Mediterranean diet combined with regular aerobic exercise and hempprotein supplementation modulates plasma circulating amino acids and improves the health status of overweight individuals

Citation: Miguel-Albarreal, A.D.; Rivero-Pino, F.; Marquez-Paradas, E.; Grao-Cruces, E.; Gonzalez-de la Rosa, T.; Montserrat-de la Paz, S.. Mediterranean Diet Combined with Regular Aerobic Exercise and Hemp Protein Supplementation Modulates Plasma Circulating Amino Acids and Improves the Health Status of Overweight Individuals. Nutrients 2024,16, 1594. https://doi.org/ 10.3390/nu16111594 Academic Editor: Roberta Masella Received: 30 April 2024 Revised: 18 May 2024 Accepted: 22 May 2024 Published: 23 May 2024 Copyright: © 2024 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 Mediterranean Diet Combined with Regular Aerobic Exercise and Hemp Protein Supplementation Modulates Plasma Circulating Amino Acids and Improves the Health Status of Overweight Individuals Antonio D. Miguel-Albarreal 1,2, Fernando Rivero-Pino 1,2 , Elvira Marquez-Paradas 1,2, Elena Grao-Cruces 1,2 , Teresa Gonzalez-de la Rosa 1,2 and Sergio Montserrat-de la Paz 1,2,* 1 Department of Medical Biochemistry, Molecular Biology, and Immunology, School of Medicine, University of Seville, Av. Sanchez Pizjuan s/n, 41009 Seville, Spain; [email protected] (A.D.M.-A.); [email protected] (E.M.-P.); [email protected] (E.G.-C.); [email protected] (T.G.-d.l.R.) 2 Instituto de Biomedicina de Sevilla (IBiS), Hospital Universitario Virgen del Rocío, CSIC, University of Seville, 41013 Seville, Spain *Correspondence: [email protected] Abstract: Plant protein is considered a sustainable health-promoting strategy to prevent metabolic syndrome. Lifestyle changes (including dietary patterns and exercise) have been demonstrated to exert an effect on human health by modulating the biochemical status in humans. The objective of this study was to assess whether supplementation with hemp protein within a Mediterranean diet context together with exercise could help to ameliorate the metabolic statuses of patients prone to developing metabolic syndrome. For this study, 23 patients followed with Mediterranean diet and engaged in aerobic exercise according to the WHO’s recommendations, while also being supplemented with hemp protein, for 12 weeks. A comparison of anthropometric, biochemical, and mineral data as well as amino acid values was made between the start and the end of the study, with the subjects acting as their own control group. Statistical analyses included a paired t-test, Wilcoxon paired test, Pearson correlation coefficient, and Sparse Partial Least Squares Discriminant Analysis to evaluate significant differences and correlations among parameters. There were statistically significant changes in total cholesterol, HDL-C (+52.3%), LDL-C ( − 54.0%), and TAG levels ( − 49.8%), but not in glucose plasma levels. Following the intervention, plasma concentrations of some amino acids, including α -aminoadipic acid, phosphoethanolamine, and 1-metylhistidine, increased, whereas those of asparagine and alanine declined. Different correlations between amino acids and the other parameters evaluated were reported and discussed. A Mediterranean diet combined with regular aerobic exercise, together with protein supplementation, can highly improve the metabolic parameters and anthropometric parameters of subjects with obesity and impaired glucose levels, ameliorating their health status and likely delaying the development of metabolic syndrome. Keywords: amino acids; plant protein; pre-diabetes; obesity; Mediterranean diet 1. Introduction The definition of overweight and obesity is abnormal or excessive deposits of fat that poses a health concern, and it has become an epidemic worldwide. Values over 25 kg/m 2 in body mass index (BMI) as considered as overweight, and over 30 kg/m 2 as obese, although others classification exist according to the waist circumference or the waist-to-height ratio. It must be noted that the associated health care costs of ameliorating the health status of overweight patients are substantial. On top of that, there is a correlation between the current rise in obesity incidence and the increase in type 2 diabetes mellitus (T2DM) prevalence, cardiovascular diseases, and cancers, among others [ 1 ]. Nearly 90% Nutrients 2024,16, 1594. https://doi.org/10.3390/nu16111594 https://www.mdpi.com/journal/nutrients Nutrients 2024,16, 1594 2 of 17 of T2DM patients are overweight or obese. Obesity increases the risk of prediabetes and T2DM significantly, particularly when it is combined with increased abdominal and intraabdominal fat distribution and triacylglyceride (TAG) content in the intrahepatic and intramuscular tissues. This is due to the fact that obesity causes β -cell dysfunction and insulin resistance [ 2 ]. In this sense, pre-diabetes is the state that precedes T2DM, one of the most prevalent diseases worldwide, defined by insulin resistance, which impairs glucose metabolism [3]. The relationship between diet and health has been widely described. In this regard, the intake of specific groups of food might help to reduce inflammation through different pathways, including the glucose metabolism and, consequently, the development of obesity through specific metabolic pathways. For this reason, in the framework of delaying or preventing the occurrence of increases in weight in adults, nutritional guidelines, such as Mediterranean diet guidelines, are being proposed as a strategy to promote health in subjects while being cost-effective at the same time [ 4 , 5 ]. The importance of preventing the onset of obesity stems from the fact that usually, this condition, generally associated with prediabetes, already includes insulin resistance and a dysfunctional β -cell population. The abnormal blood glucose levels may then upregulate inflammatory marker expression and result in the generation of reactive oxygen species (ROS), which ultimately results in vascular dysfunction. Increased ROS and inflammation may also worsen insulin resistance and impair insulin secretion in a reciprocal manner. Understanding the underlying process, particularly how protein and amino acids affect the metabolic status of diabetes, is important for the study of the transition from pre-diabetes to T2DM [6]. On top of that, numerous extensive epidemiological investigations have demonstrated a correlation between blood pressure and BMI in people who are overweight or of a normal weight. The most successful non-pharmacological therapy strategy for obese hypertension patients has been demonstrated to be weight reduction. A little weight reduction can reduce blood pressure in both hypertension and non-hypertensive people. Losing weight impacts blood pressure, reducing its levels, most likely due to an improvement in insulin sensitivity and a decrease in sympathetic nervous system activity [ 7 ]. In line with the relevance of dietary protein in the health status of humans, the current suggested dietary consumption of protein has been established at 0.8 g/kg/day for young healthy men, whereas consumption ranging from 1.0 to 1.5 g/kg/day in older adults has been reported to trigger improvements in glycemic control and muscle mass [ 8 , 9 ]. It has been demonstrated that diets with high contents of protein are beneficial in terms of glycemic markers due to satiety, lowering the postprandial glucose response and increasing thermogenesis and the ability to decrease the rate of muscle protein breakdown. Similarly, exercise has been demonstrated to synergistically help in the prevention of the progression of prediabetes by inducing favorable changes in glycemic control independent of lean body mass increases [ 9 ]. In this regard, it has been shown how exercise intervention is efficient in ameliorating inflammatory, metabolic, and lipid markers in middle-aged and older adults with diabetes [ 10 , 11 ]. According to Mthembu et al. [ 12 ], patients with T2DM may benefit from therapies like calorie restriction and exercise over the course of two to four months in order to increase their insulin sensitivity, in part by targeting the phosphoinositide 3-kinases/protein kinase B pathway. One interesting source of protein currently gaining interest in the research community is hemp protein. It has been demonstrated to be of high quality and environmentally friendly [ 13 ]. In fact, some studies have already shown positive effects of hemp protein in reducing postprandial blood glucose [ 14 ], and there is a huge body of evidence supporting that hemp protein is a foodstuff with potential health benefits [ 15 ]. To the author’s knowledge, no specific studies have been conducted to evaluate the circulating amino acids in humans after hemp protein ingestion for long periods, which is of interest considering the amount of essential amino acids this protein contains. Additional research is required in order to elucidate whether the intake of specific protein sources together with healthy lifestyle patterns could further promote an antiinflammatory response that might lead to healthier statuses for overweight patients, in Nutrients 2024,16, 1594 3 of 17 order to promote healthier ageing. In this study, a nutritional intervention (Mediterranean diet and aerobic exercise) supplemented with hemp protein was carried out in overweight patients in order to evaluate the changes in anthropometric data, biochemical parameters, amino acids in plasma, and minerals after twelve weeks, and how different parameters affected the outcome. 2. Materials and Methods 2.1. Study Design, Participants, and Eligibility Criteria A nutritional intervention following the Mediterranean diet, combined with aerobic exercise and supplemented with hemp protein, was conducted. Patients (n = 27) were recruited, and interventions were delivered at a community pharmacy located in the south of Spain. During the study, some of the patients dropped out and were not considered in the results. The final number of subjects was n = 23. The inclusion criteria were: age between 18 and 65 years old; waist circumference ≥ 94 cm in men and ≥ 88 cm in women; and metabolic equivalent < 4 (calculated through the long version of the International Physical Activity Questionnaire). The exclusion criteria were prediabetic patients on pharmacotherapy; subjects suffering from medical issues (e.g., diabetes or another chronic disease) or physical limitation; smokers; and pregnant or lactating women. The study was conducted according to the Good Practice Guidelines and in line with the principles outlined in the Helsinki Declaration of the World Medical Association. This study was approved by the Investigation Ethic Center (CEI) of Virgen Macarena and Virgen del Rocio Hospitals (Study ID 2246-N-20), and written informed consent was obtained from all participants before entering the study. Clinical Trial Registry number is NCT06129578. Before the assessment, all questions were addressed, and the participants signed informed consent forms. The participant flow chart can be seen in Supplementary Material Figure S1. 2.2. Dietary Intervention All participants followed a low-energy diet (1315 kcal/daily) based on the Mediterranean diet for 12 weeks, supplemented with 20 g/day of hemp protein. The specific menu was designed by a trainee nutritionist. The program prescribed five meals a day, with the time customized to fit the lifestyle of each patient. Based on the baseline metabolism, the energy and nutritional parameters of every patient were computed. Patients reported the dietary intake data daily. The intervention included a face-to-face visit every two weeks, individual nutrition counseling, and education sessions. Hemp protein (Myvegan, 54% of protein, 25% of fiber, 12% of fat, and 10% of carbohydrates) was provided free of charge to the participants. The following is a summary of dietary action guidelines to create the menus for the patients: legumes, two or more servings per week; fruits, three servings of fresh fruits per day; vegetables, two or more servings per day (at least one serving raw); nuts, four nuts per day (15 g); olive oil, 40–60 g per day; fish and seafood, four or more servings per week; white meat, 2 or more servings per week; eggs, 2 or more servings per week; grains and potatoes, one or more servings per week; processed meat, commercial bakery goods, and sweets were not allowed; for alcoholic drinks, occasional consumption was allowed, with, no more than one glass of wine a day; and carbonated beverages were not allowed. Regarding culinary techniques, low-fat cooking methods (microwaving, roasting, boiling, grilling, and poaching) were used. Frying was not allowed. 2.3. Physical Activity Intervention The exercise intervention was adapted from the World Health Organization physical activity recommendations, in which adults should engage in at least 150 min of moderate-to- intense aerobic physical activity per week. The exercise was assessed using a standardized questionnaire (SBQ) adapted to local habits. A smart watch (Xiaomi Mi Band 4) was provided free of charge to all participants. Exercise was monitored using Zepp Life, an app where the patients registered all activities. Nutrients 2024,16, 1594 4 of 17 2.4. Clinical Measurement Weight ( ± 0.1 kg), metabolic age, and body fat percentage were measured using a TANITA Body Composition Analyzer (Model DC 240 MA, Tanita, Barcelona, Spain). Waist circumference was measured exactly midway between the lowest rib and the peak of the iliac crest. Measurements of systolic and diastolic blood pressure (SBP and DBP) were performed with a standard method. BMI was calculated as weight (kg)/height (m) 2 . Alcohol and tobacco consumption were also registered by directly asking the participants. 2.5. Biochemical Measurements and Mineral and Amino Acid Content After an overnight fast (8–10 h), blood samples were collected at baseline (before the intervention, t 0 ) and after the 12-week intervention (t f ). Blood was collected into Vacutainer tubes for serum with separating gel and into K2EDTA tubes for plasma (Becton Dickinson, NJ, USA). The samples were centrifuged (1400 × g, 10 min at 4 ◦ C), and serum was frozen at − 80 ◦ C until analysis. Fasting blood samples were analyzed at the Department of Medical Biochemistry (School of Medicine, University of Seville). Glucose was measured using the glucose/oxidase method (Glucose GOD-PAP; Biolabo, Madrid, Spain). Total cholesterol and TAG were determined using enzymatic methods (CHOD-PAP and GPO-PAP, respectively; Roche Diagnostics, Basel, Switzerland). High-density lipoprotein-cholesterol (HDL-C) was determined after precipitation with phosphotungstic acid. Low-density lipoprotein cholesterol (LDL-C) was calculated with the Firedewald formula. Mineral content was evaluated using UV or colorimetrics commercial kits (Bioscience Medical, Madrid, Spain). The analyses of the amino acid content in plasma samples were carried out at the University of Sevilla (Microanalysis Research Service, CITIUS) using an amino acid analyzer BIOCHROM 30 (Laborservice Onken, Gründau, Germany), following an amino acid analysis method based on ion exchange chromatography with post-column derivatization with ninhydrin. 2.6. Data Preprocessing and Exploratory and Statistical Analysis Prior to statistical analysis, the identified amino acids were pre-processed, removing all amino acids with >30% missing values. No normalization method was applied. Missing values were imputed by half the minimum of each amino acid, and the overall behavior of the samples was analyzed via principal component analysis (PCA) before and after the pre-processing process. The normality of all data sets was analyzed with the Shapiro–Wilk test [16]. To test whether the intervention had led to significant changes, all parameters and variables that fit a normal distribution were subjected to a paired t-test, while those that did not fit a normal distribution were subjected to a Wilcoxon paired test. In the search for possible correlations of interest, the calculation of Pearson’s coefficient was used as a linear correlation value between variables. Correlation heat maps were produced using the ggplot2 package (v3.4.2) from the correlation matrix between amino acids and biochemical parameters and between amino acids and mineral values [17]. Finally, a sample classification model was generated using Sparse Partial Least Squares Discriminant Analysis (sPLS-DA), which finds discriminant relationships between groups in the multivariate amino acid dataset, also employing a feature selection approach to identify the subset of amino acids that is most relevant in group differentiation. The model was generated using the mixOmics package (v6.22.0), previously setting a seed of 5249 [ 18 ]. The optimal number of components and variables was obtained by applying the M-fold method as a cross-validation method with a fold value of 5, and the contributions of the variables in each of the components was analyzed. The optimal number of components and variables was obtained by applying the M-fold method as a cross-validation method with a fold value of 5. A total of 38 samples were randomly selected for model training, leaving the remaining 8 for model evaluation, and performance was evaluated by calculating the area under the curve (AUC) or ROC curve. Nutrients 2024,16, 1594 5 of 17 3. Results 3.1. Anthropometric Data As shown in Table 1, the results of the anthropometric characteristic of all the individuals in the study who completed the intervention are reported. Men’s and women’s values can be found separately in the Supplementary Material, Table S1. According to the forms and the supervised monitorization, all subjects followed the exercise guidelines and the dietary patterns as indicated. Statistically significant differences over time were observed in all the parameters except for beats per minute. Table 1. Anthropometric characteristics of the subjects before (t 0 ) and after (t f ) the intervention. Values are expressed as the arithmetic mean and confidence intervals (95% CI) of the subjects (n = 23, 35% males). pvalue < 0.05 implies a significant difference. Variable t0 (95% CI) tf (95% CI) p-Value Weight (kg) 91.19 (84.89–97.49) 82.20 (76.42–87.97) <0.001 BMI (kg/m2) 31.88 (29.90–33.86) 28.73 (26.78–30.68) <0.001 Abdominal circumference (cm) 103.23 (99.27–107.20) 91.87 (88.07–95.68) <0.001 Body Fat (%) 39.08 (35.76–42.41) 35.77 (32.31–39.22) <0.001 SBP (mmHg) 123.39 (118.44–128.34) 117.56 (112.05–123.07) 0.01718 DBP (mmHg) 81.17 (77.46–84.87) 76.82 (73.07–80.57) 0.006432 Body Water (%) 43.55 (41.32–45.77) 45.34 (43.08–47.59) <0.001 Bone Mass (Kg) 2.77 (2.55–3.00) 2.65 (2.46–2.85) 0.001574 Basal Metabolism (kcal) 1697.0 (1556.98–1837.62) 1594.78 (1474.14–1715.42) <0.001 Muscle Mass (%) 34.58 (29.37–39.78) 32.61 (28.26–36.97) <0.001 Visceral Fat (%) 10.76 (9.18–12.33) 8.97 (7.44–10.51) <0.001 Beats per minute 71.52 (66.93–76.11) 73.08 (67.95–78.22) 0.3346 Metabolic Age (years) 57.08 (53.14–61.02) 53.73 (48.86–58.61) 0.009444 BMI: body mass index; SBP and DBP: systolic and diastolic blood pressure. 3.2. Biochemical Parameters In Table 2, the results of the biochemical profiles of all the individuals in the study who completed the intervention are reported. Men’s and women’s values can be found separately in the Supplementary Material Table S2. Statistically significant differences were found for all the parameters except for glucose. The percentages of change for HDL-C and LDL-C were 52.3% and − 54.0%, respectively, while for TAG, the change was − 49.8%, and for total cholesterol, the value was reduced by −29.1%. Nutrients 2024,16, 1594 6 of 17 Table 2. Blood biochemistry values of the subjects before (t 0 ) and after (t f ) the intervention. Values are expressed as the arithmetic mean and confidence intervals (95% CI) of the subjects (n = 23, 35% males). pvalue < 0.05 implies a significant difference. Variable t0(95% CI) tf(95% CI) p-Value Glucose (mg/dL) 84.82 (77.09–92.54) 87.23 (83.82–90.63) 0.5411 TAG (mg/dL) 130.23 (94.40–166.06) 65.40 (54.24–76.57) <0.001 Total Cholesterol (mg/dL) 207.26 (191.90–222.63) 146.89 (131.53–157.01) <0.001 HDL-C (mg/dL) 47.48 (41.29–53.66) 72.29 (59.79–84.79) <0.001 LDL-C (mg/dL) 133.73 (120.09–147.38) 61.51 (49.00–74.01) <0.001 TAG: triacylglycerides; LDL-C and HDL-C: low and high-density lipoprotein cholesterol. 3.3. Mineral Content The results of the mineral contents of all the individuals in the study who completed the intervention are reported in the Table 3. Men’s and women’s values can be found separately in Supplementary Material Table S3. Statistically significant changes were observed following the intervention in the contents of calcium and phosphorus, with decreases of − 14.2% and − 56.7%, respectively, while in the case of chloride, the change reported was a statistically significant increase (+8.6%) after the intervention. The change in magnesium was not found to be statistically significant. Table 3. Serum oligoelement values of the subjects before (t 0 ) and after (t f ) the intervention. Values are expressed as the arithmetic mean and confidence intervals (95% CI) of the subjects (n = 23, 35% males). pvalue < 0.05 implies a significant difference. Variable t0 (95% CI) tf (95% CI) p-Value Calcium (mg/dL) 13.75 (12.81–14.68) 11.79 (11.27–12.32) <0.001 Phosphorus (mg/dL) 13.07 (11.45–14.69) 5.65 (5.38–5.92) <0.001 Chloride (nMol/L) 96.39 (92.02–99.86) 104.67 (101.98–107.37) <0.001 Magnesium (mg/dL) 1.97 (1.90–2.04) 2.01 (1.97–2.06) 0.3741 3.4. Plasma Circulating Amino Acids The plasma-circulating amino acids were measured in all the patients before and after the intervention. The total number of amino acids evaluated was 41, but after the processing of the data as described in Section 2.6, only 29 amino acids were included in the report (Table 4). The most significant changes found were α -aminoadipic acid (AAAA, +2587.6%), phosphoethanolamine (Pea, +99.2%), and 1-metylhistidine (Mhis_1, +57.5%) for those that increased, and asparagine ( − 47.7%) and alanine ( − 29.9%) for those for which the plasma concentration decreased. The AAAA and Mhis_1 changes were not statistically significant, but the changes observed were high because of the pretreatment and analyses applied to the data. Most of the amino acids conformed to a normal distribution and imputed the missing values by half of the minimum for each amino acid. In the exploratory analysis, PCA revealed the difference in behavior between samples, showing that, after pre-processing, component 1 separated consistently at the two intervention times (t 0 and t f ). The PCAs Nutrients 2024,16, 1594 7 of 17 performed before and after pre-processing of the data can be found in Supplementary Material Figure S2. Consequently, amino acid levels in the blood behaved differently in subjects before and after the intervention, and significant differences were found in many of them. To further explore the hypotheses, the multivariate analysis technique (sPLS-DA) was used to create a model capable of classifying and discriminating interventions according to their amino acid contents. The model was fitted with the optimal number of components (3 components) and variables, and it was observed that component 1 was the main component responsible for the separation or distinction of groups, explaining 27% of the variability of the data set (Supplementary Material Figure S3). The evaluation of the model showed AUC values of 0.9622 in the first component, making the high sensitivity of the generated model clear. The construction of the model did show findings of interest; observing the loadings, i.e., the weight of each variable in component 1 (responsible for group separation), we found that the amino acids asparagine, histidine, alanine, and phenylalanine were the four most influential (Supplementary Materials), participating notably in the characterization of the profile prior to the intervention or t0. Table 4. Plasma circulating amino acid values of the subjects before (t 0 ) and after (t f ) the intervention. Values are expressed as the arithmetic mean and confidence intervals (95% CI) of the subjects (n = 23, 35% males). pvalue < 0.05 implies a significant difference. Variable t0 (95% CI) (µmol/L) tf (95% CI) (µmol/L) p-Value Phser 7.63 (7.08–8.18) 6.91 (6.11–7.70) 0.1324 Taur 14.88 (12.04–17.72) 16.87 (13.58–20.15) 0.3382 Pea 0.86 (0.62–1.11) 1.46 (1.01–1.91) 0.01695 Urea 1004.02 (926.99–1081.06) 808.57 (716.13–901.01) 0.001193 Thr 28.05 (25.54–30.55) 23.94 (21.37–26.51) 0.008221 Ser 24.62 (22.59–26.66) 22.82 (20.48–25.17) 0.1611 Asn 23.80 (21.56–26.05) 12.01 (10.78–13.25) <0.001 Glu 10.21 (8.09–12.32) 8.21 (3.49–12.93) 0.004339 Gln 102.75 (95.52–109.98) 137.19 (118.20–156.17) <0.001 AAAA 1.23 (0.82–1.63) 1.07 (0.76–1.39) 0.482 Gly 47.61 (41.86–53.37) 43.70 (36.67–50.73) 0.03543 Ala 85.12 (77.98–92.25) 57.51 (52.40–62.63) <0.001 Aaba 4.81 (4.24–5.38) 4.83 (4.24–5.42) 0.9566 Val 49.43 (45.17–53.69) 36.54 (33.25–39.82) <0.001 Cys 8.33 (7.51–9.15) 7.96 (7.20–8.72) 0.3419 Nutrients 2024,16, 1594 8 of 17 Table 4. Cont. Variable t0 (95% CI) (µmol/L) tf (95% CI) (µmol/L) p-Value Met_Cysth 5.45 (5.04–5.87) 4.08 (3.74–4.42) <0.001 Ile 14.51 (13.06–15.97) 10.74 (9.68–11.79) <0.001 Leu 28.86 (25.99–31.73) 27.80 (25.75–29.84) 0.3178 Tyr 16.97 (15.60–18.34) 13.56 (12.69–14.43) <0.001 Phe 12.93 (12.05–13.80) 9.68 (9.10–10.26) <0.001 Amm 39.53 (36.87–42.19) 28.27 (25.04–31.49) <0.001 Orn 21.57 (19.14–24.00) 17.92 (16.13–19.70) 0.00242 Lys 38.83 (36.56–41.10) 39.34 (36.09–41.78) 0.7381 Mhis_1 3.18 (2.40–3.95) 3.72 (2.68–4.75) 0.56 His 17.04 (16.24–17.84) 13.24 (12.52–13.96) <0.001 Trp 6.32 (5.46–7.18) 6.56 (5.51–7.61) 0.72 Arg 8.73 (6.91–10.55) 9.27 (8.11–10.43) 0.4954 Sarc 15.35 (14.40–16.29) 18.42 (16.87–19.97) 0.002875 Citr 5.58 (5.09–6.06) 4.46 (4.04–4.89) <0.001 3.5. Correlation of Plasma Circulating Amino Acids and Biochemical and Mineral Parameters The connections between the 29 amino acids subjected to statistical analyses and the biochemical parameters and mineral content were investigated as depicted in Figure 1. Strong positive correlations, meaning values higher than 0.5 between several amino acids and all the biochemical parameters, were found, except for glucose and HDL-C. The TAG content was found to have correlations with alanine, valine, methionine+cystathionine, isoleucine, and phenylalanine, being the parameter with more correlations with amino acids. In the case of LDL-C, correlations with asparagine, alanine, valine, and histidine were found at the same that a negative correlation with glutamine was found. On the other hand, the HDL-C content was inversely correlated with valine, isoleucine, and phenylalanine (Figure 1). In the case of minerals, eight detected compounds (asparagine, alanine, valine, methionine+cystathionine, isoleucine, tyrosine, phenylalanine, ammonia, and histidine) were correlated with the content of phosphorus (values >0.5), with asparagine being the one with the highest value (0.76). For the other minerals, no strong correlations, either directly or inversely, were found (Figure 1). Nutrients 2024,16, 1594 9 of 17 Nutrients 2024, 16, 1594 8 of 17 (25.99–31.73) (25.75–29.84) Tyr 16.97 (15.60–18.34) 13.56 (12.69–14.43) <0.001 Phe 12.93 (12.05–13.80) 9.68 (9.10–10.26) <0.001 Amm 39.53 (36.87–42.19) 28.27 (25.04–31.49) <0.001 Orn 21.57 (19.14–24.00) 17.92 (16.13–19.70) 0.00242 Lys 38.83 (36.56–41.10) 39.34 (36.09–41.78) 0.7381 Mhis_1 3.18 (2.40–3.95) 3.72 (2.68–4.75) 0.56 His 17.04 (16.24–17.84) 13.24 (12.52–13.96) <0.001 Trp 6.32 (5.46–7.18) 6.56 (5.51–7.61) 0.72 Arg 8.73 (6.91–10.55) 9.27 (8.11–10.43) 0.4954 Sarc 15.35 (14.40–16.29) 18.42 (16.87–19.97) 0.002875 Citr 5.58 (5.09–6.06) 4.46 (4.04–4.89) <0.001 3.5. Correlation of Plasma Circulating Amino Acids and Biochemical and Mineral Parameters The connections between the 29 amino acids subjected to statistical analyses and the biochemical parameters and mineral content were investigated as depicted in Figure 1. Strong positive correlations, meaning values higher than 0.5 between several amino acids and all the biochemical parameters, were found, except for glucose and HDL-C. The TAG content was found to have correlations with alanine, valine, methionine+cystathionine, isoleucine, and phenylalanine, being the parameter with more correlations with amino acids. In the case of LDL-C, correlations with asparagine, alanine, valine, and histidine were found at the same that a negative correlation with glutamine was found. On the other hand, the HDL-C content was inversely correlated with valine, isoleucine, and phenylalanine (Figure 1). In the case of minerals, eight detected compounds (asparagine, alanine, valine, methionine+cystathionine, isoleucine, tyrosine, phenylalanine, ammonia, and histidine) were correlated with the content of phosphorus (values >0.5), with asparagine being the one with the highest value (0.76). For the other minerals, no strong correlations, either directly or inversely, were found (Figure 1). (A) Nutrients 2024, 16, 1594 9 of 17 (B) Figure 1. Correlation heat maps. (A) Represents the significant correlations (p value < 0.05) between some biochemical parameters and the amino acids quantified in the subjects after pre-processing and imputation of values. (B) Shows the significant correlations (p value < 0.05) between the same amino acids and the mineral levels quantified in the subjects. The color and size of the circle represents the Pearson correlation coefficient. Phser, phosphoserine; Taur, taurine; Pea, phosphoethanolamine; Thr, threonine; Ser, serine; Asn, asparagine; Glu, glutamate; Gln, glutamine; Sarc, sarcosine, ; AAAA, α-aminoadipic acid; Gly, glycine; Ala, alanine; Citr, citrulline; Aaba, α-aminobutiric acid; Val, valine; Cys, Cysteine; Met_Cysth, methionine+cystathionine; Ile, isoleucine; Leu, leucine; Tyr, tyrosine; Phe, phenylalanine; Amm, ammonia; Orn, ornithine; Lys, lysine; Mhis_1, 1-metylhistidine; His, histidine; Trp, tryptophane. 4. Discussion Following a healthy lifestyle (i.e., regular exercise and consumption of healthy foodstuff) is necessary to prevent the development of certain diseases, especially those related to metabolism, such as diabetes or cardiovascular diseases, which are usually associated with the described situation of obesity. Overall, the results presented in this nutritional intervention show its efficacy in regulating the health statuses of overweight patients based on the results of different parameters related to the disease. Regarding the anthropometric data, the statistical significance observed in the parameters can be directly correlated to both the foodstuff from the dietary intervention and the aerobic exercise [12], as has been demonstrated by several studies demonstrating the health benefits of the Mediterranean diet for the overall health of humans [20,21]. The differences found in some parameters between males and females are likely consequences of the human body’s physiology. Men and women have very different body compositions, as males typically have less fat mass and higher absolute and relative lean muscle mass [22], factors which were considered in the inclusion criteria regarding the weight circumference, for instance. In general, the individuals exhibited low levels of HDL-C and high values of glucose, cholesterol, TAG, and LDL-C at the start of the intervention (t0), which possible indicates their non-healthy conditions [23]. Regarding the biochemical parameters of the target population, the glucose levels did not statistically significantly change over time after the 12 weeks of the intervention (tf). This absence of an effect of the diet on glucose levels was also observed in our previous human study, without hemp protein supplementation [24]. Nonetheless, low initial glucose levels do not have to be indicative of good glucose metabolism, but this condition may be associated with hyperinsulinemia. However, considering that insulin levels were not measured in the study, we cannot draw conclusions. On top of that, this absence of variation and relatively low levels of glucose for people with obesity may be indicative of the onset of insulin resistance, which may be reversed with nutritional intervention, because insulin sensitivity is known to improve with diet and weight loss, parameters that were modified in the present study. However, it must be noted that protein supplementation does not necessarily have an effect on blood glucose levels in the long term [25], although some studies have reported that, although it depends Figure 1. Correlation heat maps. (A) Represents the significant correlations (pvalue < 0.05) between some biochemical parameters and the amino acids quantified in the subjects after pre-processing and imputation of values. (B) Shows the significant correlations (pvalue < 0.05) between the same amino acids and the mineral levels quantified in the subjects. The color and size of the circle represents the Pearson correlation coefficient. Phser, phosphoserine; Taur, taurine; Pea, phosphoethanolamine; Thr, threonine; Ser, serine; Asn, asparagine; Glu, glutamate; Gln, glutamine; Sarc, sarcosine; AAAA, α -aminoadipic acid; Gly, glycine; Ala, alanine; Citr, citrulline; Aaba, α -aminobutiric acid; Val, valine; Cys, Cysteine; Met_Cysth, methionine+cystathionine; Ile, isoleucine; Leu, leucine; Tyr, tyrosine; Phe, phenylalanine; Amm, ammonia; Orn, ornithine; Lys, lysine; Mhis_1, 1-metylhistidine; His, histidine; Trp, tryptophane. 4. Discussion Following a healthy lifestyle (i.e., regular exercise and consumption of healthy foodstuff) is necessary to prevent the development of certain diseases, especially those related to metabolism, such as diabetes or cardiovascular diseases, which are usually associated with the described situation of obesity. Overall, the results presented in this nutritional intervention show its efficacy in regulating the health statuses of overweight patients based on the results of different parameters related to the disease. Regarding the anthropometric data, the statistical significance observed in the parameters can be directly correlated to both the foodstuff from the dietary intervention and the aerobic exercise [ 11 ], as has been demonstrated by several studies demonstrating the health benefits of the Mediterranean diet for the overall health of humans [ 19 , 20 ]. The differences found in some parameters between males and females are likely consequences of the human body’s physiology. Men and women have very different body compositions, as males typically have less fat mass and higher absolute and relative lean muscle mass [ 21 ], factors which were considered in the inclusion criteria regarding the weight circumference, for instance. 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