Acute anti-obesity treatment with celastrol reduces body weight, cerebral inflammation and metabolic imbalances in mice

Acute anti-obesity treatment with celastrol reduces body weight, cerebral inflammation and metabolic imbalances in mice | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Acute anti-obesity treatment with celastrol reduces body weight, cerebral inflammation and metabolic imbalances in mice Adriana Ferreiro, Maya Holgado, Raquel González-Alday, Sara González-Soto, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9344668/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 14 You are reading this latest preprint version Abstract Background The global rise in obesity is predominantly driven by energy dense foods consumption and sedentary lifestyles that contribute to a growing burden of metabolic and neuroinflammatory comorbidities. Obesity is linked to a chronic low-grade inflammatory profile, as well as to a localized neuroendocrine imbalance and inflammatory response in the brain, including regions regulating energy homeostasis, reward and motivational centers. Anti-obesity medications that reduce body weight are being extensively used across the world, and the specific cerebral mechanisms underlying its action are yet to be clarified. Methods We investigated the cerebral and systemic effects inherent to obesity development and treatment with celastrol, an anti-obesity and anti-inflammatory agent, in a murine model of diet-induced obesity (DIO) using a multimodal approach. We characterized obesity progression and celastrol treatment by comparing body weight, food intake, changes in brain microstructure by in vivo magnetic resonance imaging (MRI) and ex vivo by immunofluorescence, investigated its metabolic rearrangements using 1 H high-resolution magic angle spinning spectroscopy and draw the hormonal profiles between DIO and control animals, with or without treatment. Results Our findings indicate that obesity induces detectable neuroinflammation, evident through diffusion MRI alterations and increased microglial activation. Treatment resulted in significant body weight reduction, diffusion MRI signal changes, particularly in the hypothalamus, a decrease in microglial activation, a regularization of cerebral osmolyte concentrations, decreased cellular proliferation and astrocytic metabolism markers, and anti-inflammatory cytokine changes. Conclusions These results support the role of celastrol as an anti-obesity treatment, acting through anti-inflammatory mechanisms in the hypothalamus, and prove MRI techniques as valid tools to characterize its effects. Brain Celastrol Magnetic Resonance Imaging Magnetic Resonance Spectroscopy Inflammation Obesity Mouse Metabolism Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Background Consumption of energy dense foods, such as high-fat and sugar diets (HFHS), combined with sedentary lifestyles, are among the most important environmental factors predisposing to obesity. During obesity, the accumulation of elevated fat stores triggers a low-grade systemic inflammation, characterized by an abnormal cytokine production and the activation of a network of inflammatory signaling pathways (Wellen and Hotamisligil 2005), which boost the development of the obesity-related diseases (Hotamisligil 2006 ). Particularly, inflammation affects multiple organs including, pancreas, liver, cardiovascular system and the brain, eventually leading to the disruption of global metabolic homeostasis (Uranga and Keller 2019 ). HFHS feeding in rodents is an extensively used model to investigate its onset and development (De Moura E Dias et al. 2021; Nilsson et al. 2012). In the last decade, several studies using animal models of diet-induced obesity (DIO) revealed the activation of a localized inflammatory response in the hypothalamus (Hyp) after short term HF feeding that induces a defective control of energy homeostasis and development of leptin and insulin resistance (Thaler et al. 2012 ; Valdearcos et al. 2017a; Zhang et al. 2008). During fat-rich diets consumption, long-chain saturated fatty acids (SFAs) cross the blood-brain barrier and bind to the pro-opiomelanocortin neurons in the arcuate nucleus (ARC) of the Hyp (Posey et al. 2009 ). This binding triggers the activation of inflammatory signaling cascades, prompting the expression of pro-inflammatory genes. In this circumstances, glial cells experience morphological, physiological and functional modifications that enable an inflammatory process against the accumulation of SFAs (García-Cáceres et al. 2019a; Ramalho et al. 2018; Valdearcos et al. 2017a; Valdearcos et al. 2014). Particularly, astrocytes develop a reactive phenotype in the ARC detected immunohistochemically 24 hours after HF intake (Buckman et al. 2015 ; Horvath et al. 2010 ; Thaler et al. 2012 ), release inflammatory cytokines(Gupta et al. 2012 ), and, in response to high leptin levels, trigger microvascular remodeling within the Hyp (Gruber et al. 2021; Yi et al. 2012). Interestingly, few studies have reported that is the carbohydrate component of fat-rich diets which initiates such inflammatory cascade, including microglial activation and angiogenesis (Gao et al. 2017). In mammals, the homeostatic system interacts with motivational and rewarding behaviors via the mesocorticolimbic complex (MC) and reward centers (RC), respectively (Ferrario et al. 2016 ), cerebral structures that participate in the control of food intake and can exert relevant roles in favoring obesity development. MC structures include abundant dopamine projections from the ventral tegmental area to the prefrontal cortex, amygdala, hippocampus (Hipp), nucleus accumbens (NAc) and infralimbic area (ILA), correlating potential appetite stimuli to associated rewards, thus creating motivational connections (Berridge 2009). RCs include regions from the orbitofrontal cortex, amygdala and NAc, and grant food with its pleasurable properties (Saper et al. 2002). Interestingly, some of the implicated MC and RC regions also express inflammatory signals during obesity development (Cazettes et al. 2011). Celastrol, a pentacyclic triterpene extracted from the roots of the Tripterygium Wilfordi plant, has been revealed as a promising anti-inflammatory agent with anti-obesity effects (Liu et al. 2015 ). In long term DIO animals, celastrol administration induces body weight (BW) decreases up to 45%, reducing food intake and blocking energy expenditure. Such BW reduction is thought to be achieved through increased leptin sensitivity, reduced hypothalamic neuronal ER stress, and regulating energy metabolism, deactivation of hypothalamic inflammation (Seyfried and Hankir 2019 ), lipid metabolism and even gut microbiota (Xu et al. 2021 ), with the consequent reestablishment of glucose tolerance and insulin sensitivity (Feng et al. 2019 ). However, the exact mechanisms by which celastrol increases leptin sensitivity, or glucose tolerance and insulin sensitivity, are not yet fully understood, and some contradictory results have been reported (Saito et al. 2019). A variety of neuroimaging methods have shown that obesity is associated with brain inflammation, alterations in the cerebral microstructure, metabolism, and function. Among them, magnetic resonance imaging (MRI) techniques, such as diffusion MRI (dMRI) have provided evidence of cerebral inflammation during high-fat diet (HFD) feeding in mice, and on patients with obesity (Le Bihan 2013). dMRI, and particularly diffusion tensor imaging (DTI) methods are widely used in clinics and in basic research and have revealed important cerebral alterations during obesity (Lizarbe et al. 2020 a). For example, it has been described that obese patients and mice depict higher diffusivity in particular areas of the brain, as compared to non-obese individuals, which has been proposed to be a consequence of vasogenic edema related to obesity-induced blood brain barrier permeability changes (Cheung et al. 2009; Thomas et al. 2019 ) but the underlying mechanisms have not been elucidated. Notably, other MRI techniques, such as T 2 -weighted imaging or magnetization transfer imaging (MTI) have also been used to reveal changes in brain microstructure in the context of obesity-induced brain changes (Rosenbaum et al. 2022). On the other hand, magnetic resonance spectroscopy (MRS) techniques, such as high-resolution magic angle spinning (HRMAS), are sensitive enough to detect diet-induced metabolic changes in small regions of the mouse brain, such as the hypothalamus (Campillo et al. 2022 ; Frost et al. 2014 ). Interestingly, the cerebral metabolic changes induced by anti-obesity medications administration are not yet completely understood. In this sense, the implementation of imaging and spectroscopic techniques is endowed to provide vital information on the cerebral changes underlying obesity development and treatment. On these grounds, we designed an experimental setup in which we administered either HFHS or low-fat low sugar (LFLS) diets to male and female animals and followed the development of obesity and its effects on the brain by MRI, glial markers by quantitative immunofluorescence, as well as the plasma concentrations of the main peptides and hormones involved in appetite and energy balance, assessed via ELISA. Subsequently, we treated the animals with celastrol and the effects on the brain were assessed using MRI, regional neurochemical profiles using 1 H HRMAS, as well as immunofluorescence and ELISA analyses. Using this methodology, we tested the hypothesis that celastrol induces detectable brain changes in DIO mice observable in vivo using DTI parameters and magnetization transfer ratio (MTR) and focusing on four regions involved in both homeostatic and non-homeostatic control of food intake: the Hyp, the Hipp, the NAc, and the ILA. Additionally, we postulated that these changes would be corroborated ex vivo using immunofluorescence, 1 H HRMAS and ELISA techniques. 2. Methods 2.1 Experimental design C57BL/6 mice, bred and housed in our institutional animal facility, were accommodated in groups of 2–5 animals, with 12-hour/12-hour light/dark cycle, controlled humidity (45–55%), temperature (21–23ºC) and ad libitum access to food and water. At nine weeks old, mice were randomly divided into two different dietary groups, LFLS (Research Diets, D12450Hi, 10 kcal% Fat) or a HFHS diet (Research Diets, D08112601i, 45kcal% Fat with 30 kcal% Sucrose). BW and food intake were recorded weekly. Following a 20-week period of diet, we administered i.p. either celastrol (C0869, Sigma-Aldrich, Merck) diluted it in dimethyl sulfoxide (DMSO) (1%) and PBS to a final concentration of 0.04 mg/mL, or only PBS solution with 1% DMSO. Each animal received a dose of 0.25 mg/kg or vehicle solution for three consecutive days. Experimental methods to assess the cerebral effects of diet consumption and subsequent treatment with celastrol included: MRI scans after 20 weeks of diet (“ diet effects”) to male and female animals and post-treatment (“ treatment effects”) to the same animal batches (i); neurochemical profiles by HRMAS of the brain regions of interest (ii); histological markers of astrogliosis and microgliosis (iii); and plasma levels of main hormones involved in appetite regulation and energy balance (iv) (Table 1 ). Additionally, plasma was analyzed also before any treatment (n = 10 LFLS and n = 10 HFHS, 50% females). Sample sizes were estimated based on previous studies of MRI quantification of cerebral changes during obesity (Campillo et al. 2022 ), with similar expected effect sizes and statistical power, and assuming that the rest of techniques exhibit at least comparable effects. Table 1 Distribution of number of animals for each group depending on the diet, technique and treatment. Treatment Celastrol Vehicle Diet/Technique MRI Hist HR-MAS Elisa MRI Hist HR-MAS Elisa HFHS 7♀ 7♂ 2♀ 2♂ 6 ♀6♂ 5 ♀5♂ 7 ♀7♂ 2♀ 2♂ 3 ♀4♂ 5 ♀5♂ LFLS 7♀8♂ 2♀ 2♂ 7 ♀8♂ 6 ♀ 6♂ 2♀ 2♂ 7 ♀8♂ 2.2 MRI acquisition MRI scans of the mouse brain were performed on a Bruker Biospec 7T system (Bruker Biospin, Ettlingen, DE) and the management software was Paravision 6.0.1, equipped with a 1 H mouse head surface coil with a volume transmitter (90mm diameter gradient insert 360mT/m). During the MRI experiments, each mouse was individually anesthetized in a methacrylate induction box with isoflurane (2% 1 L/min O 2 ) and sustained throughout the acquisition using a nose mask with isoflurane (1% 1 L/min O 2 ). The anesthetized animals were placed on a bed equipped with a circulating warm water bath to maintain their body temperature at approximately 37°C. The head of each mouse was fixed using a tooth-bar and ear-bars. Throughout the experiment, the body temperature and respiration of the animals were continuously monitored. Localization of the regions of interest (ROI), was achieved by acquiring axial T 2 -weighted anatomical images, using a rapid acquisition with relaxation enhancement sequence with the following parameters: FOV = 21 × 21 mm 2 , repetition time (TR) = 2500 ms, echo time (TE) = 27 ms, RARE factor = 8, number of averages (Av) = 1, 5 slices in an axial orientation, slice thickness = 1.25 mm. DTI data were acquired using a Stejskal–Tanner sequence (TR = 3000 ms, TE = 32.56 ms, gradient separation (Δ) = 20 ms, gradient duration (δ) = 4 ms, FOV = 21 x 21 mm 2 , slice thickness of 1.25 mm, diffusion gradients in 15 uniformly distributed directions, b = 400 smm − 2 and 1800 smm − 2 , and 3 b = 0 smm − 2 ). Two sets of MTI (TR = 2500 ms, TE = 9.8 ms, and Av = 1) were acquired, either with an MT pulse applied (MT ON, N = 50 train of radio frequency pulses, power = 5.5 µT, offset = 1500 Hz) or without (MT OFF), and the corresponding MTR calculated as the normalized subtraction of the corresponding signal intensities. 2.3 MRI processing MRI data were processed using a software based on Python, to obtain the mean diffusivity (MD), fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD), and MTR maps, for the Pre- and Post -treatment datasets, respectively, with Dipy (Garyfallidis et al. 2014 ). The pre-processing pipeline encompassed the use of the Patch2self (Fadnavis et al. 2020) noise reduction filter for DTI and the adaptive soft coefficient matching (ASCM) filter (Coupé P. et al. 2012 ) for MTR. The Hyp, Hipp, NAc and ILA areas were manually delineated using ImageJ (U. S. National Institutes of Health, Bethesda, Maryland, USA, https://imagej.nih.gov/ij/ ) with a standard mouse atlas as a reference ( http://www.brain-map.org ). Pixel values of each parametric map were automatically filtered to remove extreme outliers (1st/3th quartile ± 1.5*interquartile range) and regions close to cerebrospinal fluid (CSF) from the ventricles (MD > 1300 µm 2 /s) and MTR < 0. From the remaining pixels, mean values were calculated for each region. 2.4 HRMAS Immediately after the MRI sessions, a group of animals was euthanized under the effects of anesthesia, using a high-power microwave (TMW-4012C 5 kW, Muromachi Kikai Co. Ltd., Japan). This method applies focused microwave irradiation to the brain and preserves in vivo metabolic state of the cerebral tissue by rapid denaturation of enzymes responsible for protein dephosphorylation (O’Callaghan and Sriram 2004). After euthanasia, brains were extracted from the skull, and tissue samples were collected from the same regions selected in MRI analysis: Hyp, Hipp, ILA and NAc and stored immediately in liquid nitrogen, to be preserved at -80°C to prevent deterioration. HRMAS experiments were performed on an 11.7 T Bruker Avance Neo vertical system (Bruker Biospin, Ettlingen, DE) operating at a proton frequency of 500.13 MHz and Topspin 4.1.4 software. The preparation of the sample consisted of introducing 10–15 mg of tissue into a zirconium rotor (diameter of 4 mm), 50 µl of D 2 O was added and then hermetically sealed. The measurements were performed at a constant temperature of 277 K with a spinning rate of 5000 Hz. For each sample, two spectra were acquired using a Carr-Purcell-Meiboom-Gill sequence (128 scans, a relaxation delay of 5 s, a water suppression pulse of 2s, 32K data points and two different TE of 36 ms and 144 ms, to account for both the metabolites with strong J-coupling, and also obtain cleaner baselines (Oz and Tkáč 2011 )). The data was processed using LCModel, a software designed for the quantification of metabolites (Provencher 2001). To achieve this, LCModel fits each spectrum as a linear combination of their correspondent values from a homemade database. Such database was created by acquiring the spectra of individual metabolite from the brain. For each metabolite, the software calculates its concentration, the estimated percentage standard deviation (%SD), and concentrations normalized to total creatine (PCr + Cr) content. The data base is composed of the following metabolites and macromolecules: alanine (Ala), aspartate (Asp), choline (Cho), creatine (Cr), GABA, glucose (Glc), glutamine (Gln), glutamate (Glu), glycine (Gly), glycerylphosphorylcholine (GPC), glutathione (GSH), lactate (Lac), leucine (Leu), myo-inositol (mI), N-acetyl-aspartate (NAA), N-acetylaspartylglutamate (NAAG), phosphocholine (PCh), phosphocreatine (PCr), phenylalanine (Phe), taurine (Tau), the lipids including Lip13a, Lip09, Lip20, macromoelcules such as MM09, MM20, MM12 and the sums Cho + GPC+PCh, NAA+NAAG, Cr + PCr, Glu + Gln, MM14 + Lip13a+Lip13b+MM12, MM09 + Lip09 and MM20 + Lip20, among others. Only metabolites with a %SD less than 30% were included in the statistics analysis. 2.5 Histology After image acquisitions, a group of mice were transcardially perfused with phosphate-buffered saline (PBS) and paraformaldehyde (PFA). Their brains were removed and placed in 4% PFA for 24 h, followed by the immersion in a 30% sucrose solution for 48 h, and then fixed in OCT (Tissue-Tek, Miles, Elkhart, In., EEUU) to be subsequently stored at -80ºC to ensure cryopreservation. Frozen coronal sections were obtained using a cryostat (Shandon Cryotome E; Thermofisher Scientific Inc, Waltham, Massachusetts, USA) at a thickness of 7 µm and mounted on glass slides. Coronal sections were first treated with a solution of PBS (3% H 2 O 2 and 10% methanol) and then blocked in PBS with 10% normal donkey serum (0.1% Gly, 0.02% Triton-X). The sections were then incubated overnight at 4ºC with primary antibodies for Iba-1 (DAKO,1:500) and GFAP (Merck Millipore, 1:600) to label microglia and astrocytes respectively. Following PBS washes, the sections were incubated with the specific secondary antibodies for Iba-1 (Alexa Fluor 594) and GFAP 1 (Alexa Fluor 488) (Thermo Fisher Scientific, 1:300). Hoechst 33258 (Molecular Probes, 24 Invitrogen) was used for nuclear staining. The procedure followed is detailed in (Fernández-Sevilla et al. 2022) and (Fernández-Sevilla et al. 2020 ). Slides were mounted with Prolong Gold (Life Technologies) and images were acquired using a Nikom Eclipse Ci fluorescence microscopy with a Nikon Digital Sight DS-U3 camera and Nis-Elements D Viewer software. For the acquisition of images, a 20x objective was used for the Hyp and Hipp, and a 10x objective for the ILA and NAc. For each animal, a total of 21 images were captured from the Hyp (3 images from the ARC, 2 from the ventromedial nucleus (VMN), 2 from the paraventricular nucleus (PVN) per slice), 12 images from the Hipp (4 per slice), 12 images from the NAc (4 per slice) and 9 images from the ILA (per slice), with each animal having 3 slices. Image analysis was conducted using ImageJ. The quantification process involved each image containing the entire quadrant, or a specific area of interest, excluding bubbles or non-brain tissue regions. In each image, we measured both the area fraction occupied by cells (%OA) and the number of cells (C/A). For the area fraction occupied by cells, the values from all images were added and then divided by the number of images for each animal and brain region. The count of cells per image was normalized by dividing by the total area of that image. Then, the normalized values were added and divided by the number of images. 2.6 ELISA Blood samples were collected after 12 weeks of either HFHS or LFLS feeding. An additional group, only feeding with a HFHS diet, were treated with either celastrol or a vehicle solution for three consecutive days. Plasma concentrations of C-Peptide, ghrelin, glucagon-like peptide-1 (GLP-1), interleukin-6 (IL-6), glucagon, insulin, leptin, peptide YY (PYY) and tumor necrosis factor-α (TNFα) were measured using the MILLIPLEX Mouse Metabolic Hormone Expanded Panel kit (Millipore MMHMAG-44K). For each animal, 0.5–0.8 mL of blood was collected by cardiac puncture while the animals were anesthetized. Following blood collection, all mice were euthanized. The experimental procedures for sample collection, storage, preparation of reagents for immunoassay and immunoassay procedure were performed following the instructions of the MMHMAG-44K mouse panel protocols. To determine the concentrations of each hormone in the samples, we utilized standard curves fitted using a four-parameter logistic curve-fitting algorithm (O’Connell et al. 1993). Hormone levels were expressed in pg/mL. To assess the statistical variation among replicates, we utilized the coefficient of variation (%CV). The mean values were calculated only when the %CV was below 10%. 2.7 Data analysis All data and statistical analysis were performed using R (R Core Team 2021) and considering brain regions as biologically independent measures, and thus tested separately. BW and food intake changes during treatment were assessed building corresponding linear mixed effects (lme) models, with time (pre, 24h, 48h and 72h), diet (HFHS or LFLS), sex (male or female) and type of treatment (vehicle or celastrol) as main predictors, and all interactions considered. This was achieved using the lmer function of the lme4 package (Bates et al. 2015 ), with subsequent Type II Wald chisquare tests tests and post-hoc contrasts using emmeans (Lenth 2017), and corrected for multiple comparisons by false discovery rate adjustment (FDR). MRI and immunofluorescence variables were analyzed in two steps. First, values from the pre-treatment were checked for corresponding BW, diet , sex and diet:sex interaction dependance, using a linear model with the lm function, followed Type-II or Type-III ANOVA tests (for non-significant or significant interactions, respectively). Next, for those MRI or immunofluorescence image pre-treatment parameters that showed significant effects of either diet or diet:sex , further tests were performed on the post-treatment condition, adding to the linear model the type of treatment, the double (treatment: diet , treatment:sex ) and triple ( treatment:diet:sex ) interactions as predictors. When significant interactions were found, post-hoc differences at each level with FDR correction were performed. In the case of the Hyp in the immunofluorescence images, an effect of nuclei subregion was added to the linear model, since hypothalamic nuclei are expected to potentially have different roles in appetite control and energy balance (Timper and Brüning 2017 ), and thus a lme model was tested using the lmer function. HRMAS data was subjected to a random forest (RF) analysis using the randomForest function from the randomForest package (Liaw and Wiener 2001 ), to discriminate groups based on the metabolite profile, with the type of treatment as the outcome, and all metabolite ratios to PCr + Cr as predictors (including all regions and TEs). Briefly, RF is a machine learning method that uses decision trees created by using bootstrap samples of a training data, and a random feature selection in tree induction (Breiman 2001 ). The procedure included five different steps, including (i): a repeated 5-fold cross-validation (CV) scheme, where full dataset was randomly partitioned into five approximately equal folds. Four folds (80% of the data) were used to train the model, and the remaining fold (20%) served as the validation set; (ii): all possible training-testing combinations within the 5-folds were subjected to RF analysis. Using the tuneRF function from the randomForest package, each mtry (number of variables randomly sampled at each split) was tuned to select the mtry value that minimized the out of bag (OOB) error. For each mtry , a forest with 500 trees was grown, and the configuration associated with the lowest OOB error rate was retained, resulting in a an optimum mtry = 15. The final RF for that repetition was then refitted on the entire dataset using the selected mtry and 500 trees, and the OOB error from this model was recorded; (iii): this whole procedure was repeated 20 times with different initial random partitions, yielding 100 train–validation cycles in total. Across the 20 repetitions we obtained a distribution of OOB error estimates, from which the mean OOB error and its variability was extracted as measures of predictive performance and stability, resulting in OBB mean ± SD of 0.26 ± 0.01 of all iterations; (iv): for each of the 20 final RF models, we extracted the Mean Decrease in Gini index as a global indicator of how much each metabolite contributed to node purity and classification accuracy. This produced 20 separate importance profiles . To identify robust biomarkers, we analyzed the stability of the importance rankings across repetitions, and metabolites that were ranked in the top 10 in at least 75% of the repetitions (i.e. in ≥ 15 out of 20 runs) were considered stable important metabolites , that were retained further analysis; and (v): the final RF model, using the optimized mtry , was tested in a 70/30 (training/testing) split, and confusion matrix assessed. Finally, ANOVA of the stable important metabolites were performed to assess the classical statistical significance of type of treatment, and the effects on those metabolites of diet, sex, and corresponding interactions, with FDR corrections for post-hoc multiple comparisons. 3. Results 3.1 Physiological changes After 20 weeks of diet diversification, male and female animals fed with HFHS diet revealed higher BW and food intake values, as compared to LFLS, according to an obese phenotype (Fig. 1 , “Pre-dose” values). BW measurements during treatment evolved differently depending on the type of diet consumed, treatment received and animal’s sex, as reported by results from the lme model and subsequent Type III Wald chisquare tests, with significant time:diet:treatment:sex interaction on BW (χ = 20.3, df = 3, p < 0.001). Particularly, animals administered with celastrol showed BW decay, with significantly reduced values from 24h (all males and HFHS females) or from 48h (LFLS females) (Fig. 1 top panels , Table 2 ). Animals receiving vehicle exhibited either no significant BW alterations or small increases (Table 2 ). Average intake per cage during the days of i.p. administration decreased very remarkably in all celastrol-batches, and vehicle administered showed either no changes (LFLS females) or delayed and fewer decreases, significant only for HFHS females from 48h, and for males LFLS at 72h (Fig. 1 , bottom panels , Table 2 ). Table 2 Post-hoc tests after lme analysis of BW and intake changes with time. P-values are FDR adjusted. BW and Intake post-hoc tests Contrast BW DF BW T-ratio BW P-value Intake DF Intake T-ratio Intake P-value HFHS Vehicle Females 24h- Pre Dose 150 1.8 > 0.05 138 -0.4 > 0.05 48h- Pre Dose 150 2.1 > 0.05 138 -1.5 > 0.05 72h– Pre Dose 150 1.9 > 0.05 138 -3.0 0.0083 HFHS Celastrol Females 24h- Pre Dose 150 -2.9 0.0048 138 -4.0 0.0001 48h- Pre Dose 150 -6.6 < 0.0001 138 -7.5 < 0.0001 72h– Pre Dose 150 -9.8 < 0.0001 138 -10.8 0.05 138 -1.4 > 0.05 48h- Pre Dose 150 0.7 > 0.05 138 -2.3 > 0.05 72h– Pre Dose 150 1.9 > 0.05 138 -0.3 > 0.05 LFLS Celastrol Females 24h- Pre Dose 150 -0.3 > 0.05 138 -0.3 > 0.05 48h- Pre Dose 150 -3.2 0.0028 138 -4.3 < 0.0001 72h– Pre Dose 150 -4.5 < 0.0001 138 -5.0 0.05 138 -2.7 0.0069 48h- Pre Dose 150 0.2 > 0.05 138 -4.2 0.0001 72h– Pre Dose 150 0.2 > 0.05 138 -4.7 < 0.0001 HFHS Celastrol Males 24h- Pre Dose 150 -3.7 0.0003 138 -4.0 0.0001 48h- Pre Dose 150 -6.2 < 0.0001 138 -6.8 < 0.0001 72h– Pre Dose 150 -11.5 < 0.0001 138 -11.7 0.05 138 0.6 > 0.05 48h- Pre Dose 150 2.3 0.0312 138 -2.6 0.0166 72h– Pre Dose 150 3.5 0.0016 138 -4.3 0.0001 LFLS Celastrol Males 24h- Pre Dose 150 -5.1 < 0.0001 138 -4.7 < 0.0001 48h- Pre Dose 150 -9.8 < 0.0001 138 -7.5 < 0.0001 72h– Pre Dose 150 -13.1 < 0.0001 138 -9.6 < 0.0001 3.2 HFHS diet and celastrol effects on brain MRI parameters Analysis of the brain DTI parameters of HFHS or LFLS mice, namely MD, AD, RD, FA and MTR, revealed significantly higher FA on obese mice, both in the Hyp (F = 5.2, p < 0.05), and Hipp (F = 6.1, p < 0.05), and lower RD in the hyp (F = 4.1, p = 0.05) (Fig. 2 C). After treatment, hypothalamic FA remained significantly higher on HFHS male mice when treated with vehicle, and not celastrol (significant treatment:diet:sex interaction, df = 1, F = 4.1, p = 0.05) and post-hoc significance on vehicle male HFHS Vs LFLS (df = 23, t = -3.2, p adj <0.05) (Fig. 2 D, left panel ), while RD showed no further differences between dies, regardless of treatment (Fig. 2 D, bottom right ). In the hippocampus, no treatment effect was reported, and FA values continued significantly elevated on obese mice (F = 4.3, p < 0.05) (Fig. 2 D, top right ). 3.3 HFHS diet and celastrol effects on astrocytes and microglia The immunofluorescence images of the brains of diet-induced obese mice showed enlarged and more numerous microglia and astrocytes, as compared to non-obese animals (Fig. 3 A). Quantification and statistical testing of the number of microglial cells and its % of occupied area revealed significant increases with HFHS diet in all regions (χ hyp = 3.6, p hyp =0.05, F hipp =8.3, p hipp <0.05, F ILA =12.8, p ILA <0.05, F NAc =25.8, p NAc <0.01 for % occupied area, and χ hyp = 3.9, p hyp <0.05, F hipp =7.9, p hipp <0.05, F IL =22.9, p ILA <0.01, F NAc =22.4, p NAc <0.01 for its number) (Fig. 3 B and 3 C). On astrocytes, its area and number were found to be elevated in HFHS mice in the Hyp, Hipp and NAc -not in ILA- (χ hyp = 10.0, p hyp <0.005, F hip =8.2, p hipp <0.05, F NAc =14.7, p NAc <0.05 for % of area, and χ hyp = 7.4, p hyp <0.01, F hipp =15.9, p hipp <0.05, F NAc =17.3, p NAc <0.05 for astrocytic number) (Fig. 3 D and 3 E). Treatment with celastrol induced a general decrease in the number and area of cells on HFHS mice quantified in the Hyp images, as compared to vehicle-only mice (Fig. 3 F). Tests revealed that such decrease in number and area occupied was significant in the microglial cells of HFHS mice treated with celastrol, as compared to HFHS treated with vehicle, in the Hyp ( diet:treatment χ = 4.8, p < 0.05, post-hoc HFHS celastrol Vs HFHS vehicle df = 7.4, t = -3.5 and p < 0.05 for % of occupied area by microglia, and diet:treatment χ = 4.3, p hyp <0.05, post-hoc HFHS celastrol Vs HFHS vehicle df = 7.3, t = -3.6, p < 0.05 for its number) and Hipp ( diet:treatment F = 6.3, p < 0.05, post-hoc HFHS celastrol Vs HFHS vehicle df = 8, t = -3.3 and p < 0.05 for % of occupied area by microglia, and diet:treatment F = 6.7, p < 0.05, post-hoc HFHS celastrol Vs HFHS vehicle df = 8, t = -3.4, p < 0.05 for its number) (Fig. 3 G and 3 H). Astrocytes quantification post-treatment revealed less remarkable effects of celastrol than in microglia, with both number and occupied area in the Hyp being still higher in HFHS mice, as compared to LFLS (χ = 11.7, p < 0.005 for % of area occupied by astrocytes, and χ hyp = 9.4, p hyp < 0.005 for its number). In this region, treatment with celastrol to HFHS mice resulted in diminished area occupied by astrocytes, as compared to those treated with vehicle, but tests did not reach statistical significance (Fig. 3 I-J). 3.3 HFHS diet and celastrol effects on blood plasma Analysis of blood plasma after 20 weeks of diet revealed that mice under HFHS diet had lower ghrelin and higher leptin levels, in comparison to LFLS group (df = 1, F ghrelin = 5.7, p ghrelin < 0.05, F leptin = 5.8, p leptin < 0.05) Fig. 4 A). The rest of the hormones measured showed high variability between animals, and no other relevant effects between diet batches were found (Table 3 ). To evaluate the effects of celastrol on serum levels, we compared samples from HFHS mice treated with either celastrol or vehicle. The analysis revealed a significant interaction Sex:Treatment (F = 27.0, p < 0.001) revealing that changes were occurring in males ( post-hoc Celastrol VsVehicle Males df = 12, t = 7.6, p adj < 0.001) (Fig. 4 B). Table 3 Plasma values after either HFHS or LFLS diet consumption, and HFHS values after treatment with celastrol or vehicle. Concentration (pg/mL)/Diet and condition HFHS LFLS HFHS-vehicle HFHS-celastrol Ghrelin 22.15 ± 10.62 38.79 ± 20.64 23.70 ± 13.39 22.30 ± 9.34 Glucagon 54.31 ± 56.94 83.67 ± 36.99 38.95 ± 31.28 21.96 ± 6.76 Insulin 5569.9 ± 3065.48 3322.02 ± 3162.92 2833.15 ± 2608.38 1542.38 ± 1156.7 PYY 123.66 ± 39.90 134.66 ± 44.28 97.86 ± 63.39 131.94 ± 43.80 3.4 Cerebral metabolic changes of celastrol by HRMAS HRMAS acquisition of the cerebral samples resulted in very good quality spectra in all regions and animals, with a signal-to-noise ratio ranging from 17 to 26 and full width at half maximum linewidths around 2.5 Hz (Fig. 5 A). Subsequent fitting of the spectra to the metabolic database yielded a good adjustment, and LCModel provided the relative concentration of metabolites for each sample. Amongst the derived stable important metabolites delivered by the RF analysis, we found metabolites from all regions, with the sum of Cho + GPC + PCh in the Hyp, and Tau in the Hipp and ILA appearing in the 20 repetitions as amongst the top-10 metabolites having the higher GiniInx, and GSH in the Hyp, Cho + GPC + PCh in the ILA, Glc in NAc and Tau in the Hyp appearing in between 15 and 19 repetitions. A representative mean decreases in Gini index plot of metabolites is shown in Fig. 5 B. The final RF was applied to a 70/30 training/testing partition, yielding a confusion matrix predicting 6/8 subjects to the celastrol group (2/6 incorrectly assigned to vehicle), and 4/4 from the vehicle group correctly allocated (0.83 accuracy). ANOVA tests of the stable important metabolites yielded significant effects of either type, or interactions type:diet , type:sex , in all metabolites except on hypothalamic Tau (Table 4 ). Notably, Tau ratios to PCr + Cr were reduced in the celastrol-treated group compared to the vehicle group in both the ILA (Fig. 5 C, Table 4 ). Similarly, Cho + GPC + PCh ratio to PCr + Cr was significantly elevated in the ILA and Hyp (both TE) (Fig. 5 D and Table 4 ). Additionally, the celastrol-treated male mice showed decreased Glc ratios than the vehicle-administered male animals (Fig. 5 E). HFHS-celastrol group exhibited lower GSH/PCr + Cr than the HFHS-vehicle animals, with values after treatment reaching similar levels of LFLS (Fig. 5 F). Table 4 Main effects and interactions of type of treatment, sex and diet, on stable important metabolites TE, comparing the vehicle and celastrol groups. C: Tau. D: Cho + GPC + PCh. E: Glc, F: GSH. (*padj < 0.05, ** padj < 0.01, *** padj < 0.001). Effect/Metabolite Cho + GPC+PCh Hyp Tau ILA Tau Hipp GSH Hyp Cho + GPC+ PCh ILA Glc NAc TE = 144 TE = 36 TE = 144 TE = 36 TE = 36 TE = 144 TE = 144 TE = 36 Type F = 13.7, p < 0.001 F = 5.3, p < 0.05 F = 23.0, p < 0.001 F = 13.6, p < 0.001 Type:Sex F = 8.6, p < 0.01. F = 4.1, p < 0.05 Post-hoc Celastrol Vs Vehicle males df = 37, t =-5, p < 0.001 df = 37, t =-2.9, p < 0.05 Type:Diet F = 4.2, p < 0.05 F = 4.9, p < 0.05 Post-hoc Celastrol Vs Vehicle df = 37, t =-4.8, p < 0.001 (LFLS) df = 37, t =-3.8, p = 0.001 (HFHS) 4. Discussion In this work, we have characterized the effects of celastrol as an anti-obesity and anti-inflammatory agent on an animal model of diet-induced obesity. First, and prior to any treatment, we characterized obesity development by comparing BW, food consumption, in vivo MRI markers of neuroinflammation, ex vivo indicators of astrogliosis and microgliosis, systemic blood hormonal changes and cerebral metabolic profiles between obese and non-obese animals. Results indicate that the obese phenotype is characterized by an inflammatory state in the brain, which can be quantified in vivo by increased FA and reduced RD, and ex vivo by microglial and astrocyte augmented number and reactive shapes. Next, we administered either treatment with celastrol or its vehicle (DMSO) and observed that the obesity-induced alterations could be modified by the treatment, including a BW decrease, decreases on the in vivo FA values, particularly in the Hyp, and microglial reduction of both number and area, revealing an anti-inflammatory action mechanism of the tested compound. Additionally, we could characterize the cerebral metabolic changes induced by celastrol, which suggest the regularization of osmolytes, and measure systemic blood changes that reveal major changes on an anti-inflammatory cytokine. 4.1 HFHS diet increases FA regionally and celastrol reverts changes Among MRI methods, DTI has been instrumental in detecting microstructural changes in both grey and white matter across various neurodegenerative and pathological conditions, in humans as well as in animal models. The biological interpretation of changes in diffusion coefficients requires, however, several considerations, including the tissue composition of the region investigated. Indeed, alterations in white or grey matter induce distinct diffusion patterns, and, vice versa, a comparable variation of a diffusion coefficient may arise from diverse biological responses. For instance, as white matter is composed mainly of neuronal bundles, which favor diffusion along its parallel direction, an integrity loss of the fibers would yield decreased FA values, as it has been reported in the corpus callosum of subjects with obesity (Daoust et al. 2021 ). At the same time, in grey matter, gliosis –a state with increased cellularity and larger and irregular cellular bodies- has been related to increased FA (Lizarbe et al. 2020 b). In subcortical areas, where grey matter is also predominant, another study found decreased MD, AD and FA with body mass index in young patients with obesity, but increased FA in older patients (Tweedale et al. 2024 ). In their work, authors interpreted the decreased diffusivity coefficients as potentially reflecting gliosis, and proposed to link the higher FA to an anisotropic inflammatory process, as it was positively associated with C-reactive protein values. In our study, we report that animals fed with a HFHS diet show increased FA and decreased RD in grey matter regions, significantly in the Hyp and Hipp, which agrees well with increases in cellularity and a gliosis-associated anisotropic change of cellular shape, and, thus, inflammation, in line with the previous studies. Notably, our MRI in vivo results are supported by immunofluorescence images showing microgliosis and astrocytosis on HFHS brains. Moreover, such cellular changes were assessed not only visually, but also as statistically relevant increased astrocytic and microglial surfaces, increased number of astrocytes and microglia. Consequently, and supported by our ex vivo images, we can infer that the HFHS-induced increased FA and reduced RD, are reflecting a diffusion in the extracellular space that is restricted by the anisotropic growth of the cellular bodies composing the Hyp and Hipp. After the anti-obesity treatment, we could find, in the Hyp, that differences between FA of HFHS or LFLS mice where only maintained in male vehicle-administered animals; in other words, that animals treated with celastrol showed no-longer-increased FA values in the Hyp. Besides, in this region, after treatment, RD was no longer different between diet groups, but no specific treatment-effect could be robustly statistically reported. In the Hipp, on the contrary, FA remained elevated in HFHS after 3 days of administration, as compared to LFLS, independently of the type of treatment. Results in the Hyp are supported by the immunofluorescence images, where we found significantly lower number and area occupied by microglia, in animals treated with celastrol, as compared to mice that received the vehicle-only solution. Thus, results are consistent with an anti-inflammatory action of celastrol, specific to the Hyp, that can be quantified in vivo by an FA reduction, and supported by immunofluorescence images. 4.2 Microglia reacts first Quantification of immunofluorescence images from IBA1 and GFAP markers showed that HFHS consumption induced very remarkable effects in both microglial cells and astrocytes, with the changes of microglia being significant in the four regions investigated, and the effects on astrocytes absent only in the ILA. In both cell types, caloric diet resulted in augmented number and area occupied by the cells. This is on agreement with previous results of our group (Lizarbe et al. 2019) and others (García-Cáceres et al. 2019b), and has been consistently linked to a the diet-induced inflammatory process (Valdearcos et al. 2017b). Indeed, neuroinflammation is characterized by cellular proliferation and by an enhanced proportion of type M1 microglia, which have amoeboid shapes, and thus bigger cellular bodies that yield higher % of occupied area values, than the non-pro-inflammatory ramified M2-type (Tam and Ma 2014 ). After treatment, HFHS animals administered with celastrol depicted a robust response in microglial cells in the Hyp and Hipp, and both the area occupied by microglia and their numbers were comparable to those from LFLS mice, and significantly lower than vehicle-treated HFHS. This celastrol-induced reversal is in line with the response to other anti-obesity treatments, which withdraw the pathological changes of microglial activation (Berkseth et al. 2014 ; Marinho et al. 2026 ; Rong et al. 2025 ). Particularly, the reduced % of area occupied by cells is on agreement with a switch from M1 -amoeboid- to M2 -ramified- microglial types, similar to findings after other pharmacological interventions (Wang et al. 2024 ). Our results indicating that the number of cells also decreased may suggest that treatment induced tissue reorganization and normalization. Studies assessing proliferation of microglia after stroke demonstrated that, after an insult, microglial cells tend to migrate to the lesion focus, adopting hypertrophic shapes, while that during recovery tend to disaggregate and recover more ramified and random shapes (Kikhia et al. 2025). Thus, the reduction of Iba1 + microglial number that we detected after celastrol treatment could reflect both a reduction in activated subpopulations and a spatial redistribution with a phenotype switch to a less inflamed state, that is more difficult to detect with our counting strategies, rather than a global decrease in microglial total number. Finally, the fact that changes were quantifiable in the Hyp and Hipp reinforce the hypothesis of both areas being are primary responders of high-fat diet consumption, and treatment (Plantera et al. 2025 ). Astrocytes were less affected by treatment, and GFAP-derived parameters of number and % of occupied area remained different between diet groups, regardless of the treatment/vehicle received. In the images, we could see how celastrol specific treatment reduced both the area and number of astrocytes in the Hyp, but such diminish did not reach the statistical threshold. Hypothalamic astrocytes are known to become reactive in DIO, but most studies find that, from one hand, microglial activation is earlier and more robustly quantifiable than astrocytosis (Jais and Brüning 2017 ), and, from the other, that microglial depletion or inhibition reduces subsequent astrogliosis, supporting the idea that astrocyte activation is at least partly downstream of microglial signaling (André et al. 2016 ). Our results support the fact that microglia react first to celastrol treatment, prompting an anti-inflammatory response that is only partially extended to astrocytes. 4.3 Effects of celastrol on brain metabolism The effects of celastrol in cerebral metabolism were assessed by HRMAS, by comparing the neurochemical profile from celastrol-treated or vehicle-administered brain region samples. A random forest analysis revealed that the sum of the choline-containing compounds, Cho + GPC+PCh, and Tau were the metabolites that best classified the animals depending on their treatment, in the Hyp, ILA and Hipp, to a lesser extent, with the two metabolites being reduced in celastrol-treated mice. Choline-containing compounds are known to be MRS markers of membrane turnover and cellular proliferation (Duarte et al. 2012). In the context of obesity, increased Cho concentrations are thought to be linked to chronic low-grade neuroinflammation and glial activation, consistent with existence of inflammatory signaling within the central nervous system (Vuković et al. 2026 ). The fact that Cho compounds are within the metabolites that best classify between treated and non-treated animals, and that their value is significantly lower in the celastrol group, agrees well with a decreased inflammatory state of the treated mice, as compared to non-treated. On the other hand, previous works on DIO have quantified increases of Tau in the Hyp, Hipp and cortex, and such rise has been proposed to represent a compensatory neuroprotective response to metabolic stress (Duarte et al. 2012; Lizarbe et al. 2019). Moreover, Tau supplementation is known to reduce body weight on HFD animals (Figueroa et al. 2016 ), via anti-inflammatory effects (Ahmed et al. 2024 ) and it has been shown to accumulate in the Hipp of DIO animals acting as a counteracting beneficial response to metabolic stress (Garcia-Serrano et al. 2023 ). Thus, the lower values reported here in treated animals are consistent with a celastrol-induced anti-inflammatory response that does no further need such metabolic stress-induced elevation of Tau levels. Other than Tau and the Cho compounds, GSH and Glc in certain brain regions were also amongst the top-10 metabolites with higher classification indexes. GSH is the main small‑molecule antioxidant in brain cells, and impairment of its function is linked as the result of neurological diseases, or during aging (Iskusnykh et al. 2022 ). Astrocytes contain substantially higher GSH than neurons, and changes in cerebral GSH in some human diseases are thought to be, at least in part, the consequence of alterations of astrocytic GSH, and/or of changes in the GSH metabolism of astrocytes (Dringen and Arend 2025 ). Indeed, during inflammation, astrocytes experience increased oxidative and inflammatory load, which typically drives upregulation of antioxidant systems, including the GSH pathway, to protect both themselves and neighboring neurons. In this sense, our results showing higher GSH levels in the hypothalamus un HFHS mice treated with vehicle, as compared to HFHS celastrol animals, which reach similar levels than LFLS mice, are on agreement with an obesity-induced GSH increase that can be reverted with celastrol. Glc, on the other hand, as measured in the NAc, appeared also as a stable important metabolite distinguishing between classes, depicting significantly lower values in treated male mice, as compared to non-treated. A decrease in Glc values after celastrol administration fits well with a switch from an obesity-associated hypermetabolic brain towards a more normalized glucose handling, and is on agreement with other anti-obesity treatments (Sewaybricker and Schur 2021 ). 4.4 Systemic effects of celastrol Serum analysis of blood hormones from obese and non-obese mice showed a diet effect on the concentrations of ghrelin and leptin (p < 0.05) that matched the expected hypothesis -decreased ghrelin during HFHS (Briggs et al. 2010 ), increased leptin-(Frederich et al. 1995 ). In HSHS mice treated with celastrol, IL-6 retained the p < 0.05 threshold, with obese male animals treated much larger values than vehicle-only. IL-6 is a cytokine that plays a pivotal role in inflammatory responses, with diverse effects on regulating the immune response and metabolism via different signaling pathways, and it is known to particularly play an anti-inflammatory role in DIO related inflammatory diseases (Yang et al. 2025 ). The increased values reported after treatment suggest that the BW decreases effects of celastrol administration are mediated by anti-inflammatory effects of IL-6. Notably, these effects could only be reported in male animals. Interestingly, this agrees well with males showing larger BW and intake decreases with celastrol than females, and with the hyp FA recovery on this specific group. Future studies, however, should consider potentially increasing sample sizes, as well as controlling feeding conditions prior to the blood extraction, to increase the statistical power of these tests and confirm the sexual-differences reported in the IL-6 measurements. Conclusions By using a wide range of methodological assessments, we have covered the physiological, systemic and central effects of celastrol administration to DIO and lean animals and can conclude that it serves as an anti-obesity drug via anti-inflammatory mechanisms in the hypothalamus that can be detected in vivo by DTI, and ex vivo by histological, hormonal and cerebral metabolic changes. We believe that our results may prompt further interest in using celastrol as an anti-inflammatory agent during obesity and other pathologies involving inflammation, and that DTI methods could be used as biomarkers of its action. Limitations Our study investigates the anti-inflammatory and anti-obesity effects of celastrol during an acute administration. Interestingly, with only three doses, very high BW decreases were achieved, and indicators of both central and systemic anti-inflammatory and metabolic changes were reported. Future studies should assess a longer administration period, like treatments on human population, potentially using lower dosages, and determine the corresponding effects. In our in vivo MRI study, even DTI reported consistent changes with diet and treatment , effect sizes of the corresponding parameters were lower than those observed in the ex vivo images by immunofluorescence of Iba1 and GFAP. Moreover, DTI could only quantify treatment response in the Hyp, while immunofluorescence images of Iba1, the microglial marker, detected changes both in the Hyp and Hipp. From one hand, and in order to provide more specificity regarding the compartmentalization of cellular changes, we suspect that the implementation of microstructural multicompartment tissue biophysical models (Garcia-Hernandez et al.) could likely detect stronger effects on the corresponding diffusion parameters, and future studies should address this with more advanced acquisition strategies and biophysical models. On the other hand, however, this may also be reflecting stronger effects on the Hyp than in the Hipp, which is consistent with previous reports, which propose hypothalamic microglia as first responders for energy balance, while hippocampal microglia mediate longer-term cognitive consequences of metabolic stress (Plantera et al. 2025 ). In this study, we could not detect any changes on MTR values, neither with the diet nor with treatment effects, although previous studies did (Campillo et al. 2022 ). What the mentioned study reported, however, were MTR values that increased in a within-subject manner during obesity development, changes that lean animals did not exhibit, but no direct comparison between obese and non-obese group was assessed. In this sense, in the present work, potential between-subjected variability may have precluded the finding of relevant differences between groups. Another limitation of our study involves the high variability of the serum hormone results, which may have precluded the finding of some expected relevant effects, widely described in obesity. For example, values of the proinflammatory cytokine TNFα, which are known to increase during obesity (Makki et al. 2013 ), showed, in our study, high variability within animal groups, and no robust trends could be found. Indeed, many of the hormones studied here are dependent on feeding conditions, circadian rhythms and metabolic context (Lages et al. 2026 ), in general. Future studies aiming at disentangling the plasma inflammatory profile of obesity and anti-obesity treatment could improve such assessment by standardizing the measurements and performing blood extracting after a fasting period. Abbreviations AD Axial diffusivity Ala Alanine ANOVA Analysis of variance ARC Arcuate nucleus ASCM Adaptive soft coefficient matching Asp Aspartate Av Number of averages b Diffusion weighting factor BW Body weight Cho Choline Cr Creatine CSF Cerebrospinal fluid CV Coefficient of variation DIO Diet–induced obesity DMSO Dimethyl sulfoxide DTI Diffusion tensor imaging dMRI Diffusion magnetic resonance imaging ELISA Enzyme–linked immunosorbent assay ER Endoplasmic reticulum FA Fractional anisotropy FDR False discovery rate FOV Field of view GABA Gamma–aminobutyric acid GFAP Glial fibrillary acidic protein GLP 1–Glucagon–like peptide–1 Glc Glucose Gln Glutamine Glu Glutamate Gly Glycine GPC Glycerylphosphorylcholine GSH Glutathione HF High–fat HFD High–fat diet HFHS High–fat high–sugar Hipp Hippocampus HRMAS High–resolution magic angle spinning ¹H HRMAS Proton high–resolution magic angle spinning Hyp Hypothalamus IBA1 Ionized calcium–binding adaptor molecule 1 i.p. Intraperitoneal IL 6–Interleukin 6 ILA Infralimbic area Lac Lactate Leu Leucine LFLS Low–fat low–sugar Lip Lipids lme Linear mixed–effects model MC Mesocorticolimbic complex MD Mean diffusivity mI Myo–inositol MM Macromolecules MRI Magnetic resonance imaging MRS Magnetic resonance spectroscopy MTR Magnetization transfer ratio MTI Magnetization transfer imaging NAAG N–acetylaspartylglutamate NAc Nucleus accumbens NAA N–acetyl–aspartate OCT Optimal cutting temperature compound OOB Out–of–bag PBS Phosphate–buffered saline PCh Phosphocholine PCr Phosphocreatine PFA Paraformaldehyde Phe Phenylalanine PVN Paraventricular nucleus PYY Peptide YY RD Radial diffusivity RC Reward centers RF Random forest ROI Region of interest SD Standard deviation SFAs Saturated fatty acids Tau Taurine TE Echo time TNFα Tumor necrosis factor alpha TR Repetition time VMN Ventromedial nucleus Declarations Ethics approval and consent to participate All animal handling protocols included in this work were performed by specialized personnel at our institute’s animal facility (Reg. No. ES280790000188) and approved by the ethical committee of the Institute of Biomedical Research Sols-Morreale, CSIC and the Community of Madrid, complying with national (R.D. 53/2013) and European Community guidelines (2010/63/UE) and with the ARRIVE guidelines (ethics approval number PROEX 288/42). Consent for publication Authors and their institutions consent the publication of this work. Availability of data and materials The datasets generated and/or analysed during the current study are not publicly available due the need for a formal data sharing agreement but are available from the corresponding author on reasonable request. Competing interests The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Funding This work was supported by the “Proyectos de Generación de Conocimiento 2021” [Knowledge Generation Projects 2021] call, funded by the Spanish Ministry of Science and Innovation (MCIN/AEI/10.13039/501100011033) and by the European Union NextGeneration EU/PRTR, under grant numbers [PID2021-123068OB-I00 to A.V., PID2021-122528OB-I00 to PL-L, grant PID2021-126888OA-I00 to B.L.], scholarship PRE2022-105662 to A.F. and scholarship PIPF-2022/SAL-GL-25871 to R.G-A. Authors’ Contributions Conceptualization, B.L.; Methodology, A.F., A.V., P.L.-L. and B.L.; Investigation, A.F, M.H, R.G-A, S.G.-S., L.M.F-S.; Software, A.F., R.G.-A. and B.L., Formal Analysis, A.F., and B.L., Resources, B.L. and A.V., Writing – Original Draft, A.F., and B.L.; Writing – Review & Editing, all authors.; Visualization, A.F. and B.L., Funding Acquisition, A.V., P.L.-L. and B.L., Project Administration, P.L.-L. and B.L., Supervision, B.L. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9344668","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":621601965,"identity":"763b7776-5e60-4349-ac87-2377d3dfa8d6","order_by":0,"name":"Adriana Ferreiro","email":"","orcid":"","institution":"Institute for Biomedical Research Sols-Morreale","correspondingAuthor":false,"prefix":"","firstName":"Adriana","middleName":"","lastName":"Ferreiro","suffix":""},{"id":621601967,"identity":"6702724c-4016-4c9b-9f49-b465ea627d94","order_by":1,"name":"Maya Holgado","email":"","orcid":"","institution":"Institute for Biomedical Research Sols-Morreale","correspondingAuthor":false,"prefix":"","firstName":"Maya","middleName":"","lastName":"Holgado","suffix":""},{"id":621601968,"identity":"8fdcc7ac-8f27-400d-88b6-82a7acbf50b9","order_by":2,"name":"Raquel González-Alday","email":"","orcid":"","institution":"Institute for Biomedical Research Sols-Morreale","correspondingAuthor":false,"prefix":"","firstName":"Raquel","middleName":"","lastName":"González-Alday","suffix":""},{"id":621601969,"identity":"7ff69fb9-518c-4fcc-918f-18ac3b9de8e2","order_by":3,"name":"Sara González-Soto","email":"","orcid":"","institution":"Complutense University of Madrid","correspondingAuthor":false,"prefix":"","firstName":"Sara","middleName":"","lastName":"González-Soto","suffix":""},{"id":621601970,"identity":"b1143b39-29ec-45bb-9444-cb2a6a8996a8","order_by":4,"name":"Lidia Fernandez-Sevilla","email":"","orcid":"","institution":"King Juan Carlos University","correspondingAuthor":false,"prefix":"","firstName":"Lidia","middleName":"","lastName":"Fernandez-Sevilla","suffix":""},{"id":621601971,"identity":"e2842d37-8bf8-47f5-8cb9-b0a98556710a","order_by":5,"name":"Angeles Vicente","email":"","orcid":"","institution":"Complutense University of Madrid","correspondingAuthor":false,"prefix":"","firstName":"Angeles","middleName":"","lastName":"Vicente","suffix":""},{"id":621601972,"identity":"fe5456cc-499e-4514-ba2a-171947f5c638","order_by":6,"name":"Pilar Lopez-Larrubia","email":"","orcid":"","institution":"Institute for Biomedical Research Sols-Morreale","correspondingAuthor":false,"prefix":"","firstName":"Pilar","middleName":"","lastName":"Lopez-Larrubia","suffix":""},{"id":621601973,"identity":"1f9e9f03-1960-461f-b84b-ea6f2363402b","order_by":7,"name":"Blanca Lizarbe","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIiWNgGAWjYBACA3bGBiB1gIGfmbGNSC3MUC2SzcRrAVMHGAwOMLARp8Wcmbn5w889d+SMjzO3PWBss4tmYG9/gFeLZTNjg2HPs2fGZocZ2w0Y25JzG3jOGOB32GHGhgSeA4cTtx1mbJNgbGPObZDIIeAXoJaDfw4crt/cDNZSn9sg/xy/w4BaGpuBtiQAgw6k5TDQFgb8DgP6pZlZ5sAzwxkgvyScO57bxpODX4s5e/vjj28O3JHn7z/+7MGHsurcfvbj+B2GChKAmMjYGQWjYBSMglGADwAA53tIkNsThKgAAAAASUVORK5CYII=","orcid":"","institution":"Institute for Biomedical Research Sols-Morreale","correspondingAuthor":true,"prefix":"","firstName":"Blanca","middleName":"","lastName":"Lizarbe","suffix":""}],"badges":[],"createdAt":"2026-04-07 11:54:57","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9344668/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9344668/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106976272,"identity":"aa2f0a83-eb53-4afa-8d96-a948e8511912","added_by":"auto","created_at":"2026-04-15 10:48:01","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":142588,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePhysiological changes during the three-day treatment period with either celastrol or vehicle\u003c/strong\u003e. Changes in BW (top) and food intake (bottom) after 20 weeks of diet (Pre-dose) and at different time points after treatment initiation for females (left) and males (right), in both the HFHS (blue colors) and LFLS (orange) cohorts. Food intake was calculated as average values per cage. All values are expressed as mean ± standard deviation (*p\u003csub\u003eadj\u003c/sub\u003e\u0026lt; 0.05, **p\u003csub\u003eadj\u003c/sub\u003e\u0026lt; 0.01, ***p\u003csub\u003eadj\u003c/sub\u003e \u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9344668/v1/e8ed5617b99be9d42d79f1cf.png"},{"id":106976270,"identity":"012df360-c561-4ea4-b009-5f48778430cb","added_by":"auto","created_at":"2026-04-15 10:48:01","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":218730,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDTI brain changes with diet and treatment. A:\u003c/strong\u003e Parametric maps of RD before treatment of the brain slice containing the hypothalamus of representative LFLS (top) and HFHS (bottom) mice.\u0026nbsp; \u003cstrong\u003eB\u003c/strong\u003e: Violin plots plus values from individual animal quantification of the regional MRI parameters that showed significant differences between diet groups, namely FA in the Hyp and Hip (left panels) and RD in the Hyp (right panel), with HFHS mice depicted in green, and LFLS in light blue. The horizontal bars and confident intervals represent corresponding mean±CI values of the linear model estimated by the \u003cem\u003eemmeans\u003c/em\u003e function. \u003cstrong\u003eC\u003c/strong\u003e. Parametric maps of FA after treatment of the brain slice containing the hypothalamus of representative LFLS (top) and HFHS (bottom) mice, under vehivle (left) or celastrol (right) administration.\u0026nbsp; \u003cstrong\u003eD\u003c/strong\u003e: Violin plots plus individual representation of the regional parameters that showed significant differences either between diet or treatment groups, including FA in the Hyp (left panels), FA in the Hipp (top right) and RD in the Hyp (bottom right). RD after treatment did not show further significances between diet groups.\u0026nbsp; Results from HFHS mice are depicted in green, and LFLS in light blue. The horizontal bars and confident intervals represent the corresponding mean±CI values of the linear model estimated by the \u003cem\u003eemmeans\u003c/em\u003e function (*p\u003csub\u003eadj\u003c/sub\u003e \u0026lt; 0.05, ** p\u003csub\u003eadj\u003c/sub\u003e \u0026lt; 0.01, *** p\u003csub\u003eadj\u003c/sub\u003e \u0026lt; 0.001).\u0026nbsp;\u0026nbsp;\u0026nbsp;\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9344668/v1/115886d7c53dccbdd2525642.png"},{"id":106994285,"identity":"120b2247-e60b-4525-8fd2-b55784105357","added_by":"auto","created_at":"2026-04-15 15:07:17","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":239000,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHFHS diet and celastrol effects on astrocytes and microglia. A\u003c/strong\u003e: Representative immunofluorescence images comparing the ARC Hyp from a LFLS mouse (left) and a HFHS fed animal (right), before treatment, showing IBA-1 staining (red) for microglia cells localization (top) and GFAP staining (green) for astrocytes localization (bottom). Cell nuclei were stained with DAPI (blue). Images were acquired using a 20x objective. \u003cstrong\u003eB-C\u003c/strong\u003e: Violin plots superimposed to individual points representation of microglial values of % of occupied area (%A) and counts by area (C/A), before treatment, from LFLS mice (light blue) and HFHS animals (green). \u003cstrong\u003eD-E\u003c/strong\u003e: Violin plots superimposed to individual points representation of astrocytic quantification of % of occupied area and counts by area, before treatment, of LFLS and HFHS mice. \u003cstrong\u003eF:\u003c/strong\u003e After treatment immunofluorescence images of the PVH Hyp region, comparing celastrol (left) or vehicle (right) treatments, showing IBA-1 and GFAP staining for microglia (red, top panels) and astrocytes (green, bottom panels) cells localization. \u003cstrong\u003eG-H: \u003c/strong\u003eAfter treatment\u003cstrong\u003e \u003c/strong\u003equantification of microglial values of % of occupied area (%A) and counts by area (C/A), with \u003cstrong\u003ev\u003c/strong\u003eiolin plots superimposed to individual points of vehicle (yellow) and celastrol-treated (blue) mice. \u003cstrong\u003eD-E\u003c/strong\u003e: Violin plots superimposed to individual point representation of astrocytic quantification of % of occupied area and counts by area, after vehicle (yellow) or celastrol (blue) treatments.\u0026nbsp; The horizontal bars and confident intervals represent corresponding mean±CI values of the linear model estimated by the \u003cem\u003eemmeans\u003c/em\u003e function (*p\u003csub\u003eadj\u003c/sub\u003e \u0026lt; 0.05, ** p\u003csub\u003eadj\u003c/sub\u003e \u0026lt; 0.01, *** p\u003csub\u003eadj\u003c/sub\u003e \u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9344668/v1/3cf3e65ca4f5d9d21baa9a9d.png"},{"id":106994378,"identity":"056c0d29-9ab5-45f1-b53b-d2319c9b7795","added_by":"auto","created_at":"2026-04-15 15:08:07","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":74886,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHormonal changes. A: \u003c/strong\u003eGhrelin and\u003cstrong\u003e B\u003c/strong\u003e: Leptin violin plots superposed to experimental concentrations (dots) and mean±CI values of the linear model estimated by the \u003cem\u003eemmeans \u003c/em\u003efunction, of HFHS and LFLS mice before any treatment. Only these two hormones showed significant differences with diet. \u003cstrong\u003eC\u003c/strong\u003e: Values of IL-6 measured after treatment, in female and male vehicle or Castrol-treated mice. No other hormone or cytokine show statistical differences between treatment.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9344668/v1/97b6072c69b699bc6ea86af7.png"},{"id":107480424,"identity":"a3512a93-fa38-43d5-b534-14dd61f26c65","added_by":"auto","created_at":"2026-04-22 02:10:15","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":159024,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEx-vivo\u0026nbsp;HRMAS Spectroscopy Post-Treatment. A:\u003c/strong\u003e\u0026nbsp;Representative \u003csup\u003e1\u003c/sup\u003eH HRMAS spectrum of ILA. \u003cstrong\u003eB:\u003c/strong\u003e Mean Decrease Gini values of the metabolites exhibiting the highest importance scores in the random forest analysis. \u003cstrong\u003eC-F\u003c/strong\u003e: Boxplots representing the concentrations of metabolites normalized to total creatine content (Cr + PCr), from the different brain areas and TE, comparing the vehicle and celastrol groups.\u0026nbsp; \u003cstrong\u003eC: \u003c/strong\u003eTau. \u003cstrong\u003eD: \u003c/strong\u003eCho + GPC + PCh.\u003cstrong\u003e E: \u003c/strong\u003eGlc, \u003cstrong\u003eF:\u003c/strong\u003e GSH. (*p\u003csub\u003eadj\u003c/sub\u003e \u0026lt; 0.05, ** p\u003csub\u003eadj\u003c/sub\u003e \u0026lt; 0.01, *** p\u003csub\u003eadj\u003c/sub\u003e \u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-9344668/v1/6e32e19d85c3430918e97e08.png"},{"id":107705031,"identity":"769e62e9-2a71-40b6-95a2-88a121f4003d","added_by":"auto","created_at":"2026-04-24 09:06:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1374769,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9344668/v1/5e548ee5-6177-46d2-9fea-9e164f2a23dc.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Acute anti-obesity treatment with celastrol reduces body weight, cerebral inflammation and metabolic imbalances in mice","fulltext":[{"header":"1. Background","content":"\u003cp\u003eConsumption of energy dense foods, such as high-fat and sugar diets (HFHS), combined with sedentary lifestyles, are among the most important environmental factors predisposing to obesity. During obesity, the accumulation of elevated fat stores triggers a low-grade systemic inflammation, characterized by an abnormal cytokine production and the activation of a network of inflammatory signaling pathways (Wellen and Hotamisligil 2005), which boost the development of the obesity-related diseases (Hotamisligil \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Particularly, inflammation affects multiple organs including, pancreas, liver, cardiovascular system and the brain, eventually leading to the disruption of global metabolic homeostasis (Uranga and Keller \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHFHS feeding in rodents is an extensively used model to investigate its onset and development (De Moura E Dias et al. 2021; Nilsson et al. 2012). In the last decade, several studies using animal models of diet-induced obesity (DIO) revealed the activation of a localized inflammatory response in the hypothalamus (Hyp) after short term HF feeding that induces a defective control of energy homeostasis and development of leptin and insulin resistance (Thaler et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Valdearcos et al. 2017a; Zhang et al. 2008). During fat-rich diets consumption, long-chain saturated fatty acids (SFAs) cross the blood-brain barrier and bind to the pro-opiomelanocortin neurons in the arcuate nucleus (ARC) of the Hyp (Posey et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). This binding triggers the activation of inflammatory signaling cascades, prompting the expression of pro-inflammatory genes. In this circumstances, glial cells experience morphological, physiological and functional modifications that enable an inflammatory process against the accumulation of SFAs (Garc\u0026iacute;a-C\u0026aacute;ceres et al. 2019a; Ramalho et al. 2018; Valdearcos et al. 2017a; Valdearcos et al. 2014). Particularly, astrocytes develop a reactive phenotype in the ARC detected immunohistochemically 24 hours after HF intake (Buckman et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Horvath et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Thaler et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), release inflammatory cytokines(Gupta et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), and, in response to high leptin levels, trigger microvascular remodeling within the Hyp (Gruber et al. 2021; Yi et al. 2012). Interestingly, few studies have reported that is the carbohydrate component of fat-rich diets which initiates such inflammatory cascade, including microglial activation and angiogenesis (Gao et al. 2017).\u003c/p\u003e \u003cp\u003eIn mammals, the homeostatic system interacts with motivational and rewarding behaviors via the mesocorticolimbic complex (MC) and reward centers (RC), respectively (Ferrario et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), cerebral structures that participate in the control of food intake and can exert relevant roles in favoring obesity development. MC structures include abundant dopamine projections from the ventral tegmental area to the prefrontal cortex, amygdala, hippocampus (Hipp), nucleus accumbens (NAc) and infralimbic area (ILA), correlating potential appetite stimuli to associated rewards, thus creating motivational connections (Berridge 2009). RCs include regions from the orbitofrontal cortex, amygdala and NAc, and grant food with its pleasurable properties (Saper et al. 2002). Interestingly, some of the implicated MC and RC regions also express inflammatory signals during obesity development (Cazettes et al. 2011).\u003c/p\u003e \u003cp\u003eCelastrol, a pentacyclic triterpene extracted from the roots of the \u003cem\u003eTripterygium Wilfordi\u003c/em\u003e plant, has been revealed as a promising anti-inflammatory agent with anti-obesity effects (Liu et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In long term DIO animals, celastrol administration induces body weight (BW) decreases up to 45%, reducing food intake and blocking energy expenditure. Such BW reduction is thought to be achieved through increased leptin sensitivity, reduced hypothalamic neuronal ER stress, and regulating energy metabolism, deactivation of hypothalamic inflammation (Seyfried and Hankir \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), lipid metabolism and even gut microbiota (Xu et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), with the consequent reestablishment of glucose tolerance and insulin sensitivity (Feng et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, the exact mechanisms by which celastrol increases leptin sensitivity, or glucose tolerance and insulin sensitivity, are not yet fully understood, and some contradictory results have been reported (Saito et al. 2019).\u003c/p\u003e \u003cp\u003eA variety of neuroimaging methods have shown that obesity is associated with brain inflammation, alterations in the cerebral microstructure, metabolism, and function. Among them, magnetic resonance imaging (MRI) techniques, such as diffusion MRI (dMRI) have provided evidence of cerebral inflammation during high-fat diet (HFD) feeding in mice, and on patients with obesity (Le Bihan 2013). dMRI, and particularly diffusion tensor imaging (DTI) methods are widely used in clinics and in basic research and have revealed important cerebral alterations during obesity (Lizarbe et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003ea). For example, it has been described that obese patients and mice depict higher diffusivity in particular areas of the brain, as compared to non-obese individuals, which has been proposed to be a consequence of vasogenic edema related to obesity-induced blood brain barrier permeability changes (Cheung et al. 2009; Thomas et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) but the underlying mechanisms have not been elucidated. Notably, other MRI techniques, such as T\u003csub\u003e2\u003c/sub\u003e-weighted imaging or magnetization transfer imaging (MTI) have also been used to reveal changes in brain microstructure in the context of obesity-induced brain changes (Rosenbaum et al. 2022). On the other hand, magnetic resonance spectroscopy (MRS) techniques, such as high-resolution magic angle spinning (HRMAS), are sensitive enough to detect diet-induced metabolic changes in small regions of the mouse brain, such as the hypothalamus (Campillo et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Frost et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Interestingly, the cerebral metabolic changes induced by anti-obesity medications administration are not yet completely understood. In this sense, the implementation of imaging and spectroscopic techniques is endowed to provide vital information on the cerebral changes underlying obesity development and treatment.\u003c/p\u003e \u003cp\u003e On these grounds, we designed an experimental setup in which we administered either HFHS or low-fat low sugar (LFLS) diets to male and female animals and followed the development of obesity and its effects on the brain by MRI, glial markers by quantitative immunofluorescence, as well as the plasma concentrations of the main peptides and hormones involved in appetite and energy balance, assessed via ELISA. Subsequently, we treated the animals with celastrol and the effects on the brain were assessed using MRI, regional neurochemical profiles using \u003csup\u003e1\u003c/sup\u003eH HRMAS, as well as immunofluorescence and ELISA analyses.\u003c/p\u003e \u003cp\u003eUsing this methodology, we tested the hypothesis that celastrol induces detectable brain changes in DIO mice observable \u003cem\u003ein vivo\u003c/em\u003e using DTI parameters and magnetization transfer ratio (MTR) and focusing on four regions involved in both homeostatic and non-homeostatic control of food intake: the Hyp, the Hipp, the NAc, and the ILA. Additionally, we postulated that these changes would be corroborated \u003cem\u003eex vivo\u003c/em\u003e using immunofluorescence, \u003csup\u003e1\u003c/sup\u003eH HRMAS and ELISA techniques.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Experimental design\u003c/h2\u003e \u003cp\u003eC57BL/6 mice, bred and housed in our institutional animal facility, were accommodated in groups of 2\u0026ndash;5 animals, with 12-hour/12-hour light/dark cycle, controlled humidity (45\u0026ndash;55%), temperature (21\u0026ndash;23\u0026ordm;C) and \u003cem\u003ead libitum\u003c/em\u003e access to food and water. At nine weeks old, mice were randomly divided into two different dietary groups, LFLS (Research Diets, D12450Hi, 10 kcal% Fat) or a HFHS diet (Research Diets, D08112601i, 45kcal% Fat with 30 kcal% Sucrose). BW and food intake were recorded weekly. Following a 20-week period of diet, we administered i.p. either celastrol (C0869, Sigma-Aldrich, Merck) diluted it in dimethyl sulfoxide (DMSO) (1%) and PBS to a final concentration of 0.04 mg/mL, or only PBS solution with 1% DMSO. Each animal received a dose of 0.25 mg/kg or vehicle solution for three consecutive days.\u003c/p\u003e \u003cp\u003eExperimental methods to assess the cerebral effects of diet consumption and subsequent treatment with celastrol included: MRI scans after 20 weeks of diet (\u0026ldquo;\u003cem\u003ediet\u003c/em\u003e effects\u0026rdquo;) to male and female animals and post-treatment (\u0026ldquo;\u003cem\u003etreatment\u003c/em\u003e effects\u0026rdquo;) to the same animal batches (i); neurochemical profiles by HRMAS of the brain regions of interest (ii); histological markers of astrogliosis and microgliosis (iii); and plasma levels of main hormones involved in appetite regulation and energy balance (iv) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Additionally, plasma was analyzed also before any treatment (n\u0026thinsp;=\u0026thinsp;10 LFLS and n\u0026thinsp;=\u0026thinsp;10 HFHS, 50% females). Sample sizes were estimated based on previous studies of MRI quantification of cerebral changes during obesity (Campillo et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), with similar expected effect sizes and statistical power, and assuming that the rest of techniques exhibit at least comparable effects.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistribution of number of animals for each group depending on the diet, technique and treatment.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreatment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eCelastrol\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eVehicle\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiet/Technique\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHist\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR-MAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eElisa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHist\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHR-MAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eElisa\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHFHS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7♀ 7♂\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2♀ 2♂\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 ♀6♂\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5 ♀5♂\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7 ♀7♂\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2♀ 2♂\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3 ♀4♂\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5 ♀5♂\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLFLS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7♀8♂\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2♀ 2♂\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 ♀8♂\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6 ♀ 6♂\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2♀ 2♂\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7 ♀8♂\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 MRI acquisition\u003c/h2\u003e \u003cp\u003eMRI scans of the mouse brain were performed on a Bruker Biospec 7T system (Bruker Biospin, Ettlingen, DE) and the management software was Paravision 6.0.1, equipped with a \u003csup\u003e1\u003c/sup\u003eH mouse head surface coil with a volume transmitter (90mm diameter gradient insert 360mT/m). During the MRI experiments, each mouse was individually anesthetized in a methacrylate induction box with isoflurane (2% 1 L/min O\u003csub\u003e2\u003c/sub\u003e) and sustained throughout the acquisition using a nose mask with isoflurane (1% 1 L/min O\u003csub\u003e2\u003c/sub\u003e). The anesthetized animals were placed on a bed equipped with a circulating warm water bath to maintain their body temperature at approximately 37\u0026deg;C. The head of each mouse was fixed using a tooth-bar and ear-bars. Throughout the experiment, the body temperature and respiration of the animals were continuously monitored. Localization of the regions of interest (ROI), was achieved by acquiring axial T\u003csub\u003e2\u003c/sub\u003e-weighted anatomical images, using a rapid acquisition with relaxation enhancement sequence with the following parameters: FOV\u0026thinsp;=\u0026thinsp;21 \u0026times; 21 mm\u003csup\u003e2\u003c/sup\u003e, repetition time (TR)\u0026thinsp;=\u0026thinsp;2500 ms, echo time (TE)\u0026thinsp;=\u0026thinsp;27 ms, RARE factor\u0026thinsp;=\u0026thinsp;8, number of averages (Av)\u0026thinsp;=\u0026thinsp;1, 5 slices in an axial orientation, slice thickness\u0026thinsp;=\u0026thinsp;1.25 mm.\u003c/p\u003e \u003cp\u003eDTI data were acquired using a Stejskal\u0026ndash;Tanner sequence (TR\u0026thinsp;=\u0026thinsp;3000 ms, TE\u0026thinsp;=\u0026thinsp;32.56 ms, gradient separation (Δ)\u0026thinsp;=\u0026thinsp;20 ms, gradient duration (δ)\u0026thinsp;=\u0026thinsp;4 ms, FOV\u0026thinsp;=\u0026thinsp;21 x 21 mm\u003csup\u003e2\u003c/sup\u003e, slice thickness of 1.25 mm, diffusion gradients in 15 uniformly distributed directions, b\u0026thinsp;=\u0026thinsp;400 smm\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e and 1800 smm\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e, and 3 b\u0026thinsp;=\u0026thinsp;0 smm\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e). Two sets of MTI (TR\u0026thinsp;=\u0026thinsp;2500 ms, TE\u0026thinsp;=\u0026thinsp;9.8 ms, and Av\u0026thinsp;=\u0026thinsp;1) were acquired, either with an MT pulse applied (MT ON, N\u0026thinsp;=\u0026thinsp;50 train of radio frequency pulses, power\u0026thinsp;=\u0026thinsp;5.5 \u0026micro;T, offset\u0026thinsp;=\u0026thinsp;1500 Hz) or without (MT OFF), and the corresponding MTR calculated as the normalized subtraction of the corresponding signal intensities.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 MRI processing\u003c/h2\u003e \u003cp\u003eMRI data were processed using a software based on Python, to obtain the mean diffusivity (MD), fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD), and MTR maps, for the Pre- and Post\u003cem\u003e-treatment\u003c/em\u003e datasets, respectively, with Dipy (Garyfallidis et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The pre-processing pipeline encompassed the use of the Patch2self (Fadnavis et al. 2020) noise reduction filter for DTI and the adaptive soft coefficient matching (ASCM) filter (Coup\u0026eacute; P. et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) for MTR. The Hyp, Hipp, NAc and ILA areas were manually delineated using ImageJ (U. S. National Institutes of Health, Bethesda, Maryland, USA, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://imagej.nih.gov/ij/\u003c/span\u003e\u003cspan address=\"https://imagej.nih.gov/ij/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) with a standard mouse atlas as a reference (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.brain-map.org\u003c/span\u003e\u003cspan address=\"http://www.brain-map.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Pixel values of each parametric map were automatically filtered to remove extreme outliers (1st/3th quartile\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5*interquartile range) and regions close to cerebrospinal fluid (CSF) from the ventricles (MD\u0026thinsp;\u0026gt;\u0026thinsp;1300 \u0026micro;m\u003csup\u003e2\u003c/sup\u003e/s) and MTR\u0026thinsp;\u0026lt;\u0026thinsp;0. From the remaining pixels, mean values were calculated for each region.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 HRMAS\u003c/h2\u003e \u003cp\u003eImmediately after the MRI sessions, a group of animals was euthanized under the effects of anesthesia, using a high-power microwave (TMW-4012C 5 kW, Muromachi Kikai Co. Ltd., Japan). This method applies focused microwave irradiation to the brain and preserves \u003cem\u003ein vivo\u003c/em\u003e metabolic state of the cerebral tissue by rapid denaturation of enzymes responsible for protein dephosphorylation (O\u0026rsquo;Callaghan and Sriram 2004). After euthanasia, brains were extracted from the skull, and tissue samples were collected from the same regions selected in MRI analysis: Hyp, Hipp, ILA and NAc and stored immediately in liquid nitrogen, to be preserved at -80\u0026deg;C to prevent deterioration.\u003c/p\u003e \u003cp\u003eHRMAS experiments were performed on an 11.7 T Bruker Avance Neo vertical system (Bruker Biospin, Ettlingen, DE) operating at a proton frequency of 500.13 MHz and Topspin 4.1.4 software. The preparation of the sample consisted of introducing 10\u0026ndash;15 mg of tissue into a zirconium rotor (diameter of 4 mm), 50 \u0026micro;l of D\u003csub\u003e2\u003c/sub\u003eO was added and then hermetically sealed. The measurements were performed at a constant temperature of 277 K with a spinning rate of 5000 Hz. For each sample, two spectra were acquired using a Carr-Purcell-Meiboom-Gill sequence (128 scans, a relaxation delay of 5 s, a water suppression pulse of 2s, 32K data points and two different TE of 36 ms and 144 ms, to account for both the metabolites with strong J-coupling, and also obtain cleaner baselines (Oz and Tk\u0026aacute;č \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2011\u003c/span\u003e)).\u003c/p\u003e \u003cp\u003eThe data was processed using LCModel, a software designed for the quantification of metabolites (Provencher 2001). To achieve this, LCModel fits each spectrum as a linear combination of their correspondent values from a homemade database. Such database was created by acquiring the spectra of individual metabolite from the brain. For each metabolite, the software calculates its concentration, the estimated percentage standard deviation (%SD), and concentrations normalized to total creatine (PCr\u0026thinsp;+\u0026thinsp;Cr) content. The data base is composed of the following metabolites and macromolecules: alanine (Ala), aspartate (Asp), choline (Cho), creatine (Cr), GABA, glucose (Glc), glutamine (Gln), glutamate (Glu), glycine (Gly), glycerylphosphorylcholine (GPC), glutathione (GSH), lactate (Lac), leucine (Leu), myo-inositol (mI), N-acetyl-aspartate (NAA), N-acetylaspartylglutamate (NAAG), phosphocholine (PCh), phosphocreatine (PCr), phenylalanine (Phe), taurine (Tau), the lipids including Lip13a, Lip09, Lip20, macromoelcules such as MM09, MM20, MM12 and the sums Cho\u0026thinsp;+\u0026thinsp;GPC+PCh, NAA+NAAG, Cr\u0026thinsp;+\u0026thinsp;PCr, Glu\u0026thinsp;+\u0026thinsp;Gln, MM14\u0026thinsp;+\u0026thinsp;Lip13a+Lip13b+MM12, MM09\u0026thinsp;+\u0026thinsp;Lip09 and MM20\u0026thinsp;+\u0026thinsp;Lip20, among others. Only metabolites with a %SD less than 30% were included in the statistics analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Histology\u003c/h2\u003e \u003cp\u003eAfter image acquisitions, a group of mice were transcardially perfused with phosphate-buffered saline (PBS) and paraformaldehyde (PFA). Their brains were removed and placed in 4% PFA for 24 h, followed by the immersion in a 30% sucrose solution for 48 h, and then fixed in OCT (Tissue-Tek, Miles, Elkhart, In., EEUU) to be subsequently stored at -80\u0026ordm;C to ensure cryopreservation. Frozen coronal sections were obtained using a cryostat (Shandon Cryotome E; Thermofisher Scientific Inc, Waltham, Massachusetts, USA) at a thickness of 7 \u0026micro;m and mounted on glass slides.\u003c/p\u003e \u003cp\u003eCoronal sections were first treated with a solution of PBS (3% H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e and 10% methanol) and then blocked in PBS with 10% normal donkey serum (0.1% Gly, 0.02% Triton-X). The sections were then incubated overnight at 4\u0026ordm;C with primary antibodies for Iba-1 (DAKO,1:500) and GFAP (Merck Millipore, 1:600) to label microglia and astrocytes respectively. Following PBS washes, the sections were incubated with the specific secondary antibodies for Iba-1 (Alexa Fluor 594) and GFAP 1 (Alexa Fluor 488) (Thermo Fisher Scientific, 1:300). Hoechst 33258 (Molecular Probes, 24 Invitrogen) was used for nuclear staining. The procedure followed is detailed in (Fern\u0026aacute;ndez-Sevilla et al. 2022) and (Fern\u0026aacute;ndez-Sevilla et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Slides were mounted with Prolong Gold (Life Technologies) and images were acquired using a Nikom Eclipse Ci fluorescence microscopy with a Nikon Digital Sight DS-U3 camera and Nis-Elements D Viewer software.\u003c/p\u003e \u003cp\u003eFor the acquisition of images, a 20x objective was used for the Hyp and Hipp, and a 10x objective for the ILA and NAc. For each animal, a total of 21 images were captured from the Hyp (3 images from the ARC, 2 from the ventromedial nucleus (VMN), 2 from the paraventricular nucleus (PVN) per slice), 12 images from the Hipp (4 per slice), 12 images from the NAc (4 per slice) and 9 images from the ILA (per slice), with each animal having 3 slices.\u003c/p\u003e \u003cp\u003eImage analysis was conducted using ImageJ. The quantification process involved each image containing the entire quadrant, or a specific area of interest, excluding bubbles or non-brain tissue regions. In each image, we measured both the area fraction occupied by cells (%OA) and the number of cells (C/A). For the area fraction occupied by cells, the values from all images were added and then divided by the number of images for each animal and brain region. The count of cells per image was normalized by dividing by the total area of that image. Then, the normalized values were added and divided by the number of images.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 ELISA\u003c/h2\u003e \u003cp\u003eBlood samples were collected after 12 weeks of either HFHS or LFLS feeding. An additional group, only feeding with a HFHS diet, were treated with either celastrol or a vehicle solution for three consecutive days. Plasma concentrations of C-Peptide, ghrelin, glucagon-like peptide-1 (GLP-1), interleukin-6 (IL-6), glucagon, insulin, leptin, peptide YY (PYY) and tumor necrosis factor-α (TNFα) were measured using the MILLIPLEX Mouse Metabolic Hormone Expanded Panel kit (Millipore MMHMAG-44K). For each animal, 0.5\u0026ndash;0.8 mL of blood was collected by cardiac puncture while the animals were anesthetized. Following blood collection, all mice were euthanized. The experimental procedures for sample collection, storage, preparation of reagents for immunoassay and immunoassay procedure were performed following the instructions of the MMHMAG-44K mouse panel protocols.\u003c/p\u003e \u003cp\u003eTo determine the concentrations of each hormone in the samples, we utilized standard curves fitted using a four-parameter logistic curve-fitting algorithm (O\u0026rsquo;Connell et al. 1993). Hormone levels were expressed in pg/mL. To assess the statistical variation among replicates, we utilized the coefficient of variation (%CV). The mean values were calculated only when the %CV was below 10%.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Data analysis\u003c/h2\u003e \u003cp\u003eAll data and statistical analysis were performed using R (R Core Team 2021) and considering brain regions as biologically independent measures, and thus tested separately. BW and food intake changes during treatment were assessed building corresponding linear mixed effects (lme) models, with time (pre, 24h, 48h and 72h), diet (HFHS or LFLS), sex (male or female) and type of treatment (vehicle or celastrol) as main predictors, and all interactions considered. This was achieved using the \u003cem\u003elmer\u003c/em\u003e function of the lme4 package (Bates et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), with subsequent Type II Wald chisquare tests tests and post-hoc contrasts using \u003cem\u003eemmeans\u003c/em\u003e (Lenth 2017), and corrected for multiple comparisons by false discovery rate adjustment (FDR). MRI and immunofluorescence variables were analyzed in two steps. First, values from the pre-treatment were checked for corresponding BW, \u003cem\u003ediet\u003c/em\u003e, \u003cem\u003esex\u003c/em\u003e and \u003cem\u003ediet:sex\u003c/em\u003e interaction dependance, using a linear model with the \u003cem\u003elm\u003c/em\u003e function, followed Type-II or Type-III ANOVA tests (for non-significant or significant interactions, respectively). Next, for those MRI or immunofluorescence image pre-treatment parameters that showed significant effects of either \u003cem\u003ediet\u003c/em\u003e or \u003cem\u003ediet:sex\u003c/em\u003e, further tests were performed on the \u003cem\u003epost-treatment\u003c/em\u003e condition, adding to the linear model the type of treatment, the double \u003cem\u003e(treatment: diet\u003c/em\u003e, \u003cem\u003etreatment:sex\u003c/em\u003e) and triple (\u003cem\u003etreatment:diet:sex\u003c/em\u003e) interactions as predictors. When significant interactions were found, post-hoc differences at each level with FDR correction were performed. In the case of the Hyp in the immunofluorescence images, an effect of \u003cem\u003enuclei subregion\u003c/em\u003e was added to the linear model, since hypothalamic nuclei are expected to potentially have different roles in appetite control and energy balance (Timper and Br\u0026uuml;ning \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and thus a lme model was tested using the \u003cem\u003elmer\u003c/em\u003e function.\u003c/p\u003e \u003cp\u003eHRMAS data was subjected to a random forest (RF) analysis using the \u003cem\u003erandomForest\u003c/em\u003e function from the randomForest package (Liaw and Wiener \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), to discriminate groups based on the metabolite profile, with the \u003cem\u003etype\u003c/em\u003e of treatment as the outcome, and all metabolite ratios to PCr\u0026thinsp;+\u0026thinsp;Cr as predictors (including all regions and TEs). Briefly, RF is a machine learning method that uses decision trees created by using bootstrap samples of a training data, and a random feature selection in tree induction (Breiman \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). The procedure included five different steps, including (i): a repeated 5-fold cross-validation (CV) scheme, where full dataset was randomly partitioned into five approximately equal folds. Four folds (80% of the data) were used to train the model, and the remaining fold (20%) served as the validation set; (ii): all possible training-testing combinations within the 5-folds were subjected to RF analysis. Using the \u003cem\u003etuneRF\u003c/em\u003e function from the randomForest package, each \u003cem\u003emtry\u003c/em\u003e (number of variables randomly sampled at each split) was tuned to select the \u003cem\u003emtry\u003c/em\u003e value that minimized the out of bag (OOB) error. For each \u003cem\u003emtry\u003c/em\u003e, a forest with 500 trees was grown, and the configuration associated with the lowest OOB error rate was retained, resulting in a an optimum \u003cem\u003emtry\u003c/em\u003e\u0026thinsp;=\u0026thinsp;15. The final RF for that repetition was then refitted on the entire dataset using the selected \u003cem\u003emtry\u003c/em\u003e and 500 trees, and the OOB error from this model was recorded; (iii): this whole procedure was repeated 20 times with different initial random partitions, yielding 100 train\u0026ndash;validation cycles in total. Across the 20 repetitions we obtained a distribution of OOB error estimates, from which the \u003cem\u003emean OOB error\u003c/em\u003e and its \u003cem\u003evariability\u003c/em\u003e was extracted as measures of predictive performance and stability, resulting in OBB\u003csub\u003emean\u003c/sub\u003e \u0026plusmn; SD of 0.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01 of all iterations; (iv): for each of the 20 final RF models, we extracted the \u003cem\u003eMean Decrease in Gini index\u003c/em\u003e as a global indicator of how much each metabolite contributed to node purity and classification accuracy. This produced 20 separate \u003cem\u003eimportance profiles\u003c/em\u003e. To identify robust biomarkers, we analyzed the stability of the importance rankings across repetitions, and metabolites that were ranked in the top 10 in at least 75% of the repetitions (i.e. in \u0026ge;\u0026thinsp;15 out of 20 runs) were considered \u003cem\u003estable important metabolites\u003c/em\u003e, that were retained further analysis; and (v): the final RF model, using the optimized \u003cem\u003emtry\u003c/em\u003e, was tested in a 70/30 (training/testing) split, and confusion matrix assessed. Finally, ANOVA of the stable important metabolites were performed to assess the classical statistical significance of type of treatment, and the effects on those metabolites of diet, sex, and corresponding interactions, with FDR corrections for post-hoc multiple comparisons.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Physiological changes\u003c/h2\u003e \u003cp\u003eAfter 20 weeks of diet diversification, male and female animals fed with HFHS diet revealed higher BW and food intake values, as compared to LFLS, according to an obese phenotype (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, \u0026ldquo;Pre-dose\u0026rdquo; values). BW measurements during treatment evolved differently depending on the type of diet consumed, treatment received and animal\u0026rsquo;s sex, as reported by results from the lme model and subsequent Type III Wald chisquare tests, with significant \u003cem\u003etime:diet:treatment:sex\u003c/em\u003e interaction on BW (χ\u0026thinsp;=\u0026thinsp;20.3, df\u0026thinsp;=\u0026thinsp;3, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Particularly, animals administered with celastrol showed BW decay, with significantly reduced values from 24h (all males and HFHS females) or from 48h (LFLS females) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u003cb\u003etop panels\u003c/b\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Animals receiving vehicle exhibited either no significant BW alterations or small increases (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Average intake per cage during the days of i.p. administration decreased very remarkably in all celastrol-batches, and vehicle administered showed either no changes (LFLS females) or delayed and fewer decreases, significant only for HFHS females from 48h, and for males LFLS at 72h (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, \u003cb\u003ebottom panels\u003c/b\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePost-hoc tests after lme analysis of BW and intake changes with time. P-values are FDR adjusted.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBW and Intake post-hoc tests\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eContrast\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBW DF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBW T-ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBW P-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIntake DF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eIntake T-ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eIntake P-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eHFHS Vehicle Females\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24h- Pre Dose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48h- Pre Dose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72h\u0026ndash; Pre Dose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0083\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eHFHS Celastrol Females\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24h- Pre Dose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48h- Pre Dose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-6.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72h\u0026ndash; Pre Dose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-9.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-10.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eLFLS Vehicle Females\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24h- Pre Dose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48h- Pre Dose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72h\u0026ndash; Pre Dose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eLFLS Celastrol Females\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24h- Pre Dose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48h- Pre Dose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-4.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72h\u0026ndash; Pre Dose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eHFHS Vehicle Males\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24h- Pre Dose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0069\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48h- Pre Dose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-4.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72h\u0026ndash; Pre Dose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eHFHS Celastrol Males\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24h- Pre Dose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48h- Pre Dose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-6.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72h\u0026ndash; Pre Dose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-11.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-11.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eLFLS Vehicle Males\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24h- Pre Dose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48h- Pre Dose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0166\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72h\u0026ndash; Pre Dose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-4.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eLFLS Celastrol Males\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24h- Pre Dose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48h- Pre Dose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-9.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72h\u0026ndash; Pre Dose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-13.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-9.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 HFHS diet and celastrol effects on brain MRI parameters\u003c/h2\u003e \u003cp\u003eAnalysis of the brain DTI parameters of HFHS or LFLS mice, namely MD, AD, RD, FA and MTR, revealed significantly higher FA on obese mice, both in the Hyp (F\u0026thinsp;=\u0026thinsp;5.2, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and Hipp (F\u0026thinsp;=\u0026thinsp;6.1, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and lower RD in the hyp (F\u0026thinsp;=\u0026thinsp;4.1, p\u0026thinsp;=\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). After treatment, hypothalamic FA remained significantly higher on HFHS male mice when treated with vehicle, and not celastrol (significant \u003cem\u003etreatment:diet:sex\u003c/em\u003e interaction, df\u0026thinsp;=\u0026thinsp;1, F\u0026thinsp;=\u0026thinsp;4.1, p\u0026thinsp;=\u0026thinsp;0.05) and post-hoc significance on vehicle male HFHS Vs LFLS (df\u0026thinsp;=\u0026thinsp;23, t = -3.2, p\u003csub\u003eadj\u003c/sub\u003e\u0026lt;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD, \u003cb\u003eleft panel\u003c/b\u003e), while RD showed no further differences between dies, regardless of treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD, \u003cb\u003ebottom right\u003c/b\u003e). In the hippocampus, no treatment effect was reported, and FA values continued significantly elevated on obese mice (F\u0026thinsp;=\u0026thinsp;4.3, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD, \u003cb\u003etop right\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3 HFHS diet and celastrol effects on astrocytes and microglia\u003c/h2\u003e \u003cp\u003eThe immunofluorescence images of the brains of diet-induced obese mice showed enlarged and more numerous microglia and astrocytes, as compared to non-obese animals (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Quantification and statistical testing of the number of microglial cells and its % of occupied area revealed significant increases with HFHS diet in all regions (χ\u003csub\u003ehyp\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;3.6, p\u003csub\u003ehyp\u003c/sub\u003e=0.05, F\u003csub\u003ehipp\u003c/sub\u003e=8.3, p\u003csub\u003ehipp\u003c/sub\u003e \u0026lt;0.05, F\u003csub\u003eILA\u003c/sub\u003e=12.8, p\u003csub\u003eILA\u003c/sub\u003e\u0026lt;0.05, F\u003csub\u003eNAc\u003c/sub\u003e=25.8, p\u003csub\u003eNAc\u003c/sub\u003e\u0026lt;0.01 for % occupied area, and χ\u003csub\u003ehyp\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;3.9, p\u003csub\u003ehyp\u003c/sub\u003e\u0026lt;0.05, F\u003csub\u003ehipp\u003c/sub\u003e=7.9, p\u003csub\u003ehipp\u003c/sub\u003e\u0026lt;0.05, F\u003csub\u003eIL\u003c/sub\u003e =22.9, p\u003csub\u003eILA\u003c/sub\u003e\u0026lt;0.01, F\u003csub\u003eNAc\u003c/sub\u003e=22.4, p\u003csub\u003eNAc\u003c/sub\u003e\u0026lt;0.01 for its number) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). On astrocytes, its area and number were found to be elevated in HFHS mice in the Hyp, Hipp and NAc -not in ILA- (χ\u003csub\u003ehyp\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;10.0, p\u003csub\u003ehyp\u003c/sub\u003e\u0026lt;0.005, F\u003csub\u003ehip\u003c/sub\u003e =8.2, p\u003csub\u003ehipp\u003c/sub\u003e\u0026lt;0.05, F\u003csub\u003eNAc\u003c/sub\u003e =14.7, p\u003csub\u003eNAc\u003c/sub\u003e \u0026lt;0.05 for % of area, and χ\u003csub\u003ehyp\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;7.4, p\u003csub\u003ehyp\u003c/sub\u003e \u0026lt;0.01, F\u003csub\u003ehipp\u003c/sub\u003e=15.9, p\u003csub\u003ehipp\u003c/sub\u003e\u0026lt;0.05, F\u003csub\u003eNAc\u003c/sub\u003e =17.3, p\u003csub\u003eNAc\u003c/sub\u003e \u0026lt;0.05 for astrocytic number) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). Treatment with celastrol induced a general decrease in the number and area of cells on HFHS mice quantified in the Hyp images, as compared to vehicle-only mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF). Tests revealed that such decrease in number and area occupied was significant in the microglial cells of HFHS mice treated with celastrol, as compared to HFHS treated with vehicle, in the Hyp (\u003cem\u003ediet:treatment\u003c/em\u003e χ\u0026thinsp;=\u0026thinsp;4.8, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, post-hoc HFHS celastrol Vs HFHS vehicle df\u0026thinsp;=\u0026thinsp;7.4, t = -3.5 and p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for % of occupied area by microglia, and \u003cem\u003ediet:treatment\u003c/em\u003e χ\u0026thinsp;=\u0026thinsp;4.3, p\u003csub\u003ehyp\u003c/sub\u003e \u0026lt;0.05, post-hoc HFHS celastrol Vs HFHS vehicle df\u0026thinsp;=\u0026thinsp;7.3, t = -3.6, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for its number) and Hipp (\u003cem\u003ediet:treatment\u003c/em\u003e F\u0026thinsp;=\u0026thinsp;6.3, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, post-hoc HFHS celastrol Vs HFHS vehicle df\u0026thinsp;=\u0026thinsp;8, t = -3.3 and p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for % of occupied area by microglia, and \u003cem\u003ediet:treatment\u003c/em\u003e F\u0026thinsp;=\u0026thinsp;6.7, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, post-hoc HFHS celastrol Vs HFHS vehicle df\u0026thinsp;=\u0026thinsp;8, t = -3.4, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for its number) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eH). Astrocytes quantification post-treatment revealed less remarkable effects of celastrol than in microglia, with both number and occupied area in the Hyp being still higher in HFHS mice, as compared to LFLS (χ\u0026thinsp;=\u0026thinsp;11.7, p\u0026thinsp;\u0026lt;\u0026thinsp;0.005 for % of area occupied by astrocytes, and χ\u003csub\u003ehyp\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;9.4, p\u003csub\u003ehyp\u003c/sub\u003e \u0026lt; 0.005 for its number). In this region, treatment with celastrol to HFHS mice resulted in diminished area occupied by astrocytes, as compared to those treated with vehicle, but tests did not reach statistical significance (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eI-J).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.3 HFHS diet and celastrol effects on blood plasma\u003c/h2\u003e \u003cp\u003eAnalysis of blood plasma after 20 weeks of diet revealed that mice under HFHS diet had lower ghrelin and higher leptin levels, in comparison to LFLS group (df\u0026thinsp;=\u0026thinsp;1, F\u003csub\u003eghrelin\u003c/sub\u003e = 5.7, p\u003csub\u003eghrelin\u003c/sub\u003e\u0026lt; 0.05, F\u003csub\u003eleptin\u003c/sub\u003e = 5.8, p\u003csub\u003eleptin\u003c/sub\u003e \u0026lt; 0.05) Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). The rest of the hormones measured showed high variability between animals, and no other relevant effects between diet batches were found (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). To evaluate the effects of celastrol on serum levels, we compared samples from HFHS mice treated with either celastrol or vehicle. The analysis revealed a significant interaction \u003cem\u003eSex:Treatment\u003c/em\u003e (F\u0026thinsp;=\u0026thinsp;27.0, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) revealing that changes were occurring in males (\u003cem\u003epost-hoc\u003c/em\u003e Celastrol VsVehicle Males df\u0026thinsp;=\u0026thinsp;12, t\u0026thinsp;=\u0026thinsp;7.6, p\u003csub\u003eadj\u003c/sub\u003e \u0026lt; 0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePlasma values after either HFHS or LFLS diet consumption, and HFHS values after treatment with celastrol or vehicle.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConcentration (pg/mL)/Diet and condition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHFHS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLFLS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHFHS-vehicle\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHFHS-celastrol\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGhrelin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e22.15\u0026thinsp;\u0026plusmn;\u0026thinsp;10.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e38.79\u0026thinsp;\u0026plusmn;\u0026thinsp;20.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e23.70\u0026thinsp;\u0026plusmn;\u0026thinsp;13.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e22.30\u0026thinsp;\u0026plusmn;\u0026thinsp;9.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlucagon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e54.31\u0026thinsp;\u0026plusmn;\u0026thinsp;56.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e83.67\u0026thinsp;\u0026plusmn;\u0026thinsp;36.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e38.95\u0026thinsp;\u0026plusmn;\u0026thinsp;31.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e21.96\u0026thinsp;\u0026plusmn;\u0026thinsp;6.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsulin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e5569.9\u0026thinsp;\u0026plusmn;\u0026thinsp;3065.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e3322.02\u0026thinsp;\u0026plusmn;\u0026thinsp;3162.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e2833.15\u0026thinsp;\u0026plusmn;\u0026thinsp;2608.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e1542.38\u0026thinsp;\u0026plusmn;\u0026thinsp;1156.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePYY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e123.66\u0026thinsp;\u0026plusmn;\u0026thinsp;39.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e134.66\u0026thinsp;\u0026plusmn;\u0026thinsp;44.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e97.86\u0026thinsp;\u0026plusmn;\u0026thinsp;63.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e131.94\u0026thinsp;\u0026plusmn;\u0026thinsp;43.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Cerebral metabolic changes of celastrol by HRMAS\u003c/h2\u003e \u003cp\u003eHRMAS acquisition of the cerebral samples resulted in very good quality spectra in all regions and animals, with a signal-to-noise ratio ranging from 17 to 26 and full width at half maximum linewidths around 2.5 Hz (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Subsequent fitting of the spectra to the metabolic database yielded a good adjustment, and LCModel provided the relative concentration of metabolites for each sample. Amongst the derived \u003cem\u003estable important metabolites\u003c/em\u003e delivered by the RF analysis, we found metabolites from all regions, with the sum of Cho\u0026thinsp;+\u0026thinsp;GPC\u0026thinsp;+\u0026thinsp;PCh in the Hyp, and Tau in the Hipp and ILA appearing in the 20 repetitions as amongst the top-10 metabolites having the higher GiniInx, and GSH in the Hyp, Cho\u0026thinsp;+\u0026thinsp;GPC\u0026thinsp;+\u0026thinsp;PCh in the ILA, Glc in NAc and Tau in the Hyp appearing in between 15 and 19 repetitions. A representative \u003cem\u003emean decreases in Gini index\u003c/em\u003e plot of metabolites is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB. The final RF was applied to a 70/30 training/testing partition, yielding a confusion matrix predicting 6/8 subjects to the celastrol group (2/6 incorrectly assigned to vehicle), and 4/4 from the vehicle group correctly allocated (0.83 accuracy).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eANOVA tests of the stable important metabolites yielded significant effects of either type, or interactions \u003cem\u003etype:diet\u003c/em\u003e, \u003cem\u003etype:sex\u003c/em\u003e, in all metabolites except on hypothalamic Tau (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Notably, Tau ratios to PCr\u0026thinsp;+\u0026thinsp;Cr were reduced in the celastrol-treated group compared to the vehicle group in both the ILA (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC, Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Similarly, Cho\u0026thinsp;+\u0026thinsp;GPC\u0026thinsp;+\u0026thinsp;PCh ratio to PCr\u0026thinsp;+\u0026thinsp;Cr was significantly elevated in the ILA and Hyp (both TE) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD and Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Additionally, the celastrol-treated male mice showed decreased Glc ratios than the vehicle-administered male animals (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). HFHS-celastrol group exhibited lower GSH/PCr\u0026thinsp;+\u0026thinsp;Cr than the HFHS-vehicle animals, with values after treatment reaching similar levels of LFLS (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMain effects and interactions of type of treatment, sex and diet, on stable important metabolites TE, comparing the vehicle and celastrol groups. C: Tau. D: Cho\u0026thinsp;+\u0026thinsp;GPC\u0026thinsp;+\u0026thinsp;PCh. E: Glc, F: GSH. (*padj\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ** padj\u0026thinsp;\u0026lt;\u0026thinsp;0.01, *** padj\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEffect/Metabolite\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eCho\u0026thinsp;+\u0026thinsp;GPC+PCh Hyp\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eTau ILA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTau Hipp\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGSH Hyp\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCho\u0026thinsp;+\u0026thinsp;GPC+ PCh ILA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eGlc NAc\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTE\u0026thinsp;=\u0026thinsp;144\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTE\u0026thinsp;=\u0026thinsp;36\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTE\u0026thinsp;=\u0026thinsp;144\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTE\u0026thinsp;=\u0026thinsp;36\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTE\u0026thinsp;=\u0026thinsp;36\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTE\u0026thinsp;=\u0026thinsp;144\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTE\u0026thinsp;=\u0026thinsp;144\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eTE\u0026thinsp;=\u0026thinsp;36\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eType\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;13.7, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;5.3, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;23.0, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;13.6, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eType:Sex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;8.6, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;4.1, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePost-hoc Celastrol Vs Vehicle males\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003edf\u0026thinsp;=\u0026thinsp;37, t =-5, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003edf\u0026thinsp;=\u0026thinsp;37, t =-2.9, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eType:Diet\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;4.2, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;4.9, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePost-hoc Celastrol Vs Vehicle\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003edf\u0026thinsp;=\u0026thinsp;37, t =-4.8, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 (LFLS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003edf\u0026thinsp;=\u0026thinsp;37, t =-3.8, p\u0026thinsp;=\u0026thinsp;0.001 (HFHS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this work, we have characterized the effects of celastrol as an anti-obesity and anti-inflammatory agent on an animal model of diet-induced obesity. First, and prior to any treatment, we characterized obesity development by comparing BW, food consumption, \u003cem\u003ein vivo\u003c/em\u003e MRI markers of neuroinflammation, \u003cem\u003eex vivo\u003c/em\u003e indicators of astrogliosis and microgliosis, systemic blood hormonal changes and cerebral metabolic profiles between obese and non-obese animals. Results indicate that the obese phenotype is characterized by an inflammatory state in the brain, which can be quantified \u003cem\u003ein vivo\u003c/em\u003e by increased FA and reduced RD, and \u003cem\u003eex vivo\u003c/em\u003e by microglial and astrocyte augmented number and reactive shapes. Next, we administered either treatment with celastrol or its vehicle (DMSO) and observed that the obesity-induced alterations could be modified by the treatment, including a BW decrease, decreases on the \u003cem\u003ein vivo\u003c/em\u003e FA values, particularly in the Hyp, and microglial reduction of both number and area, revealing an anti-inflammatory action mechanism of the tested compound. Additionally, we could characterize the cerebral metabolic changes induced by celastrol, which suggest the regularization of osmolytes, and measure systemic blood changes that reveal major changes on an anti-inflammatory cytokine.\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.1 HFHS diet increases FA regionally and celastrol reverts changes\u003c/h2\u003e \u003cp\u003eAmong MRI methods, DTI has been instrumental in detecting microstructural changes in both grey and white matter across various neurodegenerative and pathological conditions, in humans as well as in animal models. The biological interpretation of changes in diffusion coefficients requires, however, several considerations, including the tissue composition of the region investigated. Indeed, alterations in white or grey matter induce distinct diffusion patterns, and, vice versa, a comparable variation of a diffusion coefficient may arise from diverse biological responses. For instance, as white matter is composed mainly of neuronal bundles, which favor diffusion along its parallel direction, an integrity loss of the fibers would yield decreased FA values, as it has been reported in the corpus callosum of subjects with obesity (Daoust et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). At the same time, in grey matter, gliosis \u0026ndash;a state with increased cellularity and larger and irregular cellular bodies- has been related to increased FA (Lizarbe et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003eb). In subcortical areas, where grey matter is also predominant, another study found decreased MD, AD and FA with body mass index in young patients with obesity, but increased FA in older patients (Tweedale et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In their work, authors interpreted the decreased diffusivity coefficients as potentially reflecting gliosis, and proposed to link the higher FA to an anisotropic inflammatory process, as it was positively associated with C-reactive protein values. In our study, we report that animals fed with a HFHS diet show increased FA and decreased RD in grey matter regions, significantly in the Hyp and Hipp, which agrees well with increases in cellularity and a gliosis-associated anisotropic change of cellular shape, and, thus, inflammation, in line with the previous studies. Notably, our MRI \u003cem\u003ein vivo\u003c/em\u003e results are supported by immunofluorescence images showing microgliosis and astrocytosis on HFHS brains. Moreover, such cellular changes were assessed not only visually, but also as statistically relevant increased astrocytic and microglial surfaces, increased number of astrocytes and microglia. Consequently, and supported by our \u003cem\u003eex vivo\u003c/em\u003e images, we can infer that the HFHS-induced increased FA and reduced RD, are reflecting a diffusion in the extracellular space that is restricted by the anisotropic growth of the cellular bodies composing the Hyp and Hipp.\u003c/p\u003e \u003cp\u003eAfter the anti-obesity treatment, we could find, in the Hyp, that differences between FA of HFHS or LFLS mice where only maintained in male vehicle-administered animals; in other words, that animals treated with celastrol showed no-longer-increased FA values in the Hyp. Besides, in this region, after treatment, RD was no longer different between diet groups, but no specific treatment-effect could be robustly statistically reported. In the Hipp, on the contrary, FA remained elevated in HFHS after 3 days of administration, as compared to LFLS, independently of the type of treatment. Results in the Hyp are supported by the immunofluorescence images, where we found significantly lower number and area occupied by microglia, in animals treated with celastrol, as compared to mice that received the vehicle-only solution. Thus, results are consistent with an anti-inflammatory action of celastrol, specific to the Hyp, that can be quantified \u003cem\u003ein vivo\u003c/em\u003e by an FA reduction, and supported by immunofluorescence images.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Microglia reacts first\u003c/h2\u003e \u003cp\u003eQuantification of immunofluorescence images from IBA1 and GFAP markers showed that HFHS consumption induced very remarkable effects in both microglial cells and astrocytes, with the changes of microglia being significant in the four regions investigated, and the effects on astrocytes absent only in the ILA. In both cell types, caloric diet resulted in augmented number and area occupied by the cells. This is on agreement with previous results of our group (Lizarbe et al. 2019) and others (Garc\u0026iacute;a-C\u0026aacute;ceres et al. 2019b), and has been consistently linked to a the diet-induced inflammatory process (Valdearcos et al. 2017b). Indeed, neuroinflammation is characterized by cellular proliferation and by an enhanced proportion of type M1 microglia, which have amoeboid shapes, and thus bigger cellular bodies that yield higher % of occupied area values, than the non-pro-inflammatory ramified M2-type (Tam and Ma \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). After treatment, HFHS animals administered with celastrol depicted a robust response in microglial cells in the Hyp and Hipp, and both the area occupied by microglia and their numbers were comparable to those from LFLS mice, and significantly lower than vehicle-treated HFHS. This celastrol-induced reversal is in line with the response to other anti-obesity treatments, which withdraw the pathological changes of microglial activation (Berkseth et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Marinho et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2026\u003c/span\u003e; Rong et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Particularly, the reduced % of area occupied by cells is on agreement with a switch from M1 -amoeboid- to M2 -ramified- microglial types, similar to findings after other pharmacological interventions (Wang et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Our results indicating that the number of cells also decreased may suggest that treatment induced tissue reorganization and normalization. Studies assessing proliferation of microglia after stroke demonstrated that, after an insult, microglial cells tend to migrate to the lesion focus, adopting hypertrophic shapes, while that during recovery tend to disaggregate and recover more ramified and random shapes (Kikhia et al. 2025). Thus, the reduction of Iba1\u0026thinsp;+\u0026thinsp;microglial number that we detected after celastrol treatment could reflect both a reduction in activated subpopulations and a spatial redistribution with a phenotype switch to a less inflamed state, that is more difficult to detect with our counting strategies, rather than a global decrease in microglial total number. Finally, the fact that changes were quantifiable in the Hyp and Hipp reinforce the hypothesis of both areas being are primary responders of high-fat diet consumption, and treatment (Plantera et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAstrocytes were less affected by treatment, and GFAP-derived parameters of number and % of occupied area remained different between diet groups, regardless of the treatment/vehicle received. In the images, we could see how celastrol specific treatment reduced both the area and number of astrocytes in the Hyp, but such diminish did not reach the statistical threshold. Hypothalamic astrocytes are known to become reactive in DIO, but most studies find that, from one hand, microglial activation is earlier and more robustly quantifiable than astrocytosis (Jais and Br\u0026uuml;ning \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and, from the other, that microglial depletion or inhibition reduces subsequent astrogliosis, supporting the idea that astrocyte activation is at least partly downstream of microglial signaling (Andr\u0026eacute; et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Our results support the fact that microglia react first to celastrol treatment, prompting an anti-inflammatory response that is only partially extended to astrocytes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Effects of celastrol on brain metabolism\u003c/h2\u003e \u003cp\u003eThe effects of celastrol in cerebral metabolism were assessed by HRMAS, by comparing the neurochemical profile from celastrol-treated or vehicle-administered brain region samples. A random forest analysis revealed that the sum of the choline-containing compounds, Cho\u0026thinsp;+\u0026thinsp;GPC+PCh, and Tau were the metabolites that best classified the animals depending on their treatment, in the Hyp, ILA and Hipp, to a lesser extent, with the two metabolites being reduced in celastrol-treated mice.\u003c/p\u003e \u003cp\u003eCholine-containing compounds are known to be MRS markers of membrane turnover and cellular proliferation (Duarte et al. 2012). In the context of obesity, increased Cho concentrations are thought to be linked to chronic low-grade neuroinflammation and glial activation, consistent with existence of inflammatory signaling within the central nervous system (Vuković et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). The fact that Cho compounds are within the metabolites that best classify between treated and non-treated animals, and that their value is significantly lower in the celastrol group, agrees well with a decreased inflammatory state of the treated mice, as compared to non-treated. On the other hand, previous works on DIO have quantified increases of Tau in the Hyp, Hipp and cortex, and such rise has been proposed to represent a compensatory neuroprotective response to metabolic stress (Duarte et al. 2012; Lizarbe et al. 2019). Moreover, Tau supplementation is known to reduce body weight on HFD animals (Figueroa et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), via anti-inflammatory effects (Ahmed et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and it has been shown to accumulate in the Hipp of DIO animals acting as a counteracting beneficial response to metabolic stress (Garcia-Serrano et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Thus, the lower values reported here in treated animals are consistent with a celastrol-induced anti-inflammatory response that does no further need such metabolic stress-induced elevation of Tau levels. Other than Tau and the Cho compounds, GSH and Glc in certain brain regions were also amongst the top-10 metabolites with higher classification indexes. GSH is the main small‑molecule antioxidant in brain cells, and impairment of its function is linked as the result of neurological diseases, or during aging (Iskusnykh et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Astrocytes contain substantially higher GSH than neurons, and changes in cerebral GSH in some human diseases are thought to be, at least in part, the consequence of alterations of astrocytic GSH, and/or of changes in the GSH metabolism of astrocytes (Dringen and Arend \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Indeed, during inflammation, astrocytes experience increased oxidative and inflammatory load, which typically drives upregulation of antioxidant systems, including the GSH pathway, to protect both themselves and neighboring neurons. In this sense, our results showing higher GSH levels in the hypothalamus un HFHS mice treated with vehicle, as compared to HFHS celastrol animals, which reach similar levels than LFLS mice, are on agreement with an obesity-induced GSH increase that can be reverted with celastrol. Glc, on the other hand, as measured in the NAc, appeared also as a stable important metabolite distinguishing between classes, depicting significantly lower values in treated male mice, as compared to non-treated. A decrease in Glc values after celastrol administration fits well with a switch from an obesity-associated hypermetabolic brain towards a more normalized glucose handling, and is on agreement with other anti-obesity treatments (Sewaybricker and Schur \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Systemic effects of celastrol\u003c/h2\u003e \u003cp\u003eSerum analysis of blood hormones from obese and non-obese mice showed a \u003cem\u003ediet\u003c/em\u003e effect on the concentrations of ghrelin and leptin (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) that matched the expected hypothesis -decreased ghrelin during HFHS (Briggs et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), increased leptin-(Frederich et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). In HSHS mice treated with celastrol, IL-6 retained the p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 threshold, with obese male animals treated much larger values than vehicle-only. IL-6 is a cytokine that plays a pivotal role in inflammatory responses, with diverse effects on regulating the immune response and metabolism via different signaling pathways, and it is known to particularly play an anti-inflammatory role in DIO related inflammatory diseases (Yang et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The increased values reported after treatment suggest that the BW decreases effects of celastrol administration are mediated by anti-inflammatory effects of IL-6. Notably, these effects could only be reported in male animals. Interestingly, this agrees well with males showing larger BW and intake decreases with celastrol than females, and with the hyp FA recovery on this specific group. Future studies, however, should consider potentially increasing sample sizes, as well as controlling feeding conditions prior to the blood extraction, to increase the statistical power of these tests and confirm the sexual-differences reported in the IL-6 measurements.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eBy using a wide range of methodological assessments, we have covered the physiological, systemic and central effects of celastrol administration to DIO and lean animals and can conclude that it serves as an anti-obesity drug via anti-inflammatory mechanisms in the hypothalamus that can be detected \u003cem\u003ein vivo\u003c/em\u003e by DTI, and \u003cem\u003eex vivo\u003c/em\u003e by histological, hormonal and cerebral metabolic changes. We believe that our results may prompt further interest in using celastrol as an anti-inflammatory agent during obesity and other pathologies involving inflammation, and that DTI methods could be used as biomarkers of its action.\u003c/p\u003e \u003cp\u003e \u003cem\u003eLimitations\u003c/em\u003e \u003c/p\u003e \u003cp\u003eOur study investigates the anti-inflammatory and anti-obesity effects of celastrol during an acute administration. Interestingly, with only three doses, very high BW decreases were achieved, and indicators of both central and systemic anti-inflammatory and metabolic changes were reported. Future studies should assess a longer administration period, like treatments on human population, potentially using lower dosages, and determine the corresponding effects.\u003c/p\u003e \u003cp\u003eIn our \u003cem\u003ein vivo\u003c/em\u003e MRI study, even DTI reported consistent changes with \u003cem\u003ediet\u003c/em\u003e and \u003cem\u003etreatment\u003c/em\u003e, effect sizes of the corresponding parameters were lower than those observed in the \u003cem\u003eex vivo\u003c/em\u003e images by immunofluorescence of Iba1 and GFAP. Moreover, DTI could only quantify treatment response in the Hyp, while immunofluorescence images of Iba1, the microglial marker, detected changes both in the Hyp and Hipp. From one hand, and in order to provide more specificity regarding the compartmentalization of cellular changes, we suspect that the implementation of microstructural multicompartment tissue biophysical models (Garcia-Hernandez et al.) could likely detect stronger effects on the corresponding diffusion parameters, and future studies should address this with more advanced acquisition strategies and biophysical models. On the other hand, however, this may also be reflecting stronger effects on the Hyp than in the Hipp, which is consistent with previous reports, which propose hypothalamic microglia as first responders for energy balance, while hippocampal microglia mediate longer-term cognitive consequences of metabolic stress (Plantera et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study, we could not detect any changes on MTR values, neither with the \u003cem\u003ediet\u003c/em\u003e nor with \u003cem\u003etreatment\u003c/em\u003e effects, although previous studies did (Campillo et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). What the mentioned study reported, however, were MTR values that increased in a within-subject manner during obesity development, changes that lean animals did not exhibit, but no direct comparison between obese and non-obese group was assessed. In this sense, in the present work, potential between-subjected variability may have precluded the finding of relevant differences between groups.\u003c/p\u003e \u003cp\u003eAnother limitation of our study involves the high variability of the serum hormone results, which may have precluded the finding of some expected relevant effects, widely described in obesity. For example, values of the proinflammatory cytokine TNFα, which are known to increase during obesity (Makki et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), showed, in our study, high variability within animal groups, and no robust trends could be found. Indeed, many of the hormones studied here are dependent on feeding conditions, circadian rhythms and metabolic context (Lages et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2026\u003c/span\u003e), in general. Future studies aiming at disentangling the plasma inflammatory profile of obesity and anti-obesity treatment could improve such assessment by standardizing the measurements and performing blood extracting after a fasting period.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAxial diffusivity\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAla\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAlanine\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eANOVA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAnalysis of variance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eARC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eArcuate nucleus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eASCM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAdaptive soft coefficient matching\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAsp\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAspartate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAv\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNumber of averages\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eb\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDiffusion weighting factor\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBW\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBody weight\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCho\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCholine\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCr\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCreatine\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCSF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCerebrospinal fluid\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCoefficient of variation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDIO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDiet\u0026ndash;induced obesity\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDMSO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDimethyl sulfoxide\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDTI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDiffusion tensor imaging\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003edMRI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDiffusion magnetic resonance imaging\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eELISA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEnzyme\u0026ndash;linked immunosorbent assay\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eER\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEndoplasmic reticulum\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFractional anisotropy\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFDR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFalse discovery rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFOV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eField of view\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGABA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGamma\u0026ndash;aminobutyric acid\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGFAP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGlial fibrillary acidic protein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGLP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e1\u0026ndash;Glucagon\u0026ndash;like peptide\u0026ndash;1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGlc\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGlucose\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGln\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGlutamine\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGlu\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGlutamate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGly\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGlycine\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGPC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGlycerylphosphorylcholine\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGSH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGlutathione\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHigh\u0026ndash;fat\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHFD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHigh\u0026ndash;fat diet\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHFHS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHigh\u0026ndash;fat high\u0026ndash;sugar\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHipp\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHippocampus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHRMAS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHigh\u0026ndash;resolution magic angle spinning\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026sup1;H HRMAS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eProton high\u0026ndash;resolution magic angle spinning\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHyp\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHypothalamus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIBA1\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIonized calcium\u0026ndash;binding adaptor molecule 1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ei.p.\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIntraperitoneal\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e6\u0026ndash;Interleukin 6\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eILA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInfralimbic area\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLac\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLactate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLeu\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLeucine\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLFLS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLow\u0026ndash;fat low\u0026ndash;sugar\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLip\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLipids\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003elme\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLinear mixed\u0026ndash;effects model\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMesocorticolimbic complex\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMean diffusivity\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003emI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMyo\u0026ndash;inositol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMacromolecules\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMRI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMagnetic resonance imaging\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMRS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMagnetic resonance spectroscopy\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMTR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMagnetization transfer ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMTI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMagnetization transfer imaging\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNAAG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eN\u0026ndash;acetylaspartylglutamate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNAc\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNucleus accumbens\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNAA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eN\u0026ndash;acetyl\u0026ndash;aspartate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOCT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOptimal cutting temperature compound\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOOB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOut\u0026ndash;of\u0026ndash;bag\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePBS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePhosphate\u0026ndash;buffered saline\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCh\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePhosphocholine\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCr\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePhosphocreatine\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePFA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eParaformaldehyde\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePhe\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePhenylalanine\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePVN\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eParaventricular nucleus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePYY\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePeptide YY\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRadial diffusivity\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eReward centers\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRandom forest\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRegion of interest\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStandard deviation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSFAs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSaturated fatty acids\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTau\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTaurine\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEcho time\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTNFα\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTumor necrosis factor alpha\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRepetition time\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eVMN\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eVentromedial nucleus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAll animal handling protocols included in this work were performed by specialized personnel at our institute\u0026rsquo;s animal facility (Reg. No. ES280790000188) and approved by the ethical committee of the Institute of Biomedical Research Sols-Morreale, CSIC and the Community of Madrid, complying with national (R.D. 53/2013) and European Community guidelines (2010/63/UE) and with the ARRIVE guidelines (ethics approval number PROEX 288/42).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAuthors and their institutions consent the publication of this work.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are not publicly available due the need for a formal data sharing agreement but are available from the corresponding author on reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the \u0026ldquo;Proyectos de Generaci\u0026oacute;n de Conocimiento 2021\u0026rdquo; [Knowledge Generation Projects 2021] call, funded by the Spanish Ministry of Science and Innovation (MCIN/AEI/10.13039/501100011033) and by the European Union NextGeneration EU/PRTR, under grant numbers [PID2021-123068OB-I00 to A.V., PID2021-122528OB-I00 to PL-L, grant PID2021-126888OA-I00 to B.L.], scholarship PRE2022-105662 to A.F. and scholarship PIPF-2022/SAL-GL-25871 to R.G-A.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthors\u0026rsquo; Contributions\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, B.L.; Methodology, A.F., A.V., P.L.-L. and B.L.; Investigation, A.F, M.H, R.G-A, S.G.-S., L.M.F-S.; Software, A.F., R.G.-A. and B.L., Formal Analysis, A.F., and B.L., Resources, B.L. and A.V., Writing \u0026ndash; Original Draft, A.F., and B.L.; Writing \u0026ndash; Review \u0026amp; Editing, all authors.; Visualization, A.F. and B.L., Funding Acquisition, A.V., P.L.-L. and B.L., Project Administration, P.L.-L. and B.L., Supervision, B.L.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAcknowledgements\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe are deeply indebted to Patricia S\u0026aacute;nchez, Mar\u0026iacute;a Rodr\u0026iacute;guez and Mrs. Teresa Navarro (CSIC) and the IIBM Sebastian Cerd\u0026aacute;n NMR Facility for expert support during MRI acquisitions and granting rapid access to the MR instrumentation.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAhmed K, Choi H-N, Park J-S, Kim Y-G, Bae MK, Yim J-E. 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Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://linkinghub.elsevier.com/retrieve/pii/S0092867408010088\u003c/span\u003e\u003cspan address=\"https://linkinghub.elsevier.com/retrieve/pii/S0092867408010088\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"molecular-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mome","sideBox":"Learn more about [Molecular Medicine](https://molmed.biomedcentral.com)","snPcode":"10020","submissionUrl":"https://submission.springernature.com/new-submission/10020/3","title":"Molecular Medicine","twitterHandle":"@MolecularMedic1","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Brain, Celastrol, Magnetic Resonance Imaging, Magnetic Resonance Spectroscopy, Inflammation, Obesity, Mouse, Metabolism","lastPublishedDoi":"10.21203/rs.3.rs-9344668/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9344668/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe global rise in obesity is predominantly driven by energy dense foods consumption and sedentary lifestyles that contribute to a growing burden of metabolic and neuroinflammatory comorbidities. Obesity is linked to a chronic low-grade inflammatory profile, as well as to a localized neuroendocrine imbalance and inflammatory response in the brain, including regions regulating energy homeostasis, reward and motivational centers. Anti-obesity medications that reduce body weight are being extensively used across the world, and the specific cerebral mechanisms underlying its action are yet to be clarified.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWe investigated the cerebral and systemic effects inherent to obesity development and treatment with celastrol, an anti-obesity and anti-inflammatory agent, in a murine model of diet-induced obesity (DIO) using a multimodal approach. We characterized obesity progression and celastrol treatment by comparing body weight, food intake, changes in brain microstructure by \u003cem\u003ein vivo\u003c/em\u003e magnetic resonance imaging (MRI) and \u003cem\u003eex vivo\u003c/em\u003e by immunofluorescence, investigated its metabolic rearrangements using \u003csup\u003e1\u003c/sup\u003eH high-resolution magic angle spinning spectroscopy and draw the hormonal profiles between DIO and control animals, with or without treatment.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eOur findings indicate that obesity induces detectable neuroinflammation, evident through diffusion MRI alterations and increased microglial activation. Treatment resulted in significant body weight reduction, diffusion MRI signal changes, particularly in the hypothalamus, a decrease in microglial activation, a regularization of cerebral osmolyte concentrations, decreased cellular proliferation and astrocytic metabolism markers, and anti-inflammatory cytokine changes.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThese results support the role of celastrol as an anti-obesity treatment, acting through anti-inflammatory mechanisms in the hypothalamus, and prove MRI techniques as valid tools to characterize its effects.\u003c/p\u003e","manuscriptTitle":"Acute anti-obesity treatment with celastrol reduces body weight, cerebral inflammation and metabolic imbalances in mice","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-15 10:47:56","doi":"10.21203/rs.3.rs-9344668/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-23T14:53:36+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-21T13:41:19+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-21T10:29:39+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-12T06:37:01+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-10T09:35:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"15300094526075652265797259018380886191","date":"2026-04-10T07:21:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"129433158835393217921872239777938906553","date":"2026-04-08T20:14:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"122969362169771464079931637523991901747","date":"2026-04-08T11:04:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"302034672115973492008343052215085371281","date":"2026-04-08T06:58:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"119055679979401232699509186387867514602","date":"2026-04-08T03:12:11+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-08T03:04:20+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-08T02:44:21+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-08T02:43:49+00:00","index":"","fulltext":""},{"type":"submitted","content":"Molecular Medicine","date":"2026-04-07T11:38:47+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"molecular-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mome","sideBox":"Learn more about [Molecular Medicine](https://molmed.biomedcentral.com)","snPcode":"10020","submissionUrl":"https://submission.springernature.com/new-submission/10020/3","title":"Molecular Medicine","twitterHandle":"@MolecularMedic1","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b4c340a0-4a32-41f4-8efd-ead90a5fe15c","owner":[],"postedDate":"April 15th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-18T12:54:34+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-15 10:47:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9344668","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9344668","identity":"rs-9344668","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}