Urinary metabolomics study on the protective role of cocoa in Zucker diabetic rats via 1H-NMR-based approach

Citation: Fernández-Millán, E.; Ramos, S.; Álvarez-Cilleros, D.; Samino, S.; Amigó, N.; Correig, X.; Chagoyen, M.; Álvarez, C.; Martín, M.Á. Urinary Metabolomics Study on the Protective Role of Cocoa in Zucker Diabetic Rats via 1H-NMR-Based Approach. Nutrients 2022,14, 4127. https://doi.org/ 10.3390/nu14194127 Academic Editor: Andrea Fabbri Received: 15 September 2022 Accepted: 30 September 2022 Published: 4 October 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). nutrients Article Urinary Metabolomics Study on the Protective Role of Cocoa in Zucker Diabetic Rats via 1H-NMR-Based Approach Elisa Fernández-Millán1,2,*, Sonia Ramos 3, David Álvarez-Cilleros 3, Sara Samino 2,4 , Nuria Amigó2,4,5 , Xavier Correig 2,4,6 , Mónica Chagoyen 7, Carmen Álvarez 1,2 and MaríaÁngeles Martín2,3,* 1Department of Biochemistry and Molecular Biology, Faculty of Pharmacy (UCM), 28040 Madrid, Spain 2CIBER of Diabetes and Associated Metabolic Disease (CIBERDEM), ISCIII, 28029 Madrid, Spain 3Institute of Food Science, Technology and Nutrition (ICTAN-CSIC), 28040 Madrid, Spain 4Pere Virgili Institute for Health Research (IISPV), 43007 Tarragona, Spain 5Biosfer Teslab, 43201 Reus, Spain 6Department of Electronic Engineering, Rovira i Virgili University (URV), 43003 Tarragona, Spain 7National Centre for Biotechnology (CNB-CSIC), 28049 Madrid, Spain *Correspondence: [email protected] (E.F.-M.); [email protected] (M.Á.M.) Abstract: Cocoa constitutes one of the richest sources of dietary flavonoids with demonstrated anti-diabetic potential. However, the metabolic impact of cocoa intake in a diabetic context remains unexplored. In this study, metabolomics tools have been used to investigate the potential metabolic changes induced by cocoa in type 2 diabetes (T2D). To this end, male Zucker diabetic fatty rats were fed on standard (ZDF) or 10% cocoa-rich diet (ZDF-C) from week 10 to 20 of life. Cocoa supplementation clearly decreased serum glucose levels, improved glucose metabolism and produced significant changes in the urine metabolome of ZDF animals. Fourteen differential urinary metabolites were identified, with eight of them significantly modified by cocoa. An analysis of pathways revealed that butanoate metabolism and the synthesis and degradation of branched-chain amino acids and ketone bodies are involved in the beneficial impact of cocoa on diabetes. Moreover, correlation analysis indicated major associations between some of these urine metabolites (mainly valine, leucine, and isoleucine) and body weight, glycemia, insulin sensitivity, and glycated hemoglobin levels. Overall, this untargeted metabolomics approach provides a clear metabolic fingerprint associated to chronic cocoa intake that can be used as a marker for the improvement of glucose homeostasis in a diabetic context. Keywords: polyphenols; urine metabolites; type 2 diabetes; branched-chain aminoacids; untargeted metabolomics 1. Introduction Type 2 Diabetes (T2D) is the most prevalent metabolic disease in the world, with a large socio-sanitary impact due to its chronic macroand micro-vascular complications [ 1 ]. T2D is characterized by the elevation of blood glucose resulting from defects in insulin secretion, insulin action, or both. [ 2 ]. There are several glucose-lowering drugs available for the treatment of diabetes; however, most of them have potential adverse effects and are not completely effective in preventing the progression of the disease [ 3 ]. Therefore, more research in this area is crucial to develop alternative therapeutic agents or strategies that might reduce the harmful effect of diabetes and its associated complications. Natural products rich in phenolic compounds represent promising candidates for the treatment of diabetes, due to their good effectiveness in glucose metabolism, and their low cost and toxicity [ 4 ]. Particularly, flavonoids, the most abundant phenolic compounds in fruits and vegetables, have been described as multitarget agents with great therapeutic potential for diabetes management [ 5 ]. In this regard, cocoa constitutes one of the richest sources of dietary flavonoids, mainly flavanols which is gaining importance in research Nutrients 2022,14, 4127. https://doi.org/10.3390/nu14194127 https://www.mdpi.com/journal/nutrients Nutrients 2022,14, 4127 2 of 14 since it has been proven to reduce the pathogenesis of diabetes and its complications [ 6 ]. Notably, our previous studies have demonstrated that cocoa supplementation improves the glucose metabolism in diabetic animals [ 7 ] and has beneficial effects on associated diabetic complications, mainly arterial stiffness [ 8 ] and nephropathy [ 9 ]. Several mechanisms have been described as being involved in these positive outcomes [ 10 ], including a possible prebiotic effect of cocoa [ 11 ]. However, the precise underlying mechanisms of these antidiabetic actions are not fully understood. In this sense, knowing how cocoa modulates metabolic pathways significantly affected in diabetes could be essential to further clarify its final biological actions. Over the last years, metabolomics approaches have been successfully applied to evaluate metabolite alterations related to metabolic disease, such as diabetes [ 12 ], as well as to investigate the metabolic response to a nutritional intervention by characterizing the disturbed metabolites between control and treated groups [ 13 ]. Untargeted metabolomic approaches are especially useful, as they offer information on the levels of endogenous small molecule metabolites, presented in a biological sample without a priori information [ 14 ]. This comprehensive analysis unravels metabolite profile modifications and detects alterations in biological pathways and biochemical processes, providing further insight into the molecular mechanisms involved in the nutritional intervention. Particularly, nuclear magnetic resonance (NMR) is the most common analytical technique used to reveal profiles of metabolites involved in the tricarboxylic acid (TCA) cycle, amino acid, and carbohydrates metabolism, all of them suggested as significant pathways disturbed in diabetic models [ 15 ]. Indeed, the NMR study of biofluids has been proven to be useful to investigate the anti-diabetic activity of several plants’ bioactive compounds [ 15 – 18 ] as well as dietary flavonoids [ 19 , 20 ]. However, at present, there are no studies evaluating the metabolic impact of cocoa consumption in a diabetic setting, which could certainly help to better understand the mechanisms underlying its anti-diabetic activity. Therefore, this study was aimed to identify potential metabolic changes that could be involved in the beneficial effect of cocoa on diabetes. To this end, we have performed an untargeted NMR-based metabolomics analysis along with a multivariate analysis in an in vivo model of T2D, the Zucker diabetic fatty (ZDF) rats. Our strategic approach has revealed significant differences in the urine metabolic profiles between control (ZDF) and cocoa supplemented (ZDF-C) diabetic animals. In addition, main differential metabolites and metabolic pathways affected by the cocoa intake in diabetic animals have been identified. Overall, these results provide new insights on the mechanism underlying the impact of cocoa on T2D, improving the functional understanding of its anti-diabetic effects. 2. Materials and Methods 2.1. Diets, Animals, and Experimental Design Diets were prepared from an AIN-93G formulation (Panlab S.L., Barcelona, Spain). A cocoa rich-diet (10%) was produced by adding 100 g/kg of natural Forastero cocoa powder (a kind gift from Idilia Foods, Barcelona, Spain) to AIN-93G diet. The total polyphenolic content of the cocoa powder, as determined with the Folin-Ciocalteu method, was 2.4 g/100 g on a dry matter basis. The main cocoa flavonoids were determined by LC-MS, as previously described [ 8 ]. Monomeric epicatechin (382.0 mg/100 g) and catechin (115.2 mg/100 g) were the major flavanols in the extract, together with appreciable amounts of procyanidins B1 (35.2 mg/100 g) and B2 (132.2 mg/100 g). The resulting cocoa diet was isoenergetic and its composition is given in Supplementary Table S1. Male ZDF rats and their Zucker lean controls (ZL) were purchased from Charles River Laboratories (L’arbresle, France) at 9 weeks of age. Animals were acclimated for one week under standard controlled conditions (21 ◦ C ± 1 ◦ C; 12 h day/night cycle). After that, the ZDF rats were randomly sorted into two groups (eight animals per group) that received the standard AIN-93G diet (ZDF), or the same control diet supplemented with 10% of cocoa (ZDF-C), for 10 weeks. The lean Zucker rats (ZL) (n= 6) received the standard AIN-93G diet. During the experiment, food and water were available ad libitum. Body weight and Nutrients 2022,14, 4127 3 of 14 blood glucose in overnight fasted animals were followed weekly during the entire study and food intake was monitored three times per week. The animals were treated according to the European (2010/63/EU) and Spanish (RD 53/2013) legislation on Care and Use of Experimental Animals and the experiments were approved by the Ethics Committee from Comunidad de Madrid (PROEX 304/15). 2.2. Biochemical Determinations At 20 weeks of age, the animals were fasted overnight, and blood samples were collected for biochemical analysis. Blood glucose was determined using an Accounted Glucose Analyser (LifeScan España, Madrid, Spain). Serum insulin and Hb1Ac were analyzed with ELISA kits (Rat Insulin, Mercodia, Uppsala, Sweden; HbA1c Kit Spinreact, BioAnalitica, Madrid, Spain). The fasting plasma concentrations of both glucose and insulin were used to calculate the indices of homeostatic model assessment of the insulin resistance (HOMA-IR) and insulin secretion (HOMA-B) according to the following formulas: HOMAIR = fasting insulin (mU/mL) X fasting glucose (mM)/22.5 and HOMA-B = 20 X fasting insulin (mU/mL)/[fasting glucose (mM) − 3.5], respectively. Triacylglycerols (TG), HDLCholesterol and LDL-Cholesterol were determined in serum by kits (BioSystems, Madrid, Spain) as described elsewhere [8]. One week before the end of the study, a glucose tolerance test was performed. Briefly, after overnight fasting, 35% glucose solution (2 g/kg of body weight) (Sigma Chemical, Madrid, Spain) was administrated to the rats by intraperitoneal injection. Blood samples were obtained from the tail vein before the glucose load (t = 0) and at, 30, 60, 90, and 120 min, after the glucose administration and glucose levels were measured using an Accounted Glucose Analyzer (LifeScan España, Madrid, Spain). The integrated glucose response (area under the curve, AUC) over a period of 120 min after glucose overload was also calculated. 2.3. Preparation of Urine Samples and NMR Analysis The 23 h urine samples were collected by means of metabolic cages at the end of the experiment, and were prepared and analyzed using standardized and optimized protocols, as previously described [ 21 ]. Briefly, the urine samples were thawed at room temperature before 540 µ L of urine was mixed with 60 µ L of buffer (KH 2 PO 4 , 1.5 M, pH 7.4 made up in 2 H 2 O) containing 5.8 mM mM trimethylsilyl propionate (TSP) and 2 mM NaN 3 . Following centrifugation (12,000 × g, 4 ◦ C, 5 min) to remove solids, 550 µ L of sample were transferred into 5 mm SampleJet NMR tubes and immediately loaded onto a refrigerated SampleJet robot (Bruker Biospin, Germany) and maintained at 4 ◦C until NMR analysis The NMR area associated with the concentration of each metabolite was obtained after the spectral analysis by using an in-house lineshape fitting based on an algorithm developed to deconvolute the pre-processed NMR spectra by using Lorentzian and Gaussian functions to minimize the fitting error. The NMR areas were transformed into concentration units by using specific conversion factors depending on the proton numbers of the molecular structure generating the signal and a TSP internal standard as previously described [ 22 ]. Finally, the metabolite concentrations were normalized by using PQN normalization [ 23 ] to avoid urine dilution effects. Resonances assignment and metabolite identification was verified using the Chenomx and the Human Metabolome Database (HMDB). 2.4. Statistical and Multivariate Analysis The data from biochemical parameters were tested for homogeneity of variances by the test of Levene; for multiple comparisons, one-way ANOVA was followed by a Tukey test when variances were homogeneous, or by the Tamhane test when variances were not homogeneous. All the data were presented as mean ± standard deviation (SD). For the metabolomics study, the median and interquartile range concentration of each metabolite was compared by using non-parametric Wilcoxon rank-sum text. A GraphPad Prism version 8.00 (GraphPad software, Inc., La Jolla, CA, USA) was used. The level of significance was p< 0.05. Nutrients 2022,14, 4127 4 of 14 Multivariate analysis was used to analyze the metabolomics differences of urine of the different groups. Principal components analysis (PCA) was performed to describe the metabolic profiles among groups. This unsupervised method allows us to identify the maximum number of uncorrelated principal components that together explain the maximum amount of variance in the NMR metabolomic data set. Prior to running the PCA, we auto scaled the input data and then transformed to create a composite score for each principal component. The loading analysis of the principal components was used to identify the most relevant molecular components. Orthogonal partial least squares discriminant analysis (OPLS-DA) was applied as the supervised regression modelling for discriminating groups. The validity of the models against overfitting was estimated by the parameter R2Y, and the predictive ability was described by Q2 values. Furthermore, the differential metabolites of variable importance in the projection (VIP) from the OPLSDA were utilized to screen the biomarkers. Relationship strength between differential metabolites and physiological parameters was assessed using the two tailed Pearson’s correlation test. The correlation was considered significant when the absolute value of Pearson’s correlation coefficient r was > 0.5. Finally, identified metabolites were submitted to the metabolite set enrichment analysis (MSEA) module in MetaboAnalyst 5.0 using HMDB identifiers. The Kyoto Encyclopedia of Genes and Genomes (KEGG) was used as input. 3. Results 3.1. Effect of Cocoa Supplementation on Physiological Parameters The metabolic characteristics of the ZL and ZDF animals are shown in Table 1. At 20 weeks of life, the body weight of ZDF and ZDF-C rats were significantly increased as compared to non-diabetic ZL animals. Likewise, both ZDF groups showed hyperglycemia, hyperinsulinemia, increased HbA1c levels, and glucose intolerance (AUC), which confirmed their hyperphagic and diabetic state. However, all these parameters were significantly improved in diabetic animals that were supplemented with cocoa. Moreover, cocoa intake was also effective in reducing insulin resistance (HOMA-IR) and glucose intolerance (AUC) in ZDF rats, as well as increasing beta-cell function (HOMA-B). On the contrary, the serum levels of HDL-Cholesterol, LDL-Cholesterol, and TG, were significantly elevated in both ZDF groups in comparison to the ZL group, and there were no differences between those animals fed with standard (ZDF) or with cocoa diet (ZDF-C). These results indicate that a cocoa rich diet significantly improved glucose homeostasis but not lipid profile, in 20-weeks old ZDF rats. Table 1. Biological parameters of Zucker lean rats (ZL), Zucker diabetic fatty rats fed with control diet for 10 weeks (ZDF), and Zucker diabetic fatty rats fed with cocoa diet for 10 weeks (ZDF-C). Data represent the means ±SD of 6–8 animals. Different letters denote statistically significant differences, p< 0.05. ZL ZDF ZDF-C Body weight (g) 329 ±41 a444 ±82 b400 ±12 c Glucose levels (mg/dL) 86 ±90 a238 ±71 b118 ±11 c Insulin levels (ng/mL) 0.40 ±0.02 a4.36 ±0.50 b1.14 ±0.22 c HbA1c (%) 4.38 ±0.19 a10.40 ±1.58 b6.09 ±0.79 c AUC (mmol/L/min) 1803 ±96 a4044 ±60 b3049 ±33 c HOMA IR 2.60 ±0.20 a90.96 ±14.26 b12.22 ±1.36 c HOMA B 149.81 ±22.21 a169.52 ±46.20 a227.83 ±14.32 b TG (mmol/L) 0.39 ±0.09 a2.74 ±0.24 b2.87 ±0.31 b HDL-Cholesterol (mmol/L) 2.27 ±0.22 a2.85 ±0.37 b3.08 ±0.32 b LDL-Cholesterol (mmol/L) 0.92 ±0.09 a2.28 ±0.33 b2.82 ±0.31 c 3.2. Metabolic Analysis of Urine Samples An untargeted approach using 1 H-NMR techniques was used to explore the metabolomics changes in the urine of ZL, ZDF and ZDF-C animals. A total of 37 metabolites were identified and quantified in the urine of lean and diabetic rats (Supplementary Table S2). A representative Nutrients 2022,14, 4127 5 of 14 1 H-NMR spectrum of one of the urine samples with the metabolite assignment is shown in Supplementary Figure S1. To obtain a preliminary understanding of the overall differences in metabolites between groups, and the degree of variability among the samples within the group, we achieved an exploratory PCA on the entire dataset. As shown in Figure 1A, the first two components (PC1 and PC2) were enough to identify specific group molecular characteristics, explaining 55% of the variance in the data (37% and 18%, respectively). PC1 showed a clear separation among non-diabetic ZL and the two diabetic groups, driven by higher concentration of creatinine, 2-oxoisocaproate and nicotinamide-N-oxide in the ZL group, together with a lower concentration of glucose and 4-hydroxy phenylacetate (Figure 1B). On the other side, PC2 was not enough to separate ZDF-C and ZDF groups, but it can be achieved using PC1 and PC2 together. Altogether, our results strongly indicated that cocoa supplementation modify the urinary metabolic profile of ZDF diabetic animals. Nutrients 2022, 14, x FOR PEER REVIEW 5 of 15 AUC (mmol/L/min) 1803 ± 96 a 4044 ± 60 b 3049 ± 33 c HOMA IR 2.60 ± 0.20 a 90.96 ± 14.26 b 12.22 ± 1.36 c HOMA B 149.81 ± 22.21 a 169.52 ± 46.20 a 227.83 ± 14.32 b TG (mmol/L) 0.39 ± 0.09 a 2.74 ± 0.24 b 2.87 ± 0.31 b HDL-Cholesterol (mmol/L) 2.27 ± 0.22 a 2.85 ± 0.37 b 3.08 ± 0.32 b LDL-Cholesterol (mmol/L) 0.92 ± 0.09 a 2.28 ± 0.33 b 2.82 ± 0.31 c 3.2. Metabolic Analysis of Urine Samples An untargeted approach using 1H-NMR techniques was used to explore the metabolomics changes in the urine of ZL, ZDF and ZDF-C animals. A total of 37 metabolites were identified and quantified in the urine of lean and diabetic rats (Supplementary Table S2). A representative 1H-NMR spectrum of one of the urine samples with the metabolite assignment is shown in Supplementary Figure S1. To obtain a preliminary understanding of the overall differences in metabolites between groups, and the degree of variability among the samples within the group, we achieved an exploratory PCA on the entire dataset. As shown in Figure 1A, the first two components (PC1 and PC2) were enough to identify specific group molecular characteristics, explaining 55% of the variance in the data (37% and 18%, respectively). PC1 showed a clear separation among non-diabetic ZL and the two diabetic groups, driven by higher concentration of creatinine, 2-oxoisocaproate and nicotinamide-N-oxide in the ZL group, together with a lower concentration of glucose and 4-hydroxy phenylacetate (Figure 1B). On the other side, PC2 was not enough to separate ZDF-C and ZDF groups, but it can be achieved using PC1 and PC2 together. Altogether, our results strongly indicated that cocoa supplementation modify the urinary metabolic profile of ZDF diabetic animals. Figure 1. (A) Principal components analysis (PCA) score plots from urine samples from normal lean (ZL), diabetic (ZDF), and cocoa-supplemented diabetic (ZDF-C) rats, at 20 weeks of life. (B) Loading plot of PC1. 3.3. Identification of Potential Biomarkers Associated with the Cocoa Ingestion in ZDF Rats Based on this initial observation, the OPLS-DA supervised method was employed to explain the differences caused by cocoa intake in diabetic Zucker rats. As shown in Figure Figure 1. ( A ) Principal components analysis (PCA) score plots from urine samples from normal lean (ZL), diabetic (ZDF), and cocoa-supplemented diabetic (ZDF-C) rats, at 20 weeks of life. ( B ) Loading plot of PC1. 3.3. Identification of Potential Biomarkers Associated with the Cocoa Ingestion in ZDF Rats Based on this initial observation, the OPLS-DA supervised method was employed to explain the differences caused by cocoa intake in diabetic Zucker rats. As shown in Figure 2A, a clear separation between the two groups (ZDF and ZDF-C) was observed as demonstrated by the OPLS-DA scores scatter plot. Moreover, R2Y and Q2 parameters were relatively high with good predictive ability and reliability (R2Y = 0.812 and Q2 = 0.729, respectively), which was able to manifest the changed trend in metabolites between groups. In order to discover the potential metabolites contributing to the group classification, the variable importance in the projection (VIP) values of the obtained multivariate analysis OPLS-DA model was used. Establishing a cut-off VIP values above 1.0 and the significance level p< 0.05, we found 14 endogenous metabolites that could be identified as potential biomarkers (Figure 2B). For the cocoa intake, the most common metabolites enrichment were valine, isoleucine, leucine, 2-oxoglutarate, alanine, and hippurate. On the other hand, 3-indolelsulfate, D_unknow4 metabolite, acetoacetate, urea, 4-hidroxyphenillactate, suberate, formate, and glucose, were enriched in the diabetic control rats. Nutrients 2022,14, 4127 6 of 14 Nutrients 2022, 14, x FOR PEER REVIEW 6 of 15 2A, a clear separation between the two groups (ZDF and ZDF-C) was observed as demonstrated by the OPLS-DA scores scatter plot. Moreover, R2Y and Q2 parameters were relatively high with good predictive ability and reliability (R2Y = 0.812 and Q2 = 0.729, respectively), which was able to manifest the changed trend in metabolites between groups. In order to discover the potential metabolites contributing to the group classification, the variable importance in the projection (VIP) values of the obtained multivariate analysis OPLS-DA model was used. Establishing a cut-off VIP values above 1.0 and the significance level p < 0.05, we found 14 endogenous metabolites that could be identified as potential biomarkers (Figure 2B). For the cocoa intake, the most common metabolites enrichment were valine, isoleucine, leucine, 2-oxoglutarate, alanine, and hippurate. On the other hand, 3-indolelsulfate, D_unknow4 metabolite, acetoacetate, urea, 4-hidroxyphenillactate, suberate, formate, and glucose, were enriched in the diabetic control rats. Figure 2. (A) Orthogonal partial least squares discriminant analysis (OPLS-DA) score plots from urine samples of diabetic (ZDF) and cocoa-supplemented diabetic (ZDF-C) rats at 20 weeks of life. (B) VIP values derived from OPLS-DA. The blue and red boxes on the right indicate whether the mean metabolite abundance is increased (red) or decreased (blue) in ZDF-C vs. ZDF. For these 14 metabolites, we further assessed their differential urine concentration between groups, yielding eight metabolites (valine, leucine, isoleucine, acetoacetate, urea, hippurate, 3indolelsulfate, and D_unknow4) that were significantly modified by the cocoa intake (Figure 3). Figure 2. ( A ) Orthogonal partial least squares discriminant analysis (OPLS-DA) score plots from urine samples of diabetic (ZDF) and cocoa-supplemented diabetic (ZDF-C) rats at 20 weeks of life. ( B ) VIP values derived from OPLS-DA. The blue and red boxes on the right indicate whether the mean metabolite abundance is increased (red) or decreased (blue) in ZDF-C vs. ZDF. For these 14 metabolites, we further assessed their differential urine concentration between groups, yielding eight metabolites (valine, leucine, isoleucine, acetoacetate, urea, hippurate, 3indolelsulfate, and D_unknow4) that were significantly modified by the cocoa intake (Figure 3). Nutrients 2022, 14, x FOR PEER REVIEW 7 of 15 Figure 3. Altered urine metabolites between the diabetic (ZDF) and cocoa-supplemented diabetic (ZDF-C) rats at 20 weeks of life: (A) valine, (B) leucine, (C) isoleucine, (D) acetoacetate, (E) urea, (F) hippurate, (G) 3indolelsulfate, and (H) D_unknow4 metabolite levels. Data represent the means ± SD of 6–8 animals. Different letters denote statistically significant differences, p < 0.05. 3.4. Correlation Analysis between Urine Metabolites and Biomarkers Associated with Cocoa Intake Next, we investigate the relationship between the significant metabolites identified in the urine of diabetic animals and the clinical parameters related to glucose and lipid metabolisms. To this end, a Pearson’s correlation analysis was conducted. The results were presented as a correlation heatmap, where green color indicates a positive correlation and red color indicates a negative one (Figure 4). A significant positive association was found between the levels of acetoacetate, 3-indolelsulfate, and the D_unknown4 metabolite and increased body weight, glycaemia, and HbA1c, as well as with insulin resistance (HOMA-IR) and glucose intolerance (AUC). However, valine and isoleucine were negatively correlated with all these parameters. Likewise, the levels of leucine showed significantly negative correlations with glucose, HbA1c, and AUC. The results also showed that urea exhibited a positive and significant association with the increased levels of glucose and HbA1c. Interestingly, hippurate levels, despite of all the other metabolites, were positively linked with an increase in the beta cell function (HOMA-B). On the other hand, increased LDL levels were positively associated with valine, isoleucine, and leucine. Figure 3. Altered urine metabolites between the diabetic (ZDF) and cocoa-supplemented diabetic (ZDF-C) rats at 20 weeks of life: ( A ) valine, ( B ) leucine, ( C ) isoleucine, ( D ) acetoacetate, ( E ) urea, ( F ) hippurate, ( G ) 3indolelsulfate, and ( H ) D_unknow4 metabolite levels. Data represent the means ±SD of 6–8 animals. Different letters denote statistically significant differences, p< 0.05. Nutrients 2022,14, 4127 7 of 14 3.4. Correlation Analysis between Urine Metabolites and Biomarkers Associated with Cocoa Intake Next, we investigate the relationship between the significant metabolites identified in the urine of diabetic animals and the clinical parameters related to glucose and lipid metabolisms. To this end, a Pearson’s correlation analysis was conducted. The results were presented as a correlation heatmap, where green color indicates a positive correlation and red color indicates a negative one (Figure 4). A significant positive association was found between the levels of acetoacetate, 3-indolelsulfate, and the D_unknown4 metabolite and increased body weight, glycaemia, and HbA1c, as well as with insulin resistance (HOMA-IR) and glucose intolerance (AUC). However, valine and isoleucine were negatively correlated with all these parameters. Likewise, the levels of leucine showed significantly negative correlations with glucose, HbA1c, and AUC. The results also showed that urea exhibited a positive and significant association with the increased levels of glucose and HbA1c. Interestingly, hippurate levels, despite of all the other metabolites, were positively linked with an increase in the beta cell function (HOMA-B). On the other hand, increased LDL levels were positively associated with valine, isoleucine, and leucine. Nutrients 2022, 14, x FOR PEER REVIEW 8 of 15 Figure 4. Heatmap of correlation between the main significantly altered urine metabolites and biochemical biomarkers related to diabetes. Pearson correlation values were used for the matrix. Green color indicates a positive correlation, whereas red color indicates a negative correlation; the intensity of the color represents the degree of association. * Represents adjusted p < 0.05. 3.5. Analysis of Biomarker Networks and Reconstruction of Metabolic Pathways To further explore the metabolic pathways associated to the cocoa intake in diabetic animals, we performed a metabolite set enrichment analysis (MSEA) using the KEGG database as a framework (MetaboAnalyst 5.0). The enriched analysis showed the main metabolic routes modulated by the cocoa intake under diabetic conditions (Figure 5). Among them, butanoate metabolism, valine, leucine, and isoleucine degradation and biosynthesis, pantothenate and CoA biosynthesis, and synthesis and degradation of ketone bodies, were significantly enriched (p < 0.05) (Table 2). Therefore, these pathways represent the potential targeted pathways of cocoa intake in the diabetic condition. Based on the above results, a correlation network graph responding to the cocoa intake was constructed (Figure 6). Figure 4. Heatmap of correlation between the main significantly altered urine metabolites and biochemical biomarkers related to diabetes. Pearson correlation values were used for the matrix. Green color indicates a positive correlation, whereas red color indicates a negative correlation; the intensity of the color represents the degree of association. * Represents adjusted p< 0.05. 3.5. Analysis of Biomarker Networks and Reconstruction of Metabolic Pathways To further explore the metabolic pathways associated to the cocoa intake in diabetic animals, we performed a metabolite set enrichment analysis (MSEA) using the KEGG database as a framework (MetaboAnalyst 5.0). The enriched analysis showed the main metabolic routes modulated by the cocoa intake under diabetic conditions (Figure 5). Among them, butanoate metabolism, valine, leucine, and isoleucine degradation and biosynthesis, pantothenate and CoA biosynthesis, and synthesis and degradation of ketone bodies, were significantly enriched (p< 0.05) (Table 2). Therefore, these pathways represent the potential targeted pathways of cocoa intake in the diabetic condition. Based on the above results, a correlation network graph responding to the cocoa intake was constructed (Figure 6). Nutrients 2022,14, 4127 8 of 14 Nutrients 2022, 14, x FOR PEER REVIEW 9 of 15 Figure 5. Metabolite set enrichment analysis of significantly altered urine metabolites according to the KEGG database. Table 2. Top pathways enriched with metabolites having significantly altered abundance in Zucker diabetic fatty rats fed with cocoa diet (ZDF-C), as identified by the pathway analysis using MetaboAnalyst. Top Pathways Total Compounds Hits p Value FDR * Metabolites Identified Butanoate metabolism 15 2 0.0015 0.026 Acetoacetate, 2-oxoglutarate Valine, leucine and isoleucine degradation 40 5 0.0023 0.026 Isoleucine, Leucine, Valine, Acetoacetate, 4Methyl-2-oxopentanoate Pantothenate and CoA biosynthesis 19 1 0.0032 0.026 Valine Synthesis and degradation of ketone bodies 5 1 0.0051 0.031 Acetoacetate Valine, leucine and isoleucine biosynthesis 8 5 0.01 0.047 Isoleucine, Leucine, Valine, Threonine, Acetoacetate, Aminoacyl-tRNA biosynthesis 48 5 0.019 0.047 Isoleucine, Leucine, Valine, Alanine, Lysine * FDR is the p value adjusted using False Discovery Rate. Figure 5. Metabolite set enrichment analysis of significantly altered urine metabolites according to the KEGG database. Table 2. Top pathways enriched with metabolites having significantly altered abundance in Zucker diabetic fatty rats fed with cocoa diet (ZDF-C), as identified by the pathway analysis using MetaboAnalyst. Top Pathways Total Compounds Hits pValue FDR * Metabolites Identified Butanoate metabolism 15 2 0.0015 0.026 Acetoacetate, 2-oxoglutarate Valine, leucine and isoleucine degradation 40 5 0.0023 0.026 Isoleucine, Leucine, Valine, Acetoacetate, 4-Methyl-2-oxopentanoate Pantothenate and CoA biosynthesis 19 1 0.0032 0.026 Valine Synthesis and degradation of ketone bodies 5 1 0.0051 0.031 Acetoacetate Valine, leucine and isoleucine biosynthesis 8 5 0.01 0.047 Isoleucine, Leucine, Valine, Threonine, Acetoacetate, Aminoacyl-tRNA biosynthesis 48 5 0.019 0.047 Isoleucine, Leucine, Valine, Alanine, Lysine * FDR is the pvalue adjusted using False Discovery Rate. Nutrients 2022,14, 4127 9 of 14 Nutrients 2022, 14, x FOR PEER REVIEW 10 of 15 Figure 6. Schematic representation depicting the interrelationships of the disturbed metabolic pathways identified by 1H NMR urine analysis. Green (significantly increased), and red (not significantly increased), as compared to ZDF (p < 0.05 is significant). 4. Discussion The anti-diabetic potential of cocoa has previously been described both in pre-diabetic [7] and in diabetic ZDF animals [8]. Cocoa exerts these beneficial effects via multiple mechanisms, including antioxidant and anti-inflammatory effects, as well as by increasing both insulin secretion and insulin action [10]. Likewise, cocoa intake can modify the composition of the gut microbiota in ZDF rats, and these changes have been closely associated with the improved glucose homeostasis [11]. Herein, we show for the first time that chronic cocoa supplementation for 10 weeks significantly modified the urinary profile of diabetic fatty rats. These findings are in concordance with those of Massot-Cladera et al. [24] who exposed that the consumption of a cocoa-rich diet for 3 weeks resulted in a different urinary metabolic pattern in normal Wistar rats. In contrast, in a recent human intervention study, chronic consumption ofprocyanidin-rich cocoa (10 weeks) was found to have a marginal impact on serum metabolome in male endurance athletes [25]. Likewise, the biological response of a free-living population to the daily consumption of dark chocolate for two weeks only had a significant metabolic impact in subjects with essential high anxiety [26]. Together, these data seem to indicate that the effects of cocoa on the human metabolome are highly dependent on the stress situations of individuals, highlighting the beneficial implications of cocoa on the metabolic response to stress. According to this, our results further support the positive effects of the cocoa consumption on stress-associated metabolic abnormalities, like those that appear in animals with diabetes. In the present study, we applied NMR-based metabolomics and multivariate analysis, including PCA and OPLS-DA, to study the metabolic profile of ZDF rats and discriminate the protective effect of a cocoa-rich diet. A total of 14 potential biomarkers associated with diabetes and obesity were identified, which were related to disturbed metabolic pathways involving valine, leucine and isoleucine biosynthesis, synthesis and degradation of ketone bodies, butanoate metabolism, pantothenate and CoA biosynthesis and aminoacyl-tRNA biosynthesis, among others. The chronic supplementation of diet with 10% of cocoa powder interfered in these metabolic pathways by restoring potential biomarkers back to normality or even improving them, which might be part of the protective mechanism of cocoa on the disrupted diabetic metabolism. Figure 6. Schematic representation depicting the interrelationships of the disturbed metabolic pathways identified by 1 H NMR urine analysis. Green (significantly increased), and red (not significantly increased), as compared to ZDF (p< 0.05 is significant). 4. Discussion The anti-diabetic potential of cocoa has previously been described both in pre-diabetic [ 7 ] and in diabetic ZDF animals [ 8 ]. Cocoa exerts these beneficial effects via multiple mechanisms, including antioxidant and anti-inflammatory effects, as well as by increasing both insulin secretion and insulin action [ 10 ]. Likewise, cocoa intake can modify the composition of the gut microbiota in ZDF rats, and these changes have been closely associated with the improved glucose homeostasis [ 11 ]. Herein, we show for the first time that chronic cocoa supplementation for 10 weeks significantly modified the urinary profile of diabetic fatty rats. These findings are in concordance with those of Massot-Cladera et al. [ 24 ] who exposed that the consumption of a cocoa-rich diet for 3 weeks resulted in a different urinary metabolic pattern in normal Wistar rats. In contrast, in a recent human intervention study, chronic consumption ofprocyanidin-rich cocoa (10 weeks) was found to have a marginal impact on serum metabolome in male endurance athletes [ 25 ]. Likewise, the biological response of a free-living population to the daily consumption of dark chocolate for two weeks only had a significant metabolic impact in subjects with essential high anxiety [ 26 ]. Together, these data seem to indicate that the effects of cocoa on the human metabolome are highly dependent on the stress situations of individuals, highlighting the beneficial implications of cocoa on the metabolic response to stress. According to this, our results further support the positive effects of the cocoa consumption on stress-associated metabolic abnormalities, like those that appear in animals with diabetes. In the present study, we applied NMR-based metabolomics and multivariate analysis, including PCA and OPLS-DA, to study the metabolic profile of ZDF rats and discriminate the protective effect of a cocoa-rich diet. A total of 14 potential biomarkers associated with diabetes and obesity were identified, which were related to disturbed metabolic pathways involving valine, leucine and isoleucine biosynthesis, synthesis and degradation of ketone bodies, butanoate metabolism, pantothenate and CoA biosynthesis and aminoacyl-tRNA biosynthesis, among others. The chronic supplementation of diet with 10% of cocoa powder interfered in these metabolic pathways by restoring potential biomarkers back to normality or even improving them, which might be part of the protective mechanism of cocoa on the disrupted diabetic metabolism.