Abstract
Soldiers in active military service need optimal physical fitness for
successfully carrying out their operations. Therefore, their health
status is regularly checked by army doctors. These inspections include
physical parameters such as the body-mass index (BMI), functional
tests, and biochemical studies. If a medical exam reveals an
individual’s excess weight, further examinations are made, and
corrective actions for weight lowering are initiated. The collection of
urine is non-invasive and therefore attractive for frequent metabolic
screening. We compared the chemical profiles of urinary samples of 146
normal weight, excess weight, and obese soldiers of the Mexican Army,
using untargeted metabolomics with liquid chromatography coupled to
high-resolution mass spectrometry (LC-MS). In combination with data
mining, statistical and metabolic pathway analyses suggest increased
S-adenosyl-L-methionine (SAM) levels and changes of amino acid
metabolites as important variables for overfeeding. We will use these
potential biomarkers for the ongoing metabolic monitoring of soldiers
in active service. In addition, after validation of our results, we
will develop biochemical screening tests that are also suitable for
civil applications.
Keywords: Metabolic status, Metabolomics, Military service, Soldiers,
Public health, Obesity, Data mining
Introduction
Many professionals require a certain level of physical fitness for
their work, particularly first-line responders such as firefighters,
paramedics, and military personnel. To ensure their operability, they
require, in addition to training, good eating habits and periodic
review of their health status.
Overweight and obesity are present in most populations and are the
origin of numerous metabolic diseases ([40]Kaplan, 1989; [41]Tchernof &
Després, 2013; [42]Cirulli et al., 2019). The World Health Organization
(WHO) recognizes obesity as a global epidemic ([43]James, 2008).
In Mexico, the prevalence of overweight and obesity is dramatically
high at about 75% ([44]Instituto Nacional de Salud Pública (MX), 2018).
Thus, the Mexican official standard NOM-008-SSA3-2010 for the
comprehensive management of obesity defines obesity as a public health
problem in Mexico due to its magnitude and impact. Criteria for health
management should support the early detection, prevention,
comprehensive treatment, and control of the growing number of patients
([45]Secretaría de Gobernación (MX), 2010).
Soldiers of the Mexican Army have regular exams of their health state
by a military doctor. Since overweight and obese soldiers could present
risks for their own health and missions, mainly in the special bodies
such as paratroopers, they are sent to lose weight in particular
training camps such as the “Center for improving lifestyle and health”
in Mexico City. Furthermore, the social security institute’s law for
the Mexican Armed Forces considers soldiers with a Body Mass Index
(BMI) greater than 30 as incapable of active service ([46]Cámara de
Diputados (MX), 2019). This medical assessment of the soldiers measures
vital signs, weight, height, calculating the BMI, clinical history, and
a meticulous clinical examination of the body’s apparatus and systems.
Additional laboratory and cabinet studies are indicated if the doctor
identifies alterations or abnormalities in these clinical analyses. All
these studies could reveal possible diseases. However, for the case of
overweight and obesity, the diagnosis is currently only based on the
calculation of the BMI without considering important aspects such as
the patient’s physiological and metabolic status.
Metabolites in body fluids can be analyzed to assess the nutrition and
endogenous changes associated with overweight and obesity, using
techniques such as nuclear magnetic resonance (NMR) and mass
spectrometry (MS) ([47]Xie, Waters & Schirra, 2012; [48]Zhang, Sun &
Wang, 2013). Usually, invasive studies such as blood analyses explore
the patients’ metabolic changes and monitor corrective actions. On the
other hand, non-invasive tests are generally limited to phenotypic
measurements such as body mass index.
Analyzing urine would be more convenient for patients and provide
information on the metabolism and pathways involved in particular
conditions ([49]Braga, 2017). Urine is a biofluid that contains
different molecules generated by the organism’s metabolism that must be
eliminated and represents an excellent source of human sample material
because it is available non-invasively. Typically, various molecules
are altered simultaneously in diseased people ([50]Bruzzone et al.,
2021).
Artificial intelligence and machine learning algorithms can support
medical diagnosis ([51]Hatwell, Gaber & Azad, 2020). Classification is
the most widely implemented machine learning task in the medical
sector, employing, for example, the Adaptive Boost algorithm
([52]Freund, 2001). Adaptive Boost pre-processing also helps to select
the most important features automatically from high dimensional data
and decision trees ([53]Rangini & Jiji, 2013).
This study used untargeted metabolomics based on mass spectrometry to
analyze urine from military personnel with normal and excess weight
(overweight and obesity). Using Ada Boost data mining, we created a
classification model and identified possible biomarkers for monitoring
the metabolic state of soldiers and the early diagnosis of deviations.
Materials and Methods
Participants and sample preparation
Participants were recruited from the Military Medical Sciences Center,
Mexico City, Mexico. Inclusion criteria were: both sexes, active
military service, and signed consent to participate voluntarily.
Participants answered a questionnaire to identify risk factors for
obesity; the next day, nutritional status was assessed by bioelectrical
impedance.
The Body-Mass-Index (BMI) was calculated using [54]Eq. (1), according
to the WHO definition ([55]World Health Organization (WHO), 2021):
[MATH: BMI=massheight2 :MATH]
(1)
with the person’s weight measured in kilograms (kg) and the person’s
height in meters (m).
Following the WHO system, soldiers with a BMI equal to or higher than
25 were classified as ‘overweight,’ and those with a BMI equal to or
above 30 as ‘obese’ ([56]World Health Organization (WHO), 2021).
The first urine of the day was collected at 6 am, and the samples were
frozen at −60 °C until their processing. Urine samples were thawed and
centrifuged at 850 g for 5 min for metabolomics analysis. Ten L of each
sample were diluted in 90 L of chromatography-mass spectrometry (LC-MS)
grade water (1:9 v/v) and transferred to vials for UPLC-MS analysis.
Untargeted metabolomics by HPLC-MS
LC-MS grade acetonitrile, water, and acetic acid were purchased from JT
Baker (Brick Town, NJ, USA). Samples were analyzed with a Dionex
UltiMate 3000 HPLC (Thermo Scientific, Waltham, MA, USA) coupled to an
Orbitrap Fusion Tribrid Mass Spectrometer (Thermo Scientific) with an
electrospray ionization source. We used an AccuCore C18 column (4.6 ×
150 mm, 2.6 m) to separate metabolites using a binary gradient elution
of solvents A and B, similar to the method described by
[57]López-Hernández et al. (2019). In short, the mobile phase was A:
0.5% acetic acid in water; B: 0.5% acetic acid in acetonitrile. The
mobile phase was delivered at a flow rate of 0.5 mL/min, initially with
1% B, followed by a linear gradient to 15% B over 3 min. Solvent B was
increased to 50% within 3 min. Over the next 4 min, the gradient was
ramped up to 90% B with a plateau for 2 min. The amount of B was then
decreased to 50% in 2 min. 2 min later, the solvent B was lowered to
15%, and finally, solvent B returned to initial conditions(1%) until
the end of the chromatographic run (18 min). The column temperature was
controlled at 40 °C. The injection volume was 20 L.
Data were acquired in positive electrospray ionization (ESI+) mode with
the capillary voltage set to 3.5 kV, the Ion Transfer Tube Temperature
to 350 °C, and Vaporizer Temp to 400 °C. The desolvation gas was
nitrogen with a flow rate of 50 UA (arbitrary units). The detector type
was Orbitrap at a resolution of 120,000. Data were acquired from
50–2,000 m/z in Full Scan mode with an AGC target of 2.0E5. Before the
analysis, the mass spectrometer was calibrated with LTQ ESI Positive
Ion Calibration Solution (Pierce, Thermo Scientific).
Conversion of raw files to mzML
We used the docker version of the ProteoWizard msconvert tool
([58]https://proteowizard.sourceforge.io/) ([59]Kessner et al., 2008).
To reduce disk space and memory use during file processing, we
downsampled the data to 32-bit, peak picking, and zlib compression:
Processing of mzML files with KNIME
For mass spectrometry raw data processing and generation of an aligned
feature matrix, we employed the OpenMS nodes ([60]Sturm et al., 2008;
[61]Pfeuffer et al., 2017; [62]Röst et al., 2016) of the KNIME
Analytics Platform ([63]https://www.knime.com) ([64]Berthold et al.,
2009; [65]Alka et al., 2020). [66]Figure 1 represents the KNIME
workflow for the raw data processing and matrix generation. The exact
parameters of each step are documented in the workflow.knime workflow
file, provided as [67]Supplementary Files at Zenodo (see ‘Data
Availability’ statement below). For preparing the resulting table of
aligned features for the MetaboAnalyst Web Server ([68]Xia et al.,
2009), we edited the .CSV file with vim ([69]https://www.vim.org/),
using the CSV vim plugin ().
Figure 1. KMIME-Workflow for processing the urinary metabolomics data.
[70]Figure 1
[71]Open in a new tab
The final result is an aligned matrix of features.
Statistical analyses with MetaboAnalyst
For metabolic classification models, we used the web-based version of
MetaboAnalyst ([72]https://www.metaboanalyst.ca/) ([73]Xia et al.,
2009; [74]Chong, Yamamoto & Xia, 2019; [75]Wishart, 2020). We applied
the one-factor statistical analysis for peak intensities in a plain
text file, with unpaired samples in columns.
The MetaboAnalyst report for the uploaded data is provided as a
[76]Supplemental File.
First, we filtered the raw data by the interquartile range (IQR),
normalized it by the median, and applied a square root transformation.
Further, we used auto-scaling, i.e., the values were mean-centered and
divided by the standard deviation of each variable.
Metabolic pathway enrichment and metabolite identification
For identifying metabolic pathway enrichment and likely involved
metabolites, we used the Functional Analysis (MS peaks) tool of
MetaboAnalyst ([77]Li et al., 2013). We specified a mass search against
the Human Metabolome Database (HMDB, [78]https://hmdb.ca) ([79]Wishart
et al., 2018; [80]Wishart et al., 2022), with 10 ppm mass tolerance in
positive mode. We filtered raw data by the interquartile range (IQR),
normalized by the median, and applied a square root transformation.
Further, we used auto-scaling, i.e., the values were mean-centered and
divided by the standard deviation of each variable (the same data
preparation as for statistics above). For the Mummichog algorithm, we
set a p-value cutoff of 0.25 (default top: 10% peaks). We used the
pathway library of Homo sapiens MFN pathway/metabolite sets (a meta
library) with at least five entries.
The chemical structure and function of metabolites and the
identifications from the Mummichog analysis were searched in the KEGG
database ([81]https://www.genome.jp/kegg/compound/) ([82]Kanehisa et
al., 2014), BiGG ([83]http://bigg.ucsd.edu/universal/metabolites/)
([84]King et al., 2016), the Edinburgh human metabolic network
reconstruction ([85]Ma et al., 2007) and the above-mentioned HMDB.
Results
Body-Mass-Index (BMI) and body fat content of participants
[86]Table 1 summarizes statistical data of the 153 participants. Of the
67 women and 86 men, 66 presented normal weight, 62 had overweight, and
25 were obese. Comparing female and male soldiers, the latter exhibited
a higher prevalence of overweight and obesity. As expected, the groups
with higher BMI also presented a higher body fat content, suggesting
metabolic differences between these groups.
Table 1. General characteristics and anthropometric measurements of the
soldiers by normal weight, overweight and obesity (Data are presented as mean
± SD).
n Normal weight Overweight Obesity Global
66 62 25 153
Age [years] 27.74 ± 3.53 29.81 ± 4.53 37.83 ± 6.79 30.20 ± 5.73
Age range 22–45 22–45 29–49 22–49
Gender
Female (% n) 43 (28.1) 18 (11.8) 6 (3.9) 67 (43.8)
Male (% n) 23 (15.0) 44 (28.8) 19 (12.4) 86 (56.2)
Weight [kg] 61.05 ± 7.32 75.46 ± 6.18 84.02 ± 12.29 70.79 ± 11.77
Height [m] 1.62 ± 0.05 1.66 ± 0.06 1.60 ± 0.05 1.63 ± 0.06
BMI [kg/m^2] 23.02 ± 1.45 27.08 ± 1.33 33.33 ± 2.41 26.39 ± 3.88
Body fat [%] 25.09 ± 6.97 27.51 ± 6.28 34.63 ± 4.75 27.7. ± 7.10
[87]Open in a new tab
Notes.
BMI
Body Mass Index
Urinary metabolomics raw data processing and filtering
[88]Figure 2 shows the number of features in the different sample
groups and blank samples. We removed data sets of presumably empty
samples and technical outliers by comparing the number of features with
blank injections and eliminating all analyses with less than 4,000
features.
Figure 2. Clean-up of raw data.
[89]Figure 2
[90]Open in a new tab
Sample data sets with less than 4,000 features were removed. (A)
Boxplot of features (A) before clean-up, (B) after removal of samples
with less than 4,000 features. A total of 120 data sets of healthy,
overweight and obese individuals were used for further analyses.
After clean-up, 52 samples of healthy, 47 overweight, and 21 obese
individuals were left. We used these 120 data sets for further
analysis. The healthy group showed 5,717 to 9,657, the overweight group
5,559 to 10,447, and the obese group 5,575 to 9,436 features.
Identification of metabolic identities with MetaboAnalyst
First, we applied a cluster analysis with the sparse PLS-DA (sPLS-DA)
algorithm ([91]Lê Cao, Boitard & Besse, 2011), which indicates distinct
metabolic identities of healthy, overweight, and obese individuals.
However, the clustering is far from perfect, and especially the group
of overweight individuals does not separate well from the other groups
([92]Fig. 3A). We discussed the difficulty of clustering metabolic data
in an earlier paper ([93]Winkler, 2015).
Figure 3. Metabolic identity of healthy, overweight and obese groups.
[94]Figure 3
[95]Open in a new tab
(A) The clusters of sPLS-DA show overlapping of the three sample
classes. The healthy and obese group can be more clearly discriminated,
whereas the overweight group is located in between them. (B) OPLS-DA
scores separate the samples of healthy individuals from overweight and
obese soldiers.
To test if we could distinguish between healthy participants and
others, we joined the overweight and obese groups and applied an
orthogonal projection to latent structures data analysis (OPLS-DA)
([96]Trygg & Wold, 2002). As a result, two clusters were separated
reasonably well, (1) samples of healthy individuals and (2) samples of
overweight and obese soldiers ([97]Fig. 3B).
The classification is imperfect; however, the graphics represent the
medical situation of clearly healthy, obviously sick, and patients in
transition. Consequently, we can discriminate between two metabolic
identities of normal-weight and overweight/obese soldiers.
Statistical analysis of fold-changes
Using the same parameters for uploading the data (see ‘Methods’), but
only defining two groups, i.e., healthy and obese-overweight, we
created the Volcano plot shown in [98]Fig. 4. We did this analysis in
the one-factor statistical analysis module of MetaboAnalyst. We defined
non-parametric Wilcoxon rank-sum tests, a fold-change of 1.3 and a
p-value threshold of 0.1 (raw), with equal group variance.
Figure 4. The Volcano plot shows metabolic features with a P-value <0.1 and a
fold-change of 1.3.
[99]Figure 4
[100]Open in a new tab
Two hundred twenty-five significant differential variables were
detected and subjected to an Adaptive Boost data mining analysis.
Adaptive boost analysis
The preselected 225 variables were loaded into R/Rattle ([101]Williams,
2009; [102]Williams, 2011) for further evaluation and split into three
partitions for training, validation, and testing (70/15/15). Variables
with missing values were deleted. The following parameters were used:
[103]Table 2 summarizes the results of the model building process. The
overall error of the model is 5.5%, with an average class error of
5.75%.
Table 2. Predictive classification model with the Adaptive Boost algorithm.
Predicted
Actual Healthy Obese-overweight Error [%]
Training Healthy 44 0 0.0
Obese-overweight 0 58 0.0
Validation Healthy 6 3 33.3
Obese-overweight 2 10 16.7
Testing Healthy 9 2 18.2
Obese-overweight 1 11 8.3
Overall Healthy 59 5 7.8
Obese-overweight 3 79 3.7
[104]Open in a new tab
Consequently, the classification between healthy and obese-overweight
persons based on urinary metabolomics profiles is highly reliable,
considering natural variations.
The important variables that contribute most to correct classification
are shown in [105]Fig. 5.
Figure 5. Variable importance for the predictive Adaptive Boost
classification model.
[106]Figure 5
[107]Open in a new tab
Biomarker analysis
[108]Table 3 lists important variables from the Ada Boost analysis with
at least a 1.3-fold significant change. Those ions are possible
biomarkers for weight-related metabolic studies.
Table 3. Important variables from the Ada Boost analysis with at least
1.3-fold significant change.
Ada Boost m/z FC log2 (FC) raw.pval −log10 (p)
1 305.096085357725 0.67706 −0.56264 0.000000054252 7.2656
2 176.05534607238 0.76713 −0.38246 0.00081848 3.087
3 114.053383082002 1.3627 0.44649 0.000069642 4.1571
4 258.127823892932 1.4759 0.56159 0.0010258 2.989
5 176.10230666151 1.3729 0.45718 0.022281 1.6521
6 82.9609575200155 0.68689 −0.54184 0.039329 1.4053
7 246.167018958163 1.566 0.64711 0.041643 1.3805
8 153.091303342611 1.4299 0.51588 0.012894 1.8896
9 104.99663756284 0.75266 −0.40993 0.014395 1.8418
10 227.101700473198 1.968 0.97672 0.013038 1.8848
11 208.063674165656 1.4688 0.55469 0.098829 1.0051
12 187.002131945098 0.75863 −0.39852 0.032069 1.4939
13 115.075775049445 0.6563 −0.60758 0.0017274 2.7626
14 192.105233415702 0.60822 −0.71733 0.00025415 3.5949
15 204.121253887635 1.924 0.94407 0.099638 1.0016
16 222.080121719522 1.788 0.83835 0.010779 1.9674
17 80.9549688491325 0.70797 −0.49824 0.04125 1.3846
18 218.134680226487 2.1311 1.0916 0.039707 1.4011
19 211.06880722364 1.3152 0.39528 0.010779 1.9674
20 175.023674939912 0.75944 −0.39698 0.094865 1.0229
21 304.149677463601 1.3526 0.43569 0.0025023 2.6017
22 276.180382062822 0.58665 −0.76942 0.011404 1.9429
23 260.144346264144 1.7745 0.82742 0.034686 1.4598
24 199.096606327732 0.69475 −0.52543 0.00054643 3.2625
25 139.998348382386 0.68953 −0.53631 0.050208 1.2992
26 195.087746674809 1.7269 0.78819 0.017119 1.7665
27 176.066233961146 0.72685 −0.46027 0.00081848 3.087
28 286.128705723401 1.388 0.47301 0.0055271 2.2575
29 174.911397524627 1.4127 0.49845 0.0085721 2.0669
30 211.144964577744 1.322 0.40276 0.016049 1.7946
[109]Open in a new tab
Notes.
Ada Boost
Ada Boost rank
m/z
mass-to-charge ratio of feature
FC
fold-change
pval
p-value
Mummichog analysis: metabolic pathway enrichment
To explore affected metabolic pathways and facilitate the
identification of metabolites, we performed a Mummichog analysis in
MetaboAnalyst (see ‘Methods’).
As indicated in [110]Table 4 and [111]Fig. 6, five pathways
demonstrated enrichment above the defined threshold limits:
Table 4. Enriched pathways from the Mummichog analysis.
Pathway Pathway tot. Hits tot. Hits sig. Expected FET EASE Gamma Emp.
Hits Emp. Pathway No. Cpd. Hits
Urea cycle/amino group metabolism 85 50 10 3.7797 0.0045702 0.0136
0.039704 0 0 P1 [112]C00062; [113]C04441; [114]C04692; [115]C00437;
[116]C00073; [117]C00019; [118]C00242; [119]C01449; [120]C01250;
[121]C00547; [122]C00049
Alanine and Aspartate Metabolism 30 20 5 1.334 0.016982 0.065906
0.041654 0 0 P2 [123]C00062; [124]C00940; [125]C01042; [126]C00402;
[127]C00049
Drug metabolism - cytochrome P450 53 48 7 2.3567 0.079575 0.17018
0.046002 0 0 P3 [128]C16582; [129]C16604; [130]C16550; [131]C07501;
[132]C16609; [133]C16584; [134]C16586
Aspartate and asparagine metabolism 114 77 9 5.0692 0.14967 0.25437
0.050052 0 0 P4 [135]C00437; [136]C01239; CE1938; [137]C00402;
[138]C05932; [139]C00062; [140]C02571; [141]C04540; [142]C03078;
[143]C03415; CE1943; [144]C00049
Lysine metabolism 52 28 4 2.3123 0.17608 0.38004 0.057276 0 0 P5
[145]C00019; [146]C06157; [147]C03793; [148]C01259
Ubiquinone Biosynthesis 10 7 2 0.44467 0.10051 0.43686 0.061142 0 0 P6
[149]C01179; [150]C00019
Vitamin B3 (nicotinate and nicotinamide) metabolism 28 19 3 1.2451
0.18615 0.44767 0.061929 0 0 P7 [151]C00062; [152]C00019; [153]C00049
Vitamin B1 (thiamin) metabolism 20 9 2 0.88933 0.15545 0.5223 0.067899
0 0 P8 [154]C06157; [155]C16255
Tyrosine metabolism 160 103 9 7.1147 0.43083 0.57147 0.072443 0 0 P9
[156]C05350; [157]C00019; [158]C05852; [159]C03758; [160]C02505;
[161]C00547; CE5547; [162]C00642; [163]C00082; [164]C05576;
[165]C07453; [166]C00355; [167]C01179; [168]C00268; [169]C05584;
[170]C05587; [171]C05588; [172]C04043; CE2174; CE2176; CE2173
Arginine and Proline Metabolism 45 38 4 2.001 0.35481 0.58556 0.073852
0 0 P10 [173]C00062; [174]C00073; [175]C00019; [176]C00049; [177]C05933
Biopterin metabolism 22 14 2 0.97827 0.3058 0.68367 0.085412 2 0.02 P11
[178]C04244; [179]C00268; [180]C00082
Pyrimidine metabolism 70 45 4 3.1127 0.48368 0.70125 0.08789 0 0 P12
[181]C00214; [182]C00881; [183]C00475; [184]C00049
Tryptophan metabolism 94 74 6 4.1799 0.54076 0.70613 0.088605 0 0 P13
[185]C05647; [186]C00019; [187]C05651; [188]C02220; [189]C00078;
[190]C00268; [191]C00328; [192]C04409; [193]C03227; [194]C00525
Starch and Sucrose Metabolism 33 15 2 1.4674 0.33598 0.70875 0.088995 0
0 P14 CE2837; [195]C01083; [196]C00208
Vitamin B9 (folate) metabolism 33 16 2 1.4674 0.36578 0.73186 0.092598
0 0 P15 [197]C01045; [198]C00504
Butanoate metabolism 34 20 2 1.5119 0.47883 0.80744 0.10716 1 0.01 P16
[199]C05548; [200]C02727
Porphyrin metabolism 43 20 2 1.9121 0.47883 0.80744 0.10716 0 0 P17
[201]C05520; [202]C00931
Xenobiotics metabolism 110 59 4 4.8913 0.7018 0.8572 0.1204 0 0 P18
[203]C00870; [204]C14853; [205]C06205; [206]C14871
Histidine metabolism 33 25 2 1.4674 0.60163 0.87285 0.12555 8 0.08 P19
[207]C00439; [208]C00019
Methionine and cysteine metabolism 94 47 3 4.1799 0.73432 0.89655
0.13469 0 0 P20 [209]C08276; [210]C00019; [211]C00073
Sialic acid metabolism 107 28 2 4.7579 0.66429 0.90095 0.13661 0 0 P21
[212]C00140; [213]C00645; [214]C00243
Purine metabolism 80 53 3 3.5573 0.80598 0.93105 0.15258 0 0 P22
[215]C00499; [216]C00242; [217]C00049
Galactose metabolism 41 34 2 1.8231 0.7658 0.93997 0.15864 0 0 P23
[218]C00140; [219]C05400; [220]C05402; [221]C05399; [222]C00243;
[223]C00089
Glycine, serine, alanine and threonine metabolism 88 60 3 3.9131
0.86848 0.95761 0.17378 1 0.01 P24 [224]C00062; [225]C00019;
[226]C00073
Androgen and estrogen biosynthesis and metabolism 95 71 3 4.2243
0.93142 0.98074 0.20732 0 0 P25 [227]C02538; [228]C05293; [229]C00019;
[230]C03917; [231]C04373; [232]C04295; [233]C00523
Glycero-phospholipid metabolism 156 49 2 6.9368 0.9118 0.98298 0.21248
1 0.01 P26 [234]C00019; [235]C00670
Leukotriene metabolism 92 54 2 4.0909 0.93745 0.98885 0.22988 0 0 P27
[236]C03577; CE5140; CE4995
C21-steroid hormone biosynthesis and metabolism 112 81 2 4.9803 0.99121
0.99889 0.31857 0 0 P28 [237]C03917; [238]C02538; [239]C04373;
[240]C00523
Hyaluronan Metabolism 8 4 1 0.35573 0.28138 1 1 0 0 P29 [241]C00140
Glycolysis and Gluconeogenesis 49 32 1 2.1789 0.93051 1 1 0 0 P30
[242]C01136
Hexose phosphorylation 20 16 1 0.88933 0.73463 1 1 2 0.02 P31
[243]C01083; [244]C00089
Keratan sulfate degradation 68 6 1 3.0237 0.391 1 1 0 0 P32 [245]C00140
Carnitine shuttle 72 23 1 3.2016 0.8521 1 1 0 0 P33 pcrn
Alkaloid biosynthesis II 10 6 1 0.44467 0.391 1 1 0 0 P34 egme
Parathio degradation 6 5 1 0.2668 0.33844 1 1 0 0 P35 [246]C00870
Electron transport chain 7 3 1 0.31127 0.21943 1 1 0 0 P36 [247]C00390
Vitamin H (biotin) metabolism 5 5 1 0.22233 0.33844 1 1 0 0 P37
[248]C00120
De novo fatty acid biosynthesis 106 22 1 4.7135 0.83919 1 1 0 0 P38
[249]C06429
Vitamin A (retinol) metabolism 67 41 1 2.9793 0.96749 1 1 0 0 P39
[250]C16679; [251]C16677; [252]C16680
Valine, leucine and isoleucine degradation 65 26 1 2.8903 0.88497 1 1
14 0.14 P40 [253]C00123; [254]C00407
Fatty Acid Metabolism 63 15 1 2.8014 0.71158 1 1 0 0 P41 [255]C02571
Heparan sulfate degradation 34 5 1 1.5119 0.33844 1 1 0 0 P42
[256]C00140
TCA cycle 31 18 1 1.3785 0.77539 1 1 0 0 P43 [257]C00390
Arachidonic acid metabolism 95 75 1 4.2243 0.99823 1 1 0 0 P44
[258]C04741; [259]C04843; [260]C14782; [261]C14814; [262]C00639
Phosphatidyl-inositol phosphate metabolism 59 29 1 2.6235 0.91057 1 1 0
0 P45 [263]C01235
Prostaglandin formation from arachidonate 78 61 1 3.4684 0.99409 1 1 0
0 P46 [264]C04741; [265]C05959; [266]C00639
Vitamin B6 (pyridoxine) metabolism 11 8 1 0.48913 0.48401 1 1 3 0.03
P47 [267]C00314
N-Glycan Degradation 16 8 1 0.71147 0.48401 1 1 1 0.01 P48 [268]C00140
Vitamin B12 (cyanocobalamin) metabolism 9 3 1 0.4002 0.21943 1 1 0 0
P49 [269]C00019
Carbon fixation 10 10 1 0.44467 0.5629 1 1 0 0 P50 [270]C00049
Nitrogen metabolism 6 4 1 0.2668 0.28138 1 1 4 0.04 P51 [271]C00049
Drug metabolism - other enzymes 31 22 1 1.3785 0.83919 1 1 5 0.05 P52
[272]C16631
Aminosugars metabolism 69 25 1 3.0682 0.87491 1 1 3 0.03 P53
[273]C00140; [274]C00645
Beta-Alanine metabolism 20 15 1 0.88933 0.71158 1 1 11 0.11 P54
[275]C00049
Prostaglandin formation from dihomo gama-linoleic acid 11 8 1 0.48913
0.48401 1 1 0 0 P55 [276]C04741
[277]Open in a new tab
Notes.
Pathway tot.
total number of compounds in this pathway
Hits tot.
total of putative hits for this pathway
Hits sig.
significant hits
Expected
randomly expected hits
FET
Fisher’s exact test
EASE
adjusted FET
Gamma
gamma corrected p-value
Emp.
empirical compounds, such as adducts
Cpd.
compound (with KEGG database identifier)
The compounds corresponding to the database identifiers are provided as
a [278]Table S1.
Figure 6. Enriched pathways from the Mummichog analysis.
[279]Figure 6
[280]Open in a new tab
* •
Urea cycle/amino group metabolism
* •
Alanine and aspartate metabolism
* •
Drug metabolism—cytochrome P450
* •
Aspartate and asparagine metabolism
* •
Ubiquinone biosynthesis.
Especially the appearance of urea cycle/amino group metabolism as the
first hit gives confidence to the Mummichog algorithm since no
information about the origin of the samples was given to the
MetaboAnalyst platform.
Thus, ions assigned to metabolites of enriched pathways have increased
confidence in our further discussion.
Discussion
Classification of normal weight vs. overweight-obese, based on metabolic
signature
To develop a predictive classification model, we used the untargeted
LC-MS features with at least a 1.3-fold change. The features correspond
to ions with a particular retention time. Although a 30% increased or
decreased metabolite level might not be critical for health, it can
indicate a disturbed pathway.
Identifying compounds corresponding to the features is theoretically
possible. However, the reliable assignment of metabolites is tedious
([281]Rathahao-Paris et al., 2015; [282]Jeffryes et al., 2015;
[283]Fuente et al., 2019; [284]Djoumbou-Feunang et al., 2019;
[285]Dührkop et al., 2019), and the data mining models are helpful
without knowing the related compounds ([286]Winkler, 2015). Thus, we
limited the identification of compounds to important variables.
The OPLS-DA analysis already indicated distinct metabolic identities
([287]Fig. 3B) for normal weight and overweight-obese individuals. A
predictive model that we developed with the Adaptive Boost algorithm
was able to classify normal weight and overweight-obese individuals
with an overall error of 5.5% ([288]Table 2). Notably, the highest
errors were found in the validation and testing data of healthy
soldiers wrongly classified as overweight or obese. These assignments
could indicate a possible tendency of the soldiers to gain weight.
The Adaptive Boost model demonstrates metabolic differences between
normal weight and overweight-obese individuals, which can be used for
classification. Further, the Adaptive Boost could provide a sensitive
method to estimate the metabolic state and the tendency of a person to
gain weight. However, additional studies are necessary to evaluate the
performance of Adaptive Boost models with untargeted metabolic data as
a predictive tool in clinical diagnostics and treatment.
Metabolic pathways in obesity-overweight and potential biomarkers
Compiling the biomarker candidate ions with likely metabolite
identifications resulted in [289]Fig. 7.
Figure 7. Green pathways contain at least one unique putative compound.
[290]Figure 7
[291]Open in a new tab
Green putative compounds are unique for one pathway.
Several ions and the metabolic pathway integration-derived metabolites
hint at S-adenosyl-L-methionine (SAM). A previous study reported a 42%
increase of SAM in the serum of test persons who were overfed by 1,250
kcal per day and gained weight above the median ([292]Elshorbagy et
al., 2016). SAM is synthesized from methionine and ATP and is a key
metabolite since it donates methyl groups to different molecules, such
as DNA, RNA, proteins, and lipids, in enzymatic reactions. The
demethylated S-adenosyl-homocysteine (SAH) is hydroxylated by
adenosylhomocysteinase, resulting in adenosine and homocysteine.
Methionine synthase builds methionine by transferring a methyl group
from 5-methyl-tetrahydrofolate to homocysteine ([293]Finkelstein,
2000).
Several of these reactions have been reported to be altered in obesity.
For example, high serum levels of homocysteine have been correlated
with reduced high-density lipoprotein (HDL) levels. The accumulation of
homocysteine comes with lower SAM and SAH levels, leading to a
diminished production of phosphatidylcholine, which is essential for
the production of low-density lipoproteins (LDL) and very-low-density
lipoproteins (VLDL) ([294]Obeid & Herrmann, 2009). Hyperlipidemia with
increased serum homocysteine increases the risk of developing an
atherosclerotic disease in overweight patients ([295]Glueck et al.,
1995). In addition, elevated serum homocysteine is related to hepatic
steatosis. The later effect was pronounced with low folate intake
([296]Gulsen et al., 2005). Strikingly, we also found the folate
metabolism affected in our present study.
Another altered SAM-related pathway, we detected, is related to
nicotinamide metabolism. Nicotinamide-N-methyl transferase (NNMT)
methylates nicotinamide, using SAM as a methyl donor ([297]Ramsden et
al., 2017). As a result, NNMT is enriched in adipose tissue and the
liver of patients with obesity and type 2 diabetes mellitus (DM2)
([298]Kraus et al., 2014).
The possibility of detecting excess food energy intake in urine by
measuring SAM would provide a non-invasive method for monitoring
patients during weight-loss diets and professionals who require high
physical fitness, such as soldiers. Thus, the level of SAM will be
assayed in the following study during the treatment of obese military
personnel.
In addition, several ions that putatively correspond to compounds from
amino acid metabolism were identified. Changes in amino acid levels and
related metabolites in obese patients have been reported in several
studies ([299]Xie, Waters & Schirra, 2012; [300]Maltais-Payette et al.,
2018; [301]Yu et al., 2018). Therefore, our finding is expectable.
However, since we found the alteration of amino acid pathways through a
variable importance analysis of untargeted metabolomics data, we
suggest a high relevance of amino acid-related biomarkers compared to
other groups of compounds such as TCA-cycle metabolites.
Therefore, besides the SAM level, we will investigate the role of amino
acid metabolism in obesity and weight reduction in future studies.
Conclusions
An Ada Boost model based on urinary metabolomics data could
discriminate obese and overweight from healthy military personnel with
a low overall error rate of 5.5%, indicating a metabolic signature
related to the excessive ingestion of food.
Important variables from data mining, statistical analyses, and
metabolic pathway enrichment analysis suggest S-adenosyl-methionine
(SAM) as a possible urine biomarker for overfeeding. Increased SAM
levels were found for overfed people in plasma, but monitoring SAM in
urine could be used daily for close follow-up of patients, for example,
in the treatment of losing weight or persons that need a high level of
physical fitness, such as soldiers.
As well, the amino acid metabolism showed significant changes.
Therefore, in ongoing studies, we include SAM, amino acid metabolism
compounds, and acylcarnitines for evaluating the metabolic state of
military personnel. In the future, our results will support the design
of low-cost biochemical assays for the broad public.
Supplemental Information
Table S1. Common names of compounds for KEGG and BiGG Models
identifiers.
The article uses compounds’ KEGG and BiGG Models identifiers. This
table lists the common chemical names of the metabolites.
[302]Click here for additional data file.^ (29.5KB, ods)
DOI: 10.7717/peerj.13754/supp-1
Acknowledgments