# Metabolic response in blood and cerebrospinal fluid during feed restriction: Identification of potential metabolic signals associated with individual feed intake rebound

M.N. Viera, A.L. Astessiano, E. Jorge-Smeding, A.I. Trujillo

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## ► To cite this version:

M.N. Viera, A.L. Astessiano, E. Jorge-Smeding, A.I. Trujillo. Metabolic response in blood and cerebrospinal fluid during feed restriction: Identification of potential metabolic signals associated with individual feed intake rebound. Journal of Dairy Science, 2025, 108 (10), pp.11001-11013. 10.3168/jds.2025-27016. hal-05509064

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# Metabolic response in blood and cerebrospinal fluid during feed restriction: Identification of potential metabolic signals associated with individual feed intake rebound

M. N. Viera,<fnref n="1" />* A. L. Astessiano,<fnref n="2" /> E. Jorge-Smeding,<fnref n="2" />† and A. I. Trujillo<fnref n="2" />

## ABSTRACT

The aim of this study was to evaluate the effects of feed restriction and refeeding on metabolic and AA profiles in blood serum, and on cerebrospinal fluid (CSF) profiles during the feed restriction period, using a controlled feeding model. Additionally, the study aims to identify metabolic signals generated during feed restriction that may help explain individual variability in the feed intake recovery. Following 2 wk of adaptation, 8 primiparous Holstein cows housed in individual stalls with free access to water were fed ad libitum for 5 d (basal period), followed by 50% feed restriction for 5 d, and then returned to ad libitum feeding for 5 d (refeeding period). Dry matter intake and milk yield were determined daily. On d 4, 9, and 14 before morning feeding, blood serum samples were collected by puncture of the coccygeal vessels, whereas CSF samples were obtained from the cisterna magna on d 4 and 9. A targeted analysis of AA was performed using GC time-of-flight MS method in both fluids. The blood serum metabolite and hormone concentrations were affected by the period. In general, more significant increases in AA concentrations were observed in the CSF during feed restriction compared with blood serum, particularly for EAA and branched-chain AA. In blood serum, only a few AA showed significant changes during the refeeding period, whereas in CSF several AA, including Val, Glu, and Lys, increased during feed restriction. A partial least squares regression model using CSF data successfully predicted feed intake rebound measured as the change in the DMI (ΔDMI) during the refeeding relative to feed restriction. This model included 12 predictors; among them, Cys showed a positive association with the ΔDMI, whereas the other AA and glucose exhibited a negative association. In contrast,

no valid model was obtained when using serum AA concentrations as predictors. Short-term feed restriction induced metabolic, hormonal, and AA changes in both serum and CSF. Variability in refeeding response was linked to CSF glucose and AA concentrations during restriction, suggesting that altered blood-brain barrier selectivity or central AA production may be associated with feed intake regulation.

**Key words:** voluntary feed intake, recovery, cerebrospinal fluid, amino acids

## INTRODUCTION

High-yielding dairy cows in early lactation undergo a state of negative energy balance (NEB) and nutrient balance due to downregulated feed intake that is insufficient to sustain the increased demand for milk synthesis (Ingvartsen et al., 2003). This period is marked by endocrine and metabolic changes, including increased lipolysis and nutrient partitioning toward the mammary gland, resulting in elevated plasma concentration of non-esterified fatty acids (NEFA), BHB, and pro-inflammatory cytokines. The end of the NEB involves the timely restoration of adequate feed intake, which is essential to support metabolic recovery and sustain milk production (Grummer, 1995; Drackley, 1999). The metabolic alterations are closely connected to the control of voluntary feed intake, which is influenced by a complex interaction of metabolic (Santos et al., 2024), neuroendocrine (Roche et al., 2008), inflammatory (Brown and Bradford, 2021), and hepatic oxidative signals (Allen et al., 2009; Soutto et al., 2020) that are integrated primarily at the level of the hypothalamus and brainstem. It has been demonstrated that cerebrospinal fluid (CSF) metabolites have a potential regulatory role in the control of feed intake. For example, glucose infusion into the CSF of sheep reduced feed intake within 2 h (Seoane and Baile, 1972), and elevated BHB concentrations in the CSF in dairy cows during NEB suggest a hypophagic effect, as demonstrated under in vitro cell culture conditions (Lae-

<footnote n="1">¹Facultad de Veterinaria, Departamento de Biociencias, Universidad de la República, Montevideo 13000, Uruguay</footnote>

<footnote n="2">²Facultad de Agronomía, Departamento de Producción Animal y Pasturas, Universidad de la República, Montevideo 12900, Uruguay</footnote>

<footnote>Received May 29, 2025.</footnote>

<footnote>Accepted July 25, 2025.</footnote>

<footnote>*Corresponding author: mariaviera2091@gmail.com</footnote>

<footnote>†Current address: UMR Herbivores, INRAE, VetAgro Sup, 63122 Saint-Genès-Champanelle, France.</footnote>
ger et al., 2012a, 2013). It is known that, in the brain, AA may also act as signals of energy or protein status, influencing neural circuits that regulate feeding. In addition, AA serve as precursors for many neurotransmitters and can affect brain function and feed intake regulation by modulating neurotransmitter synthesis—specifically through the production of Glu, Asp, and Gly—and by competing for transport of Trp and Tyr across the blood-brain barrier (BBB). These mechanisms are well established in monogastric species (He and Wu, 2020) but remain poorly defined in ruminants, especially during the refeeding period.

In dairy cows, changes in the concentrations of Gln, Leu, Ile, and Val (Laeger et al., 2012b, 2013), as well as the activation of pro-peptides and pro-hormones involved in energy homeostasis (Kuhla et al., 2015), have been observed in the CSF during NEB. Together, these findings suggest that CSF not only reflects systemic metabolic changes but may also contribute to understand mechanisms involved in the regulation of voluntary feed intake during periods of NEB.

There are individual differences in the recovery of voluntary food intake after a period of NEB, but the mechanisms driving this variability remain unclear, as do the extent of the changes that occur in CSF and blood serum metabolite profiles. We hypothesize that the variability of the recovery of feed intake in the refeeding period is associated with changes in CSF and blood serum metabolic profiles during feed restriction. Therefore, this study aimed to evaluate the effects of feed restriction and refeeding on metabolic and AA profiles in blood serum, and on CSF profiles during the restriction period, using a controlled feeding model. Additionally, the study aims to identify metabolic signals generated during feed restriction that may help explain individual variability in feed intake recovery.

## MATERIALS AND METHODS

The experiment was carried out at the Estación Experimental Mario A. Cassinoni, Facultad de Agronomía (Paysandú, Uruguay, 32°S, 58°W) during September 2019. Animal use and procedures were approved by the Animal Experimentation Committee (CEUA) of Facultad de Agronomía (CEUA ID: 1169).

### Experimental Design and Animal Management

The study used 8 primiparous Holstein cows in mid lactation selected from the experimental station herd (18.50 ± 4.10 kg/d milk yield, 472 ± 41 kg of BW, 2.50 ± 0.19 of BCS, and 109 ± 28 DIM at the beginning of the experiment; mean ± SD). All cows had 2 wk of adaptation to the TMR diet and were housed in individual tie stalls with

free access to water. The study was carried out for 15 d and comprised 3 consecutive periods. During the first 5 d (d 0 to 4) cows were fed ad libitum (basal period), from d 5 to 9 cows were fed 50% of the individual average daily DMI of the previous ad libitum period (feed restriction period) and, finally, from d 10 to 14, cows were returned to ad libitum conditions (refeeding period). Cows were fed once daily (0700 h) a TMR diet formulated to meet requirements for maintenance and milk production (NRC, 2001). Feed intake was measured daily by difference of feed offered and refused and expressed as DMI. Cows were milked twice daily at 0500 and 1500 h, and individual milk yields were recorded electronically at each milking (DairyPlan C21 herd and parlor management, GEA Farm Technologies Ltd., Cambridge, New Zealand). At the beginning and the end of each feeding period, milk samples were obtained and analyzed for fat, protein, and lactose content by an infrared spectrophotometric method (COLAVECO, Nueva Helvecia, Uruguay). Energy-corrected milk and energy retained in milk (ERM) were calculated according to Equation 1 (Tyrrell and Reid, 1965) and Equation 2 (NRC, 2001), respectively. The energy balance (EB) was calculated as NASEM (2021; Equation 3). All variables were averaged within each of the 3 feeding periods (basal, feed restriction, and refeeding) for statistical analysis:

$$ECM\left(\frac{\text{kg}}{\text{d}}\right) = \left[ 12.82 \times \text{fat}\left(\frac{\text{kg}}{\text{d}}\right) \right] + \left[ 7.13 \times \text{protein}\left(\frac{\text{kg}}{\text{d}}\right) \right] + \left[ 0.323 \times \text{milk}\left(\frac{\text{kg}}{\text{d}}\right) \right] \quad [1]$$

$$\text{ERM}\left(\frac{\text{Mcal}}{\text{d}}\right) = (0.0929 \times \% \text{ fat}) + (0.0547 \times \% \text{ protein}) + (0.0395 \times \% \text{ lactose}) \quad [2]$$

$$\text{EB}\left(\frac{\text{Mcal}}{\text{d}}\right) = \text{NE}_L \text{ intake}\left(\frac{\text{Mcal}}{\text{d}}\right) - \left[ \text{NE}_L \text{ maintenance}\left(\frac{\text{Mcal}}{\text{d}}\right) + \text{NE}_L \text{ milk}\left(\frac{\text{Mcal}}{\text{d}}\right) \right] \quad [3]$$

Ration samples were collected daily during each period to make a composite sample that was grounded (1 mm, Wiley mill, Thomas Scientific) and analyzed at the Laboratory de Nutrición Animal (Facultad de Agronomía, Uruguay) for DM, OM, and total N (AOAC, 2019; methods 934.01, 942.05, and 984.13, respectively). Neutral detergent fiber, ADF, and ADL were measured sequentially using an ANKOM200 Fiber Analyzer (ANKOM
<table><caption>**Table 1.** Ingredients and nutrient composition (SD in parentheses) of TMR (% of DM unless noted)</caption><thead><tr><th>Item</th><th>TMR</th></tr></thead><tbody><tr><td><strong>Ingredient</strong></td><td></td></tr><tr><td>Sorghum silage</td><td>75.60</td></tr><tr><td>Corn grain, ground</td><td>5.60</td></tr><tr><td>Soybean meal</td><td>16.80</td></tr><tr><td>Urea</td><td>0.70</td></tr><tr><td>Sodium chloride</td><td>0.60</td></tr><tr><td>Calcium carbonate</td><td>0.30</td></tr><tr><td>Dicalcium phosphate</td><td>0.20</td></tr><tr><td>NutriBov<sup>1</sup></td><td>0.10</td></tr><tr><td>Sulfur</td><td>0.08</td></tr><tr><td><strong>Nutrient composition</strong></td><td></td></tr><tr><td>DM (% as fed)</td><td>31.40 (0.72)</td></tr><tr><td>OM</td><td>92.10 (0.38)</td></tr><tr><td>CP</td><td>12.50 (0.28)</td></tr><tr><td>Ether extract</td><td>1.10 (0.40)</td></tr><tr><td>NDF<sup>2</sup></td><td>49.50 (0.88)</td></tr><tr><td>ADF<sup>2</sup></td><td>26.60 (2.64)</td></tr><tr><td>ADL<sup>2</sup></td><td>0.64 (0.05)</td></tr><tr><td>NEL<sup>3</sup> (Mcal/kg of DM)</td><td>1.54 (0.04)</td></tr></tbody></table>

Technology Corp., Fairport, NY) based on the procedure described by Van Soest et al. (1991). A heat-stable amylase was used in the analysis of NDF; NDF and ADF contents were expressed as ash-free residues. Chemical composition of the TMR is available in Table 1.

## Blood Serum and CSF Sampling

Blood samples were obtained from coccygeal vessels using empty tubes (BD Vacutainer) after morning milking and before feeding at the end of each period (d 4, 9, and 14). The serum was separated by centrifugation (3,000 × g, 10 min, room temperature) and divided in 2 aliquots. One aliquot of serum was frozen at -80°C for AA quantification and the other was stored at -20°C until determination of classical metabolites.

The collection of CSF samples was performed only at the end of basal and feed restriction periods (d 4 and 9, respectively) due to the very invasive nature of the procedure. The CSF was collected aseptically from the cisterna magna by puncturing the atlanto-occipital space using sterile spinal needles (21 gauge × 120 mm; St Jean et al., 1997). The animals were sedated with xylazine (Kensol Koning, Buenos Aires, Argentina; dosage: 0.05-0.10 mg/kg, intravenous), the puncture site was shorn, washed, disinfected, and locally anesthetized and the animal's head was bent at a 90° angle to open up the intervertebral gap. The CSF samples were obtained

after morning milking and before feeding, and they were divided into 2 aliquots for the same purposes as serum samples.

## Metabolites and Hormone Analysis

All metabolites for both fluids were determined spectrophotometrically using a 96-well microplate reader (Multiskan FC, Thermo Fisher Scientific) and commercial kits (Biosystems SA, Barcelona, Spain, for glucose, cholesterol, urea; and Randox Laboratories, Crumlin, UK, for NEFA and BHB). All samples were analyzed in one assay for each metabolite and intra-assay coefficient of variation for all metabolite determinations did not exceed 10%. Serum glucagon was measured using RIA kit (#GL-32K, Millipore) specific for glucagon in serum (Sheahan, 2014). Intra-assay coefficients of variation for control 1 (50 pg/mL) and 2 (150 pg/mL) were 8% and 4%, respectively. Blood serum concentrations of insulin were measured using an immunoradiometric assay with a commercial kit (DIAsource INS-IRMA Kit, Louvain-la-Neuve, Belgium) previously used in cattle (Astessiano et al., 2015). The assay detection limit was 1.52 mIU/mL, and intra-assay coefficients of variation for control 1 (23.40 mIU/mL) and 2 (76.30 mIU/mL) were 6.2% and 2.2%, respectively. The revised quantitative insulin sensitivity check index (RQUICKI) was calculated according to Holtenius and Holtenius (2007; Equation 4):

$$RQUICKI = \frac{1}{\left[ \log\left(\text{glucose, mg/dL}\right) + \log\left(\text{insulin, } \frac{\mu\text{U}}{\text{mL}}\right) + \log\left(\text{NEFA, mmol/L}\right) \right]} \quad [4]$$

## AA Absolute Quantification

Amino acid absolute quantitative analysis procedures were performed by using gas chromatography time-of-flight mass spectrometry (GCToF/MS) according to Fiehn (2016) at the core laboratory West Metabolomic Center (University of California-Davis Genome Center, Davis, CA) to determine free AA concentrations from blood serum and CSF. A column of Restek 95% dimethyl/5% diphenyl polysiloxane RTX-5MS (Restek Corporation) with a measure of 30 m long × 0.25 mm of internal diameter and with 0.25-µm film was used for chromatography analyses. A Leco Pegasus IV mass spectrometer (St. Joseph, MI) was used with unit mass resolution at 17 spectra/s from 85 to 500 Da and ionization energy set in -70 eV and equipped with an 1,800 V detector voltage, 280°C transfer line, and a 250°C ion source. Chromatographic data were pre-processed with-

<footnote n="1">¹NutriBov mix (Agropecuária Toledo Pena, Lavras, Brazil) contained Zn, 12,000 mg/kg; Cu, 4,000 mg/kg; Se, 40 mg/kg; I, 200 mg/kg; Co, 40 mg/kg; Mn, 16,000 mg/kg; vitamin A, 1,600,000 IU/kg; vitamin D₃, 320,000 IU/kg, and vitamin E, 6,000 IU/kg.</footnote>

<footnote n="2">²NDF, ADF, and ADL were measured including heat-stable amylase, and contents were expressed as ash-free residues.</footnote>

<footnote n="3">³Estimated according to Acosta (2004).</footnote>
out smoothing and peak width was set at 3 s, the baseline subtraction was done just above the noise level, and automatic mass spectral deconvolution and peak detection at signal/noise levels of 5:1 were automatically performed throughout the chromatogram. Annotation of derivatized ions was performed using the BinBase algorithm based on deconvoluted spectra and peak metadata (retention index, unique ion, spectral similarity, signal/noise ratio, peak purity) from the LecoChromaTOF software (version 2.32) using a multitiered filtering system with stringent thresholds, specifically developed for GCToF/ MS data annotation (Skogerson et al., 2011). To measure AA concentrations, two 8-point curves spanning the range of 10 to 0.05 ng on the column were created and compared with the samples. A quadratic trendline was used to convert peak heights into analyte amounts on the column. Concentrations were then calculated by dividing the analyte amount on the column by the sample amount on the column.

## Statistical Analysis

Data of both fluids were analyzed as repeated measures using the MIXED procedure (SAS University Edition, SAS Institute Inc., Cary, NC). The model included the period (basal, feed restriction, and refeeding) as the fixed effect, the cow as a random effect, and DIM at the beginning of the experiment as a covariate. The period was the repetition factor, and due to the equally spaced sampling periods, the first-order autoregressive covariance structure was used (Little et al., 2006). Other covariate structures were tested and discarded due to greater Akaike information criterion and Bayesian information criterion values. Cerebrospinal fluid data were only available for the basal and feed restriction periods and were analyzed accordingly. In contrast, milk yield, ECM, DMI, and milk composition were recorded throughout the 3 periods and analyzed using the average values per period. Least squares means and associated standard errors were obtained, and multiple comparisons were adjusted with Tukey correction. A significant difference was considered when $P \le 0.05$ and trends if $0.05 < P \le 0.10$. Total AA (TAA) was defined as the sum of all analyzed AA. Total NEAA was defined as the sum of Ala, Asn, Asp, Cys, Glu, Gln, Gly, Pro, Ser, and Tyr, and the total EAA as the sum of His, Ile, Leu, Met, Phe, Thr, Trp, and Val. Additionally, the branched-chain amino acid (BCAA)/TAA and BCAA/EAA ratios were calculated.

To explore the possible associations between blood serum and CSF metabolites during feed restriction and the individual DMI rebound during the subsequent ad libitum refeeding period, data were subjected to partial least squares regression (PLS-R) using XLSTAT software (version 2022.1; Addinsoft, 2022). This a su-

pervised multivariate method used to build predictive models for a given trait (response variable; Wold et al., 2010). The response variable (ΔDMI) was defined as the change between DMI during the first day of the refeeding (DMI<sub>refeeding</sub>) and the average DMI during the feed restriction period (DMI<sub>feed restriction</sub>; Equation 5), and the predictors of ΔDMI were the concentrations of metabolites in serum and CSF during the feed restriction period:

$$ \Delta \text{DMI} = \frac{\text{DMI}_{\text{refeeding}} - \text{DMI}_{\text{feed restriction}}}{\text{DMI}_{\text{feed restriction}}} \quad [5] $$

We selected d 1 of the refeeding period for ΔDMI because it reflects the acute compensatory response in intake immediately after the feed restriction period. This rebound is typically the most pronounced and variable across individuals, as reported in previous studies involving dairy cows subjected to feed restriction and refeeding (Ferraretto et al., 2014; Abdelatty et al., 2017; Goetz et al., 2024). In contrast, DMI tends to stabilize or decline after the first few days of refeeding, making d 1 the most informative time point to assess individual variability in feed intake recovery. Separate PLS-R models were developed for blood serum and CSF metabolites obtained during the feed restriction period. Each dataset included AA and classical metabolites such as glucose, insulin, urea, cholesterol, BHB, and NEFA. The initial number of variables included in the analysis was 26 for blood serum and 22 for CSF. Together with the metabolite's concentrations, the DIM was also included in the predictor's dataset, in analogy to the covariate considered in the ANOVA previously stated. A PLS-R model was considered valid and further refined if the predictive ability based on cross-validation ($Q^2$), was >0 for the first run. Cross-validation of the model was performed through a specific application of the jack-knife method (leave-one-out rule), and the number of components retained was selected according to change of $Q^2$ between 2 consecutive components ($\Delta Q^2 \ge 0.975$) in the first run of the model based on the entire dataset. The goodness of fit and goodness of predictability were assessed by coefficient of determination for $Y$ ($R^2Y$) and $Q^2$, respectively (Hennigsson et al., 2001). Values of $R^2Y$ closer to 1 indicate better goodness of fit, while $Q^2 >0.50$ were considered as the threshold of predictive relevance (Barroso et al., 2010). In addition, the model was refined through variable reduction based on the variable importance in projection (VIP), a measure of the weight of each variable relative to others. In each round, predictors with VIP ≥1.0 were retained, and the model was rerun until the $Q^2$ increase was <0.0975, obtaining a final model (Chong and Jun, 2005) of 12 predictors.
<figure><img src="image_4.png" /><figcaption>**Figure 1.** Daily DMI (kg/d) of Holstein cows across the basal (d 0–4), feed restriction (d 5–9), and refeeding (d 10–14) periods. Feed restriction was 50% of the basal intake, and refeeding returned to ad libitum conditions. The feed restriction period is highlighted in gray.</figcaption></figure>

## RESULTS

### Cow Performance

The DMI decreased during feed restriction (–50%) compared with the basal, and then it increased during the refeeding (+50%) achieving similar DMI as in the basal period (Figure 1). Milk yield (–15%), ECM (–19%), and ERM decreased (–20%; *P* < 0.001 for all variables) dur-

ing feed restriction compared with the basal period. The EB was negative (–7.32 Mcal/d) during feed restriction period and was positive (5.33 Mcal/d) during the refeeding period. All of the milk components decreased their concentration from basal to feed restriction (–5%; *P* < 0.001); however, the percentage of protein increased (*P* < 0.001) and the fat tended to increase (*P* < 0.06) during the refeeding period (Table 2).

<table><caption>**Table 2.** Dry matter intake, milk yield, energy balance (EB), and milk composition in Holstein cows in 3 consecutive feeding periods<fnref n="1" /></caption><thead><tr><th rowspan="2">Item</th><th colspan="3">Period<sup>2</sup></th><th rowspan="2">SE</th><th rowspan="2">P-value</th></tr><tr><th>Basal</th><th>Feed restriction</th><th>Refeeding</th></tr></thead><tbody><tr><td>DMI (kg/d)</td><td>18.01<sup>a</sup></td><td>9.70<sup>b</sup></td><td>18.62<sup>a</sup></td><td>0.54</td><td>&lt;0.001</td></tr><tr><td>Milk yield (kg/d)</td><td>20.37<sup>a</sup></td><td>17.23<sup>b</sup></td><td>17.88<sup>b</sup></td><td>0.84</td><td>&lt;0.001</td></tr><tr><td>ECM (kg/d)</td><td>22.92<sup>a</sup></td><td>18.53<sup>b</sup></td><td>19.67<sup>b</sup></td><td>0.81</td><td>&lt;0.001</td></tr><tr><td>ERM (Mcal of NEL/d)</td><td>16.20<sup>a</sup></td><td>12.98<sup>b</sup></td><td>13.79<sup>b</sup></td><td>0.58</td><td>&lt;0.001</td></tr><tr><td>EB (Mcal of NEL/d)</td><td>3.54<sup>b</sup></td><td>-7.32<sup>c</sup></td><td>5.33<sup>a</sup></td><td>0.81</td><td>&lt;0.001</td></tr><tr><td>Milk composition</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Fat (%)</td><td>4.39<sup>a</sup></td><td>4.11<sup>b</sup></td><td>4.23<sup>ab</sup></td><td>0.09</td><td>0.06</td></tr><tr><td>Fat (kg)</td><td>0.88<sup>a</sup></td><td>0.70<sup>c</sup></td><td>0.75<sup>b</sup></td><td>0.03</td><td>&lt;0.001</td></tr><tr><td>Protein (%)</td><td>3.52<sup>a</sup></td><td>3.27<sup>c</sup></td><td>3.39<sup>b</sup></td><td>0.03</td><td>&lt;0.001</td></tr><tr><td>Protein (kg)</td><td>0.70<sup>a</sup></td><td>0.56<sup>b</sup></td><td>0.60<sup>b</sup></td><td>0.03</td><td>&lt;0.001</td></tr><tr><td>Lactose (%)</td><td>5.15<sup>a</sup></td><td>4.98<sup>b</sup></td><td>4.99<sup>b</sup></td><td>0.07</td><td>0.04</td></tr><tr><td>Lactose (kg)</td><td>1.05<sup>a</sup></td><td>0.86<sup>b</sup></td><td>0.90<sup>b</sup></td><td>0.05</td><td>&lt;0.001</td></tr><tr><td>TS (kg)</td><td>2.64<sup>a</sup></td><td>2.12<sup>b</sup></td><td>2.25<sup>b</sup></td><td>0.10</td><td>&lt;0.001</td></tr></tbody></table>

<footnote>a–cDifferent superscripts in the same row indicate significant differences (*P* ≤ 0.05).</footnote>

<footnote n="1">¹Values represent the mean of the 5-d period for each variable. Milk composition values correspond to the average of 2 samples collected at the beginning and end of each period. ERM = energy retained in milk.</footnote>

<footnote n="2">²Basal: feed ad libitum (0 to 4 d); feed restriction: feed 50% of ad libitum (5 to 9 d); refeeding: feed ad libitum (10 to 14 d).</footnote>
## Metabolites and Hormones in Blood Serum and CSF

The blood serum metabolite and hormone concentrations were affected by the period (*P* < 0.05; Table 3). Serum concentrations of NEFA and BHB increased from basal to feed restriction and then decreased during the refeeding period to similar values as in the basal period (*P* < 0.001; Table 3). Glucose concentration decreased from the basal to refeeding period (*P* < 0.01; Table 3). Cholesterol concentrations increased during feed restriction (*P* = 0.05; Table 3). Urea concentration remained stable during feed restriction and then increased (*P* < 0.01; Table 3) during the refeeding period. Insulin concentration and the RQUICKI index decreased from basal to feed restriction and during the refeeding period increased to similar values as in the basal period (*P* < 0.05; Table 3). Glucagon concentration and insulin/glucagon ratio tended to be affected by the feeding period, increasing from the feed restriction to refeeding period (*P* = 0.04; Table 3).

In CSF, glucose concentration tended to decrease (*P* = 0.09) from basal to feed restriction period, BHB was not affected, and cholesterol and NEFA concentrations were not detectable (Table 3).

**AA Concentrations in Blood Serum.** Serum concentrations of Tyr, Glu, Cys, and Asp maintained their concentration from basal to feed restriction but increased during the refeeding period (*P* < 0.05; Table 4). Asparagine and Gln concentration decreased (*P* < 0.01) during feed restriction and then increased (*P* < 0.01) during refeeding, but only Asn concentration reached greater concentrations relative to the basal period (*P* < 0.01). The Ala, Pro, and

His concentrations were maintained from basal to feed restriction and then increased (*P* < 0.05) during refeeding recovering the basal period concentration, whereas Trp concentration tended (*P* < 0.09) to follow the same profile. For instance, Lys and Phe tended to maintained concentration during all of the feeding periods (*P* < 0.09; Table 4). However, some individual AA, such as Val, Ser, Thr, Met, Leu, Ile, and Gly concentrations, were not affected by feeding period (Table 4).

The NEAA concentration decreased (*P* = 0.02) during the feed restriction and then increased during the refeeding period (*P* < 0.01), TAA tended to be affected by period, and EAA was not affected (Table 4). The BCAA/TAA ratio increased during the feed restriction and then decreased during the refeeding period (*P* < 0.04); meanwhile, BCAA/EAA was not affected by the feeding period (Table 4).

**AA Concentrations in CSF.** The concentrations of Val, Glu, Pro, Met, Lys, Gly, and Asp increased (*P* < 0.05), whereas Phe and Ala concentration tended to increase (*P* = 0.09), and only Cys decreased (*P* = 0.02) during the feed restriction period (Table 5). However, some individual AA, such as Tyr, Ser, Thr, Trp, Leu, Ile, His, Gln, and Asn concentrations, were not affected by feeding period (Table 5).

The concentrations of total BCAA and EAA increased (*P* < 0.05) during the feeding period, but TAA and NEAA were not affected (Table 5). For instance, BCAA/TAA and BCAA/EAA increased (*P* < 0.05) during the feeding period (Table 5).

**PLS-R in CSF and Blood Serum.** No valid model was obtained to predict ΔDMI when considering plasma AA

<table><caption>**Table 3.** Concentrations of metabolites and hormones in blood serum and metabolites in cerebrospinal fluid (CSF) in Holstein cows in 3 consecutive feeding periods<fnref n="1" /></caption><thead><tr><th rowspan="2">Item</th><th colspan="4">Period<sup>2</sup></th><th rowspan="2">P-value</th></tr><tr><th>Basal</th><th>Feed restriction</th><th>Refeeding</th><th>SE</th></tr></thead><tbody><tr><td>Serum metabolite</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>NEFA (mmol/L)</td><td>0.09<sup>b</sup></td><td>0.58<sup>a</sup></td><td>0.08<sup>b</sup></td><td>0.06</td><td>&lt;0.001</td></tr><tr><td>BHB (mmol/L)</td><td>0.29<sup>b</sup></td><td>0.53<sup>a</sup></td><td>0.30<sup>b</sup></td><td>0.03</td><td>&lt;0.001</td></tr><tr><td>Glucose (mmol/L)</td><td>4.73<sup>a</sup></td><td>3.98<sup>b</sup></td><td>3.36<sup>c</sup></td><td>0.17</td><td>&lt;0.01</td></tr><tr><td>Cholesterol (mmol/L)</td><td>3.76<sup>b</sup></td><td>4.67<sup>a</sup></td><td>4.32<sup>ab</sup></td><td>0.24</td><td>0.05</td></tr><tr><td>Urea (mmol/L)</td><td>5.85<sup>b</sup></td><td>5.11<sup>b</sup></td><td>7.17<sup>a</sup></td><td>0.44</td><td>&lt;0.01</td></tr><tr><td>Insulin (IU)</td><td>9.53<sup>a</sup></td><td>6.95<sup>b</sup></td><td>9.85<sup>a</sup></td><td>1.09</td><td>0.05</td></tr><tr><td>Glucagon (pg/mL)</td><td>182.10<sup>ab</sup></td><td>150.90<sup>b</sup></td><td>187.00<sup>a</sup></td><td>12.45</td><td>0.09</td></tr><tr><td>Insulin:glucagon</td><td>1.09<sup>ab</sup></td><td>0.96<sup>b</sup></td><td>1.45<sup>a</sup></td><td>0.15</td><td>0.06</td></tr><tr><td>RQUICKI</td><td>0.56<sup>a</sup></td><td>0.42<sup>b</sup></td><td>0.61<sup>a</sup></td><td>0.04</td><td>&lt;0.01</td></tr><tr><td>CSF metabolite</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Glucose (mmol/L)</td><td>3.70</td><td>2.60</td><td>—</td><td>0.20</td><td>0.09</td></tr><tr><td>BHB (mmol/L)</td><td>0.12</td><td>0.11</td><td>—</td><td>0.02</td><td>0.61</td></tr></tbody></table>

<footnote>a-cDifferent superscripts in the same row indicate significant differences (*P* ≤ 0.05).</footnote>

<footnote n="1">¹Blood serum samples were collected at the end of each period. The CSF samples were collected only at the end of the basal and restriction periods; no CSF was obtained during refeeding.</footnote>

<footnote n="2">²Basal: feed ad libitum (0 to 4 d); feed restriction: feed 50% of ad libitum (5 to 9 d); refeeding: feed ad libitum (10 to 14 d). NEFA = nonesterified fatty acids; RQUICKI = revised quantitative insulin sensitivity check index.</footnote>
<table><caption>**Table 4.** Amino acid concentrations in blood serum of Holstein cows collected at the end of each of 3 consecutive feeding periods (basal, feed restriction, and refeeding)</caption><thead><tr><th rowspan="2">AA (µmol/L)</th><th colspan="3">Period<fnref n="1" /></th><th rowspan="2">SE</th><th rowspan="2">P-value</th></tr><tr><th>Basal</th><th>Feed restriction</th><th>Refeeding</th></tr></thead><tbody><tr><td>Tyr</td><td>34.50<sup>b</sup></td><td>28.60<sup>b</sup></td><td>44.90<sup>a</sup></td><td>3.30</td><td>&lt;0.01</td></tr><tr><td>Glu</td><td>50.20<sup>b</sup></td><td>42.80<sup>b</sup></td><td>76.00<sup>a</sup></td><td>4.90</td><td>&lt;0.01</td></tr><tr><td>Cys</td><td>24.80<sup>b</sup></td><td>24.80<sup>b</sup></td><td>25.60<sup>a</sup></td><td>0.20</td><td>0.02</td></tr><tr><td>Asp</td><td>14.00<sup>b</sup></td><td>11.20<sup>b</sup></td><td>18.90<sup>a</sup></td><td>1.20</td><td>&lt;0.01</td></tr><tr><td>Asn</td><td>35.50<sup>b</sup></td><td>28.50<sup>c</sup></td><td>44.30<sup>a</sup></td><td>2.20</td><td>&lt;0.01</td></tr><tr><td>Gln</td><td>173.70<sup>a</sup></td><td>121.00<sup>b</sup></td><td>151.20<sup>a</sup></td><td>8.00</td><td>&lt;0.01</td></tr><tr><td>Ala</td><td>138.10<sup>ab</sup></td><td>119.10<sup>b</sup></td><td>159.80<sup>a</sup></td><td>9.80</td><td>0.02</td></tr><tr><td>Pro</td><td>67.50<sup>ab</sup></td><td>49.20<sup>b</sup></td><td>79.10<sup>a</sup></td><td>8.50</td><td>0.04</td></tr><tr><td>His</td><td>55.70<sup>ab</sup></td><td>51.20<sup>b</sup></td><td>66.20<sup>a</sup></td><td>3.90</td><td>0.04</td></tr><tr><td>Trp</td><td>39.00<sup>ab</sup></td><td>34.60<sup>b</sup></td><td>43.10<sup>a</sup></td><td>2.60</td><td>0.06</td></tr><tr><td>Lys</td><td>56.70<sup>b</sup></td><td>64.40<sup>ab</sup></td><td>74.70<sup>a</sup></td><td>5.00</td><td>0.06</td></tr><tr><td>Phe</td><td>33.10<sup>b</sup></td><td>35.10<sup>ab</sup></td><td>40.70<sup>a</sup></td><td>2.40</td><td>0.09</td></tr><tr><td>Val</td><td>185.80</td><td>184.70</td><td>213.80</td><td>13.30</td><td>0.23</td></tr><tr><td>Ser</td><td>110.70</td><td>85.50</td><td>103.40</td><td>12.70</td><td>0.37</td></tr><tr><td>Thr</td><td>56.00</td><td>47.50</td><td>48.70</td><td>7.40</td><td>0.65</td></tr><tr><td>Met</td><td>2.90</td><td>2.90</td><td>3.00</td><td>0.10</td><td>0.63</td></tr><tr><td>Leu</td><td>125.10</td><td>138.40</td><td>153.00</td><td>13.10</td><td>0.34</td></tr><tr><td>Ile</td><td>186.20</td><td>197.50</td><td>206.10</td><td>14.30</td><td>0.62</td></tr><tr><td>Gly</td><td>195.50</td><td>173.40</td><td>193.30</td><td>9.20</td><td>0.16</td></tr><tr><td>TAA</td><td>1,589.50</td><td>1,442.90</td><td>1,746.00</td><td>94.80</td><td>0.09</td></tr><tr><td>BCAA</td><td>496.90</td><td>520.60</td><td>572.90</td><td>39.20</td><td>0.39</td></tr><tr><td>EAA</td><td>740.50</td><td>756.50</td><td>849.40</td><td>53.60</td><td>0.32</td></tr><tr><td>NEAA</td><td>849.00<sup>a</sup></td><td>686.40<sup>b</sup></td><td>896.70<sup>a</sup></td><td>46.20</td><td>0.01</td></tr><tr><td>BCAA/TAA</td><td>0.30<sup>b</sup></td><td>0.40<sup>a</sup></td><td>0.30<sup>b</sup></td><td>0.01</td><td>0.01</td></tr><tr><td>BCAA/EAA</td><td>0.70</td><td>0.70</td><td>0.70</td><td>0.009</td><td>0.35</td></tr></tbody></table>

concentrations as predictors ($Q^2 < 0$). However, a valid model with acceptable predictive ability and goodness of fit ($Q^2 = 0.56$; $R^2Y = 0.62$) was obtained when considering the CSF data (Table 6). This model retained only the first component and comprised 12 predictors (Cys, Pro, Phe, Asp, Tyr, Gly, His, Trp, Val, glucose, Asn, and Lys, with the first 6 showing the highest VIP values). The sign of the coefficients in the predicted model showed that cows exhibiting greater concentrations of these predictors during the feed restriction period showed a lower increase in $\Delta$DMI later in the refeeding period, except for Cys for which greater concentrations were associated with greater $\Delta$DMI.

DISCUSSION

This study comprised a short-term nutritional challenge model to investigate the productive and metabolic impacts of feed restriction followed by recovery under ad libitum condition, and the potential association between blood serum and CSF metabolites and the DMI rebound after a period of feed restriction. As expected, the feed restriction induced a state of NEB reducing milk yield, which was not recovered after the 5-d refeeding period despite partial recovery of the metabolic status. Our find-

ings indicate that the concentration of certain AA (e.g., Cys, Pro, Phe, Asp, Tyr, Gly, His, Trp, Val, Asn, and Lys) and glucose in CSF during feed restriction, but not in blood serum, were associated with the individual variation in DMI rebound.

According to the experimental design the 50% feed restriction reduced the DMI leading to a NEB state and decreased milk yield, ECM, ERM, and milk total solids, consistent with previous studies (Gross et al., 2011; Abdelatty et al., 2017; Herve et al., 2019). The lack of response in milk yield during the refeeding period may be attributed to insufficient recovery time (Gross et al., 2011; Herve et al., 2019). The greater NEFA, BHB, and cholesterol concentrations, and lower glucose, urea, insulin, and RQUICKI values during the feed restriction period were consistent with the NEB (Gross et al., 2011). Indeed, the greater insulin sensitivity, as indicated by the RQUICKI index during refeeding, may also explain the lower glucose and greater glucagon concentrations, which suggest a redirection of glucose toward peripheral tissues (De Koster and Opsomer, 2013; Cincović et al., 2014). Although RQUICKI has been used as an index of insulin sensitivity in various species, its physiological relevance and validity in dairy cows have been questioned. In the present study,

<footnote>a-c Different superscripts in the same row indicate significant differences (P ≤ 0.05).</footnote>

<footnote n="1">¹Basal: feed ad libitum (0 to 4 d); feed restriction: feed 50% of ad libitum (5 to 9 d); refeeding: feed ad libitum (10 to 14 d). TAA = total amino acids; BCAA = branched-chain amino acids.</footnote>
<table><caption>**Table 5.** Amino acid concentrations in cerebrospinal fluid (CSF) of Holstein cows collected at the end of each of 2 consecutive feeding periods (basal and feed restriction)</caption><thead><tr><th rowspan="2">AA (µmol/L)</th><th colspan="2">Period<fnref n="1" /></th><th rowspan="2">SE</th><th rowspan="2">P-value</th></tr><tr><th>Basal</th><th>Feed restriction</th></tr></thead><tbody><tr><td>Val</td><td>2.70</td><td>5.80</td><td>0.70</td><td>&lt;0.01</td></tr><tr><td>Glu</td><td>7.00</td><td>8.00</td><td>0.20</td><td>&lt;0.01</td></tr><tr><td>Pro</td><td>1.10</td><td>1.50</td><td>0.09</td><td>0.01</td></tr><tr><td>Met</td><td>1.60</td><td>1.90</td><td>0.007</td><td>0.03</td></tr><tr><td>Lys</td><td>10.60</td><td>13.40</td><td>1.20</td><td>0.04</td></tr><tr><td>Gly</td><td>6.40</td><td>11.10</td><td>1.60</td><td>0.04</td></tr><tr><td>Asp</td><td>3.10</td><td>4.30</td><td>0.04</td><td>&lt;0.01</td></tr><tr><td>Phe</td><td>4.10</td><td>5.10</td><td>0.30</td><td>0.09</td></tr><tr><td>Ala</td><td>43.80</td><td>48.80</td><td>3.00</td><td>0.09</td></tr><tr><td>Cys</td><td>3.00</td><td>2.50</td><td>14.10</td><td>0.02</td></tr><tr><td>Tyr</td><td>4.00</td><td>4.20</td><td>0.30</td><td>0.56</td></tr><tr><td>Ser</td><td>51.80</td><td>51.10</td><td>3.10</td><td>0.87</td></tr><tr><td>Thr</td><td>12.30</td><td>12.70</td><td>1.10</td><td>0.72</td></tr><tr><td>Trp</td><td>3.40</td><td>3.60</td><td>0.07</td><td>0.15</td></tr><tr><td>Leu</td><td>1.70</td><td>3.40</td><td>0.70</td><td>0.12</td></tr><tr><td>Ile</td><td>1.20</td><td>2.10</td><td>0.40</td><td>0.15</td></tr><tr><td>His</td><td>10.40</td><td>10.50</td><td>0.10</td><td>0.32</td></tr><tr><td>Gln</td><td>169.10</td><td>176.90</td><td>14.40</td><td>0.46</td></tr><tr><td>Asn</td><td>8.10</td><td>7.60</td><td>0.30</td><td>0.25</td></tr><tr><td>TAA</td><td>639.50</td><td>622.50</td><td>19.70</td><td>0.42</td></tr><tr><td>BCAA</td><td>5.60</td><td>15.30</td><td>2.90</td><td>0.03</td></tr><tr><td>EAA</td><td>48.10</td><td>63.10</td><td>5.40</td><td>0.04</td></tr><tr><td>NEAA</td><td>591.40</td><td>559.40</td><td>17.40</td><td>0.17</td></tr><tr><td>BCAA/TAA</td><td>0.009</td><td>0.02</td><td>0.004</td><td>0.02</td></tr><tr><td>BCAA/EAA</td><td>0.10</td><td>0.20</td><td>0.03</td><td>&lt;0.01</td></tr></tbody></table>

RQUICKI was included primarily to allow comparison with previous studies that employed this index in the context of feed restriction.

## AA Concentration in Blood Serum and CSF

The decrease in blood serum concentrations of TAA and NEAA along with unchanged BCAA concentrations during a short period of feed restriction is consistent with findings from Laeger et al. (2012b) and Girard et al. (2019). This pattern is also consistent with the decrease in plasma AA observed in cows during early lactation, which reflects a negative protein balance during this period (Zhou et al., 2016; Jorge-Smeding et al., 2021). In this condition, the increased metabolic demand may have led to a more pronounced reduction in other AA, especially NEAA, whereas BCAA concentrations remained relatively stable. This pattern may reflect a metabolic prioritization of BCAA homeostasis for energy supply or protein synthesis under nutritional stress. In the CSF, the increase in BCAA and EAA during feed restriction, along with the greater BCAA/TAA and EAA/TAA ratios, suggests a different metabolic regulation in the central nervous system than in blood. This may be due to increased BCAA transport across the BBB or changes in neuronal metabolism because some AA can be synthesized locally in the brain (He and Wu, 2020). In fact, AA concentrations in the CSF are lower than in the blood, with more AA being transported from the brain to the blood than from the blood to the brain (Hawkins et al., 2006). The brain adapts through compensatory neuronal changes, including AA catabolism as an alternative fuel source (Griffin and Bradshaw, 2017). In this sense, the greater concentration of BCAA in CSF explained by increased concentration of Val during the feed restriction may have played a role in supporting EB in the brain (Fernstrom,

<table><caption>**Table 6.** Outcomes of partial least squares regression (PLS-R) predictive model using metabolite concentrations as predictors of DMI variation (ΔDMI) in blood serum and cerebrospinal fluid (CSF) of Holstein cows (n = 8)<fnref n="1" /></caption><thead><tr><th rowspan="3">Item</th><th colspan="6">Fluid</th></tr><tr><th colspan="3">Blood serum</th><th colspan="3">CSF</th></tr><tr><th>Predictor</th><th>Coefficient</th><th>VIP</th><th>Predictor</th><th>Coefficient</th><th>VIP</th></tr></thead><tbody><tr><td>Q<sup>2</sup></td><td colspan="3">&lt;0 (invalid model)</td><td colspan="3">0.56</td></tr><tr><td>R<sup>2</sup>Y</td><td colspan="3">0.49</td><td colspan="3">0.62</td></tr><tr><td></td><td>—</td><td>—</td><td>—</td><td>[Cys]</td><td>3.73</td><td>↑</td></tr><tr><td></td><td>—</td><td>—</td><td>—</td><td>[Pro]</td><td>-5.58</td><td>↓</td></tr><tr><td></td><td>—</td><td>—</td><td>—</td><td>[Phe]</td><td>-1.42</td><td>↓</td></tr><tr><td></td><td>—</td><td>—</td><td>—</td><td>[Asp]</td><td>-2.39</td><td>↓</td></tr><tr><td></td><td>—</td><td>—</td><td>—</td><td>[Tyr]</td><td>-1.73</td><td>↓</td></tr><tr><td></td><td>—</td><td>—</td><td>—</td><td>[Gly]</td><td>-0.3</td><td>↓</td></tr><tr><td></td><td>—</td><td>—</td><td>—</td><td>[His]</td><td>-0.98</td><td>↓</td></tr><tr><td></td><td>—</td><td>—</td><td>—</td><td>[Trp]</td><td>-5.97</td><td>↓</td></tr><tr><td></td><td>—</td><td>—</td><td>—</td><td>[Val]</td><td>-0.39</td><td>↓</td></tr><tr><td></td><td>—</td><td>—</td><td>—</td><td>[Glucose]</td><td>-4.36</td><td>↓</td></tr><tr><td></td><td>—</td><td>—</td><td>—</td><td>[Asn]</td><td>-1.47</td><td>↓</td></tr><tr><td></td><td>—</td><td>—</td><td>—</td><td>[Lys]</td><td>-0.37</td><td>↓</td></tr></tbody></table>

<footnote n="1">¹Basal: feed ad libitum (0 to 4 d); feed restriction: feed 50% of ad libitum (5 to 9 d); refeeding: feed ad libitum (10 to 14 d). TAA = total amino acids; BCAA = branched-chain amino acids.</footnote>

<footnote n="1">¹ΔDMI = [DMI (refeeding) – DMI (feed restriction)]/DMI (feed restriction); DMI during refeeding corresponds exclusively to d 1 of that period; R²Y: coefficient of determination for Y; Q²: predictive ability based on cross-validation.</footnote>
2005). Similar to our results, previous studies reported increased BCAA concentrations in the CSF despite no change in blood in response to feed restriction in humans (Knudsen et al., 1990).

The decrease of Cys in CSF during feed restric-
tion could be attributed to the increased demand for
glutathione as an antioxidant, potentially leading to
the degradation of Cys (Dringen, 2000), because NEB
triggers oxidative stress (Abuelo et al., 2015) and it is
known that Cys availability is limiting for glutathione
synthesis (Aoyama, 2021). This observation aligns with
the trend noted in Laeger et al. (2012b), which reported
that Cys concentration in CSF tended to decrease in re-
sponse to fed restriction. Interestingly, Pro metabolism
may complement this process by providing a source of
Glu through the activation of Pro oxidase under nutrient
stress, thereby fueling glutathione synthesis and support-
ing the antioxidant defense system (Gropper et al., 2009).
Moreover, the conversion of Pro to α-ketoglutarate, a key
tricarboxylic acid (TCA) cycle intermediate, highlights
its role in energy production and metabolic adaptation
during NEB (Gropper et al., 2009; Ansia Vazquez, 2017).
As seen in Figure 2, most AA are directed toward the TCA
cycle, indicating their involvement in energy production
during this period of feed restriction. Additionally, pre-
liminary results from the current experiment (Viera et al.,
2021) showed an increase of citric and succinic acids in
CSF in response to feed restriction, these molecules be-
ing candidate markers potentially involved in intermedi-
ary energy metabolism reinforcing ours results.

Glucose and AA as Predictors of DMI Rebound

The individual variation in the DMI rebound dur-
ing the refeeding, which ranged from +50% to +120%
across cows, suggests considerable differences in how
voluntary feed intake is regulated after a short period
of feed restriction. Previous studies also have shown
an important individual variation of feed intake and
feeding behavior during transition period in dairy cows
(Rumphorst et al., 2022; Santos et al., 2024). This vari-
ability suggests a crucial role of the hypothalamus in
sensing nutrient availability, with signals potentially
originating from the CSF, which transmits metabolic
signals between the blood and brain (Roche et al., 2008;
Segarra et al., 2021). Studies have also shown that brain
AA concentrations can act as signals for energy or pro-
tein availability, influencing feeding-regulatory circuits
(Heeley and Blouet, 2016). For instance, neuropeptides
involved in regulating feed intake are synthesized as
pro-peptides, which are then processed and bind to re-
ceptors in the brain feeding centers (Kuhla et al., 2015).
Interestingly, our results suggest that the concentration
of glucose and certain AA in CSF during the restriction
period, rather than their concentration in blood, may
be involved in predicting individual variability in the
rebound of DMI. Without attempting to find the causal
mechanisms and knowing the limitation of the number
of cows, explanatory hypotheses are analyzed in rela-
tion to the observed predictive capacity in the individual
variability of DMI rebound.

<figure><img src="image_5.png" /><figcaption>**Figure 2.** Metabolic pathways of the AA identified as key predictors (in bold) in the partial least squares regression (PLS-R) model applied to dairy cows.</figcaption></figure>
The lower glucose concentration in the CSF during the restriction period stimulated a greater DMI rebound likely due to a compensatory mechanism in response to the lack of energy (Laeger et al., 2012b). Interestingly, during the restriction period glucose concentration decreased more in CSF than in serum (–30% vs. –16%) relative to the baseline, likely suggesting that CSF glucose amplified the signal of impaired nutrient status compared with the blood (Tan et al., 2023). Indeed, glucose CSF depends on blood concentrations, and once it crosses the BBB via GLUT-1 transporters (Mergenthaler et al., 2013), it can inhibit feed intake by increasing pyruvate concentrations and downregulating the AMPK activity, which ultimately reduce the expression of orexigenic neuropep- tides such as AgRP and NPY (Lee et al., 2005). The BHB concentrations in CSF did not act as a predictor of DMI rebound, contrary to our hypothesis that it might be as- sociated with the regulation of feed intake. Although the role of BHB in regulating feeding behavior remains unre- solved and controversial, some studies suggest that BHB reduces feed intake and promotes weight loss (Laeger et al., 2010). Conversely, other research indicates that BHB may exert both orexigenic and anorexigenic effects by modulating AMPK activity and AgRP expression in neurons (Paoli et al., 2015; Rojas-Morales et al., 2016).

Interestingly, most of the AA identified as predictors of DMI rebound are directly involved in neurotransmitter synthesis or function, acting either as stimulants or inhibitors. Because feed intake is heavily regulated by hypothalamic circuits and brainstem centers, any metabolic adjustment within these areas—driven by AA availability or neurotransmitter flux—is likely to be captured in the CSF profile. Cysteine was the only AA that showed a positive relationship between its concentration during the restriction period and the variation in DMI rebound. Cysteine plays a crucial role in neuronal function through its involvement in the production of hydrogen sulfide, a gaseous neuromodulator known to influence neuronal excitability. Elevated Cys concentration can increase local hydrogen sulfide synthesis in appetite-regulating brain regions, potentially activating orexigenic neurons or inhibiting anorexigenic ones (Zhou et al., 2018). In addition, Cys is essential for the synthesis of glutathione, a major intracellular antioxidant that helps maintain neuronal excitability. In the liver, Cys is primarily synthesized from Met through the trans-sulfuration pathway, and the rate of this synthesis depends on nutritional status. During feed restriction, reduced hepatic conversion of Met to Cys may diminish this anorexigenic effect, which may be associated with increased intake during refeeding. This is supported by elevated CSF Met concentration observed under restriction, indicating a possible metabolic adaptation. Although the trans-sulfuration pathway is limited in

the central nervous system (McBean, 2012), changes in peripheral metabolism may still influence brain function via altered AA availability and redox balance (Hawkins et al., 2006).

Among the AA negatively associated with ΔDMI, Trp—a precursor to serotonin, a neurotransmitter that promotes satiety—may be diverted toward the kynurenine pathway under conditions of metabolic stress, a pathway that has been associated with appetite suppression. This metabolic shift could represent as an adaptive mechanism for energy conservation in CSF potentially altering the selectivity of the BBB (Richard et al., 2009). In rodents, the transformation of His into neuronal histamine has been shown to reduce feed intake (Yoshimatsu et al., 2002). The histaminergic neurons project to key hypothalamic nuclei involved in energy homeostasis, where H1-type histamine receptors are especially important for suppressing feed intake (Khouma et al., 2023). Glycine acts as an inhibitory neurotransmitter in the central nervous system, particularly in the brainstem and spinal cord. Its elevation can enhance inhibitory signaling, potentially leading to reduced neuronal activity associated with feeding behavior. Particularly, it has been demonstrated that Gly intravenous infusion decreased the DMI and meal duration in sheep (Baile and Heinrich Martin, 1971). In addition, high brain Asp concentrations may enhance excitatory neurotransmission, leading to overactivation of appetite-suppressing pathways in the hypothalamus (Dingledine and McBain, 1999).

During states of energy restriction, the brain adapts its substrate utilization by increasingly relying on alternative energy sources, including AA such as Phe, Tyr, Lys, and BCAA, which are involved in neurotransmitter synthesis and may contribute to energy homeostasis. In general, the BCAA have been proposed to induce a decreased in the feed intake by mTOR-dependent inhibition of AgRP gene expression, suppression of AMPK activity, stimulation of cerebral BCAA degradation, or a combination of these (Blouet and Schwartz, 2010; Comesaña et al., 2018; Chen et al., 2021). In dairy cows, the elevated CSF concentrations of Val observed after parturition suggest that these BCAA have been proposed to exert central anorexigenic effects (Laeger et al., 2013) in according with our results. In a meta-analysis from 24 experiments, it was found that after correcting by the effect of the diet protein supply, the cows with lower DMI had greater plasma concentrations of BCAA (Letelier et al., 2022). Additionally, data from our team under grazing conditions showed that North American Holstein cows had greater plasma BCAA concentrations and lower DMI per unit of metabolic BW compared with New Zealand Holstein cows (Jorge-Smeding et al., 2021).
The feed restriction model mimicked NEB but did not fully capture the complexity of the periparturient period, during which multiple physiological factors affect DMI. However, the strictly controlled conditions of this study helped isolate specific metabolic responses. Although the small sample size was due to the invasive nature of CSF collection, the study nonetheless offers new insights into central AA and glucose dynamics during feed restriction and refeeding.

## CONCLUSIONS

A short-term feed restriction has induced metabolic, hormonal, and AA changes in both blood serum and CSF. The magnitude of the change in feed intake during refeeding was highly variable among cows, negatively associated with increased glucose and AA concentrations in CSF during the feed restriction, except for Cys that showed a positive relationship. These changes suggest a potential role for altered BBB selectivity or promote the central production of specific AA involved in neu-rotransmitter synthesis or signaling, thereby affecting intake regulation. Overall, these results emphasize the close relationship between metabolic status and neural mechanisms in the control of intake in dairy cows, so further research into this complex regulation is needed.

## NOTES

The study was supported by a grant from Comisión Academica de Posgrados (CAP). The authors thank Alberto Casal at the Departamento de Producción Animal for performing the CSF collection and we also thank all the Mario A. Cassinoni Experimental Unit (Paysandú, Uruguay) staff for their work taking permanent care of the experimental animals. Availability of data: Data available upon request from corresponding author. Animal use and procedures were approved by the Animal Experimentation Committee (CEUA) of Facultad de Agronomía (CEUA ID: 1169). The authors have not stated any conflicts of interest.

**Nonstandard abbreviations used:** BBB = blood-brain barrier; BCAA = branched-chain amino acid; CSF = cerebrospinal fluid; EB = energy balance; ERM = energy retained in milk; GCToF/MS = gas chromatography time-of-flight mass spectrometry; NEB = negative energy balance; NEFA = nonesterified fatty acid; PLS-R = partial least squares regression; Q² = predictive ability based on cross-validation; R²Y: coefficient of determination for Y; RQUICKI = revised quantitative insulin sensitivity check index; TAA = total amino acids; TCA = tricarboxylic acid; VIP = variable importance in projection.

## REFERENCES

Abdelatty, A. M., M. E. Iwaniuk, M. Garcia, K. M. Moyes, B. B. Teter, P. Delmonte, A. K. G. Kadegowda, M. A. Tony, F. F. Mohamad, and R. A. Erdman. 2017. Effect of short-term feed restriction on temporal changes in milk components and mammary lipogenic gene expression in mid-lactation Holstein dairy cows. J. Dairy Sci. 100:4000-4013. https://doi.org/10.3168/jds.2016-11130.

Abuelo, A., J. Hernández, J. L. Benedito, and C. Castillo. 2015. The importance of the oxidative status of dairy cattle in the periparturient period: Revisiting antioxidant supplementation. J. Anim. Physiol. Anim. Nutr. (Berl.) 99:1003-1016. https://doi.org/10.1111/jpn.12273.

Acosta, Y. 2004. Estimación del valor nutritivo para producción de leche. Pages 69-78 in Guía para la alimentación de rumiantes. Serie Técnica No. 142. J. M. Mieres, ed. Unidad de Agronegocios y Difusión del INIA.

Addinsoft. 2022. XLSTAT statistical and data analysis solution. Accessed Dec. 2, 2024. https://www.xlstat.com.

Allen, M. S., B. J. Bradford, and M. Oba. 2009. Board-invited review: The hepatic oxidation theory of the control of feed intake and its application to ruminants. J. Anim. Sci. 87:3317-3334. https://doi.org/10.2527/jas.2009-1779.

Ansia Vazquez, I. 2017. Effects of feed restriction on milk production and metabolism in mid-lactation dairy cows. PhD thesis. Department of Animal Sciences, University of Illinois, Urbana-Champaign, IL.

AOAC International. 2019. Official Methods of Analysis. 21st ed. George Latimer Jr., ed. AOAC International, Arlington, VA

Aoyama, K. 2021. Glutathione in the brain. Int. J. Mol. Sci. 22:5010. https://doi.org/10.3390/ijms22095010.

Astessiano, A. L., A. Meikle, M. Fajardo, J. Gil, D. A. Mattiauda, P. Chilibroste, and M. Carriquiry. 2015. Metabolic and endocrine profiles and hepatic gene expression of Holstein cows fed total mixed ration or pasture with different grazing strategies during early lactation. Acta Vet. Scand. 57:70. https://doi.org/10.1186/s13028-015-0163-6.

Baile, C. A., and F. Heinrich Martin. 1971. Hormones and amino acids as possible factors in control of hunger and satiety in sheep. J. Dairy Sci. 54:897-905. https://doi.org/10.3168/jds.S0022-0302(71)85938-6.

Barroso, C., G. Cepeda Carrion, and J. Roldán. 2010. Applying Maximum Likelihood and PLS on Different Sample Sizes: Studies on SERVQUAL Model and Employee Behavior Model. Pages 427-447 in Handbook of Partial Least Squares, Vol. 201, No. 0. V. E. Vinzi, W. W. Chin, J. Henseler, and H. Wang, ed. Springer, Berlin, Germany. https://doi.org/10.1007/978-3-540-32827-8_20.

Blouet, C., and G. J. Schwartz. 2010. Hypothalamic nutrient sensing in the control of energy homeostasis. Behav. Brain Res. 209:1-12. https://doi.org/10.1016/j.bbr.2009.12.024.

Brown, W. E., and B. J. Bradford. 2021. Invited review: Mechanisms of hypophagia during disease. J. Dairy Sci. 104:9418-9436. https://doi.org/10.3168/jds.2021-20217.

Chen, K., Z. Zhang, J. Li, S. Xie, L. J. Shi, Y. H. He, X. F. Liang, Q. S. Zhu, and S. He. 2021. Different regulation of branched-chain amino acid on food intake by TOR signaling in Chinese perch (*Siniperca chuatsi*). Aquaculture 530:735792. https://doi.org/10.1016/j.aquaculture.2020.735792.

Chong, I. G., and C. H. Jun. 2005. Performance of some variable selection methods when multicollinearity is present. Chemom. Intell. Lab. Syst. 78:103-112. https://doi.org/10.1016/j.chemolab.2004.12.011.

Cincović, M., B. Belić, R. Djokovic, B. Toholj, T. Hristovska, B. Delić, and M. Došenović. 2014. Insulin resistance in cows during dry period and early lactation. Pages 98-104 in Contemporary Agriculture Savremena Poljoprivreda, Vol. 63, No. 1-2. Faculty of Agriculture, University of Novi Sad, Serbia.

Comesaña, S., C. Velasco, M. Conde-Sieira, J. M. Míguez, J. L. Soengas, and S. Morais. 2018. Feeding stimulation ability and central effects of intraperitoneal treatment of L-leucine, L-valine, and L-proline on amino acid sensing systems in rainbow trout: Implication in food intake control. Front. Physiol. 9:1209. https://doi.org/10.3389/fphys.2018.01209.
De Koster, J. D., and G. Opsomer. 2013. Insulin resistance in dairy cows. Vet. Clin. North Am. Food Anim. Pract. 29:299-322. https://doi.org/10.1016/j.cvfa.2013.04.002.

Dingledine, R., and C. J. McBain. 1999. Glutamate and aspartate are the major excitatory transmitters in the brain. Pages 267-289 in Basic Neurochemistry: Molecular, Cellular and Medical Aspects. 6th ed. G. J. Siegel, B. W. Agranoff, R. W. Albers, S. K. Fisher, and M. D. Uhler, ed. Lippincott Williams & Wilkins, Philadelphia, PA.

Drackley, J. K. 1999. Biology of dairy cows during the transition period: The final frontier? J. Dairy Sci. 82:2259-2273. https://doi.org/10.3168/jds.S0022-0302(99)75474-3.

Dringen, R. 2000. Metabolism and functions of glutathione in brain. Prog. Neurobiol. 62:649-671. https://doi.org/10.1016/S0301-0082(99)00060-X.

Fernstrom, J. D. 2005. Branched-chain amino acids and brain function. J. Nutr. 135:1539S-1546S. https://doi.org/10.1093/jn/135.6.1539S.

Ferraretto, L. F., H. Gencoglu, K. S. Hackbart, A. B. Nascimento, F. Dalla Costa, R. W. Bender, J. N. Guenther, R. D. Shaver, and M. C. Wiltbank. 2014. Effect of feed restriction on reproductive and metabolic hormones in dairy cows. J. Dairy Sci. 97:754-763. https://doi.org/10.3168/jds.2013-6925.

Fiehn, O. 2016. Metabolomics by gas chromatography-mass spectrometry: Combined targeted and untargeted profiling. Curr. Protoc. Mol. Biol. 114:30-34. https://doi.org/10.1002/0471142727 mb.3004s114.

Girard, C. L., N. Vanacker, V. Beaudet, M. Duplessis, and P. Lacasse. 2019. Glucose and insulin responses to an intravenous glucose tolerance test administered to feed-restricted dairy cows receiving folic acid and vitamin B12 supplements. J. Dairy Sci. 102:6226-6234. https://doi.org/10.3168/jds.2019-16298.

Goetz, B. M., M. A. Abeyta, S. Rodriguez-Jimenez, J. Opgenorth, J. L. McGill, S. R. Fensterseifer, R. P. Arias, A. M. Lange, E. A. Galbraith, and L. H. Baumgard. 2024. Effects of a multistrain *Bacillus*-based direct-fed microbial on gastrointestinal permeability and biomarkers of inflammation during and following feed restriction in mid-lactation Holstein cows. J. Dairy Sci. 107:6192-6210. https://doi.org/10.3168/jds.2023-24352.

Griffin, J. W., and P. C. Bradshaw. 2017. Amino acid catabolism in Alzheimer's disease brain: friend or foe? Oxid. Med. Cell. Longev. 2017:5472792. https://doi.org/10.1155/2017/5472792.

Gropper, S. S., J. L. Smith, and J. L. Groff. 2009. Chapter 6: Protein. Pages 179-245 in Advanced Nutrition and Human Metabolism. 5th ed. Wadsworth, Cengage Learning.

Gross, J., H. A. van Dorland, R. M. Bruckmaier, and F. J. Schwarz. 2011. Milk fatty acid profile related to energy balance in dairy cows. J. Dairy Res. 78:479-488. https://doi.org/10.1017/S0022029911000550.

Grummer, R. R. 1995. Impact of changes in organic nutrient metabolism on feeding the transition dairy cow. J. Anim. Sci. 73:2820-2833. https://doi.org/10.2527/1995.7392820x.

Hawkins, R. A., R. L. O'Kane, I. A. Simpson, and J. R. Vina. 2006. Branched-chain amino acids: Metabolism, physiological function, and application. Amino Acids 1:3.

He, W., and G. Wu. 2020. Metabolism of amino acids in the brain and their roles in regulating food intake. Pages 167-185 in Amino Acids in Nutrition and Health: Amino Acids in Systems Function and Health. Vol. 1265. G. Wu, ed. Springer International Publishing. https://doi.org/10.1007/978-3-030-45328-2_10.

Heeley, N., and C. Blouet. 2016. Central amino acid sensing in the control of feeding behavior. Front. Endocrinol. (Lausanne) 7:148. https://doi.org/10.3389/fendo.2016.00148.

Henningsson, M., E. Sundbom, B. Å. Armelius, and P. Erdberg. 2001. PLS model building: A multivariate approach to personality test data. Scand. J. Psychol. 42:399-409. https://doi.org/10.1111/1467-9450.00252.

Herve, L., H. Quesnel, M. Véron, J. Portanguen, J. J. Gross, R. M. Bruckmaier, and M. Boutinaud. 2019. Milk yield loss in response to feed restriction is associated with mammary epithelial cell exfoliation in dairy cows. J. Dairy Sci. 102:2670-2685. https://doi.org/10.3168/jds.2018-15398.

Holtenius, P., and K. Holtenius. 2007. A model to estimate insulin sensitivity in dairy cows. Acta Vet. Scand. 49:29. https://doi.org/10.1186/1751-0147-49-29.

Ingvartsen, K. L., R. J. Dewhurst, and N. C. Friggens. 2003. On the relationship between lactational performance and health: Is it yield or metabolic imbalance that cause production diseases in dairy cattle? A position paper. Livest. Prod. Sci. 83:277-308. https://doi.org/10.1016/S0301-6226(03)00110-6.

Jorge-Smeding, E., M. Carriquiry, G. Cantalapiedra-Hijar, A. Mendoza, and A. L. Astessiano. 2021. Plasma concentrations of branched-chain amino acids differ with Holstein genetic strain in pasture-based dairy systems. Sci. Rep. 11:22414. https://doi.org/10.1038/s41598-021-01564-0.

Khouma, A., M. M. Moeini, J. Plamondon, D. Richard, A. Caron, and N. J. Michael. 2023. Histaminergic regulation of food intake. Front. Endocrinol. (Lausanne) 14:1202089. https://doi.org/10.3389/fendo.2023.1202089.

Knudsen, G. M., K. D. Pettigrew, C. S. Patlak, M. M. Hertz, and O. B. Paulson. 1990. Asymmetrical transport of amino acids across the blood-brain barrier in humans. J. Cereb. Blood Flow Metab. 10:698-706. https://doi.org/10.1038/jcbfm.1990.123.

Kuhla, B., T. Laeger, H. Husi, and W. Mullen. 2015. Cerebrospinal fluid prohormone processing and neuropeptides stimulating feed intake of dairy cows during early lactation. J. Proteome Res. 14:823-828. https://doi.org/10.1021/pr500872k.

Laeger, T., S. Görs, C. C. Metges, and B. Kuhla. 2012b. Effect of feed restriction on metabolites in cerebrospinal fluid and plasma of dairy cows. J. Dairy Sci. 95:1198-1208. https://doi.org/10.3168/jds.2011-4506.

Laeger, T., C. C. Metges, and B. Kuhla. 2010. Role of β-hydroxybutyric acid in the central regulation of energy balance. Appetite 54:450-455. https://doi.org/10.1016/j.appet.2010.04.005.

Laeger, T., R. Pohland, C. C. Metges, and B. Kuhla. 2012a. The ketone body beta-hydroxybutyric acid influences agouti-related peptide expression via AMP- activated protein kinase in hypothalamic GT1-7 cells. J. Endocrinol. 213:193-203. https://doi.org/10.1530/JOE-11-0457.

Laeger, T., H. Sauerwein, A. Tuchscherer, O. Bellmann, C. C. Metges, and B. Kuhla. 2013. Concentrations of hormones and metabolites in cerebrospinal fluid and plasma of dairy cows during the periparturient period. J. Dairy Sci. 96:2883-2893. https://doi.org/10.3168/jds.2012-5909.

Lee, K., B. Li, X. Xi, Y. Suh, and R. J. Martin. 2005. Role of neuronal energy status in the regulation of adenosine 5'-monophosphate-activated protein kinase, orexigenic neuropeptides expression, and feeding behavior. Endocrinology 146:3-10. https://doi.org/10.1210/en.2004-0968.

Letelier, P., G. I. Zanton, J. R. R. Dórea, and M. A. Wattiaux. 2022. Plasma essential amino acid concentration and profile are associated with performance of lactating dairy cows as revealed through meta-analysis and hierarchical clustering. J. Dairy Sci. 105:5044-5061. https://doi.org/10.3168/jds.2021-21028.

Little, T. D., J. A. Bovaird, and K. F. Widaman. 2006. On the merits of orthogonalizing powered and product terms: Implications for modeling interactions among latent variables. Struct. Equ. Modeling 13:497-519. https://doi.org/10.1207/s15328007sem1304_1.

McBean, G. J. 2012. The transsulfuration pathway: A source of cysteine for glutathione in astrocytes. Amino Acids 42:199-205. https://doi.org/10.1007/s00726-011-0864-8.

Mergenthaler, P., U. Lindauer, G. A. Dienel, and A. Meisel. 2013. Sugar for the brain: The role of glucose in physiological and pathological brain function. Trends Neurosci. 36:587-597. https://doi.org/10.1016/j.tins.2013.07.001.

NASEM (National Academies of Sciences, Engineering, and Medicine). 2021. Nutrient Requirements of Dairy Cattle. 8th rev. ed. The National Academies Press, Washington, DC. https://doi.org/10.17226/25806.

NRC. 2001. Nutrient Requirements of Dairy Cattle. 7th rev. ed. The National Academies Press, Washington, DC. https://doi.org/10.17226/9825.

Paoli, A., G. Bosco, E. M. Camporesi, and D. Mangar. 2015. Ketosis, ketyogenic diet and food intake control: A complex relationship. Front. Psychol. 6:27. https://doi.org/10.3389/fpsyg.2015.00027.
Richard, D. M., M. A. Dawes, C. W. Mathias, A. Acheson, N. Hill-Kap-
turczak, and D. M. Dougherty. 2009. L-tryptophan: Basic metabolic
functions, behavioral research and therapeutic indications. Int. J.
Tryptophan Res. 2:45-60. https://doi.org/10.4137/ijtr.s2129.

Roche, J. R., D. Blache, J. K. Kay, D. R. Miller, A. J. Sheahan, and D. W.
Miller. 2008. Neuroendocrine and physiological regulation of intake
with particular reference to domesticated ruminant animals. Nutr.
Res. Rev. 21:207-234. https://doi.org/10.1017/S0954422408138744.

Rojas-Morales, P., E. Tapia, and J. Pedraza-Chaverri. 2016.
β-Hydroxybutyrate: A signaling metabolite in starvation response?
Cell. Signal. 28:917-923. https://doi.org/10.1016/j.cellsig.2016.04
.005.

Rumphorst, T., T. Scheu, C. Koch, and A. Sundrum. 2022. Inter-and
intra-individual variation in the behavior of feed intake on nutrient
availability in early lactating dairy cows. Animals (Basel) 12:37.
https://doi.org/10.3390/ani12010037.

Santos, M. G. S., B. Mion, and E. S. Ribeiro. 2024. Estimations of
prepartum feed intake and its effects on transition metabolism and
subsequent milk production. J. Dairy Sci. 107:9786-9802. https://
doi.org/10.3168/jds.2023-24619.

Segarra, M., M. R. Aburto, and A. Acker-Palmer. 2021. Blood-brain
barrier dynamics to maintain brain homeostasis. Trends Neurosci.
44:393-405. https://doi.org/10.1016/j.tins.2020.12.002.

Seoane, J. R., and C. A. Baile. 1972. Effects of intraventricular (III
ventricle) injections of 2-deoxy-D-glucose, glucose and xylose on
feeding behavior of sheep. Physiol. Behav. 9:423-428. https://doi.
org/10.1016/0031-9384(72)90170-9.

Sheahan, A. J. 2014. Neuroendocrine regulation of dry matter intake
in grazing dairy cows. PhD thesis. Department of Agricultural Sci-
ences, Lincoln University, United States.

Skogerson, K., G. Wohlgemuth, D. K. Barupal, and O. Fiehn. 2011. The
volatile compound BinBase mass spectral database. BMC Bioinfor-
matics 12:321. https://doi.org/10.1186/1471-2105-12-321.

Soutto, J. P., M. Carriquiry, P. Chilibroste, A. L. Astessiano, M. Garcia-
Roche, and A. I. Trujillo. 2020. Short-term feed intake regulation
of dairy cows fed a total mixed ration or grazing forage oats. Anim.
Prod. Sci. 60:1153-1162. https://doi.org/10.1071/AN18801.

St Jean, G., K. Yvorchuk-St Jean, D. E. Anderson, and W. E. Moore.
1997. Cerebrospinal fluid constituents collected at the atlanto-
occipital site of xylazine hydrochloride sedated, healthy 8-week-old
Holstein calves. Can. J. Vet. Res. 61:108.

Tan, Q. C., X. W. Xing, J. T. Zhang, M. W. He, Y. B. Ma, L. Wu, X.
Wang, H. F. Wang, and S. Y. Yu. 2023. Correlation between blood

glucose and cerebrospinal fluid glucose levels in patients with dif-
ferences in glucose metabolism. Front. Neurol. 14:1103026. https://
doi.org/10.3389/fneur.2023.1103026.

Tyrrell, H. F., and J. T. Reid. 1965. Prediction of the energy value of
cow's milk. J. Dairy Sci. 48:1215-1223. https://doi.org/10.3168/jds
.S0022-0302(65)88430-2.

Van Soest, P. J., J. B. Robertson, and B. A. Lewis. 1991. Methods for
dietary fiber, neutral detergent fiber, and nonstarch polysaccharides
in relation to animal nutrition. J. Dairy Sci. 74:3583-3597. https://
doi.org/10.3168/jds.S0022-0302(91)78551-2.

Viera, M. N., A. L. Astessiano, E. Jorge-Smeding, and A. I. Trujillo.
2021. Associations between cerebrospinal fluid and blood serum me-
tabolomic profiles reveals candidate biomarkers of feed-restriction
in dairy cows. South Carolina, United States. International Congress
on Farm Animal Endocrinology (ICFAE), BOLFA, Triennial Lacta-
tion Symposium.

Wold, S., L. Eriksson, and N. Kettaneh. 2010. PLS in Data Mining
and Data Integration. Pages 327-357 in Handbook of Partial Least
Squares. Vol. 201, No. 0, V. E. Vinzi, W. W. Chin, J. Henseler, and
H. Wang, ed. Springer, Berlin, Germany. https://doi.org/10.1007/978
-3-540-32827-8_16.

Yoshimatsu, H., S. Chiba, D. Tajima, Y. Akehi, and T. Sakata. 2002.
Brief communication: Histidine suppresses food intake through its
conversion into neuronal histamine. Exp. Biol. Med. (Maywood)
227:63-68. https://doi.org/10.1177/153537020222700111.

Zhou, J., X. H. Lv, J. J. Fan, L. Y. Dang, K. Dong, B. Gao, A. Q. Song,
and W. N. Wu. 2018. GYY4137 promotes mice feeding behavior via
arcuate nucleus sulfur-sulfhydrylation and AMPK activation. Front.
Pharmacol. 9:966. https://doi.org/10.3389/fphar.2018.00966.

Zhou, Z., J. J. Loor, F. Piccioli-Cappelli, F. Librandi, G. E. Lobley, and
E. Trevisi. 2016. Circulating amino acids in blood plasma during
the peripartal period in dairy cows with different liver functionality
index. J. Dairy Sci. 99:2257-2267. https://doi.org/10.3168/jds.2015
-9805.

ORCIDS

M. N. Viera, https://orcid.org/0000-0002-9649-3158

A. L. Astessiano, https://orcid.org/0000-0002-1542-1815

E. Jorge-Smeding, https://orcid.org/0000-0002-3964-8619

A. I. Trujillo, https://orcid.org/0000-0003-3252-8590