Quantitative proteomics unveils potential plasma biomarkers and provides insights into the pathophysiological mechanisms underlying equine metabolic syndrome

Quantitative proteomics unveils potential plasma biomarkers and provides insights into the pathophysiological mechanisms underlying equine metabolic syndrome | 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 Quantitative proteomics unveils potential plasma biomarkers and provides insights into the pathophysiological mechanisms underlying equine metabolic syndrome Elisa María Espinosa-López, Beatriz Ortiz-Guisado, Elisa Diez de Castro, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6223672/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Jul, 2025 Read the published version in BMC Veterinary Research → Version 1 posted 11 You are reading this latest preprint version Abstract Background Equine Metabolic Syndrome (EMS) is a multifactorial endocrine disorder characterized by obesity, insulin dysregulation (ID), and an increase in the risk of laminitis, a painful condition that can lead to euthanasia in severe cases. Diagnosing EMS is challenging and often relies on clinical history including obesity, difficulty in losing weight, and recurring episodes of laminitis. The gold standard for laboratory support of an EMS diagnosis is the identification of ID, being basal insulin the simplest and most accessible method. However, various factors such as diet, age, stress, season, and testing protocols can influence results. Dynamic tests like the oral sugar test (OST) are preferred but present limitations due to low sensitivity and poor repeatability. These diagnostic challenges make EMS difficult to detect in veterinary medicine highlighting the need for an effective method of the early detection of EMS to prevent laminitis and its associated complications. Results Mass spectrometry-based proteomics represents a powerful tool to identify biomarkers and explore molecular pathways related to the underlying pathology. In the current study we established an integrated proteomics pipeline to identify plasma biomarkers for EMS diagnosis. We compared plasma proteomes from healthy horses, non-ID obese horses and animals diagnosed with EMS. This comparison revealed 76 proteins with significant changes (1% FDR) between groups. Conclusions Our study demonstrates that the complement system, the coagulation cascade and extracellular matrix remodelling pathways are altered in EMS. These findings offer new insights into the molecular basis of the development of EMS and led to the nomination of several proteins as potential biomarkers for its early detection. Equine metabolic syndrome plasma proteomics diagnostic biomarkers Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Background Equine metabolic syndrome (EMS) is a multifactorial endocrine disorder affecting horses which is analogous to the metabolic syndrome in people. Both conditions are characterized by obesity and insulin dysregulation (ID), but EMS also increases the risk of developing laminitis ( 1 ). Laminitis is a painful disease caused by injury to the tissue between the hoof and the underlying bone. Treating laminitis is particularly challenging and, in severe cases, euthanasia might be considered to prevent further suffering ( 2 , 3 ). The diagnosis of EMS is not straightforward. It may be suspected in horses with compatible history, such as those having difficulties in losing weight or considered “easy keepers”, as well as horses showing clinical signs as obesity and evidence of previous or ongoing episodes of laminitis. Currently, the gold standard for supporting a diagnosis of EMS relies on laboratory testing to investigate ID ( 1 ). Measurement of basal insulin is the simplest test to perform, as it only requires a single blood sample and established reference ranges ( 1 ). However, results may be affected by external factors including diet, age, stress, season and method of insulin measurement, among others ( 4 – 10 ). For this reason, dynamic tests are currently recommended, and the oral sugar test (OST) is preferred in clinical practice ( 1 ). The OST evaluates insulinemic response following consumption of carbohydrates, which not only reflects insulin sensitivity, but also the effect of the enteroinsular axis on insulin secretion ( 11 , 12 ). However, this test requires compliance of the horse to tolerate sugar administration and some studies have described a lack of sensitivity (especially when lower doses of carbohydrates are used) and poor repeatability ( 5 , 13 ). As a results, EMS diagnosis is considered one of the current challenges in veterinary medicine. Early diagnosis of EMS is key to prevent laminitis ( 14 ), and significant efforts have been made to identify biomarkers that enable prompt detection of EMS. Mass spectrometry–based proteomics is a powerful tool to uncover molecular pathways underlying pathophysiological conditions across various cells, tissues, and biological fluids. To our knowledge, no previous studies have used a global proteomic approach to interrogate blood in the search of protein biomarkers for the diagnosis of EMS. In the current study, we used label free quantitative proteomics to detect variations in the plasma proteome of healthy horses, non-ID obese horses, and animals diagnosed with EMS, with the aims of (a) expanding our understanding on the molecular mechanisms underlying EMS and (b) offering the foundations for the development of reliable blood-based biomarker test, which could enable early diagnosis. Our findings have the potential to contribute to improve welfare and management of horses affected by this debilitating condition. Materials and methods Study design and animals’ enrolment criteria Study design and analytical workflow are summarized in Fig. 1 . Animals enrolled in this study were horses suspected of having EMS by referring vets of both the Veterinary Hospital at the University of Córdoba (Spain) and Extremadura (Cáceres, Spain). The control group of healthy animals consisted of teaching horses housed at both hospitals whose blood samples were taken from routine blood analysis as part of their annual wellness evaluation. Both university and client-owned horses were evaluated for inclusion criteria and informed client consent was obtained before evaluation. Data collection and testing were either performed at university facilities or in the field at clients' properties. Animals underwent a clinical examination to assess their health status, including evaluation of obesity and a clinical history of laminitis episodes. Information collected also included diet (pasture, hay and or concentrate), weight gain, activity performed (dressage, breeding or teaching) and current or previous medications administered. No animals were euthanized as part of the study. Obesity and adiposity were assessed by two independent veterinarians blinded to each other and the scores from the two evaluators were averaged. Body condition score (BCS) and cresty neck score (CNS) were assigned in accordance with previous publications ( 15 , 16 ) and the presence of localised fat deposits was also recorded. None of the animals included in the study were undergoing any pharmacological treatment at the time of enrolment and subjects with suspected pituitary pars intermedia dysfunction (PPID) were excluded from the study. Basal insulin was measured in all individuals and dynamic tests were restricted to those animals suspected of EMS in order to avoid unnecessary tests being performed on healthy horses. For the dynamic tests, supplementary feed was withheld overnight, leaving only access to hay. Immediately after obtaining the baseline blood samples, corn syrup (Karo Light Corn Syrup; ACH Food Companies, Memphis, TN, USA) was administrated orally using a 60 mL syringe at 0.45 mL/kg of body weight, and further blood samples were collected 60 min after oral administration. After excluding animals that did not meet the inclusion criteria, a total of 34 animals were enrolled in the study (Supplementary Table 1). Animals were classified into three groups based on their clinical signs and the EMS diagnostic criteria ( 1 ): a control group (n = 11) consisted of healthy animals normoinsulinemic with no clinical signs of EMS or other diseases; a second control group included obese horses with no signs of ID (n = 12) (BCS ≥ 6.5/9 and basal blood insulin ≤ 30 mU/L and ≤ 90 mU/L after 60 min OST) and a third group included EMS animals, with obesity and confirmed ID (n = 11) (BCS ≥ 6.5/9, CNS ≥ 3/5, basal blood insulin > 30 mU/L and/or > 90 mU/L after 60 min OST). Plasma collection Blood samples were collected from jugular venipuncture into heparinized tubes in the morning before the horses had eaten any kind on concentrate feed. Plasma was separated by centrifugation at 2500 g for 10 min immediately after collection, and frozen at – 80 ˚C until used. Insulin quantification Insulin determinations were performed using an equine-optimized insulin ELISA (Equine Insulin ELISA, Mercodia AB, Sweden). The insulin ELISA kit showed a detection limit of 1.15 mU/L, an intra-assay coefficient < 5%, and inter-assay coefficient < 15%. Peptide mass fingerprinting (PMF) Proteins in plasma samples were separated by polyacrylamide gel electrophoresis in denaturant and reducing conditions (SDS-PAGE) ( 17 ) using 12% resolving and 4% stacking gels. Electrophoresis was performed at 200 V for 45 min. Precision Plus Protein Dual Colour Standards (Bio-Rad) were used as molecular weight markers. Gels were stained with Coomassie Blue G-250 solution (Merck). Gel plugs were removed and proteins digested as previously described ( 18 , 19 ), using trypsin. Peptide mixtures from the proteolytic reactions were analysed by matrix-assisted laser-desorption ionization–time of flight-mass spectrometry (MALDI–TOF) in a UltrafleXtreme mass spectrometer (Bruker Daltonics), operated in positive ion detection reflector mode. Samples were mixed 1:1 (v/v) with a 10 mg/mL solution of α-cyano-4-hydroxycinnamic acid in 60% acetonitrile (ACN) (v/v)/0.2% trifluoroacetic acid (TFA) (v/v), before being spotted onto the MALDI target and air-dried. Spectra were acquired at 35% laser energy with 2000 laser shots per spectrum between 900–3500 m/z. External mass calibration was performed using a mixture of des-Arg bradykinin (904.47 Da), neurotensin (1672.92 Da), corticotrophin (2465.2 Da) and oxidized insulin chain (3495.9 Da) in 50% ACN/0.1% TFA (v/v). Global quantitative proteomic survey A quantitative proteomic survey was applied to undepleted plasma samples to identify proteins which abundance changes across the investigated groups. Total protein concentration in plasma samples was determined by Bradford assay ( 20 ) using bovine serum albumin as standard. Plasma samples were diluted in 25 mM ammonium bicarbonate to obtain a final protein concentration in the digestion mixture of 0.5 µg/µL. Proteins were digested as previously described ( 18 , 19 , 21 ). In summary, proteins were first denatured using RapiGest SF surfactant (Waters Corporation) at a final concentration of 0.05% (w/v) for 10 min at 80°C. Then, proteins were reduced with 3 mM dithiothreitol for 10 min at 60°C, followed by alkylation with 9 mM iodoacetamide in the dark, at room temperature for 30 min. Finally, trypsin was added at a 50:1 ratio (protein:enzyme) and samples incubated overnight at 37°C. To stop the proteolytic reaction, TFA was added at a final concentration 0.5% (v/v), followed by incubation at 37°C for 45 min. Finally, samples were centrifuged at 13,000 g for 15 min and the supernatant collected. Tryptic peptides were analysed by LC-TIMS-MS/MS in a nanoElute nanoflow ultrahigh-pressure LC system (Bruker Daltonics, Bremen, Germany) coupled to a timsTOF Pro 2 mass spectrometer, equipped with a CaptiveSpray nanoelectrospray ion source (Bruker Daltonics). Peptide digests (200 ng) were loaded onto a Pepmap C18 capillary column (15 cm length, 75 µm ID, 1.9 µm particle size, Bruker) and separated at 30°C using a 40 min gradient at a flow rate of 300 nL/min (mobile phase A (MPA): 0.1% FA; mobile phase B (MPB): 0.1% FA in ACN). A step gradient from 0 to 30% MPB was applied over 24 min, followed by a 30 to 90% MPB step for 1 min, and finished with a 90% MPB wash for an additional 5 min for a further time. The timsTOF Pro 2 was run in Data Dependent Acquisition-Parallel Accumulation Serial Fragmentation (DDA-PASEF) mode. Mass spectra for MS and MS/MS scans were recorded between 100 and 1700 m/z. Ion mobility resolution was set to 0.85–1.30 V s/cm 2 over a ramp time of 100 ms. Data-dependent acquisition was performed using 4 PASEF MS/MS scans per cycle with a duty cycle close to 100%. A polygonal filter was applied on the m/z space and ion mobility to exclude low m/z, mainly single-charged ions from the selection of PASEF precursors. An active exclusion time of 0.4 min was applied to precursors that reached 20,000 intensity units. The collision energy was increased stepwise as a function of the ion mobility ramp, from 27 to 45 eV. Identification of proteins and quantification of their relative abundance were obtained using the proteomics software Peaks Studio X Pro (Bioinformatics solutions Inc.). Spectra were searched against the horse reference proteome downloaded from UniProt ( www.uniprot.org ) (69434 entries, downloaded on 28/11/2024). Search parameters were set to 15 ppm as mass tolerance for precursors and 0.05 Da for fragment ions, semispecific search and two miss cleavages allowed. Carbamidomethylation of cysteines was set as a fixed modification, and methionine oxidation as variable modification allowing up to 4 variable modifications per peptide. False discovery rate (FDR) was set at 1% at both peptide and protein levels, based on decoy assignments. Only proteins identified with at least 2 unique significant peptides were taken to further analysis. Proteins sharing the same set of peptides were reported as one protein group. Extracted MS1 peak areas were used for protein quantification. Features were automatically detected, and samples aligned using the Peaks label free quantification algorithm. Total ion current of the samples was used to normalize feature intensity. Label free parameters were set to 15 ppm as mass shift tolerance between samples and automatic retention time shift tolerance. Filters included the requirement of 2 peptides identified per protein and a fold change higher than 1.5. Significance was calculated by ANOVA test, setting a threshold of 1% FDR to calculate adjusted p values. Bioinformatics and statistical analysis Statistical analysis and data visualization were conducted in R (v.3.2) ( 22 ). One-way ANOVA and unpaired t-tests were used to assess significant differences between groups. P values were adjusted for multiple hypothesis testing using the Benjamini-Hochberg (FDR) procedure. A proportional Venn diagram was created using the “eulerr” R package ( 23 ). Protein abundance data were log transformed for normalization and subsequently employed to create a heatmap using the “pheatmap” R package ( 24 ). Hierarchical clustering was generated using Euclidean as distance measurement and ward.D2 as clustering method. The principal component analysis (PCA) was performed using the “FactoMineR” R package ( 25 ), on standardized data, and results visualized using the “factoextra” R package ( 26 ) setting a confidence level of 0.999 for ellipses. Go ontology analysis was performed using the web-based tool g:Profiler ( 27 ). Results Clinical characteristics of the experimental groups In the current study we compared the plasma proteome of horses diagnosed with EMS to that of healthy animals with no evidence of ID, including both obese and non-obese animals. Classification of the patients was established according to the current diagnosis of EMS, based on obesity and ID (Supplementary Table 1). A total of 34 horses were enrolled in the study: 30 Andalusian horses and four crossbreds. Among them, 21 were females and 13 males. Mean age was 11.6 ± 5.8 years. Fat patches were identified in 18 animals, all being in the EMS (9/11) or obese (9/12) groups. Significant differences among EMS and non-ID obese groups versus healthy controls were also found in BCS (p < 0.001) and CNS (p < 0.001) (EMS 8.3 ± 0.6 and 4.1 ± 0.8; non-ID obese 8.1 ± 0.5 and 3.6 ± 0.5; healthy 5.6 ± 0.9 and 2.2 ± 0.4, respectively). However, no significant differences between EMS and non-ID obese groups were found in those parameters. Five of the horses had history of previous laminitis: four in the EMS group and one in the non-ID obese group. There was no history of laminitis in the healthy group. Basal insulin was, as anticipated, significantly higher (p < 0.05) in the EMS group (54.6 ± 47.6 mU/L) than in the non-ID obese (9.6 ± 8.3 mU/L) and healthy (13.7 ± 6.5 mU/L) groups. Post OST insulin was also significantly higher (p < 0.01) in the EMS group (134.5 ± 81.9 mU/L), than in the obese group (48.9 ± 16.6 mU/L). Global quantitative proteomic survey in plasma Complexity of plasma samples from healthy (n = 11), non-ID obese (n = 12) and horses diagnosed with EMS (n = 11), evaluated by SDS-PAGE, showed that protein pattern was very similar in all three investigated groups (Fig. 2 A). Protein fraction was dominated by a band of about 66 kDa, later confirmed by PMF to contain albumin. Other notable proteins in the profile included serotransferrin (75–80 kDa), histidine-rich glycoprotein (75 kDa), Ig-like domain containing protein (approx. 50 kDa) and cathepsin D (approx. 45 kDa). Total protein concentration was significantly higher in the non-ID obese group compared to EMS (p < 0.001) (Fig. 2 B, Supplementary table 1 ). Plasma proteomes were then compared by label free quantitative proteomics. As outlined in Material and Methods, plasma proteins were digested using trypsin and the resulting peptides were analysed by LC-TIMS-MS/MS using DDA-PASEF acquisition mode. A total of 292 protein groups were identified in Peaks X Pro using as acceptance criteria of at least 2 unique significant peptides and 1% FDR at both peptide and protein levels. The identity, significance, number of unique peptides and the relative abundance of these proteins is shown in Supplementary Table 2. A total of 227 protein groups were identified in all three groups, 5 protein groups were uniquely identified in the EMS group, 14 in the healthy group and 7 in the non-ID obese group (Fig. 3 A). However, since the proteins uniquely identified in the EMS group were found in only two or fewer animals, they were excluded as viable candidates for group differentiation. Assessment of the differential abundance of proteins across the experimental groups, using Peaks label free algorithm, revealed 76 proteins with statistically significant changes among the three groups (one-way ANOVA, 1% FDR). The protein groups, significance, relative area and ratio are shown in Supplementary Table 3. Principal Component Analysis (PCA), based on protein abundances, showed that the first and the second components effectively distinguished the three conditions (Fig. 3 B). First component separates healthy from EMS animals, being able to explain a 34.7% of the total variance between samples. Top 5 proteins responsible for the variance explained by component 1 (Fig. 3 C) included vitamin D-binding protein (UniProtKB F6T0P6), protein z vitamin K dependent plasma glycoprotein (UniProtKB F6SR87), transferrin (UniProtKB F6PKE1), hyaluronan binding protein 2 (UniProtKB A0A5F5PJC3) and kininogen 1 (UniProtKB A0A5F5PUE2). Second component explained a 26.4% of the total variance between samples, separating obese from healthy and EMS individuals (Fig. 3 B). Top 5 proteins responsible for the variance explained by component 2 (Fig. 3 D) include complement C3 (UniProtKB A0A9L0RG95), beta-2-glycoprotein 1 (UniProtKB F6Z041), a serpin member (UniProtKB F7BM31), an immunoglobulin (UniProtKB A0A3Q2HY39) and leucine rich alpha-2-glycoprotein 1 (UniProtKB A0A9L0T558). Comparison between EMS and healthy groups, revealed 57 proteins which abundances were significantly different between these two groups (Supplementary Table 3). Most of the proteins (55 over 57) were more abundant in EMS animals than healthy controls while 2 were more abundant in healthy animals than EMS animals. Comparison of EMS animals with non-ID obese animals, to account for changes associated with obesity, revealed 43 proteins which abundance significantly changed between these groups. Of these, 24 were more abundant in the EMS group, while 19 were more abundant in the obese group (Supplementary Table 3). Normalized abundances of proteins showing significant differences among three groups were visualized as a heatmap (Fig. 4 ). Clustering analysis was performed on both samples and proteins based on Euclidean distances. Clustering analysis on samples (columns) showed three main clusters corresponding to the three clinical conditions: healthy, non-ID obese and EMS diagnosed. Clustering analysis on proteins (rows), based on Euclidean distances, showed three main clusters: (cluster 1) proteins specifically elevated in EMS animals, which included proteins elevated in the EMS group when compared to both control groups (obese and non-obese animals); (cluster 2) proteins associated with obesity, which includes those elevated specifically in EMS and non-ID obese animals with no significant difference between them, and (cluster 3) proteins which abundance diminished in EMS animals when compared to non-ID obese animals. As proteins in cluster 1 are specifically elevated in EMS, they could be targeted as potential candidates for diagnosis (Fig. 5 ). Enrichment analysis of these proteins highlighted their involvement in complement system activation, the coagulation cascade, cholesterol metabolism, peptidase regulator activity and the extracellular region (Fig. 6 ). Within the complement system, we observed increased levels of complement C1q subunits a (UniProt KB A0A9L0RPF6) and subunit c (UniProt KB A0A3B0ITF5), complement C2 (UniProt KB A0A3Q2IDE0) and C4a anaphylatoxin (UniProt KB A0A9L0R7H5). Proteins associated with the coagulation cascade elevated in EMS included alpha-2-macroglobulin (Uniprot KB F6QAD8 and F6RI47), angiotensinogen (UniprotKB A0A9L0T1B5) and several serine proteases inhibitors (UniprotKB A0A9L0S2K1 and A0A3Q2H3F6). Remarkably, lumican and fibulin-1, both related with ECM remodeling, exhibited the highest increases in EMS, with fold-changes of 3.53 and 2.24 respectively when compared non-ID obese. Proteins in cluster 2 (Fig. 4 ) were elevated in both non-ID obese and EMS groups vs healthy group. Among these proteins, some exhibited no significant difference between the non-ID obese and EMS groups, suggesting their association with obesity. This group included some apolipoproteins (A-II (UniProtKB A0A9L0R4P3) and C-III (UniProtKB A0A3Q2I0V8)), some proteins related to the alternative activation pathway of the complement systems (complement factor D (UniProtKB A0A3Q2LBP6) and properdin (UniProtKB F6SP74)), proteins related to the coagulation cascade (antithrombin-III (UniProtKB F7CYR1), coagulation factor IX (UniProtKB F6RFT9), coagulation factor VII (UniProtKB F7ABW7), and several inhibitors of serin proteases (i.e. fetuin A (UniProtKB A0A5F5Q0V8)). Kininogen 1 (UniProtKB A0A5F5PUE2), part of kallikrein-kinin system (KKS), an important interconnection between the complement and coagulation cascades, showed an increased abundance in both EMS and non-ID obese vs healthy controls. Additionally, among the proteins in cluster 2, we identified some proteins that were elevated in EMS and non-ID obese vs healthy controls but also showed higher levels in EMS compared to non-ID obese controls, suggesting their potential as biomarkers of disease progression. This group included hyaluronan binding protein 2 (UniProtKB A0A5F5PJC3, also known as factor VII activating protease), vitamin D binding protein (UniprotKB F6T0P6) and a carboxylic ester hydrolase (UniprotKB A0A9L0SFT5) and fetuin B (UniProtKB F6RRV1). Finally, cluster 3 highlighted some proteins showing diminished abundance in EMS compared to non-ID obese group, which can be explored as potential diagnostic biomarkers. This group included adiponectin (UniProtKB F7DZE7), complement component C3 (UniProtKB A0A9L0RG95), complement component C1r (UniProtKB A0A9L0SLY6) and members of the serpin superfamily (member 3 serpin family A (UniProtKB F6ZLR1) and member 1 serpin family D (UniProtKB F7BM31)). Discussion In the present study, we explored the differences in the plasma proteome of horses diagnosed with EMS compared to healthy horses to identify altered pathways potentially related with the pathophysiology of this syndrome and putative biomarkers for early diagnosis. We employed label free mass spectrometry-based quantitative proteomics to detect variations in the concentration of plasma proteins across three experimental groups: (a) healthy normoinsulinemic non-obese animals, (b) obese animals with no evidence of ID and (c) EMS animals (obese and ID animals). The inclusion of non-ID obese horses as a control group aimed to account for proteome changes associated with obesity, as it is a key component of EMS syndrome. In our study the categorization of animals to either obese or EMS groups was based on basal and post OST insulin concentration in blood. ID was considered when basal insulin was above 30 µU/mL /or post OST insulin was above 90 µU/mL. To determine these levels, ID was defined according to the recommendations set by the equine endocrinology group and EMS consensus. The cutoff levels were calculated for our method of quantification using a web-based insulin concentration converter developed for horses ( 28 ). Plasmatic insulin results can be influenced by several factors such as stress, diet, disease, time of sampling, method of measurement or even a short transport before testing ( 4 – 10 ), therefore, none of the horses in this study was transported shortly before the sampling procedure was performed avoiding any stress. Additionally, a similar feed privation protocol and method of insulin measurement were used in all the animals. However, it is worth noting that not all the samples were taken at the same time of the year, and some authors have described that this sole factor can have an impact in EMS classification ( 29 ). Plasma proteomics is challenging due to the large dynamic range of protein abundances, with highly abundant proteins hindering the detection of less abundant proteins. Nevertheless, recent developments in instrumentation and bioinformatic applications have improved both the identification and quantification of proteins. We employed state of the art instrumentation (tims-TOF Pro 2, Bruker) and acquisition techniques (DDA-PASEF) ( 30 ) to enhance the coverage of equine plasma proteome. This approach resulted in the coverage of approx. a 20% more of the equine plasma proteome compared to the most recent report ( 31 ). As specific features in the proteome of EMS horses we found that the concentration of multiple members of the classical activation pathway of the complement system is elevated (Supplementary Table 3, Fig. 7 ). The complement system in vertebrates is a complex protein network organized by a series of serine proteases, which are sequentially activated to cleave specific downstream proteins. The system can be activated via the classical, alternative or lectin pathways, resulting in the cleavage of component C3 and the activation of membrane attack complex (MAC), that target cell lysis ( 32 ) (Fig. 7 ). In addition, complement activation participates in several functions such as immune regulation and inflammatory process by producing anaphylatoxins. Plasma levels of some components, including C3, C3a, factor B, factor D and factor H, are associated with body mass index ( 33 ) and plasma level of complement C3 had been suggested as a potential biomarker of insulin resistance in humans ( 34 , 35 ). In good agreement with these studies, we observed that members of the complement system are elevated in EMS animals, however some of them are also elevated in non-ID obese animals, raising the question of whether they might reflect the progression of the syndrome in horses as it has been suggested for human ( 35 ). Our findings show that proteins belonging to the classical activation pathway are elevated specifically to EMS (i.e. C1q, complement C2 and C4a anaphylatoxin) while some components of the alternative pathway are shared with non-ID obese animals (factor D and properdin). As activation of the complement system can arise from different inflammatory conditions, additional research is required to better understand its role in EMS and its potential as early biomarker of disease development and/or progression. In human, metabolic syndrome displays characteristics of a hypercoagulable state, defined by increased levels of coagulating factors, inhibition of fibrinolytic pathways and platelet hypercoagulability ( 36 ). This hypercoagulable state seems to be related to adipose tissue dysregulation, oxidative stress and chronic systemic inflammation. Several studies have evaluated the value of proteins related to inflammation as blood biomarkers of EMS, obtaining contradictory results ( 37 – 43 ) and no differences were found in parameters traditionally used for the evaluation of coagulation between control horses and horses with obesity and ID ( 44 ). However, some indicators of clot strength differed between groups, suggesting a hypercoagulable tendency in EMS animals. The hypercoagulable and hypofibrinolytic states associated with obesity and metabolic syndrome are confirmed in our study by the elevation in EMS vs obese animals of several members of the coagulation cascade and two isoforms of alpha-2-macroglobulin, an inhibitor of fibrinolysis. High levels of this protein in blood have been identified as a risk factor for cardiovascular events ( 45 ). Additionally, alpha-2-macroglobulin increases in the hyperinsulinemic hoof of horses, which may be attributed to tissue damage and inflammation associated with hyperinsulinemia-induced laminitis. Although the levels of this protein have been found to be elevated in the plasma of horses with chronic laminitis, it has never been described or implicated in EMS ( 46 ). The coagulation and complement systems share some features enabling multiple interactions that can explain their association with several inflammatory and thrombotic conditions ( 47 ). One of the interactions is by the crosstalk between the kallikrein-kinin system (KKS) ( 48 ). Notably, carboxypeptidase N, which inactivates C3a from the complement system, also inactivates bradykinin from the KKS ( 49 ), then acting as connecting point between coagulation, thrombosis, inflammation, and innate immunity ( 50 ). Besides, C1q has been described as one of the ligands of histidine rich glycoprotein (HRG), a regulatory factor in the coagulation cascade with anticoagulant and antifibrinolytic properties, that is also elevated in EMS and obese animals, connecting the complement and coagulation cascades ( 51 ). Fetuin A is a hepato-adipokine that has been involved in diverse pathological processes including complications related to metabolic disorders ( 52 – 54 ). Consistent with our findings, previous studies in humans have shown that circulating fetuin A is significantly elevated in patients with obesity and metabolic syndrome ( 53 , 55 , 56 ). This glycoprotein belongs to the protease inhibitor cystatin superfamily that negatively affects glucose homeostasis and participates in adipose tissue inflammation contributing to insulin resistance development ( 57 ). It has been proposed that fetuin A induces metabolic dysfunction through a number of mechanisms that include inhibition of insulin receptor tyrosine kinase activity and the promotion of inflammation in immune cells and adipocytes ( 55 , 58 ). Fetuin B, another protease inhibitor that acts as a proinflammatory factor, is linked to the development of insulin resistance and type 2 diabetes ( 59 ) and has been nominated as putative biomarker for insulin resistance and metabolic syndrome in human ( 60 – 63 ). Given the roles of fetuin A and fetuin B in human metabolic dysfunction, we hypothesize that these two proteins may contribute to the pathophysiology of EMS. Investigating the role of fetuin A and fetuin B in horses could reveal novel insights into the mechanisms underlying EMS and provide potential targets for therapeutic interventions. Extracellular matrix (ECM) components play a critical role in adipose tissue and liver fibrosis associated with metabolic syndrome. One of the proteins with the highest changes in EMS animals is lumican, an ubiquitous leucine-rich proteoglycan that regulates the assembly of collagen fibers in the ECM ( 64 ) and inhibits matrix metalloproteinases (MMPs) ( 65 ). Different studies have revealed the association of this protein with adipocyte disfunction in the context of metabolic syndrome progression in humans and animal models ( 64 , 66 , 67 ). MMPs are key in the regulation of tissue repair ( 68 ) and the inhibition by lumican may worsen the scenario in the context of laminitis. Furthermore, inflammation and dysregulation of endogenous MMPs are implicated in the development of laminitis ( 69 ). Fibulin-1, which is elevated in EMS animals, is another ECM protein present in blood. It has been related with cardiovascular risk in human diabetes ( 70 , 71 ). Due to its association with cardiovascular complications, this protein could be also related to the onset of laminitis. Further studies are needed to explore the relationship of high levels of lumican and fibulin-1 in the presentation and severity of episodes of laminitis in EMS patients. Adiponectin (UniProtKB F7DZE7), an adipokine linked to protection against cardiovascular diseases, insulin resistance, inflammation and metabolic disease ( 72 ) is decreased in EMS vs obese non-ID animals. Previous studies indicated that normal adipocytes secrete higher amounts of the high molecular weight (HMW) form of adiponectin and retain more of the low molecular weight (LMW) form. In horses, HMW adiponectin have been reported to have an inverse relationship with body condition score and insulin levels and higher levels have been observed in lean vs obese animals ( 73 ). Insulin and insulin-like growth factors (IGFs) are central hormones in regulating metabolism, and insulin-like growth factor binding proteins (IGFBPs) represent an important link between insulin and IGF systems. Previous research suggested that IGFBPs play an important role in obesity, insulin resistance, cardiovascular risk and metabolic syndrome in humans ( 74 ). Abnormal IGFBP expression is a sensitive marker of ID and is used to identify individuals with ID at high cardiovascular risk and as an early marker of human metabolic syndrome ( 74 ). In our study we observed that an IGFBP is specifically increased in EMS animals compared to both non-ID obese and healthy animals. Furthermore, this protein is elevated in non-ID obese vs healthy animals, suggesting that it could serve as potential marker of disease progression. Further work is needed to evaluate the role of IGFBP in EMS and its value as progression biomarkers. Conclusion In this study, we compared the plasma proteomes of healthy, non-ID obese horses and EMS animals, identifying potential biomarkers that could help detect at risk animals and offer insights into the pathophysiology of the condition. Some proteins, including various members of the complement and coagulation cascades, and proteins related to ECM remodeling showed elevated abundance specifically in EMS animals, potentially offering valuable biomarkers for the early detection of the syndrome. Notably, elevated levels of classical complement pathway proteins, such as C4a anaphylatoxin and complement C2, were specific to EMS horses, suggesting their potential as diagnostic biomarkers. Additionally, ECM proteins like lumican and fibulin-1 were found to be significantly altered, suggesting their involvement in tissue remodeling and the development of laminitis. Moreover, elevated levels of certain proteins, such as alpha-2-macroglobulin, fetuin A, fetuin B and IGFBPs were found in both EMS and non-ID obese horses, supporting that obesity-related metabolic disturbances play a significant role in EMS development and identifying these proteins as potential biomarkers for monitoring disease progression. Our findings enhance our understanding of the molecular mechanisms underlying EMS and suggest several promising biomarkers for further investigation. However, additional research is needed to validate these proteins as biomarkers and to assess their potential in improving EMS management. Declarations Acknowledgements The authors gratefully acknowledge the Proteomics Unit at the Servicio Central de Apoyo a la Investigación (SCAI) from the University of Córdoba. E.M.E-L- received a PhD Fellowship from Plan Propio (University of Córdoba). Authors’ contributions GGB, ED, EAT and AD designed the study. GGB, EMEL, ED, EAT and AD obtained the funding. ED collected the samples and clinical data. EMEL and BOG performed the laboratory experiments. GGB, EMEL, BOG and ED analyzed the results. GGB, EMEL and ED wrote the main manuscript draft. EAT and AD reviewed and edited the manuscript. All authors read and approved the final manuscript. Funding This research was supported by a grant from the Waltham Foundation (2021-2023). Data availability statement The authors declare that the data supporting the findings of this study are available within the paper and its Supplementary Information files. Should any raw data files be needed in another format they are available from the corresponding author upon request. Ethics approval and consent to participate The study protocol received approval from the Institutional Committee at the Veterinary Hospital of the University of Córdoba (Spain) and the University of Extremadura (Spain). Written informed consent was obtained from the owners of all horses enrolled in the study. All diagnostic procedures were clinically indicated, aimed at benefiting the animals, and were conducted in compliance with the highest standards of veterinary practice. The authors confirm that all methods were carried out in accordance with relevant guidelines and veterinary regulations. Competing interests The authors declare no competing interests. Consent to publish Not applicable References Durham AE, et al. ECEIM consensus statement on equine metabolic syndrome. J Vet Intern Med. 2019;33:335–49. Hunt RJ. A retrospective evaluation of laminitis in horses. Equine Vet J. 1993;25:61–4. Pollard D, Wylie CE, Newton JR, Verheyen KLP. 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Elevated Circulating Fetuin-B Levels Are Associated with Insulin Resistance and Reduced by GLP-1RA in Newly Diagnosed PCOS Women. Mediators of inflammation 2020, 2483435 (2020). Xia X et al. Association of serum fetuin-B with insulin resistance and pre-diabetes in young Chinese women: evidence from a cross-sectional study and effect of liraglutide. PeerJ 9, e11869 (2021). Guzmán-Ruiz R, et al. Adipose tissue depot-specific intracellular and extracellular cues contributing to insulin resistance in obese individuals. FASEB J. 2020;34:7520–39. Brezillon S, Pietraszek K, Maquart FX, Wegrowski Y. Lumican effects in the control of tumour progression and their links with metalloproteinases and integrins. FEBS J. 2013;280:2369–81. Strieder-Barboza C, et al. Lumican modulates adipocyte function in obesity-associated type 2 diabetes. Adipocyte. 2022;11:665–75. Wolff G, et al. Diet-dependent function of the extracellular matrix proteoglycan Lumican in obesity and glucose homeostasis. Mol Metab. 2019;19:97–106. Gill SE, Parks WC. Metalloproteinases and their inhibitors: regulators of wound healing. Int J Biochem Cell Biol. 2008;40:1334–47. Kyaw-Tanner M, Pollitt CC. Equine laminitis: increased transcription of matrix metalloproteinase-2 (MMP-2) occurs during the developmental phase. Equine Vet J. 2004;36:221–5. Scholze A, et al. Plasma concentrations of extracellular matrix protein fibulin-1 are related to cardiovascular risk markers in chronic kidney disease and diabetes. Cardiovasc Diabetol. 2013;12:6. Cangemi C, et al. Fibulin-1 is a marker for arterial extracellular matrix alterations in type 2 diabetes. Clin Chem. 2011;57:1556–65. Ye JJ, Bian X, Lim J, Medzhitov R. Adiponectin and related C1q/TNF-related proteins bind selectively to anionic phospholipids and sphingolipids. Proc Natl Acad Sci U S A. 2020;117:17381–8. Wooldridge AA, et al. Evaluation of high-molecular weight adiponectin in horses. Am J Vet Res. 2012;73:1230–40. Ruan W, Lai M. Insulin-like growth factor binding protein: a possible marker for the metabolic syndrome? Acta Diabetol. 2010;47:5–14. Carter RA, Geor RJ, Burton Staniar W, Cubitt TA, Harris PA. Apparent adiposity assessed by standardised scoring systems and morphometric measurements in horses and ponies. Vet J. 2009;179:204–10. Additional Declarations No competing interests reported. Supplementary Files SupTable1patients.xlsx Supplementary Table 1: Information of animals enrolled in the study. Obesity was assessed following the 1 to 9 Henneke Body Condition Score System (BCS) (15), where 1 denotes extremely emaciated animals and 9 extremely obese animals. Adiposity was assessed following the 0 to 5 cresty neck score (CNS) (16, 75) where 0 denotes no neck crest and 5 represents a crest enlarged and permanently drooping to one side of the neck. SupTable2PeaksDB.xlsx Supplementary Table 2: Global protein identification data. Proteins were identified and their relative abundance quantified using Peaks Studio X Pro software. Spectra were compared against the horse reference proteome from UniProt (69434 entries, downloaded on 28/11/2024). Search parameters included a 15 ppm mass tolerance for precursors, 0.05 Da for fragment ions, semispecific search, and allowance for two missed cleavages. Carbamidomethylation of cysteines was set as a fixed modification, and methionine oxidation as a variable modification, with up to 4 variable modifications per peptide. A 1% false discovery rate (FDR) was applied at both peptide and protein levels, using decoy assignments. Only proteins identified with at least 2 unique, significant peptides were accepted for further analysis, and proteins sharing the same peptides were reported as one protein group. Protein quantification was performed using extracted MS1 peak areas. SupTable3Labelfree.xlsx Supplementary Table 3: Label free quantitative proteomics data. Features were automatically detected, and samples aligned using the Peaks label free quantification algorithm. The total ion current of the samples was used to normalize feature intensity. Label free parameters included 15 ppm mass shift tolerance between samples and automatic retention time shift tolerance. The analysis required at least 2 peptides identified per protein and a fold change greater than 1.5. Statistical significance was determined using an ANOVA test, with a 1% false discovery rate (FDR) threshold to calculate adjusted p-values. Unpaired t tests were performed in R and p values adjusted for multiple testing using the Benjamini-Hochberg (FDR) procedure. Additionalfile1uncroppedgel.pdf Cite Share Download PDF Status: Published Journal Publication published 02 Jul, 2025 Read the published version in BMC Veterinary Research → Version 1 posted Editorial decision: Revision requested 16 May, 2025 Reviews received at journal 28 Apr, 2025 Reviewers agreed at journal 23 Apr, 2025 Reviewers agreed at journal 23 Apr, 2025 Reviews received at journal 15 Apr, 2025 Reviewers agreed at journal 09 Apr, 2025 Reviewers invited by journal 03 Apr, 2025 Editor assigned by journal 03 Apr, 2025 Editor invited by journal 26 Mar, 2025 Submission checks completed at journal 25 Mar, 2025 First submitted to journal 25 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-6223672","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":438150437,"identity":"03f50d18-b17f-4c9a-a75e-d21bde1c0242","order_by":0,"name":"Elisa María Espinosa-López","email":"","orcid":"","institution":"University of Córdoba","correspondingAuthor":false,"prefix":"","firstName":"Elisa","middleName":"María","lastName":"Espinosa-López","suffix":""},{"id":438150439,"identity":"15f4451e-3d16-453e-98a2-b19824207efd","order_by":1,"name":"Beatriz Ortiz-Guisado","email":"","orcid":"","institution":"University of Córdoba","correspondingAuthor":false,"prefix":"","firstName":"Beatriz","middleName":"","lastName":"Ortiz-Guisado","suffix":""},{"id":438150441,"identity":"94199d90-555c-4b6d-885b-b20857aad903","order_by":2,"name":"Elisa Diez de Castro","email":"","orcid":"","institution":"University of Córdoba","correspondingAuthor":false,"prefix":"","firstName":"Elisa","middleName":"Diez","lastName":"de Castro","suffix":""},{"id":438150444,"identity":"3e99cb28-c348-482a-96a3-889ed29a6930","order_by":3,"name":"Escolástico Aguilera-Tejero","email":"","orcid":"","institution":"University of Córdoba","correspondingAuthor":false,"prefix":"","firstName":"Escolástico","middleName":"","lastName":"Aguilera-Tejero","suffix":""},{"id":438150446,"identity":"9d4ae6fc-6bda-479c-ab15-7a9ce48a33ee","order_by":4,"name":"Andy E. Durham","email":"","orcid":"","institution":"Liphook Equine Hospital","correspondingAuthor":false,"prefix":"","firstName":"Andy","middleName":"E.","lastName":"Durham","suffix":""},{"id":438150447,"identity":"41b2d45a-a732-4b69-82a1-9e6f89431033","order_by":5,"name":"Guadalupe Gómez-Baena","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAy0lEQVRIiWNgGAWjYDAC+cMHGBgbGBj4idciwZYA1iLZQLwWHgOwFoMDxOowuN1g/OLjDju7zTeSHz5gqKgjQsudA2mWM88kJ2+7kWZswHDmMGEtkg0Jx4x525iTzW7ksEkwthHhPMmGxDaglvpk4xkgLf+IcBi/RDLzY962w3YGEiAtDcxEaOE5xsY488zxBIkzz4wNEo4R4Rc29v7PHz7uqLbnbweG2IcaIhwG0iUBJBIbQMwEojQwMDB/ABL2RCoeBaNgFIyCkQgAiSc7O4420FoAAAAASUVORK5CYII=","orcid":"","institution":"University of Córdoba","correspondingAuthor":true,"prefix":"","firstName":"Guadalupe","middleName":"","lastName":"Gómez-Baena","suffix":""}],"badges":[],"createdAt":"2025-03-14 05:08:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6223672/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6223672/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12917-025-04879-6","type":"published","date":"2025-07-02T15:58:45+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":80047318,"identity":"529ba5a7-280b-4eb7-845e-717d79bb60d5","added_by":"auto","created_at":"2025-04-07 09:54:13","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":171503,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExperimental design and workflow.\u003c/strong\u003e Plasma samples were collected from horses and categorized into three groups: healthy animals with no signs of EMS or other diseases (n=11); obese horses with no signs of ID (n=12); and EMS horses, diagnosed as described in materials and methods, with obesity and confirmed ID (n=11). Blood biochemistry and complete clinical history were collected from every animal. In addition, protein profile was analysed by SDS-PAGE prior to quantitative proteomic survey. Tryptic peptides were analysed by DDA-PASEF acquisition in a LC-TIMS-MS/MS system and proteins were identified and quantified using Peaks Studio X Pro (Bioinformatics solutions Inc.).\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-6223672/v1/a2761b6e1732765d556cdc25.png"},{"id":80047321,"identity":"e50b2585-3811-45c4-b88e-3dbbd57d2c2e","added_by":"auto","created_at":"2025-04-07 09:54:14","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":263218,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProteome profiles representative of healthy, non-ID obese and EMS animals. \u003c/strong\u003eA. Protein pattern assayed by SDS-PAGE. Plasma samples (5 mg total protein loaded per well) were compared in a 12 % resolving gel. Protein identification obtained by PMF is indicated for some of the most intense bands. B. Boxplot of total protein concentration. Top and bottom of the box represent the 75 % (Q3) and 25% (Q1) percentile respectively, the line inside the box represents the median and the whiskers define 1.5 times the interquartile range from the box. Unpaired t.test was performed in R (***p \u0026lt;0.001).\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-6223672/v1/1520cdab1aa97c535f9691e5.png"},{"id":80047319,"identity":"b6735d18-b5a9-4ac4-8e76-50d782cef64b","added_by":"auto","created_at":"2025-04-07 09:54:13","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":88258,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGlobal proteomic survey.\u003c/strong\u003e A. Venn diagram showing common and unique proteins identified across the groups. B. PCA using protein abundances estimated by Peaks. The percentage in each axis denotes the percentage of the sum of the protein variances across the samples, which is explained by the correspondent principal component. The plot also shows the coordinates of individuals on the principal components with ellipses of point concentrations at 0.999 confidence level. Component 1 explains a 34.7 % of the total variance while component 2 explains 26.4 % of the variance. C. Top 5 proteins contributing to variance explained by component 1. D. Top 5 proteins contributing to variance explained by component 2.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-6223672/v1/1dab7b37f844013b0c159974.png"},{"id":80049322,"identity":"7e8de026-ef9c-462f-88ba-094f030e08f1","added_by":"auto","created_at":"2025-04-07 10:10:14","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":133593,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLabel free quantitative proteomics survey.\u003c/strong\u003e Heatmap representing the protein groups that passed the identification and quantification filters. Proteins and samples exhibiting a similar abundance trend across the samples were clustered using Euclidean as distance measurement.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-6223672/v1/87cbc9f1e7578e8e8b84e3b1.png"},{"id":80048354,"identity":"b9783bf7-4d76-40be-a3ad-1aca5a0fbcb6","added_by":"auto","created_at":"2025-04-07 10:02:14","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":227536,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBoxplots of the proteins selected as potential candidates for the diagnosis of EMS. \u003c/strong\u003eBoxplots of protein areas calculated in Peaks X. Top and bottom of the box represent the 75 % (Q3) and 25% (Q1) percentile respectively, the line inside the box represents the median. The whiskers extend to 1.5 times the interquartile range from the box. Unpaired t.tests were performed in R and p values adjusted for multiple testing using the Benjamini-Hochberg (FDR) procedure. The significance levels are as follows: N.S for non-significant, *p \u0026lt;0.05, **p \u0026lt;0.01, ***p \u0026lt;0.001.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-6223672/v1/5a3b4eeaeb0801f5b50864e7.png"},{"id":80047326,"identity":"1eb961b5-cab4-44a0-b67b-4a2ef0183b88","added_by":"auto","created_at":"2025-04-07 09:54:14","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":150098,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional enrichment analysis of proteins specifically elevated in EMS group.\u003c/strong\u003e Functional enrichment analysis was performed on proteins from cluster 1, as shown in the heatmap, in g:Profiler (https://biit.cs.ut.ee/gprofiler/gost) (27). The analysis was performed with \u003cem\u003eEquus caballus\u003c/em\u003e as the reference organism, setting default parameters (g:SCS threshold as significance threshold for multiple testing correction and a significant threshold of 0.05. GO:MF (GO molecular function), GO:CC (GO cellular component), GO:BP (GO biological process) and KEGG databases were selected as data sources.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-6223672/v1/24ba00b06668549c8163418f.png"},{"id":80047324,"identity":"439832bd-a411-4b0e-b7b3-3ee857358c5b","added_by":"auto","created_at":"2025-04-07 09:54:14","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":69942,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe complement system classical pathway is increased in EMS horses.\u003c/strong\u003e The classical pathway is initiated by the activation of the C1 complex by antigen-antibody complexes recognized by the component C1q. Upon activation, C4 and C2 are cleaved into C4a and C4b, and C2a and C2b, respectively. C4b and C2b associate to form C3 convertase, which promote the cleavage of C3 into C3a and C3b. C3b associates with C3 convertase to form C5 convertase which cleaves C5 into C5a and C5b. C3a, C4a and C5a are anaphylatoxins. C5b along with C6-C9 form the terminal membrane attack complex (MAC). Alternative pathway occurs by spontaneous hydrolysis of C3 in combination with factor B, adipsin and properdin. Several factors, known as complement regulatory proteins, regulate the activation of the complement system (factor H, factor I, C4 binding protein).\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-6223672/v1/14b36eae820f3e2361ce63a6.png"},{"id":86180057,"identity":"b0f279de-a945-485f-8db1-df9dbdbba364","added_by":"auto","created_at":"2025-07-07 16:21:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1825976,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6223672/v1/c3aed10f-d415-4900-8eba-9c2c12216dae.pdf"},{"id":80048351,"identity":"653ddd40-7455-4795-a040-6c6f59476756","added_by":"auto","created_at":"2025-04-07 10:02:14","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":14698,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Table 1: Information of animals enrolled in the study.\u003c/strong\u003e Obesity was assessed following the 1 to 9 Henneke Body Condition Score System (BCS) (15), where 1 denotes extremely emaciated animals and 9 extremely obese animals. Adiposity was assessed following the 0 to 5 cresty neck score (CNS) (16, 75) where 0 denotes no neck crest and 5 represents a crest enlarged and permanently drooping to one side of the neck.\u003c/p\u003e","description":"","filename":"SupTable1patients.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6223672/v1/7ca1873d0ad0c9e2060247e6.xlsx"},{"id":80047327,"identity":"0fe4d78f-ecc0-4b77-9944-1a7415265697","added_by":"auto","created_at":"2025-04-07 09:54:14","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":175839,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Table 2: Global protein identification data.\u003c/strong\u003e Proteins were identified and their relative abundance quantified using Peaks Studio X Pro software. Spectra were compared against the horse reference proteome from UniProt (69434 entries, downloaded on 28/11/2024). Search parameters included a 15 ppm mass tolerance for precursors, 0.05 Da for fragment ions, semispecific search, and allowance for two missed cleavages. Carbamidomethylation of cysteines was set as a fixed modification, and methionine oxidation as a variable modification, with up to 4 variable modifications per peptide. A 1% false discovery rate (FDR) was applied at both peptide and protein levels, using decoy assignments. Only proteins identified with at least 2 unique, significant peptides were accepted for further analysis, and proteins sharing the same peptides were reported as one protein group. Protein quantification was performed using extracted MS1 peak areas.\u003c/p\u003e","description":"","filename":"SupTable2PeaksDB.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6223672/v1/f5402ff7fd05c6954cd91dd4.xlsx"},{"id":80047332,"identity":"d1ac5cd4-2ea7-4fda-9b8b-81e25a767057","added_by":"auto","created_at":"2025-04-07 09:54:14","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":55105,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Table 3: Label free quantitative proteomics data.\u003c/strong\u003e Features were automatically detected, and samples aligned using the Peaks label free quantification algorithm. The total ion current of the samples was used to normalize feature intensity. Label free parameters included 15 ppm mass shift tolerance between samples and automatic retention time shift tolerance. The analysis required at least 2 peptides identified per protein and a fold change greater than 1.5. Statistical significance was determined using an ANOVA test, with a 1% false discovery rate (FDR) threshold to calculate adjusted p-values. Unpaired t tests were performed in R and p values adjusted for multiple testing using the Benjamini-Hochberg (FDR) procedure.\u003c/p\u003e","description":"","filename":"SupTable3Labelfree.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6223672/v1/35ba8d6ead143bd7488b8c26.xlsx"},{"id":80047329,"identity":"b22201a1-8ad7-4f3f-9fe9-f337bb5e2da8","added_by":"auto","created_at":"2025-04-07 09:54:14","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":185318,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1uncroppedgel.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6223672/v1/5b58b1516681593182533d62.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Quantitative proteomics unveils potential plasma biomarkers and provides insights into the pathophysiological mechanisms underlying equine metabolic syndrome","fulltext":[{"header":"Background","content":"\u003cp\u003eEquine metabolic syndrome (EMS) is a multifactorial endocrine disorder affecting horses which is analogous to the metabolic syndrome in people. Both conditions are characterized by obesity and insulin dysregulation (ID), but EMS also increases the risk of developing laminitis (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Laminitis is a painful disease caused by injury to the tissue between the hoof and the underlying bone. Treating laminitis is particularly challenging and, in severe cases, euthanasia might be considered to prevent further suffering (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe diagnosis of EMS is not straightforward. It may be suspected in horses with compatible history, such as those having difficulties in losing weight or considered \u0026ldquo;easy keepers\u0026rdquo;, as well as horses showing clinical signs as obesity and evidence of previous or ongoing episodes of laminitis. Currently, the gold standard for supporting a diagnosis of EMS relies on laboratory testing to investigate ID (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Measurement of basal insulin is the simplest test to perform, as it only requires a single blood sample and established reference ranges (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). However, results may be affected by external factors including diet, age, stress, season and method of insulin measurement, among others (\u003cspan additionalcitationids=\"CR5 CR6 CR7 CR8 CR9\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). For this reason, dynamic tests are currently recommended, and the oral sugar test (OST) is preferred in clinical practice (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). The OST evaluates insulinemic response following consumption of carbohydrates, which not only reflects insulin sensitivity, but also the effect of the enteroinsular axis on insulin secretion (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). However, this test requires compliance of the horse to tolerate sugar administration and some studies have described a lack of sensitivity (especially when lower doses of carbohydrates are used) and poor repeatability (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). As a results, EMS diagnosis is considered one of the current challenges in veterinary medicine.\u003c/p\u003e \u003cp\u003eEarly diagnosis of EMS is key to prevent laminitis (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e), and significant efforts have been made to identify biomarkers that enable prompt detection of EMS. Mass spectrometry\u0026ndash;based proteomics is a powerful tool to uncover molecular pathways underlying pathophysiological conditions across various cells, tissues, and biological fluids. To our knowledge, no previous studies have used a global proteomic approach to interrogate blood in the search of protein biomarkers for the diagnosis of EMS. In the current study, we used label free quantitative proteomics to detect variations in the plasma proteome of healthy horses, non-ID obese horses, and animals diagnosed with EMS, with the aims of (a) expanding our understanding on the molecular mechanisms underlying EMS and (b) offering the foundations for the development of reliable blood-based biomarker test, which could enable early diagnosis. Our findings have the potential to contribute to improve welfare and management of horses affected by this debilitating condition.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and animals\u0026rsquo; enrolment criteria\u003c/h2\u003e \u003cp\u003eStudy design and analytical workflow are summarized in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Animals enrolled in this study were horses suspected of having EMS by referring vets of both the Veterinary Hospital at the University of C\u0026oacute;rdoba (Spain) and Extremadura (C\u0026aacute;ceres, Spain). The control group of healthy animals consisted of teaching horses housed at both hospitals whose blood samples were taken from routine blood analysis as part of their annual wellness evaluation. Both university and client-owned horses were evaluated for inclusion criteria and informed client consent was obtained before evaluation. Data collection and testing were either performed at university facilities or in the field at clients' properties. Animals underwent a clinical examination to assess their health status, including evaluation of obesity and a clinical history of laminitis episodes. Information collected also included diet (pasture, hay and or concentrate), weight gain, activity performed (dressage, breeding or teaching) and current or previous medications administered. No animals were euthanized as part of the study.\u003c/p\u003e \u003cp\u003eObesity and adiposity were assessed by two independent veterinarians blinded to each other and the scores from the two evaluators were averaged. Body condition score (BCS) and cresty neck score (CNS) were assigned in accordance with previous publications (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e) and the presence of localised fat deposits was also recorded. None of the animals included in the study were undergoing any pharmacological treatment at the time of enrolment and subjects with suspected pituitary pars intermedia dysfunction (PPID) were excluded from the study.\u003c/p\u003e \u003cp\u003eBasal insulin was measured in all individuals and dynamic tests were restricted to those animals suspected of EMS in order to avoid unnecessary tests being performed on healthy horses. For the dynamic tests, supplementary feed was withheld overnight, leaving only access to hay. Immediately after obtaining the baseline blood samples, corn syrup (Karo Light Corn Syrup; ACH Food Companies, Memphis, TN, USA) was administrated orally using a 60 mL syringe at 0.45 mL/kg of body weight, and further blood samples were collected 60 min after oral administration.\u003c/p\u003e \u003cp\u003eAfter excluding animals that did not meet the inclusion criteria, a total of 34 animals were enrolled in the study (Supplementary Table\u0026nbsp;1). Animals were classified into three groups based on their clinical signs and the EMS diagnostic criteria (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e): a control group (n\u0026thinsp;=\u0026thinsp;11) consisted of healthy animals normoinsulinemic with no clinical signs of EMS or other diseases; a second control group included obese horses with no signs of ID (n\u0026thinsp;=\u0026thinsp;12) (BCS\u0026thinsp;\u0026ge;\u0026thinsp;6.5/9 and basal blood insulin\u0026thinsp;\u0026le;\u0026thinsp;30 mU/L and \u0026le;\u0026thinsp;90 mU/L after 60 min OST) and a third group included EMS animals, with obesity and confirmed ID (n\u0026thinsp;=\u0026thinsp;11) (BCS\u0026thinsp;\u0026ge;\u0026thinsp;6.5/9, CNS\u0026thinsp;\u0026ge;\u0026thinsp;3/5, basal blood insulin\u0026thinsp;\u0026gt;\u0026thinsp;30 mU/L and/or \u0026gt;\u0026thinsp;90 mU/L after 60 min OST).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePlasma collection\u003c/h3\u003e\n\u003cp\u003eBlood samples were collected from jugular venipuncture into heparinized tubes in the morning before the horses had eaten any kind on concentrate feed. Plasma was separated by centrifugation at 2500 \u003cem\u003eg\u003c/em\u003e for 10 min immediately after collection, and frozen at \u0026ndash; 80 ˚C until used.\u003c/p\u003e\n\u003ch3\u003eInsulin quantification\u003c/h3\u003e\n\u003cp\u003eInsulin determinations were performed using an equine-optimized insulin ELISA (Equine Insulin ELISA, Mercodia AB, Sweden). The insulin ELISA kit showed a detection limit of 1.15 mU/L, an intra-assay coefficient\u0026thinsp;\u0026lt;\u0026thinsp;5%, and inter-assay coefficient\u0026thinsp;\u0026lt;\u0026thinsp;15%.\u003c/p\u003e\n\u003ch3\u003ePeptide mass fingerprinting (PMF)\u003c/h3\u003e\n\u003cp\u003eProteins in plasma samples were separated by polyacrylamide gel electrophoresis in denaturant and reducing conditions (SDS-PAGE) (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e) using 12% resolving and 4% stacking gels. Electrophoresis was performed at 200 V for 45 min. Precision Plus Protein Dual Colour Standards (Bio-Rad) were used as molecular weight markers. Gels were stained with Coomassie Blue G-250 solution (Merck). Gel plugs were removed and proteins digested as previously described (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e), using trypsin. Peptide mixtures from the proteolytic reactions were analysed by matrix-assisted laser-desorption ionization\u0026ndash;time of flight-mass spectrometry (MALDI\u0026ndash;TOF) in a UltrafleXtreme mass spectrometer (Bruker Daltonics), operated in positive ion detection reflector mode. Samples were mixed 1:1 (v/v) with a 10 mg/mL solution of α-cyano-4-hydroxycinnamic acid in 60% acetonitrile (ACN) (v/v)/0.2% trifluoroacetic acid (TFA) (v/v), before being spotted onto the MALDI target and air-dried. Spectra were acquired at 35% laser energy with 2000 laser shots per spectrum between 900\u0026ndash;3500 m/z. External mass calibration was performed using a mixture of des-Arg bradykinin (904.47 Da), neurotensin (1672.92 Da), corticotrophin (2465.2 Da) and oxidized insulin chain (3495.9 Da) in 50% ACN/0.1% TFA (v/v).\u003c/p\u003e\n\u003ch3\u003eGlobal quantitative proteomic survey\u003c/h3\u003e\n\u003cp\u003eA quantitative proteomic survey was applied to undepleted plasma samples to identify proteins which abundance changes across the investigated groups. Total protein concentration in plasma samples was determined by Bradford assay (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) using bovine serum albumin as standard. Plasma samples were diluted in 25 mM ammonium bicarbonate to obtain a final protein concentration in the digestion mixture of 0.5 \u0026micro;g/\u0026micro;L. Proteins were digested as previously described (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). In summary, proteins were first denatured using RapiGest SF surfactant (Waters Corporation) at a final concentration of 0.05% (w/v) for 10 min at 80\u0026deg;C. Then, proteins were reduced with 3 mM dithiothreitol for 10 min at 60\u0026deg;C, followed by alkylation with 9 mM iodoacetamide in the dark, at room temperature for 30 min. Finally, trypsin was added at a 50:1 ratio (protein:enzyme) and samples incubated overnight at 37\u0026deg;C. To stop the proteolytic reaction, TFA was added at a final concentration 0.5% (v/v), followed by incubation at 37\u0026deg;C for 45 min. Finally, samples were centrifuged at 13,000 \u003cem\u003eg\u003c/em\u003e for 15 min and the supernatant collected. Tryptic peptides were analysed by LC-TIMS-MS/MS in a nanoElute nanoflow ultrahigh-pressure LC system (Bruker Daltonics, Bremen, Germany) coupled to a timsTOF Pro 2 mass spectrometer, equipped with a CaptiveSpray nanoelectrospray ion source (Bruker Daltonics). Peptide digests (200 ng) were loaded onto a Pepmap C18 capillary column (15 cm length, 75 \u0026micro;m ID, 1.9 \u0026micro;m particle size, Bruker) and separated at 30\u0026deg;C using a 40 min gradient at a flow rate of 300 nL/min (mobile phase A (MPA): 0.1% FA; mobile phase B (MPB): 0.1% FA in ACN). A step gradient from 0 to 30% MPB was applied over 24 min, followed by a 30 to 90% MPB step for 1 min, and finished with a 90% MPB wash for an additional 5 min for a further time. The timsTOF Pro 2 was run in Data Dependent Acquisition-Parallel Accumulation Serial Fragmentation (DDA-PASEF) mode. Mass spectra for MS and MS/MS scans were recorded between 100 and 1700 m/z. Ion mobility resolution was set to 0.85\u0026ndash;1.30 V s/cm\u003csup\u003e2\u003c/sup\u003e over a ramp time of 100 ms. Data-dependent acquisition was performed using 4 PASEF MS/MS scans per cycle with a duty cycle close to 100%. A polygonal filter was applied on the m/z space and ion mobility to exclude low m/z, mainly single-charged ions from the selection of PASEF precursors. An active exclusion time of 0.4 min was applied to precursors that reached 20,000 intensity units. The collision energy was increased stepwise as a function of the ion mobility ramp, from 27 to 45 eV.\u003c/p\u003e \u003cp\u003eIdentification of proteins and quantification of their relative abundance were obtained using the proteomics software Peaks Studio X Pro (Bioinformatics solutions Inc.). Spectra were searched against the horse reference proteome downloaded from UniProt (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.uniprot.org\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.uniprot.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (69434 entries, downloaded on 28/11/2024). Search parameters were set to 15 ppm as mass tolerance for precursors and 0.05 Da for fragment ions, semispecific search and two miss cleavages allowed. Carbamidomethylation of cysteines was set as a fixed modification, and methionine oxidation as variable modification allowing up to 4 variable modifications per peptide. False discovery rate (FDR) was set at 1% at both peptide and protein levels, based on decoy assignments. Only proteins identified with at least 2 unique significant peptides were taken to further analysis. Proteins sharing the same set of peptides were reported as one protein group. Extracted MS1 peak areas were used for protein quantification. Features were automatically detected, and samples aligned using the Peaks label free quantification algorithm. Total ion current of the samples was used to normalize feature intensity. Label free parameters were set to 15 ppm as mass shift tolerance between samples and automatic retention time shift tolerance. Filters included the requirement of 2 peptides identified per protein and a fold change higher than 1.5. Significance was calculated by ANOVA test, setting a threshold of 1% FDR to calculate adjusted p values.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eBioinformatics and statistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analysis and data visualization were conducted in R (v.3.2) (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). One-way ANOVA and unpaired t-tests were used to assess significant differences between groups. P values were adjusted for multiple hypothesis testing using the Benjamini-Hochberg (FDR) procedure. A proportional Venn diagram was created using the \u0026ldquo;eulerr\u0026rdquo; R package (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Protein abundance data were log transformed for normalization and subsequently employed to create a heatmap using the \u0026ldquo;pheatmap\u0026rdquo; R package (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Hierarchical clustering was generated using Euclidean as distance measurement and ward.D2 as clustering method. The principal component analysis (PCA) was performed using the \u0026ldquo;FactoMineR\u0026rdquo; R package (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e), on standardized data, and results visualized using the \u0026ldquo;factoextra\u0026rdquo; R package (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e) setting a confidence level of 0.999 for ellipses. Go ontology analysis was performed using the web-based tool g:Profiler (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eClinical characteristics of the experimental groups\u003c/h2\u003e \u003cp\u003eIn the current study we compared the plasma proteome of horses diagnosed with EMS to that of healthy animals with no evidence of ID, including both obese and non-obese animals. Classification of the patients was established according to the current diagnosis of EMS, based on obesity and ID (Supplementary Table\u0026nbsp;1). A total of 34 horses were enrolled in the study: 30 Andalusian horses and four crossbreds. Among them, 21 were females and 13 males. Mean age was 11.6\u0026thinsp;\u0026plusmn;\u0026thinsp;5.8 years. Fat patches were identified in 18 animals, all being in the EMS (9/11) or obese (9/12) groups. Significant differences among EMS and non-ID obese groups versus healthy controls were also found in BCS (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and CNS (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (EMS 8.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6 and 4.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8; non-ID obese 8.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5 and 3.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5; healthy 5.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9 and 2.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4, respectively). However, no significant differences between EMS and non-ID obese groups were found in those parameters. Five of the horses had history of previous laminitis: four in the EMS group and one in the non-ID obese group. There was no history of laminitis in the healthy group. Basal insulin was, as anticipated, significantly higher (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in the EMS group (54.6\u0026thinsp;\u0026plusmn;\u0026thinsp;47.6 mU/L) than in the non-ID obese (9.6\u0026thinsp;\u0026plusmn;\u0026thinsp;8.3 mU/L) and healthy (13.7\u0026thinsp;\u0026plusmn;\u0026thinsp;6.5 mU/L) groups. Post OST insulin was also significantly higher (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) in the EMS group (134.5\u0026thinsp;\u0026plusmn;\u0026thinsp;81.9 mU/L), than in the obese group (48.9\u0026thinsp;\u0026plusmn;\u0026thinsp;16.6 mU/L).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eGlobal quantitative proteomic survey in plasma\u003c/h2\u003e \u003cp\u003eComplexity of plasma samples from healthy (n\u0026thinsp;=\u0026thinsp;11), non-ID obese (n\u0026thinsp;=\u0026thinsp;12) and horses diagnosed with EMS (n\u0026thinsp;=\u0026thinsp;11), evaluated by SDS-PAGE, showed that protein pattern was very similar in all three investigated groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Protein fraction was dominated by a band of about 66 kDa, later confirmed by PMF to contain albumin. Other notable proteins in the profile included serotransferrin (75\u0026ndash;80 kDa), histidine-rich glycoprotein (75 kDa), Ig-like domain containing protein (approx. 50 kDa) and cathepsin D (approx. 45 kDa). Total protein concentration was significantly higher in the non-ID obese group compared to EMS (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB, Supplementary table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePlasma proteomes were then compared by label free quantitative proteomics. As outlined in Material and Methods, plasma proteins were digested using trypsin and the resulting peptides were analysed by LC-TIMS-MS/MS using DDA-PASEF acquisition mode. A total of 292 protein groups were identified in Peaks X Pro using as acceptance criteria of at least 2 unique significant peptides and 1% FDR at both peptide and protein levels. The identity, significance, number of unique peptides and the relative abundance of these proteins is shown in Supplementary Table\u0026nbsp;2. A total of 227 protein groups were identified in all three groups, 5 protein groups were uniquely identified in the EMS group, 14 in the healthy group and 7 in the non-ID obese group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). However, since the proteins uniquely identified in the EMS group were found in only two or fewer animals, they were excluded as viable candidates for group differentiation.\u003c/p\u003e \u003cp\u003eAssessment of the differential abundance of proteins across the experimental groups, using Peaks label free algorithm, revealed 76 proteins with statistically significant changes among the three groups (one-way ANOVA, 1% FDR). The protein groups, significance, relative area and ratio are shown in Supplementary Table\u0026nbsp;3. Principal Component Analysis (PCA), based on protein abundances, showed that the first and the second components effectively distinguished the three conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). First component separates healthy from EMS animals, being able to explain a 34.7% of the total variance between samples. Top 5 proteins responsible for the variance explained by component 1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC) included vitamin D-binding protein (UniProtKB F6T0P6), protein z vitamin K dependent plasma glycoprotein (UniProtKB F6SR87), transferrin (UniProtKB F6PKE1), hyaluronan binding protein 2 (UniProtKB A0A5F5PJC3) and kininogen 1 (UniProtKB A0A5F5PUE2). Second component explained a 26.4% of the total variance between samples, separating obese from healthy and EMS individuals (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Top 5 proteins responsible for the variance explained by component 2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD) include complement C3 (UniProtKB A0A9L0RG95), beta-2-glycoprotein 1 (UniProtKB F6Z041), a serpin member (UniProtKB F7BM31), an immunoglobulin (UniProtKB A0A3Q2HY39) and leucine rich alpha-2-glycoprotein 1 (UniProtKB A0A9L0T558).\u003c/p\u003e \u003cp\u003eComparison between EMS and healthy groups, revealed 57 proteins which abundances were significantly different between these two groups (Supplementary Table\u0026nbsp;3). Most of the proteins (55 over 57) were more abundant in EMS animals than healthy controls while 2 were more abundant in healthy animals than EMS animals. Comparison of EMS animals with non-ID obese animals, to account for changes associated with obesity, revealed 43 proteins which abundance significantly changed between these groups. Of these, 24 were more abundant in the EMS group, while 19 were more abundant in the obese group (Supplementary Table\u0026nbsp;3).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNormalized abundances of proteins showing significant differences among three groups were visualized as a heatmap (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Clustering analysis was performed on both samples and proteins based on Euclidean distances. Clustering analysis on samples (columns) showed three main clusters corresponding to the three clinical conditions: healthy, non-ID obese and EMS diagnosed. Clustering analysis on proteins (rows), based on Euclidean distances, showed three main clusters: (cluster 1) proteins specifically elevated in EMS animals, which included proteins elevated in the EMS group when compared to both control groups (obese and non-obese animals); (cluster 2) proteins associated with obesity, which includes those elevated specifically in EMS and non-ID obese animals with no significant difference between them, and (cluster 3) proteins which abundance diminished in EMS animals when compared to non-ID obese animals.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAs proteins in cluster 1 are specifically elevated in EMS, they could be targeted as potential candidates for diagnosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Enrichment analysis of these proteins highlighted their involvement in complement system activation, the coagulation cascade, cholesterol metabolism, peptidase regulator activity and the extracellular region (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Within the complement system, we observed increased levels of complement C1q subunits a (UniProt KB A0A9L0RPF6) and subunit c (UniProt KB A0A3B0ITF5), complement C2 (UniProt KB A0A3Q2IDE0) and C4a anaphylatoxin (UniProt KB A0A9L0R7H5). Proteins associated with the coagulation cascade elevated in EMS included alpha-2-macroglobulin (Uniprot KB F6QAD8 and F6RI47), angiotensinogen (UniprotKB A0A9L0T1B5) and several serine proteases inhibitors (UniprotKB A0A9L0S2K1 and A0A3Q2H3F6). Remarkably, lumican and fibulin-1, both related with ECM remodeling, exhibited the highest increases in EMS, with fold-changes of 3.53 and 2.24 respectively when compared non-ID obese.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eProteins in cluster 2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) were elevated in both non-ID obese and EMS groups vs healthy group. Among these proteins, some exhibited no significant difference between the non-ID obese and EMS groups, suggesting their association with obesity. This group included some apolipoproteins (A-II (UniProtKB A0A9L0R4P3) and C-III (UniProtKB A0A3Q2I0V8)), some proteins related to the alternative activation pathway of the complement systems (complement factor D (UniProtKB A0A3Q2LBP6) and properdin (UniProtKB F6SP74)), proteins related to the coagulation cascade (antithrombin-III (UniProtKB F7CYR1), coagulation factor IX (UniProtKB F6RFT9), coagulation factor VII (UniProtKB F7ABW7), and several inhibitors of serin proteases (i.e. fetuin A (UniProtKB A0A5F5Q0V8)). Kininogen 1 (UniProtKB A0A5F5PUE2), part of kallikrein-kinin system (KKS), an important interconnection between the complement and coagulation cascades, showed an increased abundance in both EMS and non-ID obese vs healthy controls. Additionally, among the proteins in cluster 2, we identified some proteins that were elevated in EMS and non-ID obese vs healthy controls but also showed higher levels in EMS compared to non-ID obese controls, suggesting their potential as biomarkers of disease progression. This group included hyaluronan binding protein 2 (UniProtKB A0A5F5PJC3, also known as factor VII activating protease), vitamin D binding protein (UniprotKB F6T0P6) and a carboxylic ester hydrolase (UniprotKB A0A9L0SFT5) and fetuin B (UniProtKB F6RRV1).\u003c/p\u003e \u003cp\u003eFinally, cluster 3 highlighted some proteins showing diminished abundance in EMS compared to non-ID obese group, which can be explored as potential diagnostic biomarkers. This group included adiponectin (UniProtKB F7DZE7), complement component C3 (UniProtKB A0A9L0RG95), complement component C1r (UniProtKB A0A9L0SLY6) and members of the serpin superfamily (member 3 serpin family A (UniProtKB F6ZLR1) and member 1 serpin family D (UniProtKB F7BM31)).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn the present study, we explored the differences in the plasma proteome of horses diagnosed with EMS compared to healthy horses to identify altered pathways potentially related with the pathophysiology of this syndrome and putative biomarkers for early diagnosis. We employed label free mass spectrometry-based quantitative proteomics to detect variations in the concentration of plasma proteins across three experimental groups: (a) healthy normoinsulinemic non-obese animals, (b) obese animals with no evidence of ID and (c) EMS animals (obese and ID animals). The inclusion of non-ID obese horses as a control group aimed to account for proteome changes associated with obesity, as it is a key component of EMS syndrome. In our study the categorization of animals to either obese or EMS groups was based on basal and post OST insulin concentration in blood. ID was considered when basal insulin was above 30 \u0026micro;U/mL /or post OST insulin was above 90 \u0026micro;U/mL. To determine these levels, ID was defined according to the recommendations set by the equine endocrinology group and EMS consensus. The cutoff levels were calculated for our method of quantification using a web-based insulin concentration converter developed for horses (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Plasmatic insulin results can be influenced by several factors such as stress, diet, disease, time of sampling, method of measurement or even a short transport before testing (\u003cspan additionalcitationids=\"CR5 CR6 CR7 CR8 CR9\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e), therefore, none of the horses in this study was transported shortly before the sampling procedure was performed avoiding any stress. Additionally, a similar feed privation protocol and method of insulin measurement were used in all the animals. However, it is worth noting that not all the samples were taken at the same time of the year, and some authors have described that this sole factor can have an impact in EMS classification (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePlasma proteomics is challenging due to the large dynamic range of protein abundances, with highly abundant proteins hindering the detection of less abundant proteins. Nevertheless, recent developments in instrumentation and bioinformatic applications have improved both the identification and quantification of proteins. We employed state of the art instrumentation (tims-TOF Pro 2, Bruker) and acquisition techniques (DDA-PASEF) (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e) to enhance the coverage of equine plasma proteome. This approach resulted in the coverage of approx. a 20% more of the equine plasma proteome compared to the most recent report (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAs specific features in the proteome of EMS horses we found that the concentration of multiple members of the classical activation pathway of the complement system is elevated (Supplementary Table\u0026nbsp;3, Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The complement system in vertebrates is a complex protein network organized by a series of serine proteases, which are sequentially activated to cleave specific downstream proteins. The system can be activated via the classical, alternative or lectin pathways, resulting in the cleavage of component C3 and the activation of membrane attack complex (MAC), that target cell lysis (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). In addition, complement activation participates in several functions such as immune regulation and inflammatory process by producing anaphylatoxins.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePlasma levels of some components, including C3, C3a, factor B, factor D and factor H, are associated with body mass index (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e) and plasma level of complement C3 had been suggested as a potential biomarker of insulin resistance in humans (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). In good agreement with these studies, we observed that members of the complement system are elevated in EMS animals, however some of them are also elevated in non-ID obese animals, raising the question of whether they might reflect the progression of the syndrome in horses as it has been suggested for human (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Our findings show that proteins belonging to the classical activation pathway are elevated specifically to EMS (i.e. C1q, complement C2 and C4a anaphylatoxin) while some components of the alternative pathway are shared with non-ID obese animals (factor D and properdin). As activation of the complement system can arise from different inflammatory conditions, additional research is required to better understand its role in EMS and its potential as early biomarker of disease development and/or progression.\u003c/p\u003e \u003cp\u003eIn human, metabolic syndrome displays characteristics of a hypercoagulable state, defined by increased levels of coagulating factors, inhibition of fibrinolytic pathways and platelet hypercoagulability (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). This hypercoagulable state seems to be related to adipose tissue dysregulation, oxidative stress and chronic systemic inflammation. Several studies have evaluated the value of proteins related to inflammation as blood biomarkers of EMS, obtaining contradictory results (\u003cspan additionalcitationids=\"CR38 CR39 CR40 CR41 CR42\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e) and no differences were found in parameters traditionally used for the evaluation of coagulation between control horses and horses with obesity and ID (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). However, some indicators of clot strength differed between groups, suggesting a hypercoagulable tendency in EMS animals. The hypercoagulable and hypofibrinolytic states associated with obesity and metabolic syndrome are confirmed in our study by the elevation in EMS vs obese animals of several members of the coagulation cascade and two isoforms of alpha-2-macroglobulin, an inhibitor of fibrinolysis. High levels of this protein in blood have been identified as a risk factor for cardiovascular events (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). Additionally, alpha-2-macroglobulin increases in the hyperinsulinemic hoof of horses, which may be attributed to tissue damage and inflammation associated with hyperinsulinemia-induced laminitis. Although the levels of this protein have been found to be elevated in the plasma of horses with chronic laminitis, it has never been described or implicated in EMS (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe coagulation and complement systems share some features enabling multiple interactions that can explain their association with several inflammatory and thrombotic conditions (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). One of the interactions is by the crosstalk between the kallikrein-kinin system (KKS) (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). Notably, carboxypeptidase N, which inactivates C3a from the complement system, also inactivates bradykinin from the KKS (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e), then acting as connecting point between coagulation, thrombosis, inflammation, and innate immunity (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). Besides, C1q has been described as one of the ligands of histidine rich glycoprotein (HRG), a regulatory factor in the coagulation cascade with anticoagulant and antifibrinolytic properties, that is also elevated in EMS and obese animals, connecting the complement and coagulation cascades (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFetuin A is a hepato-adipokine that has been involved in diverse pathological processes including complications related to metabolic disorders (\u003cspan additionalcitationids=\"CR53\" citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). Consistent with our findings, previous studies in humans have shown that circulating fetuin A is significantly elevated in patients with obesity and metabolic syndrome (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e). This glycoprotein belongs to the protease inhibitor cystatin superfamily that negatively affects glucose homeostasis and participates in adipose tissue inflammation contributing to insulin resistance development (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e). It has been proposed that fetuin A induces metabolic dysfunction through a number of mechanisms that include inhibition of insulin receptor tyrosine kinase activity and the promotion of inflammation in immune cells and adipocytes (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e). Fetuin B, another protease inhibitor that acts as a proinflammatory factor, is linked to the development of insulin resistance and type 2 diabetes (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e) and has been nominated as putative biomarker for insulin resistance and metabolic syndrome in human (\u003cspan additionalcitationids=\"CR61 CR62\" citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e). Given the roles of fetuin A and fetuin B in human metabolic dysfunction, we hypothesize that these two proteins may contribute to the pathophysiology of EMS. Investigating the role of fetuin A and fetuin B in horses could reveal novel insights into the mechanisms underlying EMS and provide potential targets for therapeutic interventions.\u003c/p\u003e \u003cp\u003eExtracellular matrix (ECM) components play a critical role in adipose tissue and liver fibrosis associated with metabolic syndrome. One of the proteins with the highest changes in EMS animals is lumican, an ubiquitous leucine-rich proteoglycan that regulates the assembly of collagen fibers in the ECM (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e) and inhibits matrix metalloproteinases (MMPs) (\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e). Different studies have revealed the association of this protein with adipocyte disfunction in the context of metabolic syndrome progression in humans and animal models (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e). MMPs are key in the regulation of tissue repair (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e) and the inhibition by lumican may worsen the scenario in the context of laminitis. Furthermore, inflammation and dysregulation of endogenous MMPs are implicated in the development of laminitis (\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e). Fibulin-1, which is elevated in EMS animals, is another ECM protein present in blood. It has been related with cardiovascular risk in human diabetes (\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e). Due to its association with cardiovascular complications, this protein could be also related to the onset of laminitis. Further studies are needed to explore the relationship of high levels of lumican and fibulin-1 in the presentation and severity of episodes of laminitis in EMS patients.\u003c/p\u003e \u003cp\u003eAdiponectin (UniProtKB F7DZE7), an adipokine linked to protection against cardiovascular diseases, insulin resistance, inflammation and metabolic disease (\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e) is decreased in EMS vs obese non-ID animals. Previous studies indicated that normal adipocytes secrete higher amounts of the high molecular weight (HMW) form of adiponectin and retain more of the low molecular weight (LMW) form. In horses, HMW adiponectin have been reported to have an inverse relationship with body condition score and insulin levels and higher levels have been observed in lean vs obese animals (\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eInsulin and insulin-like growth factors (IGFs) are central hormones in regulating metabolism, and insulin-like growth factor binding proteins (IGFBPs) represent an important link between insulin and IGF systems. Previous research suggested that IGFBPs play an important role in obesity, insulin resistance, cardiovascular risk and metabolic syndrome in humans (\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e). Abnormal IGFBP expression is a sensitive marker of ID and is used to identify individuals with ID at high cardiovascular risk and as an early marker of human metabolic syndrome (\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e). In our study we observed that an IGFBP is specifically increased in EMS animals compared to both non-ID obese and healthy animals. Furthermore, this protein is elevated in non-ID obese vs healthy animals, suggesting that it could serve as potential marker of disease progression. Further work is needed to evaluate the role of IGFBP in EMS and its value as progression biomarkers.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this study, we compared the plasma proteomes of healthy, non-ID obese horses and EMS animals, identifying potential biomarkers that could help detect at risk animals and offer insights into the pathophysiology of the condition. Some proteins, including various members of the complement and coagulation cascades, and proteins related to ECM remodeling showed elevated abundance specifically in EMS animals, potentially offering valuable biomarkers for the early detection of the syndrome. Notably, elevated levels of classical complement pathway proteins, such as C4a anaphylatoxin and complement C2, were specific to EMS horses, suggesting their potential as diagnostic biomarkers. Additionally, ECM proteins like lumican and fibulin-1 were found to be significantly altered, suggesting their involvement in tissue remodeling and the development of laminitis. Moreover, elevated levels of certain proteins, such as alpha-2-macroglobulin, fetuin A, fetuin B and IGFBPs were found in both EMS and non-ID obese horses, supporting that obesity-related metabolic disturbances play a significant role in EMS development and identifying these proteins as potential biomarkers for monitoring disease progression. Our findings enhance our understanding of the molecular mechanisms underlying EMS and suggest several promising biomarkers for further investigation. However, additional research is needed to validate these proteins as biomarkers and to assess their potential in improving EMS management.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors gratefully acknowledge the Proteomics Unit at the Servicio Central de Apoyo a la Investigaci\u0026oacute;n (SCAI) from the University of C\u0026oacute;rdoba. E.M.E-L- received a PhD Fellowship from Plan Propio (University of C\u0026oacute;rdoba).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGGB, ED, EAT and AD designed the study. GGB, EMEL, ED, EAT and AD obtained the funding. ED collected the samples and clinical data. EMEL and BOG performed the laboratory experiments. GGB, EMEL, BOG and ED analyzed the results. GGB, EMEL and ED wrote the main manuscript draft. EAT and AD reviewed and edited the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by a grant from the Waltham Foundation (2021-2023).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the data supporting the findings of this study are available within the paper and its Supplementary Information files. Should any raw data files be needed in another format they are available from the corresponding author upon request.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol received approval from the Institutional Committee at the Veterinary Hospital of the University of C\u0026oacute;rdoba (Spain) and the University of Extremadura (Spain). Written informed consent was obtained from the owners of all horses enrolled in the study. All diagnostic procedures were clinically indicated, aimed at benefiting the animals, and were conducted in compliance with the highest standards of veterinary practice. The authors confirm that all methods were carried out in accordance with relevant guidelines and veterinary regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDurham AE, et al. ECEIM consensus statement on equine metabolic syndrome. J Vet Intern Med. 2019;33:335\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHunt RJ. A retrospective evaluation of laminitis in horses. Equine Vet J. 1993;25:61\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePollard D, Wylie CE, Newton JR, Verheyen KLP. Factors associated with euthanasia in horses and ponies enrolled in a laminitis cohort study in Great Britain. 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Acta Diabetol. 2010;47:5\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarter RA, Geor RJ, Burton Staniar W, Cubitt TA, Harris PA. Apparent adiposity assessed by standardised scoring systems and morphometric measurements in horses and ponies. Vet J. 2009;179:204\u0026ndash;10.\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":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-veterinary-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [BMC Veterinary Research](http://bmcvetres.biomedcentral.com/)","snPcode":"12917","submissionUrl":"https://submission.nature.com/new-submission/12917/3?","title":"BMC Veterinary Research","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Equine metabolic syndrome, plasma proteomics, diagnostic biomarkers","lastPublishedDoi":"10.21203/rs.3.rs-6223672/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6223672/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eEquine Metabolic Syndrome (EMS) is a multifactorial endocrine disorder characterized by obesity, insulin dysregulation (ID), and an increase in the risk of laminitis, a painful condition that can lead to euthanasia in severe cases. Diagnosing EMS is challenging and often relies on clinical history including obesity, difficulty in losing weight, and recurring episodes of laminitis. The gold standard for laboratory support of an EMS diagnosis is the identification of ID, being basal insulin the simplest and most accessible method. However, various factors such as diet, age, stress, season, and testing protocols can influence results. Dynamic tests like the oral sugar test (OST) are preferred but present limitations due to low sensitivity and poor repeatability. These diagnostic challenges make EMS difficult to detect in veterinary medicine highlighting the need for an effective method of the early detection of EMS to prevent laminitis and its associated complications.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eMass spectrometry-based proteomics represents a powerful tool to identify biomarkers and explore molecular pathways related to the underlying pathology. In the current study we established an integrated proteomics pipeline to identify plasma biomarkers for EMS diagnosis. We compared plasma proteomes from healthy horses, non-ID obese horses and animals diagnosed with EMS. This comparison revealed 76 proteins with significant changes (1% FDR) between groups.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eOur study demonstrates that the complement system, the coagulation cascade and extracellular matrix remodelling pathways are altered in EMS. These findings offer new insights into the molecular basis of the development of EMS and led to the nomination of several proteins as potential biomarkers for its early detection.\u003c/p\u003e","manuscriptTitle":"Quantitative proteomics unveils potential plasma biomarkers and provides insights into the pathophysiological mechanisms underlying equine metabolic syndrome","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-07 09:54:09","doi":"10.21203/rs.3.rs-6223672/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-16T10:59:47+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-28T11:35:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"171831287990785211783225827924702816764","date":"2025-04-24T02:48:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"237704315147734425941008760875543390541","date":"2025-04-23T12:17:33+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-15T15:08:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"95024681228492708260208179625721095169","date":"2025-04-09T16:56:48+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-03T07:26:32+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-03T07:23:35+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-03-26T06:37:05+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-25T18:56:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Veterinary Research","date":"2025-03-25T18:55:29+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-veterinary-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [BMC Veterinary Research](http://bmcvetres.biomedcentral.com/)","snPcode":"12917","submissionUrl":"https://submission.nature.com/new-submission/12917/3?","title":"BMC Veterinary Research","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ba6f93a1-476b-40ea-a9e3-954829e00ea2","owner":[],"postedDate":"April 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-07-07T16:13:45+00:00","versionOfRecord":{"articleIdentity":"rs-6223672","link":"https://doi.org/10.1186/s12917-025-04879-6","journal":{"identity":"bmc-veterinary-research","isVorOnly":false,"title":"BMC Veterinary Research"},"publishedOn":"2025-07-02 15:58:45","publishedOnDateReadable":"July 2nd, 2025"},"versionCreatedAt":"2025-04-07 09:54:09","video":"","vorDoi":"10.1186/s12917-025-04879-6","vorDoiUrl":"https://doi.org/10.1186/s12917-025-04879-6","workflowStages":[]},"version":"v1","identity":"rs-6223672","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6223672","identity":"rs-6223672","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}