Abstract

   Fatty liver disease, a type of metabolic disorder, frequently occurs in
   dairy cows during the parturition period, causing a high culling rate
   and, therefore, considerable economic losses in the dairy industry
   owing to the lack of effective diagnostic methods. Here, metabolite
   biomarkers were identified and validated for the diagnosis of metabolic
   disorders. A total of 58 participant cows, including severe fatty liver
   disease and normal control groups, in the discovery set (liver biopsy
   tested, n = 18), test set (suspected, n = 20) and verification set
   (liver biopsy tested, n = 20), were strictly recruited and a sample
   collected for their feces, urine, and serum. Non-targeted GC-MS-based
   metabolomics methods were used to characterize the metabolite profiles
   and to screen in the discovery set. Eventually, ten novel biomarkers
   involved in bile acid, amino acid, and fatty acid were identified and
   validated in the test set. Each of them had a higher diagnostic ability
   than the traditional serum biochemical indicators, with an average area
   under the receiver operating characteristic curve of 0.830 ± 0.0439 (n
   = 10) versus 0.377 ± 0.182 (n = 9). Especially, combined biomarker
   panels via different metabolic pipelines had much better diagnostic
   sensitivity and specificity than every single biomarker, suggesting
   their powerful utilization potentiality for the early detection of
   fatty liver disease. Intriguingly, the serum biomarkers were confirmed
   perfectly in the verification set. Moreover, common biological pathways
   were found to be underlying the pathogenesis of fatty liver syndrome in
   cattle via different metabolic pipelines. These newly-discovered and
   non-invasive metabolic biomarkers are meaningful in reducing the high
   culling rate of cows and, therefore, benefit the sustainable
   development of the dairy industry.

   Keywords: early diagnosis, metabolic disorder, metabolomics, biomarker,
   non-alcoholic fatty liver disease (NAFLD)

1. Introduction

   Fatty liver disease has a high incidence and has, for a long time,
   existed as a common type of metabolic disorder in periparturient and
   postpartum cows, which is caused by a negative energy balance, intense
   body fat mobilization and puerperal stress, thus seriously threatening
   the subsequent milk production and even the future reproductive
   capacity of dairy cows [[38]1,[39]2,[40]3]. In the first month after
   calving, 5–10% of dairy cows had a severe form of fatty liver, and
   30–40% had a mild or moderate fatty liver, and especially for the two
   weeks after calving, dairy cows carried the greatest risk for an
   outcome of a metabolic disorder [[41]1,[42]3,[43]4]. It can be
   complicated by infectious diseases caused by immunodeficiency
   [[44]5,[45]6,[46]7] and even other health and reproductive problems,
   causing a high culling rate of dairy cows in their perinatal period,
   and thus, considerable economic losses in the dairy industry of up to
   ten million dollars annually [[47]8,[48]9]. Ultrasonic imaging and/or
   traditional serum biochemical indicators (AST, GLU, INS, NEFA, etc.)
   were used in the production practice to distinguish cows with fatty
   liver disease; however, this was limited in application because of
   their low diagnostic sensitivity or high false-negative rates
   [[49]10,[50]11,[51]12,[52]13]. A liver biopsy continues to serve as the
   only reliable diagnostic method; it is not a practical method on farms,
   however, as it requires special training and leads to a high risk of
   infection [[53]12,[54]13]. Moreover, conducting a liver biopsy further
   aggravates the suffering of diseased cows. There is still a lack of
   effective diagnostic methods for fatty liver disease in dairy cattle
   with minimum invasions.

   Circulating (serum or plasma) and terminal metabolite (milk, urine)
   biomarkers have been of recent great concern due to the identification
   of the molecular biomarkers facilitating the screening of metabolic
   disorders (such as ketosis, retained placenta, metritis, and lameness)
   in dairy cattle, and understanding the metabolism biology of cows
   during their parturition period
   [[55]7,[56]14,[57]15,[58]16,[59]17,[60]18,[61]19,[62]20]. However, few
   molecular markers have been identified for the occurrence of a fatty
   liver in dairy cows. Since fat deposition in the liver usually occurs
   ahead of other metabolic disorders, it is, therefore, urgent to
   discover novel biomarkers for the screening of fatty liver disease in
   cattle. We hypothesized that there is an inherent genetic regulation
   mechanism to regulate the occurrence of fatty liver disease in cattle.
   Our aims in this study were to develop novel non-invasive biomarkers
   (panel) to diagnose fatty liver diseased cows with greater specificity
   and sensitivity and to identify the common regulatory biological
   pathways that control the occurrence of fatty liver disease during the
   parturition period by detecting the terminal metabolite feces and
   urine, as well as the circulating metabolite (serum) using gas
   chromatography–mass spectrometry (GC–MS).

2. Materials and Methods

2.1. Study Design and Animal Participants

   In the study, a total of 38 cows in their early postpartum period were
   enrolled and divided into the discovery and test sets. Each set
   included normal control cows (normal) and fatty liver disease cows
   (FL).

   In the discovery set, the Holstein dairy cattle, being fed using the
   same management model throughout the experiment, were examined for the
   serum biochemical traits and thereby targeted for liver biopsies,
   according to the detailed procedures [[63]7,[64]19], so as to
   accurately diagnose the cows with fatty liver disease or a normal
   liver. The cows were diagnosed by the percentage of fat-deposited cells
   in the liver and then assigned to the normal or FL groups, with the
   result of less than 13% (n = 8) and more than 85% (n = 10),
   respectively. The percentage of fat-deposited cells was assessed via
   oil red staining of the biopsied liver tissues ([65]Figure S1). It is
   worth mentioning that the values of the fatty-cell percentages (%)
   quantitated by the liver biopsy in the study were suggested to be
   positively correlated with liver TAG content ([66]Figure S2). In
   contrast, the test cohort of cows (n = 20) was recruited from 178
   postpartum candidates by identifying cows as having a high Pearson
   correlation coefficient of their own biochemical indicators with that
   of the biopsy-diagnosed cows. In other words, the cows possessing
   similar biochemical indicators with a fatty liver and/or healthy-liver
   cows in the discovery set were recruited into the test set. Information
   on the biochemical indicators of the enrolled cows is in [67]Table S1.

   The discovery set was first used to identify the candidate biomarkers
   via single- and multi-dimensional detection methods ([68]Figure 1A).
   Then, the test set was used to further screen the biomarkers via the
   same method, finally defining the potential biomarkers, establishing a
   metabolite panel model, and evaluating its diagnostic performance and
   predictive ability ([69]Figure 1B).

Figure 1.

   [70]Figure 1
   [71]Open in a new tab

   Design of the study. (A) Workflow and experimental design of the study.
   (B) Data analysis clue of the study.

2.2. Serum, Urine, and Feces Sample Collection and Serum Biochemical
Indicator Measurement

   Serum, urine, and feces collection: Blood samples were collected from
   the tail vein of the hold dairy cows in the early morning before
   feeding at 7 ± 2 days after calving. The serum samples were then
   separated by centrifuging at 2500 rpm for 15 min after a 37 °C water
   bath incubation and immediately stored in liquid nitrogen for
   subsequent serum biochemical indicators detection. The urine and feces
   samples were collected using long-armed gloves and immediately put into
   liquid nitrogen for later use.

   Serum biochemical indicator detection: The serum indices tested
   include: AST (aspartate aminotransferase), ALB (albumin), BHB
   (β-hydroxybutyric acid), GLU (glucose), INS (insulin), NEFA
   (non-esterified fatty acids), SUN (serum urea nitrogen), TCHO (total
   cholesterol), TG (triglyceride), TP (total protein), UA (urea acid),
   and UERA (urea nitrogen). The indicators were detected using reagent
   kits (Nanjing Jiancheng Biological Engineering Institute, Nanjing,
   China) using an automatic biochemical analyzer according to the
   detailed manufacturers’ procedures.

   Liver tissue collection: The suspected biopsy cows were identified and
   selected according to their biochemical indicators, high NEFA, AST, and
   low GLU and INS levels. The biopsy operation was performed according to
   previous procedures [[72]7,[73]19]. The dairy cows were held in a cage
   and shaved on their side at the intersection of the 10th–11th rib and
   the middle humerus to the hip tubercle with an area of 5 × 5 cm. After
   sanitization and local anesthesia, the liver tissues were biopsied
   using the Bard Magnum biopsy system (Bard Peripheral Vascular, Inc.,
   Tempe, AZ, USA), followed by surgical suturing of the skin. The animal
   was administered ketoprofen and penicillin G procaine by intravenous
   injection immediately after the biopsy and for the next 3 to 5 days
   until complete recovery of health. The liver tissue samples were
   immediately fixed in 5% polyformaldehyde for oil red O staining.
   Eventually, the biopsied dairy cattle were diagnosed with liver health
   conditions according to the average percentage of hepatic cells
   containing lipid droplets ([74]Figure S1).

2.3. Sample Pre-Treatment and Non-Target GC–MS Analysis

   The sample pre-treatment procedures referred to the previously
   published methods with minor modifications [[75]21,[76]22]. Briefly,
   each aliquot of the 50μL serum or urine sample was mixed with 10 μL of
   the internal standard and 175 μL of methanol/chloroform (v/v = 3:1).
   The sample was vortexed vigorously and centrifuged. Each 200 μL sample
   of supernatant was transferred to an autosampler vial and then
   evaporated using a vacuum concentrator. For the feces samples, each 5
   mg of lyophilized feces sample was mixed with 10μL of the internal
   standard and extracted with 50μL of 50% methanol. The supernatant was
   carefully transferred to a microtube, and the residue was extracted
   again with 175 μL of methanol/chloroform (v/v = 3:1). The two-stepped
   supernatant was combined, and 100 μL of the mixed supernatant was
   transferred and evaporated. The evaporated samples were derivatized
   with 50 μL of methoxyamine (20 mg/mL in pyridine) at 30 °C for 2 h,
   followed by the addition of 50μL of MSTFA (1% TMCS) at 37.5 °C for
   another 1 h. Each 1μL of the derivatized sample was injected into a
   Pegasus HT GC–TOFMS system (Leco Corp., St. Joseph, MO, USA).
   Separation was performed on an Rxi-5 ms capillary column (30 m × 0.25
   mm × 0.25 μm), and the oven temperature was set at 80 °C for 2 min,
   then ramped up to 300 °C at 12 °C/min, and maintained at 300 °C for 8
   min. The mass spectra were collected with electron impact ionization
   (70 ev) at the full scan mode (m/z 50–500).

2.4. Quality Control Analysis and Model Reliability Testing

   Multivariate quality control charts, an important means and tool for
   laboratory automation and quality management, were used in the study to
   testify to the credibility of our model because the charts are analyzed
   and established based on the scoring positions and the location
   tendency of all the samples (shown as [77]Figure 2A). Further, a
   principal component analysis (PCA) and orthogonal partial least squares
   discriminant analysis (OPLS-DA) was also employed to test the
   reliability of the model and further distinguish the metabolic spectrum
   differences between the two groups in both the discovery set and test
   set ([78]Figure 2B).

Figure 2.

   [79]Figure 2
   [80]Open in a new tab

   Quality control and model credibility assessment of samples in the
   discovery set (cows diagnosed by liver biopsy) from feces (A), urine
   (B) and serum (C). (Left panel) Multivariate control chart (MCC) shows
   the metabolite profiles of all individual feces (A), urine (B) and
   serum (C) samples in the discovery set. The black dot represents a
   single sample. The orange and/or red dashed line represents the
   biological quality control range limit. (Middle panel) Orthogonal
   partial least squares discriminant analysis (OPLS-DA) maps of the
   samples from feces (A), urine (B), and serum (C) reveal a significant
   separation of the fatty liver group from the normal control group,
   without any overlapping, indicating a successful model construction in
   the study. Green and blue dots represent normal and fatty liver
   samples, respectively. (Right panel) The predictive ability (Q2Y) of
   the model is shown in OPLS-DA maps of samples from feces (A, 0.998),
   urine (B, 0.952), and serum (C, 0.901). To avoid over-fitting of the
   model, 999 random permutation tests were performed so as to
   cross-validate the three components. Their intercept values were
   −0.071, −0.021, and −0.188 for feces (A), urine (B), and serum (C),
   respectively, thereby demonstrating the model’s effectiveness.

2.5. Data Analysis and Identification of Differentially Expressed Metabolites

   The raw data generated by the GC–TOFMS were processed using a ChromaTOF
   (v4.71, Leco Corp., St. Joseph, MO, USA) for automated baseline
   denoising and smoothing, deconvolution, and peak alignment. Compound
   identification was performed by comparing both the MS similarity and
   FAMEs retention index distance with the referenced standards in the
   Jialib Mass library. The unsupervised principal component analysis
   model on the unit variance scale was used to evaluate the changes in
   the metabolic components and monitor the stability of the study. The
   supervisory model of the partial least squares discriminant analysis,
   based on one-dimensional variance scales, was employed to maximize the
   degree of dispersion between the disease and normal groups. According
   to its variable importance in projection (VIP) [[81]23], the variables
   that significantly contributed to the classification were identified.
   Thereafter, 999 replacement tests [[82]24,[83]25] were conducted to
   assess the risk of over-fitting the model. A t-test [[84]23] was used
   for the univariate analysis and a p-value < 0.05 indicated
   significance. The biomarker model was established and evaluated using
   the SPSS software, and the designed potential biomarker model was
   established and evaluated using binary logistic regression. A ROC
   (receiver operating characteristic) curve [[85]26,[86]27] was used to
   evaluate the results of the regression analysis, scoring the diagnostic
   specificity and sensitivity of the biomarker. The pathway analysis was
   performed based on a hypergeometric test [[87]28] and KEGG (Kyoto
   encyclopedia of genes and genomes) pathway, displaying the significant
   biological differences between the disease and normal groups. A violin
   map test was used to verify the significant expression difference
   between potential biomarkers.

3. Results

3.1. Workflow of the Study and the Participants Cohort

   The workflow and experiment designs of the study are shown in
   [88]Figure 1A,B. To define the biomarker candidates, 18 cows, liver
   biopsied and with a detected fat deposition, were recruited into the
   discovery set. The fat deposition content was determined by Oil Red
   staining of the biopsied liver tissues. The diagnosed cows with a
   fat-deposited content of 13.17% ± 8.20% (n = 8) for the healthy
   controls and 84.71% ± 4.83% (n = 10) for the fatty liver disease group
   ([89]Figure S1) were strictly selected, indicating their tremendous
   difference. To validate these biomarker candidates and define the
   potential biomarkers, a total of 20 cows, identified using their
   biochemical indicators by taking that possessed by biopsy-diagnosed
   fatty liver cows and/or healthy cows as the standard, were recruited
   into the test set. In other words, the cows possessing highly
   correlated biochemical indicators with a fatty liver and/or
   healthy-liver cows in the discovery set were harshly recruited into the
   test set, with Pearson correlation coefficients of 0.915 ± 0.041 in the
   suspected FL group (n = 10, p < 0.05) and 0.943 ± 0.092 in the
   suspected normal group (n = 10, p < 0.05), respectively. The
   biochemical indicators of the disease cows were significantly different
   to that of the normal cows, with elevated AST and NEFA (non-esterified
   fatty acids), and decreased GLU (glucose) levels. The detailed
   information is listed in [90]Table S1.

3.2. Metabolic Profiling, Model Establishment and Evaluation

   The results of the multivariate quality control charts showed that all
   the tested samples, including feces, urine, and serum, were located in
   an area of less than ×2 standard deviations along the x-axis, and the
   majority of the samples were close to the control limit ([91]Figure
   2A–C left). No abnormal values of these samples were undetected,
   indicating that the model in the study was reliable and the samples
   involved in the model were correct.

   In order to further testify to the reliability of the data model and
   distinguish the difference in metabolic profiles between the disease
   and normal groups, principal component analysis and an advanced
   supervised discriminant model, orthogonal partial least squares
   discriminant analysis (OPLS-DA) were performed, and the results
   indicated that the disease group (blue dots) and the control group
   (green dots) in all feces, urine, and serum samples showed obviously
   separated aggregation community and significant differences ([92]Figure
   2A–C middle); the relatively high Q2Y values of the OPLS-DA analysis,
   evaluating the metabolites contributed to the group, indicating the
   credibility of the grouping models, with 0.918, 0.952 and 0.901 in the
   feces, urine, and serum samples, respectively. Further, to avoid the
   phenomenon of over-fitting caused by the supervised model, 1000 random
   sampling tests (Permutation test) on this model were performed to
   evaluate the reliability of the model ([93]Figure 2A–C, right), and the
   Y-axis intercepts of less than zero in the three different samples
   confirmed the reliability of the represent study. The same situation
   was obtained by the samples in the test set ([94]Figure S3). These
   variable metabolites, in both the discovery and test sets, were used
   for the subsequent multivariate and univariate analyses.

3.3. Identification of Candidate Metabolic Biomarkers in Feces, Urine, and
Serum in the Discovery Set

   A high-performance GC–MS analysis was used to detect the small
   molecular metabolites in the feces, urine, and serum of perinatal dairy
   cattle. The results showed that the measured functional metabolites
   were small organic molecules with similar classifications and
   proportions in every sample type, including amino acids and amines,
   organic acids, carbohydrates, fatty acids, and lipids, nucleosides,
   sugars, vitamins, and auxiliary factors ([95]Figure S4). The obtained
   volume and category proportions of metabolites were, as expected in the
   project, supportive of the subsequent biomarker screening.

   As described in [96]Figure 1B, the first step of the screening process
   was to identify the candidate biomarkers from the discovery set. The
   candidate metabolite biomarkers with a VIP (variable importance in
   projection) of ≥ 1.0 and a p <0.05 on two principle components in each
   type of sample were firstly identified via a V-plot volcanic map
   ([97]Figure 2A–C, left). Subsequently, a univariate analysis was used
   to determine whether these metabolites were significantly altered via
   the single dimension statistical method t-test, with an FC (fold
   change) of > 1.2 and a p < 0.05 in the disease groups compared with the
   normal control groups ([98]Figure 2A–C, middle). Finally, 23 candidate
   biomarkers in feces ([99]Figure 3A, right), 7 in urine ([100]Figure 3B,
   right), and 24 in serum ([101]Figure 3C, right) were identified as
   important variables that contributed to the classifications in the
   discovery set, and mainly included amino acids, fatty acids,
   carbohydrates, and organic acid. The detailed information on these
   differential metabolites is listed in [102]Tables S2–S4.

Figure 3.

   [103]Figure 3
   [104]Open in a new tab

   Identification of the differential expressing candidate metabolites in
   feces (A), urine (B) and serum (C) samples in the discovery set. (Left
   panel) The volcano plot (multi-dimensional analysis) revealed the
   differential expressing metabolites in feces (A), urine (B) and serum
   (C) samples through the multi-criteria assessment. The green and blue
   cross-shaped buddings represent the metabolites within the cutoff value
   of variable importance in the project (VIP) ≥ 1 and p-value < 0.05 and
   0.01, respectively. The metabolites in the upper right corner of the
   image show upregulation in the fatty liver samples, and metabolites in
   the upper left corner show downregulation. (Middle panel) t-test map
   (single-dimensional analysis) of the fecal (A), urine (B), and serum
   (C) samples. The green dots represent the metabolites within the cutoff
   values of p-value < 0.05 and the log(fold change) ≥1.2. (Right panel)
   Venn diagram displays the differential expressing metabolites in the
   feces (A), urine (B), and serum (C) samples under multi-dimensional and
   single-dimensional data analysis. Green and blue represent the number
   of metabolites identified by single- and multi-dimensional analyses.
   More detailed information about the intersected common metabolites is
   listed in [105]Tables S2–S4.

3.4. Defining Potential Metabolic Biomarkers for Fatty Liver Disease

   An independent test cohort of 20 cows ([106]Figure 1), named the test
   set, was used to evaluate the reliability of these biomarker candidates
   and define the useful biomarkers. First, the same analysis methods and
   procedures were applied as per that in the discovery set ([107]Figure
   S5A,B). Thus, 24 candidate biomarkers in feces ([108]Figure S5A,
   right), 10 in urine ([109]Figure S5B, right), and 26 in serum
   ([110]Figure S5C, right) were identified as important variables that
   contributed to the classifications in the test set. The detailed
   information on these differential metabolites is listed in [111]Tables
   S5–S7.

   Second, the common candidate biomarkers, as potential biomarkers, were
   separately defined via intersecting the discovery set and test set from
   the feces, urine, and serum samples, respectively. The defined
   potential metabolite biomarkers must satisfy the following criteria:
   (1) significant differences between the normal and FL groups; (2)
   simultaneously consistent change direction between the normal and FL
   groups. Ultimately, two metabolites were retained in the feces:
   L-alpha-aminobutyric acid and behenic acid; one metabolite was retained
   in urine: 3-nitrotyrosine; and seven metabolites were retained in the
   serum: L-asparagine, palmitoleic acid, L-serine, stearic acid,
   nonadecanoic acid, petroselinic acid, and heptadecanoic acid. Their
   detailed information is shown in [112]Table 1.

Table 1.

   Basic information of the identified potential biomarkers that are
   significantly differential, expressing in the fatty liver group and
   normal group of both the discovery set ^1 and test set ^2 in feces,
   urine, and/or serum.
   Biomarker Source Biomarker Name Class FC
   (FL/Norm) ^3 p-Value HMDB ID ^4 KEGG ID ^5 Associated Pathways
   Feces L-Alpha-aminobutyric acid Amino Acid 0.534 8.50 × 10^−3 HMDB00452
   [113]C02356 /
   Behenic acid Fatty Acids 1.203 4.10 × 10^−2 HMDB00944 /
   Urine 3-Nitrotyrosine Amino Acid 0.256 3.10 × 10^−2 HMDB01904 / /
   Serum L-Asparagine Amino Acid 0.58 8.70 × 10^−4 HMDB00168 [114]C00152
   Ammonia Recycling; Aspartate Metabolism; Transcription/Translation
   Palmitoleic acid Fatty Acids 2.191 1.20 × 10^−2 HMDB03229 [115]C08362 /
   L-Serine Amino Acid 0.579 2.70 × 10^−2 HMDB00187 [116]C00065 Ammonia
   Recycling; Glycine and Serine Metabolism; Homocysteine Degradation;
   Methionine Metabolism; Sphingolipid Metabolism
   Stearic acid Fatty Acids 1.819 2.70 × 10^−2 HMDB00827 [117]C01530
   Mitochondrial Beta-Oxidation of Long Chain Saturated Fatty Acids;
   Plasmalogen Synthesis
   Nonadecanoic acid Fatty Acids 1.678 2.70 × 10^−2 HMDB00772 [118]C16535
   /
   Petroselinic acid Organic Acids 2.831 2.70 × 10^−2 HMDB02080
   [119]C08363 /
   Heptadecanoic acid Fatty Acids 2.272 3.40 × 10^−2 HMDB02259 / /
   [120]Open in a new tab

   ^1 Discovery set, samples from cows diagnosed by liver biopsy. ^2 Test
   set, samples from cows diagnosed by serological detection.^3 FC, fold
   change, the relative expression level of the metabolite in the fatty
   liver group (FL) compared to that in the normal group (Norm).^4 HMDB
   ID: ID number of the metabolite in the Human Metabolome Database. ^5
   KEGG ID: ID number of the metabolic pathway in the biological
   information database.

   These metabolites were all shown to have significantly different
   expressions in the FL groups from the normal control groups
   ([121]Figure 4), suggesting the ideal biomarkers to distinguish fatty
   liver disease cows from healthy control subjects. The fecal marker
   L-alpha-aminobutyric acid was significantly decreased in the FL group
   compared to the control group (fold change 0.534, p = 0.0085), while
   behenic acid was significantly increased (fold change 1.203, p =
   0.041). The urine marker, 3-nitrotyrosine, was significantly decreased
   in the disease group. Two serum markers, L-asparagine and L-serine, had
   significantly lower expression levels in the disease group, while the
   other five had significantly higher expression levels in the disease
   group; they were palmitoleic acid, stearic acid, nonadecanoic acid,
   petroselinic acid, and heptadecanoic acid ([122]Table 1). These
   increased NEFAs were also observed in early postpartum dairy cows.

Figure 4.

   [123]Figure 4
   [124]Open in a new tab

   Violin charts showing the identified potential diagnostic biomarkers
   having significantly different expression levels between fatty liver
   cows and normal controls in both the discovery set and test set. (A)
   Two feces markers, L-alpha-aminobutyric acid (left) and behenic acid
   (right); letters “C” and “F” represent control and disease feces,
   respectively. (B) One urine marker, 3-nitrotyrosine; letters “B” and
   “E” represent control and disease urine, respectively. (C) Seven serum
   markers, L-asparagine, L-serine, palmitoleic acid, and stearic acid
   (upper panel, left to right); nonadecanoic acid, petroselinic acid, and
   heptadecanoic acid (lower panel, from left to right). Letters “A” and
   “D” represent control and disease serum, respectively.

3.5. Validation of the Metabolic Marker Panel in the Test Set

   In order to verify the diagnostic performance of this metabolite panel
   for fatty liver disease in dairy cattle, an ROC (receiver operating
   characteristic) curve was established. The results indicated that these
   metabolites in feces, urine and/or serum were all showing a higher
   sensitivity and similar specificity to identify the FL cows from the
   normal control cows ([125]Figure 5, permutation test p-values < 0.05).
   For example, fecal metabolite L-alpha-aminobutyric acid, behenic acid,
   and urine metabolite 3-nitrotyrosine obtained AUC values of 0.863,
   0.794, and 0.802, respectively ([126]Table 2, [127]Figure 5A,B). When
   combining and forming a metabolite panel for each type of sample, it
   showed better diagnostic performance, with higher AUC values than any
   single metabolite, such as 0.975 of the combined two biomarkers in
   feces and 1.000 of the combined seven biomarkers in serum ([128]Table
   2, [129]Figure 5D). Especially, a fairly outstanding AUC value of 0.988
   was obtained if combining the three metabolite biomarkers in both feces
   and urine, which are considered non-invasive diagnostic biomarkers
   ([130]Table 2, [131]Figure 5D). Meanwhile, the diagnostic performance
   of these potential biomarkers was notably higher (AUC of 0.830 ±
   0.0439, n = 10) than that of the traditional serum biochemical
   indicators (0.377 ± 0.182, n = 9) ([132]Table 2, [133]Figure S6).
   Especially, even though AST was the most classic serum index to
   indicate a fatty liver (AUC = 0.756, [134]Table 2, [135]Figure S6),
   every single novel identified biomarker was still better than AST. The
   strict stepwise screening and validation thus identified reliable
   metabolite biomarkers for the early diagnosis of cows with fatty liver
   disease.

Figure 5.

   [136]Figure 5
   [137]Open in a new tab

   Validation of diagnostic performance of the identified ten biomarkers
   for metabolic disorders using ROC (receiver operating characteristic)
   curve (permutation test p-values < 0.05). (A) ROC curve of urine
   biomarker, 3-nitrotyrosine. (B) ROC curve of the two feces biomarkers,
   L-alpha-aminobutyric acid (blue line) and behenic acid (green line);
   (C) ROC curve of the seven feces biomarkers, heptadecanoic acid (blue
   line), L-asparagine (green line), L-serine (grey line), nonadecanoic
   acid (purple line), palmitoleic acid (yellow line), petroselinic acid
   (red line), and stearic acid (light blue line). (D) ROC curve of the
   combined biomarker panel in feces (blue line), urine (green line), and
   serum (grey line). The line with a 45° slope in each panel is the
   reference boundary line with an AUC (area under the curve) of 0.5,
   meaning the diagnostic method is effective or not. The higher the AUC
   value is, the higher the accuracy of the diagnostic method. The AUC
   values are listed in [138]Table 2.

Table 2.

   Diagnostic performance assessment of the identified metabolic
   biomarkers and the traditional biochemical indicators using the ROC
   (receiver operating characteristic) curve.
   Biomarker Source Biomarker Name AUC in Discovery Set
   (95% CI) ^2 AUC in Test Set
   (95% CI) ^3 p-Value in Violin Chart ^4
   Traditional Biochemical Indicator
   in Serum ^1 AST 0.756 / /
   UREA 0.5 / /
   ALB 0.469 / /
   INS 0.363 / /
   UA 0.338 / /
   TP 0.313 / /
   TG 0.294 / /
   TCHO 0.25 / /
   GLU 0.112 / /
   Biomarker in Feces L-Alpha-aminobutyric acid 0.863 0.825 3.40 × 10^−2
   Behenic acid 0.794 0.929 4.10 × 10^−2
   Combined biomarkers in Feces / 0.975 1 /
   Biomarker in Urine 3-Nitrotyrosine 0.802 0.841 3.10 × 10^−2
   Combined biomarkers in
   Feces and Urine / 0.988 1 /
   Biomarker in Serum L-Asparagine 0.938 0.76 2.30 × 10^−2
   Palmitoleic acid 0.85 0.81 1.20 × 10^−2
   L-Serine 0.812 0.79 2.20 × 10^−2
   Stearic acid 0.813 0.79 1.90 × 10^−2
   Nonadecanoic acid 0.813 0.84 8.70 × 10^−3
   Petroselinic acid 0.813 0.76 4.00 × 10^−2
   Heptadecanoic acid 0.8 0.84 1.70 × 10^−2
   Combined Biomarkers in Serum / 1 1 /
   [139]Open in a new tab

   ^1 AST, aspartate aminotransferase; UREA, urea nitrogen; ALB, albumin;
   INS, insulin; UA, urea acid; TP, total protein; TG, triglyceride; TCHO,
   total cholesterol; GLU, glucose. ^2 AUC (area under the curve) values
   calculated from the biomarkers from the discovery set (samples from
   cows diagnosed by liver biopsy) upon ROC (receiver operating
   characteristic) curve construction with 95% CI (confidence interval).
   ^3 AUC values calculated from biomarkers from the test set (samples
   from cows diagnosed by serology), with 95% CI (confidence interval). ^4
   p-value from the Violin Chart, indicating the biomarker has a
   significant statistical difference in expression levels between fatty
   liver and normal cows.

3.6. Associated Biological Pathways of Metabolite Biomarkers with Fatty Liver
Syndrome in Cattle

   To understand the metabolite-related pathways underlying the
   pathogenesis of metabolic disorders in dairy cattle, metabolic pathway
   enrichment analyses (MPEA) of all candidate differential metabolites
   were carried out in both the discovery and test sets. The results
   showed that the metabolic disorders in postpartum dairy cows with fatty
   livers were mainly related to as follows: arginine biosynthesis;
   alanine, aspartate, and glutamate metabolism; arginine and proline
   metabolism; biosynthesis of unsaturated fatty acids; histidine
   metabolism; cysteine and methionine metabolism; pantothenate and CoA
   biosynthesis; glycine, serine, and threonine metabolism; taurine and
   hypotaurine metabolism; beta-alanine metabolism; and pentose and
   glucuronate interconversion in feces ([140]Table S8). The above
   pathways are mainly related to the metabolism of bile acids, amino
   acids, and lipids during the progression of metabolic disorders.
   Meanwhile, these enriched metabolic pathways revealed the effects of
   energy metabolism, oxidative stress, and inflammation on the
   pathogenesis of metabolic disorders in dairy cows, especially fatty
   liver disease. Intriguingly, several common metabolic pathways were
   simultaneously enriched from feces, urine, and circulating serum, and
   three different types of metabolic systems: valine, leucine, and
   isoleucine biosynthesis; pantothenate and CoA biosynthesis; glutathione
   metabolism; biosynthesis of unsaturated fatty acids; arginine and
   proline metabolism ([141]Figure 6). This implicates the common
   metabolism pathways underlying the pathogenesis of metabolic disorders
   in dairy cattle, especially fatty liver syndrome.

Figure 6.

   [142]Figure 6
   [143]Open in a new tab

   Common biological metabolic pathways were enriched by differential
   expressing metabolites identified from feces (A), urine (B) and serum
   (C). The common pathways enriched by different metabolites identified
   from feces, urine and/or serum were indicated using different symbols
   (◆, ◇, ★, ☆, ○, ◎ and/or ●). Red represents the significance of the
   pathway when it was enriched; the redder it is, the more significant
   (with a smaller p-value) the pathway is. Dot size represents the number
   of metabolites involved in the pathway; the bigger the dot is, the more
   metabolites are involved.

3.7. Verification of Serum Biomarkers in a Third Liver Biopsy-Diagnosed
Holstein Population

   A third Holstein population, a total of 20 dairy cattle in their early
   postpartum period, were diagnosed for their liver health by liver
   biopsy and subsequent oil red staining. The normal group (Norm, n = 12)
   and fatty liver group (High, n = 8)) were diagnosed with 0.080% ±
   0.073% and 72.25% ± 7.99% of cells deposited with fat, respectively
   ([144]Figure 7A). Non-targeted metabolomics was again used to identify
   the differentially expressed metabolites in serum by the GC-TOFMS
   method. The PCA and OPLS-DA modeling validation analyses demonstrated
   the model’s effectiveness ([145]Figure 7B,C). The differential
   expressing metabolites were finally obtained by intersecting the
   metabolite set identified by OPLSDA-VIP and univariate statistics
   analyses ([146]Figure 7D and [147]Figure S7), following the threshold
   value for the differential metabolites selection: p < 0.05 and |log2FC|
   >= 0. The identified novel biomarkers, such as the fatty acids,
   Heptadecanoic acid and Palmitoleic acid, and amino acids, L-Asparagine
   and L-Serine, were perfectly verified to be up- and down-regulated in
   the fatty liver group ([148]Figure 7E), respectively, confirming their
   diagnostic sensitivity and specificity in fatty liver disease in dairy
   cattle. In addition, the common biological metabolic pathways were
   again enriched by the differential expressing metabolites identified in
   the third population, such as arginine and proline metabolism, TCA
   cycle, valine, leucine, and isoleucine biosynthesis ([149]Figure 7F).

Figure 7.

   [150]Figure 7
   [151]Open in a new tab

   Verification of the serum diagnostic marker in a third biopsied
   population. (A) New serum samples from a third dairy cow population in
   their early postpartum period; a total of 20 individuals were diagnosed
   for their liver health by liver biopsy, followed by oil red staining.
   The normal group (Norm, n = 12) and fatty liver group (High, n = 8))
   were diagnosed with 0.080% ± 0.073% and 72.25% ± 7.99% of cells
   deposited with fat, respectively. (B) PCA analysis results of the 20
   serum samples reflect reduced levels of variation in the data set. (C)
   OPLSDA modeling validation results of the new samples, with R2Y and Q2Y
   of 0.907 and 0.613, respectively. (D) Volcano plot of univariate
   analysis, identifying differential expressing metabolites in serum
   samples, including 5 up-regulated (in red dots) and 51 down-regulated
   (in blue dots). (E) Boxplot of representative up-regulated
   (Heptadecanoic acid and Palmitoleic acid) and down-regulated
   (Asparagine and Serine) metabolites in the fatty liver group confirmed
   their diagnostic sensitivity and specificity in fatty liver disease.
   (F) Similar biological metabolic pathways were enriched by differential
   expressing metabolites identified in the third population, such as
   arginine and proline metabolism, TCA cycle, valine, leucine, and
   isoleucine biosynthesis. *** p < 0.001.

4. Discussion

   Metabolic disorders of dairy cattle, such as fatty liver disease and
   ketosis, in the transition period, remain as prevalent now as they did
   20 years ago [[152]5]. Nowadays, metabolomics approaches are boomingly
   applied for the identification of metabolite panels in circulating
   and/or terminal metabolites in dairy cows that can differentiate
   metabolic disorder cows from healthy cows, including ketosis, retained
   placenta, metritis, lameness, mastitis, or displaced abomasums
   [[153]9,[154]14,[155]15,[156]16,[157]17,[158]18,[159]19,[160]20,[161]29
   ]. This study used non-targeted metabolomics and identified several
   predictive and diagnostic biomarker panels of fatty liver disease. The
   associated metabolic pathways possibly involved during the onset and
   progression of fatty liver disease are also discussed below.

4.1. Desirable and Novel Metabolite Biomarkers (Panels) to Early Diagnose
Fatty Liver Cattle Were Strictly Identified in the Study

   Metabolomics is defined as a comprehensive and fully quantitative
   analysis of all detectable metabolites (especially for small molecular
   weight molecules) within a particular biological sample to indicate an
   overview of metabolic status, which can provide new insight into the
   pathological mechanisms in diseases. Nuclear magnetic resonance (NMR)
   spectroscopy and MS techniques are the most commonly applied analytical
   platforms for metabolomics studies [[162]17,[163]18,[164]29,[165]30].
   In comparison with NMR spectroscopy, the major advantage of MS is the
   higher selectivity and sensitivity, as MS can measure analytics
   routinely in the femtomolar to the attomolar range. In MS-based
   metabolomics, GC–MS is the most frequently used platform because it
   offers structural information, high throughput, reasonable quantitative
   precision, relatively high reproducibility, and resolution (the
   sensitivity is at least two orders of magnitude higher than NMR). In
   the present study, the GC-TOFMS-based metabolomics of body fluids
   (serum and urine) and terminal metabolites (feces) were processed.
   Novel and desirable metabolite biomarkers (panels) for the screening
   and/or early diagnostic of fatty liver cattle were strictly identified
   in the study and validated as having a higher accuracy (specificity and
   sensitivity). Firstly, liver biopsy-diagnosed dairy cows, named as the
   discovery set, were applied for the primary identification of
   metabolite biomarker candidates ([166]Figure 1, [167]Figure 2 and
   [168]Figure S1). Secondly, the biomarker candidates were further
   testified (screened) by the highly suspected dairy cows in the test set
   ([169]Figure 1 and [170]Figure S3), who had consistent serum
   biochemical indicators with the biopsied diagnosed cows in the
   discovery set. Thirdly, after systematic, rigorous selection using
   multivariate and univariate statistical analyses ([171]Figure 3 and
   [172]Figure S5), the identified potential biomarkers were confirmed to
   be significantly differentially expressed in the disease and normal
   animals by the violin map test ([173]Figure 4). Finally, ROC curves
   were established to validate the diagnosis performance of these
   potential biomarkers ([174]Figure 5 and [175]Figure S6). Among the 10
   identified biomarkers, every single one had a higher diagnostic
   performance than the traditional serum indicators (0.830 ±0.0439, n =
   10 versus 0.377 ± 0.182, n = 9; [176]Table 2). Furthermore, the
   combined biomarkers had an even higher diagnostic sensitivity and
   specificity (AUC 0.975, 0.988, and 1.000, [177]Table 2) for fatty liver
   dairy cows. It is important that the identified biomarkers, such as
   Heptadecanoic acid and L-Serine ([178]Figure 7), were perfectly
   verified in a third biopsy-diagnosed population by non-targeted
   metabolomics, with 12 samples from healthy liver individuals and 8 from
   fatty liver cows. Moreover, common biological metabolic pathways were
   enriched by the differential expressing metabolites identified in the
   third population, such as arginine and proline metabolism, TCA cycle,
   valine, leucine, and isoleucine biosynthesis, again confirming the
   reliability of the novel identified biomarkers. In sum, the resounding
   successful experiment design, refined analysis methods, and rigorous
   validation highlight the meaningful reliability of the novel biomarkers
   identified in this study. Specifically, other than the advantage of
   higher diagnostic sensitivity and specificity of these identified
   biomarkers, non-invasiveness, rapidity, and convenience would be
   advocated in consideration of the issue of animal welfare and the
   concept of a healthy and sustainable dairy industry. This is because
   the diagnosis biomarkers can be detected by using terminal metabolites,
   feces, and urine. The identified non-invasive biomarkers would offer
   more potential and power to be utilized in production practices.

4.2. Dysregulated Fatty Acid Metabolism and Impaired Metabolism Capacity Were
Accompanied with Fatty Liver Cattle

   Increased fatty acid levels and decreased amino acid levels were shown
   in fatty liver animals compared to healthy ones based on different
   metabolic systems, no matter the exosomatic metabolite feces and urine
   or circulating serum. Specifically, among the ten identified novel
   biomarkers, four of them are amino acids, which showed significantly
   suppressed expression levels in the FL disease group; the other six are
   fatty acids, which showed significantly enhanced expression levels in
   the FL disease group ([179]Table 1), which was verified in a third
   biopsied Holstein population (n = 20, [180]Figure 7). The phenomenon of
   increased non-esterified fatty acids (NEFAs) has been shown to be
   closely related to the pathology of fatty liver disease in the
   perinatal period of dairy cattle, which usually results from a negative
   energy balance [[181]31,[182]32]. In the postpartum period, the
   lactation of the mammary gland slowly became increased and therefore
   increased the body lactose consumption, easily causing the cow to
   experience an insufficient sugar supply, thus promoting body fat
   mobilization. However, enhanced fat mobilization causes a dramatic
   increase in NEFA in the liver and circulating serum [[183]4,[184]33].
   Excessive NEFAs in circulating serum that cannot be oxidized will be
   re-esterified to synthesize triglycerides (TG) in the liver, which are
   difficult to transport out of the liver because of the lower activity
   of esterase in cattle, leading to an excessive accumulation of TG in
   the liver [[185]1,[186]34,[187]35,[188]36]. Therefore, the increased
   circulating fatty acids could indicate the occurrence of fatty liver
   syndrome [[189]17,[190]19,[191]35]. For example, serum palmitic acid
   was shown to increase in fatty liver disease cows ([192]Table 1). The
   study showed that the decrease in palmitic acid oxidation in the liver
   tissue positively correlated with the increase in triglycerides
   [[193]37] and negatively correlated with Decanoyl, which inhibits
   β-oxidation, blocking the ketogenesis of severe ketonemia, another type
   of metabolic disorder in dairy cows [[194]37]. On the other hand, the
   decreased amino acid level was also evidenced to be associated with
   metabolic disorders. For example, the amino acid marker
   L-α-aminobutyric acid in feces, mainly involved in the metabolism of
   cysteine and methionine ([195]Figure 6A,C and [196]Figure 7), was
   significantly reduced in the diseased group, indicating increased
   steatosis. Previous evidence showed that mice with a methionine
   metabolism-related gene deficiency developed liver diseases, mainly
   fatty livers and liver cancer [[197]38,[198]39]. Another amino acid
   biomarker in urine was also confirmed to be associated with liver
   injury. For example, nitrotyrosine is a specific marker generated by
   endogenous peroxynitrite anion (ONOO-), exhibiting different
   expressions in different body fluids and species. ONOO in the serum, as
   a strong oxidant, can effectively oxidize the tyrosine residues at the
   thiol and iron/sulfur (Fe/S) center, thereby inactivating the target
   proteins or enzymes, inhibiting respiratory enzymes, and destroying the
   mitochondrial structure, thus significantly inhibiting the ability of
   the antioxidant enzymes to scavenge oxygen-free radicals, initiating
   lipid peroxidation, and participating in liver metabolic disorders and
   disease [[199]40]. Accordingly, an enhanced fatty acid metabolism and
   suppressed uptake capacity would be two important pathological features
   of fatty liver cattle.

4.3. Common Biological Pathways Were Underlying the Pathogenesis of Fatty
Liver Syndrome in Cattle

   The biological pathway enrichment analysis revealed that the metabolic
   reprogramming was mainly related to the metabolism of fatty acids,
   amino acids, and bile acids during the pathogenesis of fatty liver
   disease in dairy cattle ([200]Tables S2–S8), such as the biosynthesis
   of unsaturated fatty acids, primary bile acid biosynthesis, valine,
   leucine, and isoleucine degradation/biosynthesis ([201]Figure 6). These
   common pathways, enriched by different resourced metabolites, provide
   insights into the underlying pathogenesis of fatty liver disease, such
   as the perturbations of energy metabolism, oxidative stress, and
   inflammation. In this study, the amino acid metabolisms/biosynthesis
   pathways were concurrently enriched by the differential expressed
   metabolites from feces, urine, and/or serum, such as glutathione
   metabolism; valine, leucine, and isoleucine biosynthesis/degradation;
   arginine and proline metabolism; etc ([202]Figure 6 and [203]Figure 7,
   [204]Table S8). Amino acid metabolism pathways have been confirmed to
   be associated with the development of ketosis and fatty liver disease
   by previous studies [[205]17,[206]41]. Firstly, these amino acid
   metabolisms were closely associated with glucogenesis and/or
   adipogenesis via the TCA cycle (tricarboxylic acid cycle) by
   synthesizing Acetyl CoA, α-ketoglutaric acid, succinyl CoA, fumaric
   acid, and/or oxaloacetic acid. The dysregulation of the amino acid
   metabolism would cause abnormal levels of ketone body—AST/ALT in the
   body fluids, which usually implicates liver injury. Secondly, special
   amino acids were shown to be associated with liver disease. For
   example, arginine, as a conditionally essential amino acid, has a
   certain regulatory effect on lipid metabolism, such as white/brown fat
   mass. Especially, L-arginine ethyl ester and chenodeoxycholic acid
   conjugate (cdcarg) were developed into novel bile acid molecules to
   treat liver disease, non-alcoholic fatty liver disease (NAFLD), and
   non-alcoholic steatohepatitis (NASH) [[207]42,[208]43]. Additionally,
   bile acids play important roles in energy metabolism, oxidative stress,
   and inflammation, being significantly associated with the levels of
   AST, ALT, and bilirubin [[209]23,[210]44]. Asparagine could prevent
   liver triglyceride elevation. A long-term diet deficient in choline and
   methionine could cause hepatocellular carcinoma and other liver
   diseases [[211]38,[212]39,[213]45]. In the present study, various amino
   acid metabolism pathways were significantly enriched, such as glycine,
   serine, threonine metabolism, alanine, aspartate, and glutamate
   metabolism, which are involved in glutathione metabolism.
   Interestingly, the glutathione metabolism was simultaneously found to
   be dysregulated in body fluids (serum, urine) and feces (this study,
   [214]Figure 6) and also liver tissues (our previous studies)
   [[215]7,[216]19]. Studies have shown that the deletion of the GNMT
   (glycine N-methyltransferase) gene leads to the development of a fatty
   liver and fibrosis in mice [[217]46,[218]47]. GNMT was found to be
   silent in human liver cancer and down-regulated in the liver of
   patients at risk of liver cancer, such as hepatitis C virus and
   alcohol-induced cirrhosis [[219]38,[220]48], suggesting its key role in
   maintaining liver health [[221]49,[222]50]. Therefore, the evidence
   that cows with fatty liver disease experienced fluctuations of
   metabolism involved in amino acids, fatty acids metabolism, oxidative
   stress and inflammation [[223]7,[224]17,[225]41] would be supportive of
   the complement of a full picture of energy metabolism in transition
   dairy cattle, so as to reveal the etiopathology of the disease.

4.4. The Identified Serum Biomarkers Were Confirmed by a Third Biopsied
Population

   The metabolomics in this study provides a practical strategy for
   screening fatty liver disease cows in their transition period using a
   small amount of feces, urine, or serum. The screening of fatty liver
   disease may be used as an index for the routine examinations of cows on
   commercial farms. In this study, the identified serum biomarkers, such
   as Heptadecanoic acid and L-Serine ([226]Figure 7), were perfectly
   verified in a third biopsy-diagnosed population by non-targeted
   metabolomics, with 12 samples taken from healthy liver individuals and
   8 samples from fatty liver cows. Moreover, common biological metabolic
   pathways were enriched by the differential expressing metabolites
   identified in the third population, such as arginine and proline
   metabolism, TCA cycle, valine, leucine, and isoleucine biosynthesis,
   again confirming the reliability of the novel identified biomarkers.

   Although the reliability of the novel identified biomarkers in the
   study was verified, the novel biomarkers identified from the feces,
   urine, and serum in this study might be single-faceted or limited due
   to the trade-off between coverage, throughput, and cost. To gain a
   relatively wide metabolite coverage, a more effective and robust mass
   spectrum-based metabolomics method could be attempted in the future. In
   addition, although a higher diagnostic sensitivity and specificity of
   these identified biomarkers were obtained in all the studied
   populations of Holstein, their application in other dairy breeds needs
   to be further investigated.

4.5. Potentiality of the Study

   When non-alcoholic fatty liver disease (NAFLD) occurs in human beings,
   it is usually accompanied by metabolic disorder syndromes and obesity,
   similar to fatty liver cows having increased plasma insulin and fatty
   acid concentration, elevated fasting aminotransferase (aspartate
   aminotransferase, AST/alanine aminotransferase, ALT; [227]Table S1)
   and/or triglycerides (TG) level, and also an abnormal lipid
   accumulation in the liver ([228]Figure S1) [[229]51,[230]52,[231]53].
   Even though the different origins of fatty acids caused by altered
   lipid homeostasis (lipolysis) and different insulin resistance status
   would differ NAFLD in patients from fatty liver disease in cattle, in
   view of the basal metabolism pathways, dairy cows with fatty liver
   disease, as an animal model, could bring inspiration on the
   etiopathology of typical NAFLD. Nowadays, NAFLD and its complications
   are currently recognized as a major health threat worldwide
   [[232]54,[233]55]. Recently, metabonomics involving the study of the
   entire metabolome [[234]56] has been identified as a promising and
   powerful tool for detecting disease progression, elucidating its
   pathology, and assessing the impact of drugs on certain pathological
   conditions [[235]23,[236]30,[237]57]. Notably, the application of
   metabolomic technology in screening for the biorecognition markers in
   fatty liver disease has been studied initially, and most of them have
   appeared in NAFLD research. For example, the combination of betaine and
   propionyl carnitine in the serum can make for a good prediction of
   liver diseases and can be used as a supplementary diagnosis method for
   the clinical fatty liver cancer diagnostic marker, alpha-fetoprotein
   (AFP) [[238]58]. Through a multicenter cross-sectional study, a
   biomarker combination consisting of Phe-Trp and GCA was identified and
   used as a test for hepatocellular carcinoma (HCC), and they were
   further developed as an effective tool to verify AFP false-negative HCC
   patients and high-risk S-HCC patients [[239]23]. The identification of
   novel and non-invasive diagnosis biomarkers for the metabolic disorders
   in the study, especially for fatty liver diseased dairy cattle, would
   be inspired and supportive for revealing the pathology and pathogenesis
   of NAFLD [[240]53,[241]54].

5. Conclusions

   In summary, novel biomarker panels consisting of amino acids and fatty
   acids were defined and validated as an effective tool for detecting
   metabolic disorders (fatty liver disease) in dairy cows by non-targeted
   GC–MS metabolomics determination. The biomarkers could discriminate the
   diseased cows with a much higher diagnostic sensitivity and specificity
   than that of the traditional serum biochemical indicators. Moreover,
   the non-invasive markers will have utilization potential in terms of
   animal welfare issues, which are nowadays highly advocated
   internationally. Therefore, we believe these metabolic biomarkers are
   meaningful for a reduction in high culling rates of cows during their
   early lactation period and also for the healthy and sustainable
   development of the dairy industry.

Acknowledgments