <xref ref-type="bibr" rid="ref3">3</xref>). During the intervention, urine and blood samples were collected at three time points, 0, 12 and 26&#x2009;weeks, and analyzed for metabolites. Previous studies using mass spectrometry (MS) have shown differences in the plasma (<xref ref-type="bibr" rid="ref4">4</xref>, <xref ref-type="bibr" rid="ref5">5</xref>), and urine metabolomes (<xref ref-type="bibr" rid="ref6">6</xref>) between subjects who followed NND or ADD. These studies have shown that the NND induces metabolic changes in the blood, mainly related to the higher intakes of whole grain, vegetables, and fish (<xref ref-type="bibr" rid="ref4">4</xref>, <xref ref-type="bibr" rid="ref5">5</xref>). It was also found that plasma concentrations of gut derived metabolites such as vaccenic acid and 3-hydroxybutyric acid were higher in NND subjects who lost more body weight, while lactic acid levels were found to be higher amongst NND subjects who maintained their body weight after intervention (<xref ref-type="bibr" rid="ref5">5</xref>).</p>
<p>This study investigates the human blood plasma metabolites using <sup>1</sup>H NMR spectroscopy. The major advantages of NMR are that it is unbiased and inherently quantitative toward a broad range of metabolite classes present in human blood plasma (<xref ref-type="bibr" rid="ref7 ref8 ref9 ref10">7&#x2013;10</xref>), and that the same NMR analysis can be exploited for robust quantification of the plasma lipoprotein profile (<xref ref-type="bibr" rid="ref7">7</xref>, <xref ref-type="bibr" rid="ref11">11</xref>). The aim of the present study is twofold; (1) to identify differences in plasma metabolites and/or lipoprotein patterns related to the diet (NND versus ADD) and (2) to investigate if metabolic changes related to NND driven weight loss are associated with specific biomarkers and if these are related the dietary intervention. This could provide additional insights into underlying mechanisms behind diet-induced weight loss.</p>
</sec>
<sec id="sec6">
<title>Experimental section</title>
<sec id="sec7">
<title>Study design</title>
<p>During a 6&#x2009;month period between October 2010 and July 2011, a randomized, parallel, and controlled dietary intervention was conducted to investigate the impact of the NND on centrally obese Danes (waist circumferences &#x2265;80&#x2009;cm for women and &#x2265;94&#x2009;cm for men). The number of recruited subjects was 181 (53 males, 128 females), between 20 and 66&#x2009;years old (average: 42&#x2009;years old). Moreover, the participants had one or more of the following characteristics: impaired fasting glucose level &#x003E;5.6&#x2009;mmol/L, plasma triglyceride concentrations &#x2265;1.7&#x2009;mmol/L, HDL-cholesterol concentrations &#x2264;1.03&#x2009;mmol/L for men and &#x2264;1.29&#x2009;mmol/L for women, and systolic/diastolic blood pressure &#x003E;130/85&#x2009;mm Hg. Exclusion criteria included: diagnosed diabetes (both type 1 and 2), total cholesterol &#x2265;9&#x2009;mmol/L, triglyceride concentration &#x2265;3&#x2009;mmol/L, familial hypercholesterolemia, food allergies contrasting with the intervention, pregnancy, or lactation. In addition, subjects who lost &#x003E;2&#x2009;kg in the preceding 2&#x2009;months were excluded.</p>
<p>The control to the NND was ADD, and the subjects were randomly divided between the two diets in a 3:2 ratio using simple block randomization, with a stratification done according to age (&#x003C;45 or&#x2009;&#x2265;&#x2009;45&#x2009;years), BMI (&#x003C;33 or&#x2009;&#x2265;&#x2009;33&#x2009;kg/m<sup>2</sup>), and whether they were enrolled as individuals or couples. The two diets differed in the composition of 15 food groups that were specific for NND, and in macronutrients, including total intake of proteins, carbohydrates, and fats. NND was characterized by a higher fish, whole grain, fruit, and vegetable consumption and by lower intakes of terrestrial meat, compared to ADD. A detailed description of the two diets is given in a previous article (<xref ref-type="bibr" rid="ref2">2</xref>).</p>
<p>An overview of the study design is given in <xref rid="fig1" ref-type="fig">Figure 1</xref>. Fasting blood samples and clinical parameters were collected at T0 (week 0, at the beginning of the trial), T1 (week 12), and T2 (week 26). The measured parameters were body weight, waist and hip circumference, sagittal diameter, blood pressure, and body composition, measured by dual-energy X-ray absorptiometry (Lunar Radiation Co). For more details see the work by Poulsen and colleagues (<xref ref-type="bibr" rid="ref2">2</xref>). The EDTA (ethylenediaminetetraacetic acid treated) blood plasma samples were prepared and stored at &#x2212;80&#x00B0;C until NMR analysis at the University of Copenhagen. A total of 146 subjects completed the intervention study including 90 NND (60 females, 30 males) and 56 ADD (40 females and 16 males) which resulted in a total of 438 plasma samples collected over the three time points.</p>
<fig position="float" id="fig1">
<label>Figure 1</label>
<caption>
<p>An overview of the study design. A total of 146 subject were included: 90 in the NND group (60 females and 30 males) and 56 in the ADD group (40 females and 16 males). Samples were collected at T0 (week 0), T1 (after 12&#x2009;weeks) and T2 (after 26&#x2009;weeks of intervention). A total of 438 plasma samples were collected over the three time points. The NMR spectra (NOESY and CPMG; see under data acquisition) were converted into a metabolite table including 65 SS, 33 SUS, and 56 BINS after processing the spectral datasets in SigMa. The lipoprotein data include the absolute concentrations (mg/dL) of 65 lipoproteins predicted from the NOESY spectra. The clinical data include different anthropometric and clinical parameters variables collected during the study, including bodyweight, height, age, and blood parameters. Dietary data included 21 variables from food diaries at T1 and T2, and additional 59 variables from shop data at T2.</p>
</caption>
<graphic xlink:href="fnut-10-1198531-g001.tif"/>
</fig>
<p>The SHOPUS study has been approved by the Regional Ethics Committee of Greater Copenhagen and Frederiksberg (H-3-2010-058) and by the Danish Data Protection Agency (2007-54-0269).</p>
</sec>
<sec id="sec8">
<title>Sample preparation and <sup>1</sup>H NMR data acquisition</title>
<p>Plasma samples were thawed on ice for 60&#x2009;min. A 300&#x2009;&#x03BC;L plasma aliquot was then mixed with 300&#x2009;&#x03BC;L phosphate buffer (pH 7.4), containing 0.8&#x2009;mg&#x2009;mL<sup>&#x2212;1</sup> trimethylsilylpropionate (TSP) in 2&#x2009;mL Eppendorf tubes (Eppendorf, Hamburg, Germany), and then transferred to 5&#x2009;mm NMR tubes (Bruker Biospin Gmbh, Rheinstetten, Germany) (<xref ref-type="bibr" rid="ref9">9</xref>). NMR analysis was performed on a Bruker Avance III 600 spectrometer (Bruker Biospin Gmbh, Rheinstetten, Germany) operating at a Larmor frequency of 600.13&#x2009;MHz for protons, equipped with a double tuned cryo-probe (TCI) set for 5&#x2009;mm sample tubes and a cooled autosampler (SampleJet). One-dimensional proton nuclear magnetic resonance (<sup>1</sup>H NMR) spectra were acquired from all plasma samples using both the Carr&#x2013;Purcell Meiboom&#x2013;Gill (CPMG) experiment and the NOESY-presat pulse sequences from Bruker&#x2019;s library. The former suppresses, apart from water resonance, also resonances from large molecules (i.e., proteins), and can thus better facilitate the identification of small molecules. The latter, instead, only suppresses water resonance, providing a more unbiased quantitative picture of the sample composition. All experiments were performed at 310&#x2009;K with a fixed receiver gain (RG) of 40.3. A total of 64 scans were acquired and the measured free induction decays (FID) were collected into 128k data points. The automation program controlling sample measurements included the acquisition routines for locking, automatic tuning and matching, shimming, pulse calibration, and optimized pre-saturation power for each sample, as well as automatic data processing including Fourier transformation (FT) of FID, with a Lorentzian line-broadening of 0.3&#x2009;Hz before FT, phasing, and baseline correction.</p>
</sec>
<sec id="sec9">
<title><sup>1</sup>H NMR data processing</title>
<p>Raw <sup>1</sup>H NMR spectra were converted to a metabolite concentration table using the SigMa software (<xref ref-type="bibr" rid="ref12">12</xref>). The SigMa based processing included reference alignment (towards the TSP signal at 0.0&#x2009;ppm) and pre-alignment of larger spectral regions using the <italic>icoshift</italic> method (<xref ref-type="bibr" rid="ref13">13</xref>), followed by interval recognition where the entire spectra are divided into smaller regions of signature signals (SS) of known human blood metabolites, signals of unknown spin systems (SUS), and BINS representing complex regions containing unresolved signals of more than one metabolite. After interval recognition, SigMa quantified SS and SUS variables using a one-component multivariate curve resolution (MCR) based modelling (<xref ref-type="bibr" rid="ref14">14</xref>). BINS are instead quantified using an integration approach, summing all data points of a given interval for each sample. As a unique feature, <sup>1</sup>H NMR spectra of plasma also contain information about the blood lipoproteins (<xref ref-type="bibr" rid="ref7">7</xref>). Extraction of this information typically relies on prediction models based on partial least squares regression of the spectral region, 1.4&#x2013;0.6&#x2009;ppm, to reference lipoprotein values stemming from ultracentrifugation (<xref ref-type="bibr" rid="ref9">9</xref>). In this work a lipoprotein dataset consisting of absolute concentrations of 65 LPs was generated from the NOESY 1H NMR spectra using previously described lipoprotein prediction models (<xref ref-type="bibr" rid="ref9">9</xref>, <xref ref-type="bibr" rid="ref15">15</xref>) (<xref rid="fig2" ref-type="fig">Figure 2</xref>). The variables include concentrations of the main lipoprotein classes (VLDL, IDL, LDL, HDL), and subclasses of these (LDL-1 to LDL-6, and HDL-1 to HDL-3, given in increasing density and decreasing size). For each subclass, cholesterol, free cholesterol, triglycerides, phospholipids, and apolipoproteins A1 and B were quantified. More detailed information on the LPs is reported in <xref ref-type="supplementary-material" rid="SM1">Supplementary Table S1</xref>.</p>
<fig position="float" id="fig2">
<label>Figure 2</label>
<caption>
<p>A representative spectrum <bold>(C)</bold> of the <sup>1</sup>H NMR spectra (NOESY) acquired on the NND subjects. Annotated signals represent some of SigMa based quantified blood plasma metabolites. The <bold>(A)</bold> aliphatic (3.1&#x2013;0.8 ppm) and <bold>(B)</bold> aromatic (8.5&#x2013;6 ppm) regions are also represented zoomed-in. The full list of annotated metabolites, with relative signal chemical shift (ppm) and multiplicity is reported in <xref ref-type="supplementary-material" rid="SM1">Supplementary Table S5</xref>.</p>
</caption>
<graphic xlink:href="fnut-10-1198531-g002.tif"/>
</fig>
</sec>
<sec id="sec10">
<title>Data analysis</title>
<p>The proton NMR spectral ensembles (NOESY and CPMG spectra) were firstly resolved into metabolite tables and LP tables for the different time points as described above. The datasets representing <bold>X</bold><sub>T1</sub> (T1; 12&#x2009;weeks) and <bold>X</bold><sub>T2</sub> (T2; 26&#x2009;weeks) were further corrected for baseline (T0 time values) as follows: <bold>&#x0394;X</bold><sub>T1</sub>&#x2009;=&#x2009;<bold>X</bold><sub>T1</sub>&#x2013;<bold>X</bold><sub>T0</sub> and <bold>&#x0394;X</bold><sub>T2</sub>&#x2009;=&#x2009;<bold>X</bold><sub>T2</sub>&#x2013;<bold>X</bold><sub>T0</sub>. This procedure yielded a total of six datasets, <bold>&#x0394;X</bold><sub>T1_NOESY,</sub> <bold>&#x0394;X</bold><sub>T1_CPMG,</sub> <bold>&#x0394;X</bold><sub>T1_LP,</sub> <bold>&#x0394;X</bold><sub>T2_NOESY,</sub> <bold>&#x0394;X</bold><sub>T2_CPMG,</sub> and <bold>&#x0394;X</bold><sub>T2_LP</sub> to be scrutinized for evaluating metabolic differences related to the diet, NND vs. ADD, and metabolic changes related to weight loss of the NND subjects. The weight loss factor was defined as described before (<xref ref-type="bibr" rid="ref5">5</xref>), by stratifying the NND subjects into two groups: <italic>weight losers</italic> (<italic>n</italic>&#x2009;=&#x2009;62) with at least 6% weight loss according to their body weight at inclusion and <italic>weight maintainers</italic> (<italic>n</italic>&#x2009;=&#x2009;52) who lost &#x2264;2% of their pre-intervention body weight. ADD subjects showed very limited weight change, and thus were excluded from this investigation (<xref ref-type="bibr" rid="ref2">2</xref>). For NND participants, consistent <italic>weight losers</italic> (&#x003E;6%) and consistent <italic>weight maintainers</italic> (&#x003C;2%) both at T1 and T2 were limited to only 22 and 19 subjects, respectively (<xref rid="fig3" ref-type="fig">Figure 3</xref>). Additional data used for stratification of subjects included dietary recordings, anthropometric and clinical parameters which have been published elsewhere (<xref ref-type="bibr" rid="ref2">2</xref>). All datasets were mean centered and scaled to unit standard deviation prior to multivariate data analysis.</p>
<fig position="float" id="fig3">
<label>Figure 3</label>
<caption>
<p>Delta body weight of subjects who were assigned to the NND diet. A total of 62 subjects were selected as BW losers (&#x003E;6%), 24 from T1 time point (&#x0394;BW&#x2009;=&#x2009;T1&#x2013;T0) and 38 from T2 time point (&#x0394;BW&#x2009;=&#x2009;T2&#x2013;T0) (22 in common between the two time points). Likewise, 52 individuals, 28 at T1 and 24 at T2 (19 in common), were identified as BW maintainers (&#x003C;2%) or even gained weight.</p>
</caption>
<graphic xlink:href="fnut-10-1198531-g003.tif"/>
</fig>
<p>Principal component analysis (PCA) (<xref ref-type="bibr" rid="ref16">16</xref>) was used to explore the datasets, for outlier detection as well as to investigate variations related to diet and weight loss. ANOVA-simultaneous component analysis (ASCA) (<xref ref-type="bibr" rid="ref17">17</xref>) with permutation testing (<italic>n</italic><sub>perm.</sub> = 1,000) was applied to quantify variations explained by the study design factors, diet (NND vs. ADD) and weight loss (NND weight losers vs. NND weight maintainers). Partial least squares-discriminant analysis (PLS-DA) (<xref ref-type="bibr" rid="ref18">18</xref>) was employed to identify metabolite patterns that discriminate between the two diet groups or weight losers vs. weight maintainers. Variable selection (variables selected 70% of the times by the model) and validation of the PLS-DA models were performed as previously described (<xref ref-type="bibr" rid="ref5">5</xref>). One-way ANOVA using false discovery rate (FDR, 5%) correction was employed to identify individual metabolites or lipoproteins that were different between the two diets or between the weight losers and weight maintainers. Data analysis was performed in MATLAB R2015b (The Mathworks Inc., Natick, MA) using customized scripts written by the authors and additionally using PLS Toolbox 8.7.1 (Eigenvector Research, Manson, United States). Scripts and data matrices are available upon request.</p>
</sec>
</sec>
<sec id="sec11" sec-type="results">
<title>Results</title>
<sec id="sec12">
<title>The SHOPUS cohort</title>
<p>The averages and standard deviations of the main parameters, relevant to this study, of the individuals following the two diets, NND and ADD, are presented in <xref rid="tab1" ref-type="table">Table 1</xref>. The NND group lost more weight, reduced their diastolic (DBP), systolic (SBP) blood pressure, and homeostatic model assessment for insulin resistance (HOMA-IR) factor during the intervention. The reduction in blood pressure was partially related to the greater weight loss in NND, but also to the specific diet pattern of NND (<xref ref-type="bibr" rid="ref2">2</xref>). Further information on the effect of NND, compared to ADD, on subjects&#x2019; clinical parameters including fat mass, waist and hip circumference, fasting insulin and glucose, and CRP can be found in Poulsen et al. (<xref ref-type="bibr" rid="ref2">2</xref>).</p>
<table-wrap position="float" id="tab1">
<label>Table 1</label>
<caption>
<p>An overview of the two dietary groups, New Nordic Diet (NND), and Average Danish Diet (ADD).</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th/>
<th align="center" valign="top">NND</th>
<th align="center" valign="top">ADD</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="top">Age (y)<xref rid="tfn1" ref-type="table-fn"><sup>a</sup></xref></td>
<td align="center" valign="top">44.15&#x2009;&#x00B1;&#x2009;13.1</td>
<td align="center" valign="top">40.8&#x2009;&#x00B1;&#x2009;13.2</td>
</tr>
<tr>
<td align="left" valign="top">Males (<italic>n</italic>)</td>
<td align="center" valign="top">30</td>
<td align="center" valign="top">16</td>
</tr>
<tr>
<td align="left" valign="top">Females (<italic>n</italic>)</td>
<td align="center" valign="top">60</td>
<td align="center" valign="top">40</td>
</tr>
<tr>
<td align="left" valign="top">Starting BW at T0 (kg)<xref rid="tfn1" ref-type="table-fn"><sup>a</sup></xref></td>
<td align="center" valign="top">91.2&#x2009;&#x00B1;&#x2009;16.2</td>
<td align="center" valign="top">90.1&#x2009;&#x00B1;&#x2009;19.3</td>
</tr>
<tr>
<td align="left" valign="top">&#x0394;BW at T1 (kg)<xref rid="tfn2" ref-type="table-fn"><sup>b</sup></xref></td>
<td align="center" valign="top">&#x2212;3.24&#x2009;&#x00B1;&#x2009;0.31</td>
<td align="center" valign="top">&#x2212;1.48&#x2009;&#x00B1;&#x2009;0.29</td>
</tr>
<tr>
<td align="left" valign="top">&#x0394;BW at T2 (kg)<xref rid="tfn2" ref-type="table-fn"><sup>b</sup></xref></td>
<td align="center" valign="top">&#x2212;4.77&#x2009;&#x00B1;&#x2009;0.48</td>
<td align="center" valign="top">&#x2212;1.46&#x2009;&#x00B1;&#x2009;0.44</td>
</tr>
<tr>
<td align="left" valign="top">&#x0394;HOMA-IR at T2<xref rid="tfn2" ref-type="table-fn"><sup>b</sup></xref></td>
<td align="center" valign="top">&#x2212;3.08&#x2009;&#x00B1;&#x2009;0.13</td>
<td align="center" valign="top">0.10&#x2009;&#x00B1;&#x2009;0.11</td>
</tr>
<tr>
<td align="left" valign="top">&#x0394;DBP at T2 (mm Hg)<xref rid="tfn2" ref-type="table-fn"><sup>b</sup></xref></td>
<td align="center" valign="top">&#x2212;0.52&#x2009;&#x00B1;&#x2009;0.79</td>
<td align="center" valign="top">&#x2212;0.08&#x2009;&#x00B1;&#x2009;0.92</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn id="tfn1">
<label>a</label>
<p>Mean&#x2009;&#x00B1;&#x2009;SD (standard deviation).</p>
</fn>
<fn id="tfn2">
<label>b</label>
<p>Mean&#x2009;&#x00B1;&#x2009;SEM (standard error of the mean).</p>
</fn>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="sec13">
<title>Description of the NMR data</title>
<p>A representative <sup>1</sup>H NMR spectrum of human blood plasma is shown in <xref rid="fig2" ref-type="fig">Figure 2</xref>. The spectrum is largely dominated by <sup>1</sup>H resonances of methyl and methylene groups corresponding to fatty acids and lipoproteins (1.4&#x2013;0.6&#x2009;ppm). Overall, the most abundant plasma metabolites included cholesterol-C18 (CH<sub>3</sub>, <italic>&#x03B4;</italic> 0.70, s), lactic acid (CH<sub>3</sub>, <italic>&#x03B4;</italic> 1.35, d, J 6.93&#x2009;Hz; CH, <italic>&#x03B4;</italic> 4.14, q, J 6.93&#x2009;Hz), alanine (CH<sub>3</sub> <italic>&#x03B4;</italic> 1.49, d, J 7.14&#x2009;Hz), valine (CH<sub>3</sub> <italic>&#x03B4;</italic> 0.98, d, CH<sub>3</sub> <italic>&#x03B4;</italic> 1.02, d, J 7.07&#x2009;Hz) and glucose (CH <italic>&#x03B4;</italic> 3.20&#x2013;4.00, m, CH <italic>&#x03B4;</italic> 5.27, d, 3.8&#x2009;Hz). Using Signature Mapping (SigMa) software the <sup>1</sup>H NMR data was converted into a metabolite table. The resulting table consisted of 154 variables: 65 were SS of known plasma metabolites, 33 SUS and 56 BINS (<xref ref-type="supplementary-material" rid="SM1">Supplementary Table S5</xref>). Both CPMG and NOESY spectra were processed in the same way, and both contained 154 metabolite variables. Furthermore, the same NMR data (only NOESY) were used for prediction of the lipoprotein profiles which generated two additional data sets, <bold>&#x0394;X</bold><sub>T1_LP</sub> and <bold>&#x0394;X</bold><sub>T2_LP</sub>, consisting of absolute concentrations of 65 lipoproteins (<xref ref-type="bibr" rid="ref9">9</xref>) (<xref ref-type="supplementary-material" rid="SM1">Supplementary Table S1</xref>).</p>
</sec>
<sec id="sec14">
<title>Diet related metabolic differences in blood plasma (NND vs. ADD)</title>
<p>Principal component analysis of the metabolite and lipoprotein datasets revealed a weak pattern related to the diet (<xref rid="fig4" ref-type="fig">Figure 4</xref>). This was subsequently confirmed by ASCA which revealed that all datasets at both T1 and T2 (except &#x0394;X<sub>T1_LP</sub>) showed a significant effect of diet with explained variations ranging from 0.6 to 4.8% (<xref ref-type="supplementary-material" rid="SM1">Supplementary Table S6</xref>). The NOESY dataset at T2 (<bold>&#x0394;X</bold><sub>T2_NOESY</sub>) showed the largest fraction (4.8%) of the variation explained by diet (<italic>p</italic>&#x2009;&#x003C;&#x2009;0.0001). Further investigation of the effect of the diet was performed using PLS-DA which allowed to find the pattern of metabolites discriminating NDD and ADD subjects using above mentioned datasets. Classification performances of the PLS-DA models developed on the six datasets after variable selection are summarized in <xref rid="tab2" ref-type="table">Table 2</xref>. The best performing PLS-DA model was developed on &#x0394;X<sub>T1_NOESY</sub> dataset (AUC&#x2009;=&#x2009;0.80, error&#x2009;=&#x2009;28%) and included 75 variables to be important markers of the diet, the second best was developed on &#x0394;X<sub>T2_NOESY</sub> dataset (AUC&#x2009;=&#x2009;0.74, error&#x2009;=&#x2009;28%, 115 variables used). The consistently selected 59 variables between these two models included 22 plasma metabolites (see <xref rid="tab3" ref-type="table">Table 3</xref>) such as amino acids (lysine, phenylalanine, tyrosine, serine, asparagine), organic acids (acetic acid, lactic acid, citric acid, fumaric acid, succinic acid), and ketone bodies (acetone, acetoacetate and 3-hydroxybutyrate) (see <xref rid="fig5" ref-type="fig">Figure 5</xref>).</p>
<fig position="float" id="fig4">
<label>Figure 4</label>
<caption>
<p>PCA biplots of the six matrices used for data analysis: &#x0394;X<sub>T1_NOESY</sub>, &#x0394;X<sub>T1_CPMG</sub>, &#x0394;X<sub>T1_LP</sub>, &#x0394;X<sub>T2_NOESY</sub>, &#x0394;X<sub>T2_CPMG</sub>, and &#x0394;X<sub>T2_LP</sub>, colored by diet class (NND in brown, ADD in light blue) and represented with either triangles (males) or circles (females) according to sex. Loadings are represented by grey squares, and the most relevant are annotated.</p>
</caption>
<graphic xlink:href="fnut-10-1198531-g004.tif"/>
</fig>
<table-wrap position="float" id="tab2">
<label>Table 2</label>
<caption>
<p>Results from PLS-DA after variable selection.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top">Matrix</th>
<th align="center" valign="top"><italic>N</italic>. variables used</th>
<th align="center" valign="top">N. LVs</th>
<th align="center" valign="top">Prediction AUC</th>
<th align="center" valign="top">Prediction error</th>
<th align="center" valign="top">Training AUC (CV)</th>
<th align="center" valign="top">Training error (CV)</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="bottom">&#x0394;X<sub>T1_NOESY</sub></td>
<td align="center" valign="bottom">75</td>
<td align="center" valign="bottom">4</td>
<td align="char" valign="bottom" char=".">0.80</td>
<td align="char" valign="bottom" char=".">0.28</td>
<td align="char" valign="bottom" char=".">0.90</td>
<td align="char" valign="bottom" char=".">0.18</td>
</tr>
<tr>
<td align="left" valign="bottom">&#x0394;X<sub>T2_NOESY</sub></td>
<td align="center" valign="bottom">115</td>
<td align="center" valign="bottom">2</td>
<td align="char" valign="bottom" char=".">0.74</td>
<td align="char" valign="bottom" char=".">0.28</td>
<td align="char" valign="bottom" char=".">0.83</td>
<td align="char" valign="bottom" char=".">0.27</td>
</tr>
<tr>
<td align="left" valign="bottom">&#x0394;X<sub>T1_CPMG</sub></td>
<td align="center" valign="bottom">55</td>
<td align="center" valign="bottom">2</td>
<td align="char" valign="bottom" char=".">0.74</td>
<td align="char" valign="bottom" char=".">0.33</td>
<td align="char" valign="bottom" char=".">0.84</td>
<td align="char" valign="bottom" char=".">0.17</td>
</tr>
<tr>
<td align="left" valign="bottom">&#x0394;X<sub>T2_CPMG</sub></td>
<td align="center" valign="bottom">89</td>
<td align="center" valign="bottom">3</td>
<td align="char" valign="bottom" char=".">0.70</td>
<td align="char" valign="bottom" char=".">0.38</td>
<td align="char" valign="bottom" char=".">0.87</td>
<td align="char" valign="bottom" char=".">0.18</td>
</tr>
<tr>
<td align="left" valign="bottom">&#x0394;X<sub>T1_LP</sub></td>
<td align="center" valign="bottom">32</td>
<td align="center" valign="bottom">1</td>
<td align="char" valign="bottom" char=".">0.51</td>
<td align="char" valign="bottom" char=".">0.55</td>
<td align="char" valign="bottom" char=".">0.37</td>
<td align="char" valign="bottom" char=".">0.57</td>
</tr>
<tr>
<td align="left" valign="bottom">&#x0394;X<sub>T2_LP</sub></td>
<td align="center" valign="bottom">38</td>
<td align="center" valign="bottom">4</td>
<td align="char" valign="bottom" char=".">0.64</td>
<td align="char" valign="bottom" char=".">0.41</td>
<td align="char" valign="bottom" char=".">0.75</td>
<td align="char" valign="bottom" char=".">0.32</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>The number of variables and latent variables used is reported for each matrix, together with the AUC and error values for the training set (88 subjects) and prediction sets (58 subjects). Cross-validation using venetian blinds, with 8 data splits and one sample per blind, was used.</p>
</table-wrap-foot>
</table-wrap>
<table-wrap position="float" id="tab3">
<label>Table 3</label>
<caption>
<p>A list of metabolite and lipoprotein variables found to be associated with the diet effect by both PLS-DA and one-way ANOVA.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top">Variable name</th>
<th align="center" valign="top">Class</th>
<th align="center" valign="top"><italic>p</italic>-value</th>
<th align="center" valign="top">Effect size</th>
<th align="center" valign="top">Median NND % variation</th>
<th align="center" valign="top">Median ADD % variation</th>
<th align="center" valign="top">Matrix</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="bottom">Methanol</td>
<td align="center" valign="top">OH</td>
<td align="center" valign="bottom">6.56E&#x2212;04</td>
<td align="char" valign="bottom" char=".">12.43</td>
<td align="char" valign="bottom" char=".">1.06</td>
<td align="char" valign="bottom" char=".">&#x2212;1.60</td>
<td align="center" valign="bottom">&#x0394;X<sub>T1_NOESY</sub></td>
</tr>
<tr>
<td align="left" valign="bottom">3-hydroxybutyrate</td>
<td align="center" valign="top">KB</td>
<td align="center" valign="bottom">1.83E&#x2212;03</td>
<td align="char" valign="bottom" char=".">8.62</td>
<td align="char" valign="bottom" char=".">1.34</td>
<td align="char" valign="bottom" char=".">&#x2212;0.67</td>
<td align="center" valign="bottom">&#x0394;X<sub>T2_NOESY</sub></td>
</tr>
<tr>
<td align="left" valign="bottom">Acetate</td>
<td align="center" valign="top">OA</td>
<td align="center" valign="bottom">6.62E&#x2212;07</td>
<td align="char" valign="bottom" char=".">21.17</td>
<td align="char" valign="bottom" char=".">1.06</td>
<td align="char" valign="bottom" char=".">&#x2212;1.83</td>
<td align="center" valign="bottom">&#x0394;X<sub>T2_NOESY</sub></td>
</tr>
<tr>
<td align="left" valign="bottom">Acetoacetate</td>
<td align="center" valign="top">KB</td>
<td align="center" valign="bottom">5.41E&#x2212;03</td>
<td align="char" valign="bottom" char=".">6.59</td>
<td align="char" valign="bottom" char=".">0.87</td>
<td align="char" valign="bottom" char=".">&#x2212;1.67</td>
<td align="center" valign="bottom">&#x0394;X<sub>T2_NOESY</sub></td>
</tr>
<tr>
<td align="left" valign="bottom">Betaine</td>
<td align="center" valign="top">AA</td>
<td align="center" valign="bottom">3.31E&#x2212;03</td>
<td align="char" valign="bottom" char=".">7.34</td>
<td align="char" valign="bottom" char=".">6.78</td>
<td align="char" valign="bottom" char=".">&#x2212;2.94</td>
<td align="center" valign="bottom">&#x0394;X<sub>T2_NOESY</sub></td>
</tr>
<tr>
<td align="left" valign="bottom">Cis-aconitate</td>
<td align="center" valign="top">OA</td>
<td align="center" valign="bottom">3.68E&#x2212;04</td>
<td align="char" valign="bottom" char=".">11.22</td>
<td align="char" valign="bottom" char=".">0.51</td>
<td align="char" valign="bottom" char=".">&#x2212;0.92</td>
<td align="center" valign="bottom">&#x0394;X<sub>T2_NOESY</sub></td>
</tr>
<tr>
<td align="left" valign="bottom">Citrate</td>
<td align="center" valign="top">AA</td>
<td align="center" valign="bottom">1.01E&#x2212;02</td>
<td align="char" valign="bottom" char=".">5.59</td>
<td align="char" valign="bottom" char=".">4.55</td>
<td align="char" valign="bottom" char=".">&#x2212;4.29</td>
<td align="center" valign="bottom">&#x0394;X<sub>T2_NOESY</sub></td>
</tr>
<tr>
<td align="left" valign="bottom">Creatine</td>
<td align="center" valign="top">AA</td>
<td align="center" valign="bottom">4.41E&#x2212;02</td>
<td align="char" valign="bottom" char=".">3.59</td>
<td align="char" valign="bottom" char=".">1.09</td>
<td align="char" valign="bottom" char=".">&#x2212;0.89</td>
<td align="center" valign="bottom">&#x0394;X<sub>T2_NOESY</sub></td>
</tr>
<tr>
<td align="left" valign="bottom">Dimethylamine</td>
<td align="center" valign="top">ONC</td>
<td align="center" valign="bottom">1.16E&#x2212;03</td>
<td align="char" valign="bottom" char=".">9.35</td>
<td align="char" valign="bottom" char=".">4.29</td>
<td align="char" valign="bottom" char=".">&#x2212;3.85</td>
<td align="center" valign="bottom">&#x0394;X<sub>T2_NOESY</sub></td>
</tr>
<tr>
<td align="left" valign="bottom">Fumarate</td>
<td align="center" valign="top">OA</td>
<td align="center" valign="bottom">2.80E&#x2212;04</td>
<td align="char" valign="bottom" char=".">11.62</td>
<td align="char" valign="bottom" char=".">1.54</td>
<td align="char" valign="bottom" char=".">&#x2212;2.09</td>
<td align="center" valign="bottom">&#x0394;X<sub>T2_NOESY</sub></td>
</tr>
<tr>
<td align="left" valign="bottom">Galactose</td>
<td align="center" valign="top">SG</td>
<td align="center" valign="bottom">1.94E&#x2212;03</td>
<td align="char" valign="bottom" char=".">8.46</td>
<td align="char" valign="bottom" char=".">3.42</td>
<td align="char" valign="bottom" char=".">&#x2212;3.58</td>
<td align="center" valign="bottom">&#x0394;X<sub>T2_NOESY</sub></td>
</tr>
<tr>
<td align="left" valign="bottom">Glucose</td>
<td align="center" valign="top">SG</td>
<td align="center" valign="bottom">3.00E&#x2212;03</td>
<td align="char" valign="bottom" char=".">7.51</td>
<td align="char" valign="bottom" char=".">3.32</td>
<td align="char" valign="bottom" char=".">&#x2212;3.79</td>
<td align="center" valign="bottom">&#x0394;X<sub>T2_NOESY</sub></td>
</tr>
<tr>
<td align="left" valign="bottom">Glutamine</td>
<td align="center" valign="top">AA</td>
<td align="center" valign="bottom">8.67E&#x2212;03</td>
<td align="char" valign="bottom" char=".">5.85</td>
<td align="char" valign="bottom" char=".">5.16</td>
<td align="char" valign="bottom" char=".">&#x2212;2.03</td>
<td align="center" valign="bottom">&#x0394;X<sub>T2_NOESY</sub></td>
</tr>
<tr>
<td align="left" valign="bottom">Leucine</td>
<td align="center" valign="top">AA</td>
<td align="center" valign="bottom">1.93E&#x2212;02</td>
<td align="char" valign="bottom" char=".">4.70</td>
<td align="char" valign="bottom" char=".">0.22</td>
<td align="char" valign="bottom" char=".">&#x2212;0.51</td>
<td align="center" valign="bottom">&#x0394;X<sub>T2_NOESY</sub></td>
</tr>
<tr>
<td align="left" valign="bottom">Lysine</td>
<td align="center" valign="top">AA</td>
<td align="center" valign="bottom">6.96E&#x2212;04</td>
<td align="char" valign="bottom" char=".">10.13</td>
<td align="char" valign="bottom" char=".">4.73</td>
<td align="char" valign="bottom" char=".">&#x2212;4.76</td>
<td align="center" valign="bottom">&#x0394;X<sub>T2_NOESY</sub></td>
</tr>
<tr>
<td align="left" valign="bottom">Methanol</td>
<td align="center" valign="top">OH</td>
<td align="center" valign="bottom">6.62E&#x2212;07</td>
<td align="char" valign="bottom" char=".">20.62</td>
<td align="char" valign="bottom" char=".">0.86</td>
<td align="char" valign="bottom" char=".">&#x2212;1.59</td>
<td align="center" valign="bottom">&#x0394;X<sub>T2_NOESY</sub></td>
</tr>
<tr>
<td align="left" valign="bottom">Methine LP</td>
<td align="center" valign="top">LP</td>
<td align="center" valign="bottom">2.49E&#x2212;02</td>
<td align="char" valign="bottom" char=".">4.35</td>
<td align="char" valign="bottom" char=".">6.17</td>
<td align="char" valign="bottom" char=".">&#x2212;1.02</td>
<td align="center" valign="bottom">&#x0394;X<sub>T2_NOESY</sub></td>
</tr>
<tr>
<td align="left" valign="bottom">Methyl LP</td>
<td align="center" valign="top">LP</td>
<td align="center" valign="bottom">3.38E&#x2212;03</td>
<td align="char" valign="bottom" char=".">7.28</td>
<td align="char" valign="bottom" char=".">5.22</td>
<td align="char" valign="bottom" char=".">&#x2212;4.61</td>
<td align="center" valign="bottom">&#x0394;X<sub>T2_NOESY</sub></td>
</tr>
<tr>
<td align="left" valign="bottom">Phenylalanine</td>
<td align="center" valign="top">AA</td>
<td align="center" valign="bottom">2.23E&#x2212;02</td>
<td align="char" valign="bottom" char=".">4.50</td>
<td align="char" valign="bottom" char=".">3.16</td>
<td align="char" valign="bottom" char=".">&#x2212;1.74</td>
<td align="center" valign="bottom">&#x0394;X<sub>T2_NOESY</sub></td>
</tr>
<tr>
<td align="left" valign="bottom">Succinate</td>
<td align="center" valign="top">OA</td>
<td align="center" valign="bottom">3.00E&#x2212;03</td>
<td align="char" valign="bottom" char=".">7.51</td>
<td align="char" valign="bottom" char=".">1.11</td>
<td align="char" valign="bottom" char=".">&#x2212;2.11</td>
<td align="center" valign="bottom">&#x0394;X<sub>T2_NOESY</sub></td>
</tr>
<tr>
<td align="left" valign="bottom">TMAO</td>
<td align="center" valign="top">ONC</td>
<td align="center" valign="bottom">1.75E&#x2212;03</td>
<td align="char" valign="bottom" char=".">8.76</td>
<td align="char" valign="bottom" char=".">4.97</td>
<td align="char" valign="bottom" char=".">&#x2212;3.44</td>
<td align="center" valign="bottom">&#x0394;X<sub>T2_NOESY</sub></td>
</tr>
<tr>
<td align="left" valign="bottom">Acetone</td>
<td align="center" valign="top">KB</td>
<td align="center" valign="bottom">3.64E&#x2212;02</td>
<td align="char" valign="bottom" char=".">7.33</td>
<td align="char" valign="bottom" char=".">21.38</td>
<td align="char" valign="bottom" char=".">&#x2212;10.94</td>
<td align="center" valign="bottom">&#x0394;X<sub>T1_CPMG</sub></td>
</tr>
<tr>
<td align="left" valign="bottom">Glutamine</td>
<td align="center" valign="top">AA</td>
<td align="center" valign="bottom">4.52E&#x2212;02</td>
<td align="char" valign="bottom" char=".">6.81</td>
<td align="char" valign="bottom" char=".">20.26</td>
<td align="char" valign="bottom" char=".">19.94</td>
<td align="center" valign="bottom">&#x0394;X<sub>T1_CPMG</sub></td>
</tr>
<tr>
<td align="left" valign="bottom">3-hydroxybutyrate</td>
<td align="center" valign="top">KB</td>
<td align="center" valign="bottom">2.93E&#x2212;04</td>
<td align="char" valign="bottom" char=".">13.45</td>
<td align="char" valign="bottom" char=".">1.31</td>
<td align="char" valign="bottom" char=".">&#x2212;2.89</td>
<td align="center" valign="bottom">&#x0394;X<sub>T2_CPMG</sub></td>
</tr>
<tr>
<td align="left" valign="bottom">Acetate</td>
<td align="center" valign="top">OA</td>
<td align="center" valign="bottom">5.55E&#x2212;07</td>
<td align="char" valign="bottom" char=".">21.55</td>
<td align="char" valign="bottom" char=".">1.03</td>
<td align="char" valign="bottom" char=".">&#x2212;1.82</td>
<td align="center" valign="bottom">&#x0394;X<sub>T2_CPMG</sub></td>
</tr>
<tr>
<td align="left" valign="bottom">Acetoacetate</td>
<td align="center" valign="top">KB</td>
<td align="center" valign="bottom">6.89E&#x2212;03</td>
<td align="char" valign="bottom" char=".">6.66</td>
<td align="char" valign="bottom" char=".">0.97</td>
<td align="char" valign="bottom" char=".">&#x2212;1.58</td>
<td align="center" valign="bottom">&#x0394;X<sub>T2_CPMG</sub></td>
</tr>
<tr>
<td align="left" valign="bottom">Acetone</td>
<td align="center" valign="top">KB</td>
<td align="center" valign="bottom">1.45E&#x2212;02</td>
<td align="char" valign="bottom" char=".">5.49</td>
<td align="char" valign="bottom" char=".">12.79</td>
<td align="char" valign="bottom" char=".">&#x2212;9.39</td>
<td align="center" valign="bottom">&#x0394;X<sub>T2_CPMG</sub></td>
</tr>
<tr>
<td align="left" valign="bottom">Betaine</td>
<td align="center" valign="top">AA</td>
<td align="center" valign="bottom">2.08E&#x2212;02</td>
<td align="char" valign="bottom" char=".">4.99</td>
<td align="char" valign="bottom" char=".">8.17</td>
<td align="char" valign="bottom" char=".">&#x2212;0.04</td>
<td align="center" valign="bottom">&#x0394;X<sub>T2_CPMG</sub></td>
</tr>
<tr>
<td align="left" valign="bottom">Cis-aconitate</td>
<td align="center" valign="top">OA</td>
<td align="center" valign="bottom">3.69E&#x2212;03</td>
<td align="char" valign="bottom" char=".">8.23</td>
<td align="char" valign="bottom" char=".">0.45</td>
<td align="char" valign="bottom" char=".">&#x2212;1.06</td>
<td align="center" valign="bottom">&#x0394;X<sub>T2_CPMG</sub></td>
</tr>
<tr>
<td align="left" valign="bottom">Citrate</td>
<td align="center" valign="top">OA</td>
<td align="center" valign="bottom">2.33E&#x2212;02</td>
<td align="char" valign="bottom" char=".">4.83</td>
<td align="char" valign="bottom" char=".">4.77</td>
<td align="char" valign="bottom" char=".">&#x2212;2.36</td>
<td align="center" valign="bottom">&#x0394;X<sub>T2_CPMG</sub></td>
</tr>
<tr>
<td align="left" valign="bottom">Creatine</td>
<td align="center" valign="top">AA</td>
<td align="center" valign="bottom">3.96E&#x2212;02</td>
<td align="char" valign="bottom" char=".">4.11</td>
<td align="char" valign="bottom" char=".">1.47</td>
<td align="char" valign="bottom" char=".">&#x2212;1.02</td>
<td align="center" valign="bottom">&#x0394;X<sub>T2_CPMG</sub></td>
</tr>
<tr>
<td align="left" valign="bottom">Dimethylamine</td>
<td align="center" valign="top">ONC</td>
<td align="center" valign="bottom">3.69E&#x2212;03</td>
<td align="char" valign="bottom" char=".">8.31</td>
<td align="char" valign="bottom" char=".">4.32</td>
<td align="char" valign="bottom" char=".">&#x2212;3.48</td>
<td align="center" valign="bottom">&#x0394;X<sub>T2_CPMG</sub></td>
</tr>
<tr>
<td align="left" valign="bottom">Ethanol</td>
<td align="center" valign="top">OH</td>
<td align="center" valign="bottom">6.74E&#x2212;03</td>
<td align="char" valign="bottom" char=".">6.79</td>
<td align="char" valign="bottom" char=".">0.30</td>
<td align="char" valign="bottom" char=".">&#x2212;0.42</td>
<td align="center" valign="bottom">&#x0394;X<sub>T2_CPMG</sub></td>
</tr>
<tr>
<td align="left" valign="bottom">Galactose</td>
<td align="center" valign="top">SG</td>
<td align="center" valign="bottom">3.69E&#x2212;03</td>
<td align="char" valign="bottom" char=".">8.22</td>
<td align="char" valign="bottom" char=".">3.32</td>
<td align="char" valign="bottom" char=".">&#x2212;2.54</td>
<td align="center" valign="bottom">&#x0394;X<sub>T2_CPMG</sub></td>
</tr>
<tr>
<td align="left" valign="bottom">Glucose</td>
<td align="center" valign="top">SG</td>
<td align="center" valign="bottom">3.69E&#x2212;03</td>
<td align="char" valign="bottom" char=".">8.10</td>
<td align="char" valign="bottom" char=".">4.01</td>
<td align="char" valign="bottom" char=".">&#x2212;3.42</td>
<td align="center" valign="bottom">&#x0394;X<sub>T2_CPMG</sub></td>
</tr>
<tr>
<td align="left" valign="bottom">Glutamine</td>
<td align="center" valign="top">AA</td>
<td align="center" valign="bottom">1.02E&#x2212;02</td>
<td align="char" valign="bottom" char=".">6.11</td>
<td align="char" valign="top" char=".">6.59</td>
<td align="char" valign="top" char=".">&#x2212;1.56</td>
<td align="center" valign="top">&#x0394;X<sub>T2_CPMG</sub></td>
</tr>
<tr>
<td align="left" valign="top">Methanol</td>
<td align="center" valign="top">OH</td>
<td align="center" valign="top">4.49E&#x2212;03</td>
<td align="char" valign="top" char=".">7.69</td>
<td align="char" valign="top" char=".">1.41</td>
<td align="char" valign="top" char=".">&#x2212;1.35</td>
<td align="center" valign="top">&#x0394;X<sub>T2_CPMG</sub></td>
</tr>
<tr>
<td align="left" valign="top">Methine LP</td>
<td align="center" valign="top">LP</td>
<td align="center" valign="top">4.42E&#x2212;02</td>
<td align="char" valign="top" char=".">3.96</td>
<td align="char" valign="top" char=".">7.06</td>
<td align="char" valign="top" char=".">&#x2212;0.07</td>
<td align="center" valign="top">&#x0394;X<sub>T2_CPMG</sub></td>
</tr>
<tr>
<td align="left" valign="top">Succinate</td>
<td align="center" valign="top">OA</td>
<td align="center" valign="top">6.01E&#x2212;04</td>
<td align="char" valign="top" char=".">11.25</td>
<td align="char" valign="top" char=".">0.89</td>
<td align="char" valign="top" char=".">&#x2212;3.27</td>
<td align="center" valign="top">&#x0394;X<sub>T2_CPMG</sub></td>
</tr>
<tr>
<td align="left" valign="top">TMAO</td>
<td align="center" valign="top">ONC</td>
<td align="center" valign="top">2.75E&#x2212;03</td>
<td align="char" valign="top" char=".">9.10</td>
<td align="char" valign="top" char=".">4.34</td>
<td align="char" valign="top" char=".">&#x2212;4.21</td>
<td align="center" valign="top">&#x0394;X<sub>T2_CPMG</sub></td>
</tr>
<tr>
<td align="left" valign="top">Main Fraction Cholesterol</td>
<td align="center" valign="top">LP</td>
<td align="center" valign="top">8.50E&#x2212;03</td>
<td align="char" valign="top" char=".">5.06</td>
<td align="char" valign="top" char=".">5.60</td>
<td align="char" valign="top" char=".">&#x2212;2.77</td>
<td align="center" valign="top">&#x0394;X<sub>T2_LP</sub></td>
</tr>
<tr>
<td align="left" valign="top">Sub Fraction Cholesterol</td>
<td align="center" valign="top">LP</td>
<td align="center" valign="top">6.90E&#x2212;03</td>
<td align="char" valign="top" char=".">5.64</td>
<td align="char" valign="top" char=".">5.68</td>
<td align="char" valign="top" char=".">&#x2212;3.17</td>
<td align="center" valign="top">&#x0394;X<sub>T2_LP</sub></td>
</tr>
<tr>
<td align="left" valign="top">HDL Cholesterol</td>
<td align="center" valign="top">LP</td>
<td align="center" valign="top">3.10E&#x2212;03</td>
<td align="char" valign="top" char=".">9.30</td>
<td align="char" valign="top" char=".">5.73</td>
<td align="char" valign="top" char=".">&#x2212;5.57</td>
<td align="center" valign="top">&#x0394;X<sub>T2_LP</sub></td>
</tr>
<tr>
<td align="left" valign="top">HDL-2b Cholesterol</td>
<td align="center" valign="top">LP</td>
<td align="center" valign="top">8.00E&#x2212;04</td>
<td align="char" valign="top" char=".">14.28</td>
<td align="char" valign="top" char=".">14.78</td>
<td align="char" valign="top" char=".">&#x2212;10.07</td>
<td align="center" valign="top">&#x0394;X<sub>T2_LP</sub></td>
</tr>
<tr>
<td align="left" valign="top">HDL-2b Free Cholesterol</td>
<td align="center" valign="top">LP</td>
<td align="center" valign="top">2.30E&#x2212;03</td>
<td align="char" valign="top" char=".">10.08</td>
<td align="char" valign="top" char=".">10.76</td>
<td align="char" valign="top" char=".">&#x2212;4.54</td>
<td align="center" valign="top">&#x0394;X<sub>T2_LP</sub></td>
</tr>
<tr>
<td align="left" valign="top">Main Fraction Phospholipids</td>
<td align="center" valign="top">LP</td>
<td align="center" valign="top">6.20E&#x2212;03</td>
<td align="char" valign="top" char=".">5.85</td>
<td align="char" valign="top" char=".">8.82</td>
<td align="char" valign="top" char=".">&#x2212;4.41</td>
<td align="center" valign="top">&#x0394;X<sub>T2_LP</sub></td>
</tr>
<tr>
<td align="left" valign="top">HDL-2b Phospholipids</td>
<td align="center" valign="top">LP</td>
<td align="center" valign="top">4.60E&#x2212;03</td>
<td align="char" valign="top" char=".">7.67</td>
<td align="char" valign="top" char=".">13.08</td>
<td align="char" valign="top" char=".">&#x2212;10.76</td>
<td align="center" valign="top">&#x0394;X<sub>T2_LP</sub></td>
</tr>
<tr>
<td align="left" valign="top">Plasma ApoA1</td>
<td align="center" valign="top">LP</td>
<td align="center" valign="top">9.20E&#x2212;03</td>
<td align="char" valign="top" char=".">5.03</td>
<td align="char" valign="top" char=".">3.00</td>
<td align="char" valign="top" char=".">&#x2212;1.81</td>
<td align="center" valign="top">&#x0394;X<sub>T2_LP</sub></td>
</tr>
<tr>
<td align="left" valign="top">Main Fraction ApoA1</td>
<td align="center" valign="top">LP</td>
<td align="center" valign="top">1.08E&#x2212;02</td>
<td align="char" valign="top" char=".">4.80</td>
<td align="char" valign="top" char=".">4.10</td>
<td align="char" valign="top" char=".">&#x2212;0.28</td>
<td align="center" valign="top">&#x0394;X<sub>T2_LP</sub></td>
</tr>
<tr>
<td align="left" valign="top">Sub Fraction ApoA1</td>
<td align="center" valign="top">LP</td>
<td align="center" valign="top">5.40E&#x2212;03</td>
<td align="char" valign="top" char=".">6.11</td>
<td align="char" valign="top" char=".">4.01</td>
<td align="char" valign="top" char=".">&#x2212;2.59</td>
<td align="center" valign="top">&#x0394;X<sub>T2_LP</sub></td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>Median variance is calculated as the median % of the individual variance from T0, for example for T1 data: median ((T1&#x2013;T0/T0)&#x002A;100). For metabolites displaying more than one significant signal, only one is shown. Representative SUS BINS are listed in <xref ref-type="supplementary-material" rid="SM1">Supplementary Table S7</xref>. AA, amino acid (and derivatives); KB, ketone body; LP, lipoprotein; OA, organic acid; OH, alcohol; ONC, organic nitrogen compounds; SG, sugar.</p>
</table-wrap-foot>
</table-wrap>
<fig position="float" id="fig5">
<label>Figure 5</label>
<caption>
<p>The area under the curve of the receiver operating characteristics (AUCROC) obtained from the PLS-DA model developed for BW loss effect. These results represent the final PLS-DA models after variable selection and were developed on <bold>&#x0394;X</bold><sub>T2_NOESY_NND_BW</sub>, using a training set of 44 subjects and tested on 18 subjects.</p>
</caption>
<graphic xlink:href="fnut-10-1198531-g005.tif"/>
</fig>
<p>To test the hypothesis that plasma metabolites and lipoproteins differ in their concentrations between the two different diet groups, one-way ANOVA was employed. Most of the metabolites and LPs found to be associated with diet from the PLS-DA based variable selection were different between NND versus ADD in ANOVA (<xref rid="tab3" ref-type="table">Table 3</xref>). All the 11 LPs which were significant for the diet effect, were found at higher levels in the NND subjects. The HDL-2b cholesterol showed the largest diet effect and were found at higher concentrations in blood of NND subjects (15% increase in NND vs. 10% decrease in ADD at T2) compared to ADD subjects. A total of 137 metabolite variables were found to be different between NND vs. ADD in one-way ANOVA when including all datasets. These metabolite variables included ketone bodies (acetone, acetoacetate, and 3-hydroxybutyrate), glucose, methanol, TMAO, organic acids like citrate and succinate, and amino acids like betaine and glutamine. The biggest effect is observed for acetate (&#x003E;20% at T2), followed by methanol (8%&#x2013;21%), 3-hydroxybutyrate (13%), and fumaric acid (12%), all found at higher levels in NND subjects.</p>
</sec>
<sec id="sec15">
<title>Comparison of blood plasma lipoproteins and metabolites between weight losers and maintainers in the NND subjects</title>
<p>Initially, a PCA was carried out on the datasets corresponding to the baseline points, T0 time point (<bold>X</bold><sub>T0_NND_BW</sub> dataset&#x2014;see <xref ref-type="supplementary-material" rid="SM1">Supplementary Table S4</xref>), and showed no trend associated with weight loss among NND subjects (data not shown). ASCA performed on the same datasets showed no effect of the weight loss at T0. Likewise, a PLS-DA model developed to differentiate between weight losers and weight gainers at T0 (<bold>X</bold><sub>T0_NND_BW</sub>) failed to classify the two groups at T0 (examples for the <bold>X</bold><sub>T0_NOESY_NND_BW</sub> datasets shown in <xref ref-type="supplementary-material" rid="SM1">Supplementary Figure S1</xref>). Accordingly, no metabotypes related to weigh loss at T0 could be identified.</p>
<p>Baseline corrected T1 and T2 datasets, however, showed weak to moderate BW effect. In PCA, no real separation is observed between weight losers and maintainers (examples for the <bold>&#x0394;X</bold><sub>T1_NOESY_NND_BW</sub> and <bold>&#x0394;X</bold><sub>T2_NOESY_NND_BW</sub> datasets shown in <xref ref-type="supplementary-material" rid="SM1">Supplementary Figure S1</xref>). However, ASCA showed a significant weight loss effect on <bold>&#x0394;</bold>T2 datasets, <bold>&#x0394;X</bold><sub>T2_CPMG_NND_BW</sub> and <bold>&#x0394;X</bold><sub>T2_NOESY_NND_BW</sub>, (<italic>p</italic>-value&#x2009;=&#x2009;0.02 and 0.04, respectively), with 3.2% and 3.4% of the variation in the data being associated with the weight loss, respectively. Interestingly, a significant weight loss effect was also observed in the <bold>&#x0394;X</bold><sub>T1_LP_NND_BW</sub> dataset (<italic>p</italic>-value&#x2009;=&#x2009;0.02 and 5.7% variation). The effect of BW loss was further investigated by PLS-DA (<xref rid="tab4" ref-type="table">Table 4</xref>). The best result (AUC&#x2009;=&#x2009;0.82, error&#x2009;=&#x2009;28%) was obtained using <bold>&#x0394;X</bold><sub>T2_NOESY_NND_BW</sub> (<xref rid="fig5" ref-type="fig">Figure 5</xref>). One signal from citrate and an unknown singlet at 7.89&#x2009;ppm, were consistently found to be BW related in PLS-DA and ANOVA (<italic>p</italic>&#x2009;&#x003C;&#x2009;0.05 with FDR correction) performed to test BW effect (<xref rid="tab5" ref-type="table">Table 5</xref>). Despite the fact that none of the LP variables shows differences between NND weight losers and maintainers, some LP, including total free cholesterol, apolipoprotein B subfractions and LDL cholesterol esters, were recurrently selected by PLS-DA models developed to classify these two types of NND subjects (<xref ref-type="supplementary-material" rid="SM1">Supplementary Table S8</xref>).</p>
<table-wrap position="float" id="tab4">
<label>Table 4</label>
<caption>
<p>Results from PLS-DA prediction of BW loss after variable selection.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top">Matrix</th>
<th align="center" valign="top"><italic>N</italic>. variables used</th>
<th align="center" valign="top"><italic>N</italic>. LVs</th>
<th align="center" valign="top">Prediction AUC</th>
<th align="center" valign="top">Prediction error</th>
<th align="center" valign="top">Training AUC (CV)</th>
<th align="center" valign="top">Training error (CV)</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="bottom">&#x0394;X<sub>T1_NOESY</sub>_<sub>NND_BW</sub></td>
<td align="center" valign="bottom">3</td>
<td align="center" valign="bottom">1</td>
<td align="char" valign="bottom" char=".">0.84</td>
<td align="char" valign="bottom" char=".">0.27</td>
<td align="char" valign="bottom" char=".">0.62</td>
<td align="char" valign="bottom" char=".">0.38</td>
</tr>
<tr>
<td align="left" valign="bottom">&#x0394;X<sub>T2_NOESY_NND_BW</sub></td>
<td align="center" valign="bottom">77</td>
<td align="center" valign="bottom">2</td>
<td align="char" valign="bottom" char=".">0.82</td>
<td align="char" valign="bottom" char=".">0.28</td>
<td align="char" valign="bottom" char=".">0.77</td>
<td align="char" valign="bottom" char=".">0.27</td>
</tr>
<tr>
<td align="left" valign="bottom">&#x0394;X<sub>T1_CPMG_NND_BW</sub></td>
<td align="center" valign="bottom">2</td>
<td align="center" valign="bottom">1</td>
<td align="char" valign="bottom" char=".">0.55</td>
<td align="char" valign="bottom" char=".">0.47</td>
<td align="char" valign="bottom" char=".">0.83</td>
<td align="char" valign="bottom" char=".">0.24</td>
</tr>
<tr>
<td align="left" valign="bottom">&#x0394;X<sub>T2_CPMG_NND_BW</sub></td>
<td align="center" valign="bottom">98</td>
<td align="center" valign="bottom">3</td>
<td align="char" valign="bottom" char=".">0.66</td>
<td align="char" valign="bottom" char=".">0.30</td>
<td align="char" valign="bottom" char=".">0.74</td>
<td align="char" valign="bottom" char=".">0.39</td>
</tr>
<tr>
<td align="left" valign="bottom">&#x0394;X<sub>T1_LP_NND_BW</sub></td>
<td align="center" valign="bottom">11</td>
<td align="center" valign="bottom">1</td>
<td align="char" valign="bottom" char=".">0.55</td>
<td align="char" valign="bottom" char=".">0.47</td>
<td align="char" valign="bottom" char=".">0.71</td>
<td align="char" valign="bottom" char=".">0.41</td>
</tr>
<tr>
<td align="left" valign="bottom">&#x0394;X<sub>T2_LP_NND_BW</sub></td>
<td align="center" valign="bottom">35</td>
<td align="center" valign="bottom">2</td>
<td align="char" valign="bottom" char=".">0.71</td>
<td align="char" valign="bottom" char=".">0.28</td>
<td align="char" valign="bottom" char=".">0.81</td>
<td align="char" valign="bottom" char=".">0.30</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>The number of variables and latent variables used is reported for each matrix, together with the area under the curve (AUC) and error values for the training set (37 subjects at T1, 44 at T2) and prediction sets (15 subjects at T1, 18 at T2). Cross-validation (CV) using venetian blinds, with 8 data splits and one sample per blind, was used.</p>
</table-wrap-foot>
</table-wrap>
<table-wrap position="float" id="tab5">
<label>Table 5</label>
<caption>
<p>Metabolites and lipoproteins selected by PLS-DA variable selection (variables selected 70% of the times by the model) and significant in one-way ANOVA on discrimination of body weight (BW) loss calculated on the matrix reported.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top">Variable name</th>
<th align="center" valign="top"><italic>p</italic>-value</th>
<th align="center" valign="top">Effect size</th>
<th align="center" valign="top">Median % variation from T0 BW losers</th>
<th align="center" valign="top">Median % variation from T0 BW maintainers</th>
<th align="center" valign="top">Matrix</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="bottom">Citrate3</td>
<td align="char" valign="bottom" char=".">0.03</td>
<td align="char" valign="bottom" char=".">19.63</td>
<td align="char" valign="bottom" char=".">14.44</td>
<td align="char" valign="bottom" char=".">&#x2212;3.79</td>
<td align="center" valign="bottom">&#x0394;X<sub>T2_NOESY_NND_BW</sub></td>
</tr>
<tr>
<td align="left" valign="bottom">SUS33_s<xref rid="tfn3" ref-type="table-fn"><sup>a</sup></xref></td>
<td align="char" valign="bottom" char=".">0.03</td>
<td align="char" valign="bottom" char=".">18.53</td>
<td align="char" valign="bottom" char=".">1.43</td>
<td align="char" valign="bottom" char=".">&#x2212;0.86</td>
<td align="center" valign="bottom">&#x0394;X<sub>T2_NOESY_NND_BW</sub></td>
</tr>
<tr>
<td align="left" valign="bottom">bin32</td>
<td align="char" valign="bottom" char=".">0.03</td>
<td align="char" valign="bottom" char=".">18.21</td>
<td align="char" valign="bottom" char=".">11.47</td>
<td align="char" valign="bottom" char=".">&#x2212;4.54</td>
<td align="center" valign="bottom">&#x0394;X<sub>T2_NOESY_NND_BW</sub></td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn id="tfn3">
<label>a</label>
<p>SUS, signature signal of unknown spin system; s, singlet.</p>
</fn>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="sec16">
<title>Associations between plasma metabolites and lipoproteins with anthropometric and clinical parameters in NND and ADD subjects</title>
<p>NND and ADD subjects were stratified separately according to their &#x0394; anthropometric and clinical variables (e.g., &#x0394;DBP<sub>T2</sub> <bold>=</bold> DBP<sub>T2</sub>&#x2013;DBP<sub>T0</sub>). One-way ANOVA was applied to evaluate differences in individual metabolite or lipoprotein levels between the two groups, low 1/3 versus high 1/3 tertiles. It was found that several metabolites, were inversely associated with the increase in DBP from baseline to T1 (increased by up to 53%), including acetoacetic acid, acetone and succinate, and T2 (increased up to 31%), including the former metabolites and another ketone body (3-hydroxybutyric acid), in the NND group (<xref ref-type="supplementary-material" rid="SM1">Supplementary Tables S9, S10</xref>). These metabolites were all found at higher levels for subjects for whom DBP decreased during the intervention (<xref rid="fig6" ref-type="fig">Figure 6</xref>). It is to be noted, though, that the signature signal of succinic acid was partially overlapping with one peak from 3-hydroxybutyrate, and thus, that could affect the result and the increase in this interval could be simply related to the ketone body increase in low DBP group.</p>
<fig position="float" id="fig6">
<label>Figure 6</label>
<caption>
<p>Boxplot of significant metabolites (from &#x0394;X<sub>T2_CPMG_NND</sub> matrix of selected subjects) related to diastolic pressure (DBP) changes: low values of &#x0394;DBP on the left (DBP decreasing during intervention) and high values on the right (DBP increasing during intervention).</p>
</caption>
<graphic xlink:href="fnut-10-1198531-g006.tif"/>
</fig>
</sec>
</sec>
<sec id="sec17" sec-type="discussions">
<title>Discussion</title>
<sec id="sec18">
<title>Metabotype and effect of diet</title>
<p>Clear metabolite patterns associated with the NND and ADD diets, respectively, were observed at T1 and T2. Metabolites from different chemical classes and from diverse parts of the metabolism were found to be part of the pattern distinguishing NND from ADD, and all these metabolite markers appeared at higher levels in the NND group. Few of them were directly related to the diet, with the possible exception of ethanol and TMAO, which may signify a higher ethanol and fish intake in NND. Ketone bodies recurrently showed up as markers of NND. These metabolites can arise from fatty acid metabolism. The increased energy % of PUFAs in NND (<xref ref-type="bibr" rid="ref2">2</xref>) could give rise to this increase in ketone bodies (<xref ref-type="bibr" rid="ref19">19</xref>, <xref ref-type="bibr" rid="ref20">20</xref>). The reduced energy intake in NND could also explain the higher levels of ketone bodies, together with an increase in TCA cycle intermediates (i.e., citrate), glucose and acetate, which previously has been correlated to a higher fat metabolism (<xref ref-type="bibr" rid="ref21">21</xref>) improved insulin regulation (<xref ref-type="bibr" rid="ref22">22</xref>), and hepatic gluconeogenesis (<xref ref-type="bibr" rid="ref23">23</xref>). The increase of ketone body metabolism has recently been proposed as beneficial in the long term, as it starts an adaptive response by activating cell-protective mechanisms, up-regulating anti-inflammatory and anti-oxidative activities, and improving mitochondrial function and growth (<xref ref-type="bibr" rid="ref23">23</xref>).</p>
<p>Glutamine, ethanol and methanol were also amongst the recurrent NND markers. Glutamine has previously been related to whole grain diets (<xref ref-type="bibr" rid="ref24">24</xref>). The level of methanol in the blood was found to be higher in NND subjects, while the opposite has been reported from urine samples (<xref ref-type="bibr" rid="ref6">6</xref>). Endogenous methanol can stem from gut microbiota fermentation (<xref ref-type="bibr" rid="ref25 ref26 ref27">25&#x2013;27</xref>) or by transformation from S-adenosyl methionine (SAM) (<xref ref-type="bibr" rid="ref27">27</xref>). Methanol has previously been related to the intake of fresh fruit and vegetables, juices and fermented beverages (<xref ref-type="bibr" rid="ref6">6</xref>) and inversely related to high fat diet (<xref ref-type="bibr" rid="ref28">28</xref>). Plasma ethanol, which may originate from alcohol intake, can also be formed endogenously or by the microbiota from acetaldehyde, which in turn can be generated from various precursors such as pyruvate or alanine (<xref ref-type="bibr" rid="ref29">29</xref>). Due to the randomization in SHOPUS an increase in alcohol intake in the NND group is less likely than a change in its microbial or endogenous formation.</p>
<p>Limited significant differences were also observed in lipoprotein levels between ADD and NND diets. The level of cholesterol in apolipoprotein A1 sub-fractions, and in particularly in HDL-2b sub-fraction, was found to be higher in NND subjects (<xref rid="fig7" ref-type="fig">Figure 7</xref>) as opposed to previously published total cholesterol level in blood (<xref ref-type="bibr" rid="ref2">2</xref>). In general, there seem to be small lipid-lowering effects in the NND group, but effect sizes are very small.</p>
<fig position="float" id="fig7">
<label>Figure 7</label>
<caption>
<p>Total, main fraction, VLDL, IDL, LDL and HDL cholesterol fractions and subfractions compared between the two diet groups (NND in brown and ADD in light blue) at T2. Cholesterol main fractions HDL, and HDL-2b (inside dotted lines) differed between the two diet groups by ANOVA.</p>
</caption>
<graphic xlink:href="fnut-10-1198531-g007.tif"/>
</fig>
<p>The changes in the LPs can be traced back to differences in macronutrient intakes of the two diets. The NND includes higher intakes of carbohydrates and PUFAs (<xref ref-type="bibr" rid="ref2">2</xref>). Changes in fat and carbohydrate intakes have previously been observed to alter apolipoprotein HDL concentrations. In particular higher carbohydrate intake, as is the case in the NND, has been shown to increase production rate of ApoA1 in specific HDL subfractions (<xref ref-type="bibr" rid="ref30">30</xref>). However, increase in HDL-cholesterol and ApoA1, have also been reported for a low carbohydrate, high protein diet (<xref ref-type="bibr" rid="ref31">31</xref>). These results are thus not in contrast with our findings, as protein intake was also higher in NND compared to ADD.</p>
<p>Together these results reveal that effects of changing dietary energy substrates on fatty acid synthesis and metabolism in humans are difficult to predict, and that effects of complex diets on blood lipoprotein fractions may not predict the relative health risk in a simple way but require long-term clinical trials and a larger cohort.</p>
</sec>
<sec id="sec19">
<title>Correlations of blood plasma metabolites and lipoproteins with anthropometric and clinical parameters</title>
<p>Acetoacetic acid occurs consistently as a marker of decreasing diastolic blood pressure (<xref rid="fig7" ref-type="fig">Figure 7</xref>). This ketone body has been shown to be part of a group of predictive markers for body weight loss (<xref ref-type="bibr" rid="ref32">32</xref>). Beneficial effects have been linked to low or medium concentrations of ketone bodies, originating through fasting, exercise or ketogenic diets, whereas higher concentrations, observed for example in diabetic ketoacidosis, may be detrimental and contribute to disease morbidity (<xref ref-type="bibr" rid="ref33">33</xref>). Higher plasma succinate levels were also found in subjects with a decrease in DBP but the result may be confounded by ketone body signals. Enhanced mitochondrial &#x03B2;-oxidation, which occurs for example in fasted state, can increase plasma levels of succinic acid (<xref ref-type="bibr" rid="ref34">34</xref>) and 3-hydroxybutyrate (<xref ref-type="bibr" rid="ref35">35</xref>). No clear metabolite pattern was found to be associated with BW losers and maintainers within the NND group, as only a few features were consistently found altered when stringent thresholds were used. The only metabolite showing a weak association with weight loss was citrate (FDR-corrected <italic>p</italic>-value for all citrate signals &#x003C;0.1). Increasing blood plasma level of citrate was also associated with weight loss in another study (<xref ref-type="bibr" rid="ref36">36</xref>) which linked with bone breakdown during weight loss.</p>
</sec>
</sec>
<sec id="sec20" sec-type="conclusions">
<title>Conclusion</title>
<p>NMR metabolite profiling of blood plasma samples from the SHOPUS intervention study has revealed metabolite and lipoprotein pattern changes related to diet change, changes in microbial metabolites, and to body weight loss of the NND group. Increasing levels of ketone bodies were detected in the NND group and are inversely associated with the decrease in diastolic blood pressure in the NND group. Moreover, body weight changes weakly altered the metabolite and lipoprotein profile in the NND group. These results show that the unbiased and untargeted <sup>1</sup>H-NMR spectroscopy enriched by the lipoprotein prediction models is able to provide a measure of the gross metabolic perturbations induced by dietary alterations and the related physiological changes experienced.</p>
</sec>
<sec id="sec21" sec-type="data-availability">
<title>Data availability statement</title>
<p>The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.</p>
</sec>
<sec id="sec22">
<title>Ethics statement</title>
<p>The studies involving human participants were reviewed and approved by Regional Ethics Committee of Greater Copenhagen 130 and Frederiksberg (H-3-2010-058) Danish Data Protection Agency (2007-54-0269). The patients/participants provided their written informed consent to participate in this study.</p>
</sec>
<sec id="sec23">
<title>Author contributions</title>
<p>LD, AA, and SE: conception and design. AT, BK, and MR: development of methodology. SE: data acquisition. AT, BK, and SE: data analysis. AT, BK, MR, LD, TL, AA, and SE: interpretation of results and writing. All authors contributed to the article and approved the submitted version.</p>
</sec>
<sec id="sec24" sec-type="funding-information">
<title>Funding</title>
<p>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 &#x201C;Optimal wellbeing, development and health for Danish children through a healthy New Nordic Diet&#x201D;. The trial was registered at <ext-link xlink:href="https://www.clinicaltrials.gov" ext-link-type="uri">www.clinicaltrials.gov</ext-link> as NCT01195610 (<ext-link xlink:href="https://clinicaltrials.gov/ct2/show/NCT01195610" ext-link-type="uri">https://clinicaltrials.gov/ct2/show/NCT01195610</ext-link>). The method for calculating the lipoprotein distributions was supported by the COUNTERSTRIKE project Danish Strategic Research Council/Innovation Foundation Denmark (grant number 4105-00015B). The contribution from LD was funded by the PRIMA grant from the Novo-Nordisk Foundation (NNF19OC0056246; PRIMA&#x2014;toward personalized dietary recommendations based on the interaction between diet, microbiome and abiotic conditions in the gut).</p>
</sec>
<sec id="conf1" sec-type="COI-statement">
<title>Conflict of interest</title>
<p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
<sec id="sec100" sec-type="disclaimer">
<title>Publisher&#x2019;s note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p>
</sec>
<sec id="sec26" sec-type="supplementary-material">
<title>Supplementary material</title>
<p>The Supplementary material for this article can be found online at: <ext-link xlink:href="https://www.frontiersin.org/articles/10.3389/fnut.2023.1198531/full#supplementary-material" ext-link-type="uri">https://www.frontiersin.org/articles/10.3389/fnut.2023.1198531/full#supplementary-material</ext-link></p>
<supplementary-material xlink:href="Data_Sheet_1.docx" id="SM1" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document" xmlns:xlink="http://www.w3.org/1999/xlink"/>
</sec>
</body>
<back>
<ref-list>
<title>References</title>
<ref id="ref1"><label>1.</label><citation citation-type="journal"><person-group person-group-type="author"><name><surname>Scalbert</surname> <given-names>A</given-names></name> <name><surname>Brennan</surname> <given-names>L</given-names></name> <name><surname>Manach</surname> <given-names>C</given-names></name> <name><surname>Andres-Lacueva</surname> <given-names>C</given-names></name> <name><surname>Dragsted</surname> <given-names>LO</given-names></name> <name><surname>Draper</surname> <given-names>J</given-names></name> <etal/></person-group>. <article-title>The food metabolome: a window over dietary exposure</article-title>. <source>Am J Clin Nutr</source>. (<year>2014</year>) <volume>99</volume>:<fpage>1286</fpage>&#x2013;<lpage>308</lpage>. doi: <pub-id pub-id-type="doi">10.3945/ajcn.113.076133</pub-id>, PMID: <pub-id pub-id-type="pmid">24760973</pub-id></citation></ref>
<ref id="ref2"><label>2.</label><citation citation-type="journal"><person-group person-group-type="author"><name><surname>Poulsen</surname> <given-names>SK</given-names></name> <name><surname>Due</surname> <given-names>A</given-names></name> <name><surname>Jordy</surname> <given-names>AB</given-names></name> <name><surname>Kiens</surname> <given-names>B</given-names></name> <name><surname>Stark</surname> <given-names>KD</given-names></name> <name><surname>Stender</surname> <given-names>S</given-names></name> <etal/></person-group>. <article-title>Health effect of the new Nordic diet in adults with increased waist circumference: a 6-mo randomized controlled trial</article-title>. <source>Am J Clin Nutr</source>. (<year>2013</year>) <volume>99</volume>:<fpage>35</fpage>&#x2013;<lpage>45</lpage>. doi: <pub-id pub-id-type="doi">10.3945/ajcn.113.069393</pub-id></citation></ref>
<ref id="ref3"><label>3.</label><citation citation-type="journal"><person-group person-group-type="author"><name><surname>Fritzen</surname> <given-names>AM</given-names></name> <name><surname>Lundsgaard</surname> <given-names>A-M</given-names></name> <name><surname>Jordy</surname> <given-names>AB</given-names></name> <name><surname>Poulsen</surname> <given-names>SK</given-names></name> <name><surname>Stender</surname> <given-names>S</given-names></name> <name><surname>Pilegaard</surname> <given-names>H</given-names></name> <etal/></person-group>. <article-title>New Nordic diet&#x2013;induced weight loss is accompanied by changes in metabolism and AMPK signaling in adipose tissue</article-title>. <source>J Clin Endocrinol Metabol</source>. (<year>2015</year>) <volume>100</volume>:<fpage>3509</fpage>&#x2013;<lpage>19</lpage>. doi: <pub-id pub-id-type="doi">10.1210/jc.2015-2079</pub-id>, PMID: <pub-id pub-id-type="pmid">26126206</pub-id></citation></ref>
<ref id="ref4"><label>4.</label><citation citation-type="journal"><person-group person-group-type="author"><name><surname>Acar</surname> <given-names>E</given-names></name> <name><surname>Gurdeniz</surname> <given-names>G</given-names></name> <name><surname>Khakimov</surname> <given-names>B</given-names></name> <name><surname>Savorani</surname> <given-names>F</given-names></name> <name><surname>Korndal</surname> <given-names>SK</given-names></name> <name><surname>Larsen</surname> <given-names>TM</given-names></name> <etal/></person-group>. <article-title>Biomarkers of individual foods, and separation of diets using untargeted LC-MS-based plasma metabolomics in a randomized controlled trial</article-title>. <source>Mol Nutr Food Res</source>. (<year>2019</year>) <volume>63</volume>:<fpage>e1800215</fpage>. doi: <pub-id pub-id-type="doi">10.1002/mnfr.201800215</pub-id></citation></ref>
<ref id="ref5"><label>5.</label><citation citation-type="journal"><person-group person-group-type="author"><name><surname>Khakimov</surname> <given-names>B</given-names></name> <name><surname>Poulsen</surname> <given-names>SK</given-names></name> <name><surname>Savorani</surname> <given-names>F</given-names></name> <name><surname>Acar</surname> <given-names>E</given-names></name> <name><surname>G&#x00FC;rdeniz</surname> <given-names>GZ</given-names></name> <name><surname>Larsen</surname> <given-names>TM</given-names></name> <etal/></person-group>. <article-title>New Nordic diet versus average Danish diet: a randomized controlled trial revealed healthy long-term effects of the new Nordic diet by GC&#x2013;MS blood plasma metabolomics</article-title>. <source>J Proteome Res</source>. (<year>2016</year>) <volume>15</volume>:<fpage>1939</fpage>&#x2013;<lpage>54</lpage>. doi: <pub-id pub-id-type="doi">10.1021/acs.jproteome.6b00109</pub-id>, PMID: <pub-id pub-id-type="pmid">27146725</pub-id></citation></ref>
<ref id="ref6"><label>6.</label><citation citation-type="journal"><person-group person-group-type="author"><name><surname>Trimigno</surname> <given-names>A</given-names></name> <name><surname>Khakimov</surname> <given-names>B</given-names></name> <name><surname>Savorani</surname> <given-names>F</given-names></name> <name><surname>Poulsen</surname> <given-names>SK</given-names></name> <name><surname>Astrup</surname> <given-names>A</given-names></name> <name><surname>Dragsted</surname> <given-names>LO</given-names></name> <etal/></person-group>. <article-title>Human urine <sup>1</sup>H NMR metabolomics reveals alterations of the protein and carbohydrate metabolism when comparing habitual average Danish diet vs. healthy new Nordic diet</article-title>. <source>Nutrition</source>. (<year>2020</year>) <volume>79-80</volume>:<fpage>110867</fpage>. doi: <pub-id pub-id-type="doi">10.1016/j.nut.2020.110867</pub-id></citation></ref>
<ref id="ref7"><label>7.</label><citation citation-type="journal"><person-group person-group-type="author"><name><surname>Aru</surname> <given-names>V</given-names></name> <name><surname>Lam</surname> <given-names>C</given-names></name> <name><surname>Khakimov</surname> <given-names>B</given-names></name> <name><surname>Hoefsloot</surname> <given-names>HC</given-names></name> <name><surname>Zwanenburg</surname> <given-names>G</given-names></name> <name><surname>Lind</surname> <given-names>MV</given-names></name> <etal/></person-group>. <article-title>Quantification of lipoprotein profiles by nuclear magnetic resonance spectroscopy and multivariate data analysis</article-title>. <source>TrAC Trends Anal Chem</source>. (<year>2017</year>) <volume>94</volume>:<fpage>210</fpage>&#x2013;<lpage>9</lpage>. doi: <pub-id pub-id-type="doi">10.1016/j.trac.2017.07.009</pub-id></citation></ref>
<ref id="ref8"><label>8.</label><citation citation-type="journal"><person-group person-group-type="author"><name><surname>Jim&#x00E9;nez</surname> <given-names>B</given-names></name> <name><surname>Holmes</surname> <given-names>E</given-names></name> <name><surname>Heude</surname> <given-names>C</given-names></name> <name><surname>Tolson</surname> <given-names>RF</given-names></name> <name><surname>Harvey</surname> <given-names>N</given-names></name> <name><surname>Lodge</surname> <given-names>SL</given-names></name> <etal/></person-group>. <article-title>Quantitative lipoprotein subclass and low molecular weight metabolite analysis in human serum and plasma by <sup>1</sup>H NMR spectroscopy in a multilaboratory trial</article-title>. <source>Anal Chem</source>. (<year>2018</year>) <volume>90</volume>:<fpage>11962</fpage>&#x2013;<lpage>71</lpage>. doi: <pub-id pub-id-type="doi">10.1021/acs.analchem.8b02412</pub-id></citation></ref>
<ref id="ref9"><label>9.</label><citation citation-type="journal"><person-group person-group-type="author"><name><surname>Khakimov</surname> <given-names>B</given-names></name> <name><surname>Hoefsloot</surname> <given-names>HCJ</given-names></name> <name><surname>Mobaraki</surname> <given-names>N</given-names></name> <name><surname>Aru</surname> <given-names>V</given-names></name> <name><surname>Kristensen</surname> <given-names>M</given-names></name> <name><surname>Lind</surname> <given-names>MV</given-names></name> <etal/></person-group>. <article-title>Human blood lipoprotein predictions from <sup>1</sup>H NMR spectra: protocol, model performances, and cage of covariance</article-title>. <source>Anal Chem</source>. (<year>2022</year>) <volume>94</volume>:<fpage>628</fpage>&#x2013;<lpage>36</lpage>. doi: <pub-id pub-id-type="doi">10.1021/acs.analchem.1c01654</pub-id>, PMID: <pub-id pub-id-type="pmid">34936323</pub-id></citation></ref>
<ref id="ref10"><label>10.</label><citation citation-type="journal"><person-group person-group-type="author"><name><surname>Savorani</surname> <given-names>F</given-names></name> <name><surname>Rasmussen</surname> <given-names>MA</given-names></name> <name><surname>Mikkelsen</surname> <given-names>MS</given-names></name> <name><surname>Engelsen</surname> <given-names>SB</given-names></name></person-group>. <article-title>A primer to nutritional metabolomics by NMR spectroscopy and chemometrics</article-title>. <source>Food Res Int</source>. (<year>2013</year>) <volume>54</volume>:<fpage>1131</fpage>&#x2013;<lpage>45</lpage>. doi: <pub-id pub-id-type="doi">10.1016/j.foodres.2012.12.025</pub-id></citation></ref>
<ref id="ref11"><label>11.</label><citation citation-type="journal"><person-group person-group-type="author"><name><surname>Otvos</surname> <given-names>JD</given-names></name> <name><surname>Jeyarajah</surname> <given-names>EJ</given-names></name> <name><surname>Bennett</surname> <given-names>DW</given-names></name> <name><surname>Krauss</surname> <given-names>RM</given-names></name></person-group>. <article-title>Development of a proton nuclear magnetic resonance spectroscopic method for determining plasma lipoprotein concentrations and subspecies distributions from a single, rapid measurement</article-title>. <source>Clin Chem</source>. (<year>1992</year>) <volume>38</volume>:<fpage>1632</fpage>&#x2013;<lpage>8</lpage>. doi: <pub-id pub-id-type="doi">10.1093/clinchem/38.9.1632</pub-id>, PMID: <pub-id pub-id-type="pmid">1326420</pub-id></citation></ref>
<ref id="ref12"><label>12.</label><citation citation-type="journal"><person-group person-group-type="author"><name><surname>Khakimov</surname> <given-names>B</given-names></name> <name><surname>Mobaraki</surname> <given-names>N</given-names></name> <name><surname>Trimigno</surname> <given-names>A</given-names></name> <name><surname>Aru</surname> <given-names>V</given-names></name> <name><surname>Engelsen</surname> <given-names>SB</given-names></name></person-group>. <article-title>Signature mapping (SigMa): an efficient approach for processing complex human urine <sup>1</sup>H NMR metabolomics data</article-title>. <source>Anal Chim Acta</source>. (<year>2020</year>) <volume>1108</volume>:<fpage>142</fpage>&#x2013;<lpage>51</lpage>. doi: <pub-id pub-id-type="doi">10.1016/j.aca.2020.02.025</pub-id>, PMID: <pub-id pub-id-type="pmid">32222235</pub-id></citation></ref>
<ref id="ref13"><label>13.</label><citation citation-type="journal"><person-group person-group-type="author"><name><surname>Savorani</surname> <given-names>F</given-names></name> <name><surname>Tomasi</surname> <given-names>G</given-names></name> <name><surname>Engelsen</surname> <given-names>SB</given-names></name></person-group>. <article-title><italic>icoshift</italic>: a versatile tool for the rapid alignment of 1D NMR spectra</article-title>. <source>J Magn Reson</source>. (<year>2010</year>) <volume>202</volume>:<fpage>190</fpage>&#x2013;<lpage>202</lpage>. doi: <pub-id pub-id-type="doi">10.1016/j.jmr.2009.11.012</pub-id></citation></ref>
<ref id="ref14"><label>14.</label><citation citation-type="journal"><person-group person-group-type="author"><name><surname>Lawton</surname> <given-names>WH</given-names></name> <name><surname>Sylvestre</surname> <given-names>EA</given-names></name></person-group>. <article-title>Self modeling curve resolution</article-title>. <source>Technometrics</source>. (<year>1971</year>) <volume>13</volume>:<fpage>617</fpage>&#x2013;<lpage>33</lpage>. doi: <pub-id pub-id-type="doi">10.1080/00401706.1971.10488823</pub-id></citation></ref>
<ref id="ref15"><label>15.</label><citation citation-type="journal"><person-group person-group-type="author"><name><surname>Monsonis Centelles</surname> <given-names>S</given-names></name> <name><surname>Hoefsloot</surname> <given-names>HCJ</given-names></name> <name><surname>Khakimov</surname> <given-names>B</given-names></name> <name><surname>Ebrahimi</surname> <given-names>P</given-names></name> <name><surname>Lind</surname> <given-names>MV</given-names></name> <name><surname>Kristensen</surname> <given-names>M</given-names></name> <etal/></person-group>. <article-title>Toward reliable lipoprotein particle predictions from NMR spectra of human blood: an interlaboratory ring test</article-title>. <source>Anal Chem</source>. (<year>2017</year>) <volume>89</volume>:<fpage>8004</fpage>&#x2013;<lpage>12</lpage>. doi: <pub-id pub-id-type="doi">10.1021/acs.analchem.7b01329</pub-id>, PMID: <pub-id pub-id-type="pmid">28692288</pub-id></citation></ref>
<ref id="ref16"><label>16.</label><citation citation-type="journal"><person-group person-group-type="author"><name><surname>Hotelling</surname> <given-names>H</given-names></name></person-group>. <article-title>Analysis of a complex of statistical variables into principal components</article-title>. <source>J Educ Psychol</source>. (<year>1933</year>) <volume>24</volume>:<fpage>417</fpage>&#x2013;<lpage>41</lpage>. doi: <pub-id pub-id-type="doi">10.1037/h0071325</pub-id></citation></ref>
<ref id="ref17"><label>17.</label><citation citation-type="journal"><person-group person-group-type="author"><name><surname>Smilde</surname> <given-names>AK</given-names></name> <name><surname>Jansen</surname> <given-names>JJ</given-names></name> <name><surname>Hoefsloot</surname> <given-names>HC</given-names></name> <name><surname>Lamers</surname> <given-names>R-JA</given-names></name> <name><surname>van der Greef</surname> <given-names>J</given-names></name> <name><surname>Timmerman</surname> <given-names>ME</given-names></name></person-group>. <article-title>ANOVA-simultaneous component analysis (ASCA): a new tool for analyzing designed metabolomics data</article-title>. <source>Bioinformatics</source>. (<year>2005</year>) <volume>21</volume>:<fpage>3043</fpage>&#x2013;<lpage>8</lpage>. doi: <pub-id pub-id-type="doi">10.1093/bioinformatics/bti476</pub-id>, PMID: <pub-id pub-id-type="pmid">15890747</pub-id></citation></ref>
<ref id="ref18"><label>18.</label><citation citation-type="journal"><person-group person-group-type="author"><name><surname>St&#x00E5;hle</surname> <given-names>L</given-names></name> <name><surname>Wold</surname> <given-names>S</given-names></name></person-group>. <article-title>Partial least squares analysis with cross-validation for the two-class problem: a Monte Carlo study</article-title>. <source>J Chemom</source>. (<year>1987</year>) <volume>1</volume>:<fpage>185</fpage>&#x2013;<lpage>96</lpage>. doi: <pub-id pub-id-type="doi">10.1002/cem.1180010306</pub-id></citation></ref>
<ref id="ref19"><label>19.</label><citation citation-type="journal"><person-group person-group-type="author"><name><surname>Cunnane</surname> <given-names>SC</given-names></name></person-group>. <article-title>Metabolism of polyunsaturated fatty acids and ketogenesis: an emerging connection</article-title>. <source>Prostaglandins Leukot Essent Fatty Acids</source>. (<year>2004</year>) <volume>70</volume>:<fpage>237</fpage>&#x2013;<lpage>41</lpage>. doi: <pub-id pub-id-type="doi">10.1016/j.plefa.2003.11.002</pub-id>, PMID: <pub-id pub-id-type="pmid">14769482</pub-id></citation></ref>
<ref id="ref20"><label>20.</label><citation citation-type="journal"><person-group person-group-type="author"><name><surname>Yang</surname> <given-names>H</given-names></name> <name><surname>Shan</surname> <given-names>W</given-names></name> <name><surname>Zhu</surname> <given-names>F</given-names></name> <name><surname>Wu</surname> <given-names>J</given-names></name> <name><surname>Wang</surname> <given-names>Q</given-names></name></person-group>. <article-title>Ketone bodies in neurological diseases: focus on neuroprotection and underlying mechanisms</article-title>. <source>Front Neurol</source>. (<year>2019</year>) <volume>10</volume>:<fpage>485</fpage>. doi: <pub-id pub-id-type="doi">10.3389/fneur.2019.00585</pub-id></citation></ref>
<ref id="ref21"><label>21.</label><citation citation-type="journal"><person-group person-group-type="author"><name><surname>Perry</surname> <given-names>RJ</given-names></name> <name><surname>Peng</surname> <given-names>L</given-names></name> <name><surname>Barry</surname> <given-names>NA</given-names></name> <name><surname>Cline</surname> <given-names>GW</given-names></name> <name><surname>Zhang</surname> <given-names>D</given-names></name> <name><surname>Cardone</surname> <given-names>RL</given-names></name> <etal/></person-group>. <article-title>Acetate mediates a microbiome&#x2013;brain&#x2013;&#x0392;-cell axis to promote metabolic syndrome</article-title>. <source>Nature</source>. (<year>2016</year>) <volume>534</volume>:<fpage>213</fpage>&#x2013;<lpage>7</lpage>. doi: <pub-id pub-id-type="doi">10.1038/nature18309</pub-id>, PMID: <pub-id pub-id-type="pmid">27279214</pub-id></citation></ref>
<ref id="ref22"><label>22.</label><citation citation-type="journal"><person-group person-group-type="author"><name><surname>Canfora</surname> <given-names>EE</given-names></name> <name><surname>Blaak</surname> <given-names>EE</given-names></name></person-group>. <article-title>Acetate: a diet-derived key metabolite in energy metabolism: good or bad in context of obesity and glucose homeostasis?</article-title> <source>Curr Opin Clin Nutr Metab Care</source>. (<year>2017</year>) <volume>20</volume>:<fpage>477</fpage>&#x2013;<lpage>83</lpage>. doi: <pub-id pub-id-type="doi">10.1097/MCO.0000000000000408</pub-id></citation></ref>
<ref id="ref23"><label>23.</label><citation citation-type="journal"><person-group person-group-type="author"><name><surname>Kolb</surname> <given-names>H</given-names></name> <name><surname>Kempf</surname> <given-names>K</given-names></name> <name><surname>R&#x00F6;hling</surname> <given-names>M</given-names></name> <name><surname>Lenzen-Schulte</surname> <given-names>M</given-names></name> <name><surname>Schloot</surname> <given-names>NC</given-names></name> <name><surname>Martin</surname> <given-names>S</given-names></name></person-group>. <article-title>Ketone bodies: from enemy to friend and guardian angel</article-title>. <source>BMC Med</source>. (<year>2021</year>) <volume>19</volume>:<fpage>313</fpage>. doi: <pub-id pub-id-type="doi">10.1186/s12916-021-02185-0</pub-id>, PMID: <pub-id pub-id-type="pmid">34879839</pub-id></citation></ref>
<ref id="ref24"><label>24.</label><citation citation-type="journal"><person-group person-group-type="author"><name><surname>Navarro</surname> <given-names>SL</given-names></name> <name><surname>Tarkhan</surname> <given-names>A</given-names></name> <name><surname>Shojaie</surname> <given-names>A</given-names></name> <name><surname>Randolph</surname> <given-names>TW</given-names></name> <name><surname>Gu</surname> <given-names>H</given-names></name> <name><surname>Djukovic</surname> <given-names>D</given-names></name> <etal/></person-group>. <article-title>Plasma metabolomics profiles suggest beneficial effects of a low-glycemic load dietary pattern on inflammation and energy metabolism</article-title>. <source>Am J Clin Nutr</source>. (<year>2019</year>) <volume>110</volume>:<fpage>984</fpage>&#x2013;<lpage>92</lpage>. doi: <pub-id pub-id-type="doi">10.1093/ajcn/nqz169</pub-id>, PMID: <pub-id pub-id-type="pmid">31432072</pub-id></citation></ref>
<ref id="ref25"><label>25.</label><citation citation-type="journal"><person-group person-group-type="author"><name><surname>Dorokhov</surname> <given-names>YL</given-names></name> <name><surname>Shindyapina</surname> <given-names>AV</given-names></name> <name><surname>Sheshukova</surname> <given-names>EV</given-names></name> <name><surname>Komarova</surname> <given-names>TV</given-names></name></person-group>. <article-title>Metabolic methanol: molecular pathways and physiological roles</article-title>. <source>Physiol Rev</source>. (<year>2015</year>) <volume>95</volume>:<fpage>603</fpage>&#x2013;<lpage>44</lpage>. doi: <pub-id pub-id-type="doi">10.1152/physrev.00034.2014</pub-id>, PMID: <pub-id pub-id-type="pmid">25834233</pub-id></citation></ref>
<ref id="ref26"><label>26.</label><citation citation-type="journal"><person-group person-group-type="author"><name><surname>Razzaghy-Azar</surname> <given-names>M</given-names></name> <name><surname>Nourbakhsh</surname> <given-names>M</given-names></name> <name><surname>Vafadar</surname> <given-names>M</given-names></name> <name><surname>Nourbakhsh</surname> <given-names>M</given-names></name> <name><surname>Talebi</surname> <given-names>S</given-names></name> <name><surname>Sharifi-Zarchi</surname> <given-names>A</given-names></name> <etal/></person-group>. <article-title>A novel metabolic disorder in the degradation pathway of endogenous methanol due to a mutation in the gene of alcohol dehydrogenase</article-title>. <source>Clin Biochem</source>. (<year>2021</year>) <volume>90</volume>:<fpage>66</fpage>&#x2013;<lpage>72</lpage>. doi: <pub-id pub-id-type="doi">10.1016/j.clinbiochem.2021.01.007</pub-id>, PMID: <pub-id pub-id-type="pmid">33539811</pub-id></citation></ref>
<ref id="ref27"><label>27.</label><citation citation-type="journal"><person-group person-group-type="author"><name><surname>Shindyapina</surname> <given-names>AV</given-names></name> <name><surname>Petrunia</surname> <given-names>IV</given-names></name> <name><surname>Komarova</surname> <given-names>TV</given-names></name> <name><surname>Sheshukova</surname> <given-names>EV</given-names></name> <name><surname>Kosorukov</surname> <given-names>VS</given-names></name> <name><surname>Kiryanov</surname> <given-names>GI</given-names></name> <etal/></person-group>. <article-title>Dietary methanol regulates human gene activity</article-title>. <source>PLoS One</source>. (<year>2014</year>) <volume>9</volume>:<fpage>e102837</fpage>. doi: <pub-id pub-id-type="doi">10.1371/journal.pone.0102837</pub-id></citation></ref>
<ref id="ref28"><label>28.</label><citation citation-type="journal"><person-group person-group-type="author"><name><surname>Lanng</surname> <given-names>SK</given-names></name> <name><surname>Zhang</surname> <given-names>Y</given-names></name> <name><surname>Christensen</surname> <given-names>KR</given-names></name> <name><surname>Hansen</surname> <given-names>AK</given-names></name> <name><surname>Nielsen</surname> <given-names>DS</given-names></name> <name><surname>Kot</surname> <given-names>W</given-names></name> <etal/></person-group>. <article-title>Partial substitution of meat with insect (<italic>Alphitobius Diaperinus</italic>) in a carnivore diet changes the gut microbiome and metabolome of healthy rats</article-title>. <source>Foods</source>. (<year>2021</year>) <volume>10</volume>:<fpage>1814</fpage>. doi: <pub-id pub-id-type="doi">10.3390/foods10081814</pub-id>, PMID: <pub-id pub-id-type="pmid">34441592</pub-id></citation></ref>
<ref id="ref29"><label>29.</label><citation citation-type="journal"><person-group person-group-type="author"><name><surname>Ostrovsky</surname> <given-names>YM</given-names></name></person-group>. <article-title>Endogenous ethanol&#x2014;its metabolic, behavioral and biomedical significance</article-title>. <source>Alcohol</source>. (<year>1986</year>) <volume>3</volume>:<fpage>239</fpage>&#x2013;<lpage>47</lpage>. doi: <pub-id pub-id-type="doi">10.1016/0741-8329(86)90032-7</pub-id>, PMID: <pub-id pub-id-type="pmid">3530279</pub-id></citation></ref>
<ref id="ref30"><label>30.</label><citation citation-type="journal"><person-group person-group-type="author"><name><surname>Andraski</surname> <given-names>AB</given-names></name> <name><surname>Singh</surname> <given-names>SA</given-names></name> <name><surname>Lee</surname> <given-names>LH</given-names></name> <name><surname>Higashi</surname> <given-names>H</given-names></name> <name><surname>Smith</surname> <given-names>N</given-names></name> <name><surname>Zhang</surname> <given-names>B</given-names></name> <etal/></person-group>. <article-title>Effects of replacing dietary monounsaturated fat with carbohydrate on HDL (high-density lipoprotein) protein metabolism and proteome composition in humans</article-title>. <source>Arterioscler Thromb Vasc Biol</source>. (<year>2019</year>) <volume>39</volume>:<fpage>2411</fpage>&#x2013;<lpage>30</lpage>. doi: <pub-id pub-id-type="doi">10.1161/ATVBAHA.119.312889</pub-id>, PMID: <pub-id pub-id-type="pmid">31554421</pub-id></citation></ref>
<ref id="ref31"><label>31.</label><citation citation-type="journal"><person-group person-group-type="author"><name><surname>Alzahrani</surname> <given-names>AH</given-names></name> <name><surname>Skytte</surname> <given-names>MJ</given-names></name> <name><surname>Samkani</surname> <given-names>A</given-names></name> <name><surname>Thomsen</surname> <given-names>MN</given-names></name> <name><surname>Astrup</surname> <given-names>A</given-names></name> <name><surname>Ritz</surname> <given-names>C</given-names></name> <etal/></person-group>. <article-title>Effects of a self-prepared carbohydrate-reduced high-protein diet on cardiovascular disease risk markers in patients with type 2 diabetes</article-title>. <source>Nutrients</source>. (<year>2021</year>) <volume>13</volume>:<fpage>1694</fpage>. doi: <pub-id pub-id-type="doi">10.3390/nu13051694</pub-id>, PMID: <pub-id pub-id-type="pmid">34067585</pub-id></citation></ref>
<ref id="ref32"><label>32.</label><citation citation-type="journal"><person-group person-group-type="author"><name><surname>Stroeve</surname> <given-names>JH</given-names></name> <name><surname>Saccenti</surname> <given-names>E</given-names></name> <name><surname>Bouwman</surname> <given-names>J</given-names></name> <name><surname>Dane</surname> <given-names>A</given-names></name> <name><surname>Strassburg</surname> <given-names>K</given-names></name> <name><surname>Vervoort</surname> <given-names>J</given-names></name> <etal/></person-group>. <article-title>Weight loss predictability by plasma metabolic signatures in adults with obesity and morbid obesity of the DiOGenes study</article-title>. <source>Obesity</source>. (<year>2016</year>) <volume>24</volume>:<fpage>379</fpage>&#x2013;<lpage>88</lpage>. doi: <pub-id pub-id-type="doi">10.1002/oby.21361</pub-id>, PMID: <pub-id pub-id-type="pmid">26813527</pub-id></citation></ref>
<ref id="ref33"><label>33.</label><citation citation-type="journal"><person-group person-group-type="author"><name><surname>Nasser</surname> <given-names>S</given-names></name> <name><surname>Vialichka</surname> <given-names>V</given-names></name> <name><surname>Biesiekierska</surname> <given-names>M</given-names></name> <name><surname>Balcerczyk</surname> <given-names>A</given-names></name> <name><surname>Pirola</surname> <given-names>L</given-names></name></person-group>. <article-title>Effects of ketogenic diet and ketone bodies on the cardiovascular system: concentration matters</article-title>. <source>World J Diabetes</source>. (<year>2020</year>) <volume>11</volume>:<fpage>584</fpage>&#x2013;<lpage>95</lpage>. doi: <pub-id pub-id-type="doi">10.4239/wjd.v11.i12.584</pub-id>, PMID: <pub-id pub-id-type="pmid">33384766</pub-id></citation></ref>
<ref id="ref34"><label>34.</label><citation citation-type="journal"><person-group person-group-type="author"><name><surname>Bjune</surname> <given-names>MS</given-names></name> <name><surname>Lindquist</surname> <given-names>C</given-names></name> <name><surname>Hallvardsdotter Stafsnes</surname> <given-names>M</given-names></name> <name><surname>Bj&#x00F8;rndal</surname> <given-names>B</given-names></name> <name><surname>Bruheim</surname> <given-names>P</given-names></name> <name><surname>Aloysius</surname> <given-names>TA</given-names></name> <etal/></person-group>. <article-title>Plasma 3-hydroxyisobutyrate (3-HIB) and methylmalonic acid (MMA) are markers of hepatic mitochondrial fatty acid oxidation in male Wistar rats</article-title>. <source>Biochim Biophys Acta Mol Cell Biol Lipids</source>. (<year>2021</year>) <volume>1866</volume>:<fpage>158887</fpage>. doi: <pub-id pub-id-type="doi">10.1016/j.bbalip.2021.158887</pub-id></citation></ref>
<ref id="ref35"><label>35.</label><citation citation-type="journal"><person-group person-group-type="author"><name><surname>Mierziak</surname> <given-names>J</given-names></name> <name><surname>Burgberger</surname> <given-names>M</given-names></name> <name><surname>Wojtasik</surname> <given-names>W</given-names></name></person-group>. <article-title>3-Hydroxybutyrate as a metabolite and a signal molecule regulating processes of living organisms</article-title>. <source>Biomol Ther</source>. (<year>2021</year>) <volume>11</volume>:<fpage>402</fpage>. doi: <pub-id pub-id-type="doi">10.3390/biom11030402</pub-id>, PMID: <pub-id pub-id-type="pmid">33803253</pub-id></citation></ref>
<ref id="ref36"><label>36.</label><citation citation-type="journal"><person-group person-group-type="author"><name><surname>Papandreou</surname> <given-names>C</given-names></name> <name><surname>Garc&#x00ED;a-Gavil&#x00E1;n</surname> <given-names>J</given-names></name> <name><surname>Camacho-Barcia</surname> <given-names>L</given-names></name> <name><surname>Toft Hansen</surname> <given-names>T</given-names></name> <name><surname>Harrold</surname> <given-names>JA</given-names></name> <name><surname>Sj&#x00F6;din</surname> <given-names>A</given-names></name> <etal/></person-group>. <article-title>Changes in circulating metabolites during weight loss are associated with adiposity improvement, and body weight and adiposity regain during weight loss maintenance: the satin study</article-title>. <source>Mol Nutr Food Res</source>. (<year>2021</year>) <volume>65</volume>:<fpage>2001154</fpage>. doi: <pub-id pub-id-type="doi">10.1002/mnfr.202001154</pub-id>, PMID: <pub-id pub-id-type="pmid">34184401</pub-id></citation></ref>
</ref-list>
<sec id="sec27">
<title>Glossary</title>
<table-wrap position="anchor" id="tab6">
<table frame="hsides" rules="groups">
<tbody>
<tr>
<td align="left" valign="top">AA</td>
<td align="left" valign="top">Amino Acid (and derivatives)</td>
</tr>
<tr>
<td align="left" valign="top">ADD</td>
<td align="left" valign="top">Average Danish Diet</td>
</tr>
<tr>
<td align="left" valign="top">ASCA</td>
<td align="left" valign="top">ANOVA Simultaneous Component Analysis</td>
</tr>
<tr>
<td align="left" valign="top">BW</td>
<td align="left" valign="top">Body Weight</td>
</tr>
<tr>
<td align="left" valign="top">AUC</td>
<td align="left" valign="top">Area Under the Curve</td>
</tr>
<tr>
<td align="left" valign="top">CRP</td>
<td align="left" valign="top">C-Reactive Protein</td>
</tr>
<tr>
<td align="left" valign="top">DBP</td>
<td align="left" valign="top">Diastolic Blood Pressure</td>
</tr>
<tr>
<td align="left" valign="top">DMA</td>
<td align="left" valign="top">Dimethylamine</td>
</tr>
<tr>
<td align="left" valign="top">FID</td>
<td align="left" valign="top">Free Induction Decay</td>
</tr>
<tr>
<td align="left" valign="top">FDR</td>
<td align="left" valign="top">False Discovery Rate</td>
</tr>
<tr>
<td align="left" valign="top">HOMA-IR</td>
<td align="left" valign="top">Homeostatic Model Assessment for Insulin Resistance</td>
</tr>
<tr>
<td align="left" valign="top">IDL</td>
<td align="left" valign="top">Intermediate Density Lipoprotein</td>
</tr>
<tr>
<td align="left" valign="top">IL6</td>
<td align="left" valign="top">Interleukin-6</td>
</tr>
<tr>
<td align="left" valign="top">LP</td>
<td align="left" valign="top">Lipoprotein</td>
</tr>
<tr>
<td align="left" valign="top">NND</td>
<td align="left" valign="top">New Nordic Diet</td>
</tr>
<tr>
<td align="left" valign="top">OA</td>
<td align="left" valign="top">Organic Acid</td>
</tr>
<tr>
<td align="left" valign="top">OH</td>
<td align="left" valign="top">Alcohol</td>
</tr>
<tr>
<td align="left" valign="top">PCA</td>
<td align="left" valign="top">Principal Component Analysis</td>
</tr>
<tr>
<td align="left" valign="top">PLS-DA</td>
<td align="left" valign="top">Partial Least Squares Discriminant Analysis</td>
</tr>
<tr>
<td align="left" valign="top">RG</td>
<td align="left" valign="top">Receiver Gain</td>
</tr>
<tr>
<td align="left" valign="top">SFA</td>
<td align="left" valign="top">Short Chain Fatty Acid</td>
</tr>
<tr>
<td align="left" valign="top">SS</td>
<td align="left" valign="top">Signature Signal</td>
</tr>
<tr>
<td align="left" valign="top">SUS</td>
<td align="left" valign="top">Signature signal of Unknown Spin System</td>
</tr>
<tr>
<td align="left" valign="top">SYS</td>
<td align="left" valign="top">Systolic Blood Pressure</td>
</tr>
<tr>
<td align="left" valign="top">TCA</td>
<td align="left" valign="top">Tricarboxylic Acid</td>
</tr>
<tr>
<td align="left" valign="top">TMAO</td>
<td align="left" valign="top">Trimethylamine-N-Oxide</td>
</tr>
<tr>
<td align="left" valign="top">TNF</td>
<td align="left" valign="top">Tumor Necrosis Factor</td>
</tr>
</tbody>
</table>
</table-wrap>
</sec>
</back>
</article>