Acute Impact of Lacticaseibacillus rhamnosus OLXAL-1 and Cistus creticus Extract on Postprandial Lipemia, Glycemia and Antioxidant Status of Healthy Adults: A Randomized, Crossover, Placebo-Controlled Clinical Trial

Abstract

Postprandial lipemia and glycemia are associated with nutrition-dependent diseases and cardiovascular risks, while antioxidant capacity affects blood circulation and inflammatory biomarkers. This study investigated the impact of Lacticaseibacillus rhamnosus OLXAL-1 and antioxidant intake on the above metabolic states in a randomized crossover design with three arms. Twenty-two healthy participants consumed meals consisting of bread, butter, and yogurt products (placebo or enriched with probiotics (intervention I)) or enriched with probiotics and antioxidants (intervention II). Blood samples were collected before and 30, 90, and 180 min after meal consumption, and biomarkers of lipemia, glycemia, and antioxidant status were examined. However, plasma total antioxidant capacity (TAC) was significantly greater in the combined intervention group compared to placebo (p = 0.04), with an increase of 0.24 mmol/L at 1.5 h postprandially; this effect was time-dependent (p = 0.025). In summary, the coexistence of antioxidants and probiotics showed limited acute effects on metabolic outcomes, with an increase in plasma total antioxidant capacity observed when probiotics and antioxidants were combined, while no significant changes were detected in the remaining biomarkers across intervention groups.

1. Introduction

Postprandial lipemia, characterized by an increase in triglyceride-rich lipoproteins after meal consumption, is a dynamic, non-steady-state condition [1]. This response is influenced by the fatty acid composition of food, including the degree of saturation and chain length, as well as carbohydrate and protein content [2]. Fatty acids serve as key substrates in metabolic pathways and represent an important energy source; however, elevated circulating levels have been associated with lipotoxicity and the activation of inflammatory pathways, which may contribute to cardiometabolic risk [3]. Fatty acids can accumulate in arterial walls, contributing to plaque formation, arterial narrowing, and increased cardiovascular risk [4]. Several factors that affect the postprandial lipemic response have been reported, including fasting hypertriglyceridemia [5], insulin resistance [6], obesity [7], exercise habits [8,9], macronutrient consumption [10] and the food matrix [3].

Following meal consumption, several mechanisms regulate blood glucose levels and maintain homeostasis, with insulin playing a central role [11]. However, consistently high blood sugar levels can lead to insulin resistance, a condition characterized by increased insulin production to regulate blood sugar levels, resulting in chronic hyperinsulinemia [12]. Postprandial glycemia refers to increased blood glucose levels after meal consumption, and is a key factor in the development of diabetes [13]. Moreover, the postprandial state is connected to the eating habits and lifestyle of each individual, as well as the glycemic index of food choices [14].

As nutrition has been shown to play a significant role in the regulation of postprandial lipemia and glycemia, functional foods have gained increasing attention, with several clinical and epidemiological studies exploring their beneficial effects in the postprandial state [15]. Several clinical trials have examined the effect of food extracts rich in carotenoids, polyphenols, and other antioxidant compounds on postprandial lipemia with promising findings [16,17,18]. Moreover, epidemiological data suggest that polyphenols in foods and beverages may have a beneficial effect on postprandial blood glucose control; however, the extent of this effect varies depending on the polyphenol source [19]. Clinical trials have confirmed the positive acute effect of food-derived polyphenols on postprandial glucose elevation and insulin secretion, supporting further investigation into additional sources of bioactive compounds [20,21,22]. However, most available evidence relates to chronic intake, while data on acute postprandial effects remain limited. Polyphenols have also been proposed as prebiotic compounds, and research has shown that indigestible polyphenols serve as a substrate for the development and optimal growth of probiotic bacteria thereby modulating gut microbiota [23,24]. The ability of polyphenols to modulate gut microbiota composition and function may contribute to improvements in glucose and lipid parameters [25].

Exploring the field of novel foods, food fortification with probiotics is a rapidly growing area of the food industry and has attracted special interest in the field of nutrition because of its beneficial properties for human health [26]. Probiotics are live bacteria that, when consumed in sufficient quantities, may confer health benefits, primarily through modulation of the gut microbiota [27,28]. The most commonly investigated and verified health benefits of probiotics are their beneficial effects on gastrointestinal outcomes; however, the effect of probiotic bacteria on metabolic outcomes has recently gained research interest [29]. Specifically, recent studies have suggested that probiotics can contribute to better glycemic control and lipid metabolism, thereby contributing to the management of postprandial glycemia [30,31] and lipemia [32,33]. Different probiotic compounds have been examined for their long-term effects on lipidemic parameters, including Limosilactobacillus fermentum, Lactobacillus acidophilus, Enterococcus faecium, and Streptococcus thermophilus, by reducing postprandial lipid biomarkers, including cholesterol and triglycerides [33,34,35,36,37], whereas evidence on short-term effects remains limited. Moreover, studies on probiotic bacteria such as Lactobacillus acidophilus and Bifidobacterium animalis do not support the hypothesis of probiotic short-term glycemic control [29]; however, a long-term study of B. animalis has shown beneficial effects on glycemic control and lipid profile [38]. To the best of our knowledge, the effects of Lacticaseibacillus rhamnosus OLXAL-1 on metabolic markers have not yet been evaluated in human subjects.

The successful development of probiotic-containing food products requires thorough knowledge of the ability of the microorganism to survive the manufacture and storage of the product is required [39]. Since there is potential for synergistic effects of probiotics and prebiotics, simultaneous supplementation or incorporation of both probiotics and antioxidants with prebiotic effects in a product may be more effective in managing postprandial glycemic and lipemic responses to a greater extent than either intervention alone [27,40]. This synergistic effect may be due to the complementary mechanisms of action of probiotics and polyphenols. Probiotics modulate the gut microbiome, whereas polyphenols can inhibit the activity of enzymes involved in lipid and carbohydrate metabolism [41]. Moreover, unabsorbed dietary polyphenols and their metabolites, phenolic acids, have been shown to promote probiotic growth, and studies have demonstrated that these bioactive compounds can enhance the growth of Lactobacillus spp. and other bacterial species [42]. However, the potential combined acute effects of specific probiotic strains and antioxidant-rich extracts on postprandial metabolism remain insufficiently explored.

Health benefits of probiotics are strain-specific and thus, specific strains can be used for specific health-claims. Novel wild-type probiotics are, firstly, screened (in vitro) for their probiotic potential and subsequent in vivo experiments and clinical trials are needed to confirm any beneficial action. In this study, the strain L. rhamnosus OLXAL-1, isolated from Greek olives (Olea europea var. rotunda), was chosen due to its strong a-glucosidase inhibition in vitro [43]. In addition, freeze-dried immobilized cells of L. rhamnosus OLXAL-1 on oat flakes have demonstrated high survival rate after vitro digestion and during vivo experiments in healthy mice [44], highlighting the appropriateness of the strain to be used as a probiotic candidate. Of note, the inhibition of a-glucosidase, along with other carbohydrate hydrolases, is considered as an effective way to manage postprandial hyperglycemia [45].

Cistus creticus, is a polyphenol- and terpene-rich Mediterranean medicinal plant that has demonstrated antioxidant activity, suggesting potential health benefits beyond its traditional uses. Its complex phytochemical profile and bioactive properties make it an attractive candidate for further research into functional foods and interventions for metabolic health. C. creticus was selected due to its traditional use as a medicinal plant and its promising pharmacological properties with many pharmacological activities [46].

This clinical trial aimed to evaluate the effects of wild-type L. rhamnosus OLXAL-1 strain, used in freeze-dried immobilized oat flakes form and C. creticus extract on postprandial lipemia, glycemia, and antioxidant status in healthy young participants. Specifically, the bioactive compounds of interest were incorporated into yogurt products and served as a high-fat meal. The study included three arms investigating the effect of probiotic fortification and the synergy of probiotic and antioxidant compounds compared to the effect of a placebo meal on certain blood markers associated with postprandial lipemia, glycemia, and antioxidant capacity. These findings may provide valuable insights on the potential benefits of probiotic- and antioxidant-enriched foods for the management of postprandial glycemic and lipid levels.

2. Materials and Methods

2.1. Intervention Meal

2.1.1. Preparation of Oat Flakes with Immobilized L. rhamnosus OLXAL-1

L. rhamnosus OLXAL-1, isolated from Greek olive fruits (Olea europea var. rotunda) that presented antidiabetic action in vitro, was used [43]. Freeze-dried immobilized L. rhamnosus OLXAL-1 on oat flakes was prepared in the Laboratory of Applied Microbiology & Biotechnology, in the Department of Molecular Biology and Genetics, Democritus University of Thrace, based in Alexandroupolis, Greece, as described previsouly [43,44]. In brief, overnight full-grown culture of L. rhamnosus OLXAL-1 was prepared after the inoculation of 10⁷ cells on food-grade medium (2.0% w/v glucose, 0.2% w/v KH₂PO₄, 0.03% w/v MgSO₄, 0.6% CH₃COONa, 2.5% w/v yeast extract and 0.005% w/v MnSO₄), at 37 °C. Cells were harvested after centrifugation (8500× g, for 10 min, at 4 °C) and immersed in sterile ¼ Ringer’s solution and oat flakes (Achaiko “Edelweiss”, Patras, Greece) were introduced in the cell suspension. After 20 min incubation, the mixture was strained and immobilized cells were washed with ¼ Ringer’s solution. The immobilized cells on oat flakes were transferred to −80 °C overnight, following freeze-drying on a BenchTop Pro (Virtis, SP Scientific, Warminster, PA, USA) freeze-dryer under pressure 30–35 Pa and condenser temperature −101 °C, for 24 h.

2.1.2. Preparation of C. creticus Extract

C. creticus extract was developed in the Laboratory of Organic Chemistry, Department of Chemistry of University of Ioannina, according to the following methodology. The fresh plant material—comprising both leaves and stems—was first treated to remove its moisture content. This was achieved by freeze-drying the samples at 0.1 mbar and 25 °C for 48 h. After drying, the material was ground into a fine powder using a commercial blender. Then, aqueous ultrasound-assisted extraction was carried out, to extract most of the bioactive compounds. Subsequently, water was added to the powder at a ratio of 10 mL per gram. The borosilicate bottle containing the resulting slurry was then placed in an ultrasonic bath operating at 200 W and 30 °C for 1 h, with the bottles being vigorously shaken every 15 min. The suspension obtained was filtered through a glass funnel fitted with a sintered glass filter (porosity 4). The filtration was concentrated by removing the solvent using a rotary evaporator. The concentrated solution was then frozen and subjected to freeze-drying at 0.01 mbar and 30 °C until complete sublimation of volatiles occurred. The final residue was collected and stored in a sealed borosilicate vial at −30 °C [46].

2.1.3. Antioxidant Activity of Lyophilized C. creticus Extract as a Yogurt Fortifier

The antioxidant potential of lyophilized C. creticus extract was examined in a pre-clinical study at three different concentrations using both cellular and non-cellular assays to assess its suitability as a functional fortifier in yogurt. The study evaluated total antioxidant capacity and total phenolic content before and after in vitro gastrointestinal digestion, as well as cellular antioxidant activity using Caco-2 epithelial cells. According to the published results, the mid-level fortification (250 mg extract per 200 g of yogurt) was identified as the optimal dose, achieving the highest cellular antioxidant activity percentage. Analysis of the phenolic profile of C. creticus extract showed it contained mainly Myricitrin, Myricetin 3-galactoside, Gallocatechin, Quercitrin (Quercetin 3-O-rhamnoside), Prodelphinidin B, Myricetin 3-arabinoside, Catechin and Quercetin 3-O-glucoside [46].

2.1.4. Meal Composition Analysis

Dietary information was calculated according to US Department of Agriculture (USDA) tables [47] as for oats, bread and butter and the label of the manufacturer as for the yogurt. Specifically, the two slices of unleavened white bread with a total weight of 80 g and 30 g of unsalted cow butter, 200 g of yogurt and 1 g of oat provided approximately 577.46 kcal, 50.06 g of available carbohydrates and total fat content of 35.18 g, as shown in Table 1. The intake of a high-fat and high-carbohydrate meal, with the specific ratio, can lead to impaired, lipemic, and glycemic, postprandial metabolic homeostasis, which has been linked to an increased cardiovascular and diabetic risk [4].

Table 1. Nutrient composition of the intervention meals according to USDA database [47].

Dietary Component	Placebo	Intervention I	Intervention II
Energy (kcal)	577.46	577.46	577.46
Protein (g)	15.97	15.97	15.97
CHO (g)	50.06	50.06	50.06
Sugars (g)	14.17	14.17	14.17
Starch (g)	30.31	30.31	30.31
Total fat (g)	35.18	35.18	35.18
Saturated (g)	5.36	5.36	5.36
Dietary fiber (g)	1.96	1.96	1.96
Sodium (mg)	385.63	385.63	385.63
Antioxidants (mg GAE/g) (mg GAE/g)	0	0	80.7

2.2. Participants

The study was promoted to potential participants through phone calls and recruitment flyers shared on social media. Those who initially agreed to take part attended in-person screening sessions. Participants were evaluated using an eligibility checklist that outlined the inclusion and exclusion criteria.

Participants aged between 18 and 65 years who were in good overall health were considered eligible for inclusion. Exclusion criteria encompassed individuals with a body mass index (BMI) greater than 40 kg/m² (indicating morbid obesity), those undergoing contraceptive treatments or using probiotic supplements (n = 6), participants with lactose intolerance or allergies/intolerances to any of the study ingredients (e.g., milk protein), pregnant or lactating women, individuals planning pregnancy, users of illicit drugs, or those with chronic alcoholism or an average daily alcohol intake exceeding 50 g. Also excluded were individuals diagnosed with chronic diseases such as cancer, active liver disease, severe kidney dysfunction, or those who had suffered a major stroke within the previous six months, as well as participants with conditions that increase the risk of bleeding or any other serious medical issues that could hinder safe participation in a dietary intervention. People deemed unreliable by the research team, those with an illness shortening life expectancy below the study’s duration, or anyone whose participation was considered unsafe (e.g., substance abuse) were not included. All participants were informed about the study’s purpose and methodology and provided written consent before beginning the trial.

In accordance with the study protocol, anthropometric data was collected using standardized procedures [48]. A medical history questionnaire was completed, and demographic data were recorded. Specifically, body weight (kg) was determined using a body composition analyzer (Tanita SC-330 P, Tokyo, Japan), height (cm) using a stadiometer (Tanita HR-001), and hip and waist circumferences (cm) with a measuring tape. To assess their health background, volunteers filled out a self-administered questionnaire containing two demographic items (sex and age) and three medical history questions: “Have you used any medication during the past six months?”, “Have you taken nutritional supplements within the last six months?”, and “Have you been diagnosed with any clinical condition?”. Additionally, participants completed a brief questionnaire to evaluate their dietary behaviors and general lifestyle habits, which included a food frequency questionnaire and items related to smoking, physical activity, and alcohol consumption during the preceding six months.

2.3. Study Design

This study employed a randomized, placebo-controlled, double-blind, crossover design. The randomization sequence was computer-generated, and both participants and data analysts were blinded to the intervention allocation. It took place in November 2022 within a specially designated facility at the Nutrition and Public Health Unit of the University of the Aegean, located in Lemnos, Greece. The study protocol received approval from the Research Ethics Committee of the University of the Aegean (approval no. 3343, 15 February 2022) and was preregistered on ClinicalTrials.gov prior to the enrollment of the first participant (identifier NCT05612243). All participants provided written informed consent before screening.

The experiment included three test sessions, during which each participant received the placebo and two treatment conditions, separated by a 1-week washout period between sessions. After screening, eligible participants were instructed to maintain their usual diet, lifestyle, and physical activity throughout the study duration. Before each test day, participants were given specific guidelines: they were to fast overnight for 10 h, avoid consuming high-fat meals the evening prior (meal suggestions were provided during screening), and refrain from alcohol consumption or vigorous exercise for 24 h before testing. On each intervention day, a 24 h dietary recall was collected using a written self-report form to document all foods and beverages consumed the day before. During each study visit, participants were served a meal high in fat and carbohydrates, followed by either the intervention or placebo meal, which had to be consumed within 15 min under the supervision of a researcher ensuring adherence to time requirements. In detail, the meal consisted of two slices of unleavened white bread (80 g), unsalted butter (30 g), and plain yogurt (200 g) containing (1) 1 g of immobilized L. rhamnosus OLXAL-1 on oat flakes (9.48 logcfu/g; 3 × 10⁹ cfu per serving) (intervention I group), or (2) 1 g of immobilized L. rhamnosus OLXAL-1 on oat flakes (9.48 log cfu/g; 3 × 10⁹ cfu per serving) and 250 mg lyophilized extract of C. creticus (intervention II group), or (3) 1 g of oat flakes without the strain or the extract (placebo group). All volunteers were asked not to consume any other food, as well as not to smoke and avoid any movement, during each trial period. A researcher maintained compliance control.

For each intervention, meals were prepared by a team investigator. Disposable materials were used for the necessary weightings, and the workbench was thoroughly cleaned using household detergents. Disposable gloves and personal protective equipment were used throughout the process, and hygiene and food safety measures were observed. The two intervention and placebo groups were identified through labeling using alphabetical codes (A, B, and C) to ensure that the treatment allocation was concealed to both the investigator and participants.

2.4. Blood Sampling

Blood samples were obtained via venipuncture at four time points during each study visit. Specifically, 10 mL of blood was drawn by a collaborating physician at 0 min (fasting, prior to meal consumption) and subsequently at 30, 90, and 180 min following the test meal. Samples were collected using clot activator tubes for serum and ethylenediaminetetraacetic acid (EDTA) and heparinized tubes for plasma. At each collection point, the tubes were centrifuged using a Thermo Scientific ST16R refrigerated centrifuge (Thermo Fisher Scientific, Waltham, MA, USA) at 3000× g and 4 °C—15 min for serum and 10 min for plasma. The resulting serum and plasma were then aliquoted and stored at −40 °C.

After the conclusion of the trial, serum samples from all-time points were analyzed using a COBAS c111 automated biochemical analyzer (Roche, Basel, Switzerland) to determine concentrations of high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), total cholesterol (TC), triglycerides (TGL), C-reactive protein (CRP), and uric acid (UA). Insulin (INS) levels were measured using a Maglumi 2000 Plus automated immunoassay system (Snibe, Shenzhen, China). EDTA plasma was used to assess total antioxidant capacity (TAC) at each time point by the Ferric Reducing Antioxidant Power (FRAP) assay following the method of Benzie et al. [49], as well as to determine blood glucose levels through biochemical analysis. The same plasma samples were also analyzed for trimethylamine-N-oxide (TMAO) content using the procedure described in [50]. In addition, heparinized plasma samples collected at baseline and 3 h post-meal were evaluated with the FRAS 5—Free Radical Analytical System to determine pro-oxidant status (d-ROM index), antioxidant potential (PAT index), and overall oxidative stress (OSI index).

2.5. Statistical Analysis

2.5.1. Sample Size Calculation

Sample size calculation revealed that 21 individuals were adequate to detect a significant group × time interaction effect with an effect size of 0.25, at 5% level with 90% chance, based on glycemic and antioxidant biomarkers as the primary outcome variables. Considering a drop out of 40%, the final sample size came up to 28 individuals. The sample size was determined using the G*Power 3.1 software (University of Düsseldorf, Düsseldorf, Germany).

2.5.2. Data Analysis

Descriptive statistics for biomarker concentrations and their incremental changes were expressed as mean ± standard deviation (SD). The distribution of data was evaluated using the Kolmogorov–Smirnov test to verify normality. Repeated-measures analysis of variance (ANOVA) was applied to examine the main effects of time and group, as well as their interaction on the measured biomarkers. The assumption of sphericity was tested using Mauchly’s test, and when it was not met, the Geisser–Greenhouse correction was applied. Post hoc analyses were conducted using the Bonferroni adjustment. For each biomarker, the incremental area under the curve (iAUC) was computed based on the trapezoidal rule, integrating the values above and below the fasting baseline concentration. Descriptive data for iAUCs are reported as mean (95% confidence interval, CI). Differences among the three intervention conditions were analyzed using one-way ANOVA. All statistical analyses were performed with SPSS V21.0 for Windows (IBM Corporation, Armonk, NY, USA) and Prism 9 (GraphPad Software Inc., San Diego, CA, USA). All tests were two-tailed, and statistical significance was defined as p < 0.05.

3. Results

3.1. Participants

Thirty-seven participants, n = 12 men and n = 25 women, were assessed for eligibility; however, twenty-eight were allocated into the treatment groups. Nine participants were excluded from the study because they either failed to meet the inclusion criteria or were unable to adhere to the scheduled study dates. Recruitment ceased after reaching the required sample size, i.e., n = 28 (n = 10 men and n = 18 women). However, two participants withdrew from the study because they did not like the meal and four withdrew due to time constraints or other commitments (Figure 1).

Figure 1. CONSORT schematic of participant recruitment, screening, and assessment [51].

Participant baseline characteristics are shown in Table 2. Participants were mostly women (59%) and of young age and overweight on average. About a third of participants were current smokers, whilst a third reported low physical activity (Table 2).

Table 2. Participants’ general baseline characteristics.

General Baseline Characteristics	n (%) or Mean (SD)
Men	9 (41%)
Age (years)	22 (1.9)
Anthropometry
Height (cm)	167.5 (9.2)
Weight (kg)	67.1 (17.4)
Body mass index (kg/m2)	23.5 (3.9)
Waist-to-hip ratio	0.79 (0.06)
General Lifestyle Habits
Current Smokers	8 (36%)
Physical Activity
Medium	5 (23%)
High	9 (41%)

Participants’ biochemical and hormonal parameters at the initial screening are presented in Table 3. Participants had, on average, normal levels for all biomarkers tested (Table 3).

Table 3. Participants’ biochemical and hormonal profiles at initial screening (n = 22).

Biochemical & Hormonological Parameters	Mean (SD)	Normal Values (mg/dL)
Total cholesterol (mg/dL)	172.6 (20.9)	<200
Glucose (mg/dL)	91.9 (6.4)	<99
Triglycerides (mg/dL)	63.4 (16.3)	<150
HDL-cholesterol (mg/dL)	62.9 (11.5)	>60
LDL-cholesterol (mg/dL)	97.1 (18.4)	<100
CRP (mg/dL)	0.06 (0.03)	<0.9
Insulin (μIU/mL)	15.3 (5.6)	3–25 μIU/mL

3.2. Postprandial Biochemical and Immunological Markers

The main effects of the intervention group and time, alongside their interaction on the postprandial levels in serum TC, HDL-C and LDL-C, TG, UA, CRP and INS, as well as in plasma glucose, following the test meals, are shown in Table 4. A significant time effect was observed for most biomarkers (p < 0.01), indicating expected postprandial fluctuations. However, no significant group or group × time interaction effects were detected for the majority of the measured parameters. A marginal interaction effect was observed for plasma glucose (p = 0.06), although it did not reach statistical significance.

Table 4. Postprandial changes in serum total, HDL-, LDL-cholesterol, triglycerides, uric acid, CRP and insulin, as well as in plasma glucose, and the significance of group and time effect, following the test meals.

		Baseline	Δ* 30 min	Δ 1.5 h	Δ 3 h	p Value Group Effect	p Value Time Effect	p Value Group × Time Interaction
Total cholesterol (mg/dL)	Placebo	166.5 (24.2)	−3.3 (2.3)	1.5 (1.1)	−2.3 (1.6)	0.33	0.002 *	0.10
	Intervention I	169.9 (26.3)	7.0 (4.5)	−10.6 (7.5)	−1.1 (0.80)
	Intervention II	170.9 (27.1)	0.55 (0.39)	−2.3 (1.6)	−3.5 (2.5)
Glucose (mg/dL)	Placebo	86.4 (10.1)	8.9 (6.3)	−14.6 (10.3)	1.1 (0.74)	0.40	0.006 *	0.06
	Intervention I	87.2 (7.2)	−0.08 (0.06)	−6.8 (4.8)	1.1 (0.77)
	Intervention II	86.1 (6.3)	−0.86 (0.61)	−4.9 (3.5)	−0.18 (0.13)
HDL-cholesterol (mg/dL)	Placebo	63.3 (11.2)	−0.70 (0.50)	−1.0 (0.73)	0.36 (0.26)	0.35	0.006 *	0.15
	Intervention I	64.4 (11.9)	2.0 (1.4)	−3.6 (2.5)	−0.14 (0.10)
	Intervention II	65.0 (12.7)	−1.1 (0.74)	−0.64 (0.45)	−0.64 (0.45)
LDL-cholesterol (mg/dL)	Placebo	92.0 (20.9)	−2.8 (2.0)	0.29 (0.21)	0.45 (0.32)	0.80	0.01 *	0.11
	Intervention I	92.4 (23.5)	2.5 (1.8)	−5.74 (4.06)	0.75 (0.53)
	Intervention II	94.5 (27.5)	−2.2 (1.6)	−0.98 (0.70)	0.14 (0.10)
Triglycerides (mg/dL)	Placebo	81.8 (27.3)	8.4 (6.0)	16.5 (11.7)	−6.8 (4.8)	0.85	0.0001 *	0.52
	Intervention I	81.9 (34.4)	17.0 (12.0)	9.1 (6.5)	−6.9 (4.9)
	Intervention II	79.1 (28.3)	11.5 (8.1)	18.1 (12.8)	−11.5 (8.1)
CRP (mg/dL)	Placebo	0.35 (0.62)	−0.002 (0.00)	−0.004 (0.00)	0.001 (0.00)	0.36	0.12	0.53
	Intervention I	0.23 (0.25)	0.006 (0.00)	−0.012 (0.00)	−0.005 (0.00)
	Intervention II	0.22 (0.23)	−0.002 (0.00)	−0.004 (0.00)	−0.003 (0.00)
Uric Acid (mg/dL)	Placebo	4.7 (1.2)	0.09 (0.06)	−0.07 (0.05)	−0.20 (0.14)	0.28	<0.0001 *	0.28
	Intervention I	4.9 (1.4)	0.27 (0.19)	−0.34 (0.24)	−0.17 (0.12)
	Intervention II	4.9 (1.3)	0.10 (0.07)	−0.05 (0.04)	−0.28 (0.20)
Insulin (μIU/mL)	Placebo	9.6 (3.6)	33.3 (23.5)	−24.7 (17.4)	−4.0 (2.8)	0.51	0.0003 *	0.67
	Intervention I	11.0 (5.8)	18.9 (13.3)	−5.3 (3.7)	−13.0 (9.2)
	Intervention II	9.8 (3.0)	26.9 (19.0)	−19.5 (13.8)	−5.6 (3.9)
TMAO	Placebo	0.85 (0.39)	0.30 (0.72)	0.67 (1.2)	0.8 (1.9)	0.30	0.72 *	0.38
	Intervention I	2.6 (3.5)	−0.82 (1.1)	−0.86 (1.43)	−0.88 (0.68)
	Intervention II	1.4 (0.56)	0.02 (0.76)	0.19 (1.5)	0.23 (1.0)

“Δ” denotes the changes in biomarkers at each time point, from the previous time point measured. Results are shown as mean (SD). * refers to statistically significant results.

3.3. Postprandial Oxidative Stress Markers

The main effect of the intervention group and time, as well as their interaction on postprandial changes in plasma TAC concentration, dRom, PAT and oxidative stress indexes, are presented in Table 5 and Figure 2a. A significant group × time interaction was observed for plasma TAC (p = 0.02), indicating a differential postprandial response between interventions. Regardless of no significant time or intervention effects, neither group × time interaction effects were observed for plasma dRom, PAT and OSI indexes (all p’s > 0.05, Table 5).

Table 5. Postprandial changes in plasma TAC concentration, dRom, PAT, OSI and TMAO indexes, and the significance of group and time effect, following the test meals.

		Baseline	Δ* 30 min	Δ 1.5 h	Δ 3 h	p Value Group Effect	p Value Time Effect	p Value Group × Time Interaction
Total plasma Antioxidant Capacity (TAC) (mmol/L)	Placebo	3.9 (0.84)	−0.13 (0.09)	−0.17 (0.12)	0.16 (0.11)	0.04 *	0.03 *	0.02 *
	Intervention I	3.9 (0.72)	−0.39 (0.28)	0.47 (0.33)	−0.24 (0.17)
	Intervention II	4.1 (0.95)	−0.12 (0.09)	0.24 (0.17)	−0.08 (0.06)
dRom (index)	Placebo	28.8 (6.5)	-	-	−1.6 (1.1)	0.31	0.29	0.75
	Intervention I	30.6 (9.2)	-	-	−1.6 (1.1)
	Intervention II	31.2 (8.2)	-	-	0.24 (0.21)
PAT (index)	Placebo	4164.4 (728.4)	-	-	203.1 (143.6)	0.88	0.89	0.35
	Intervention I	4190.5 (885.0)	-	-	−36.1 (25.5)
	Intervention II	4356.5 (737.8)	-	-	−227.3 (160.7)
Oxidative stress status	Placebo	72.9 (36.9)	-	-	2.6 (1.8)	0.43	0.25	0.24
(OSI index)	Intervention I	88.7 (55.2)	-	-	−16.2 (11.5)
	Intervention II	94.0 (45.2)	-	-	−9.0 (6.4)

“Δ” denotes the changes in biomarkers at each time point, from the previous time point measured. Results are shown as mean (SD). * refers to statistically significant results.

Figure 2. (a) Postprandial responses to the test meals on plasma TAC. (b) The incremental Areas Under Curve (iAUCs) in the three study groups, for plasma TAC, over 3 h after the intake of the test meals.

The incremental Areas Under Curve (iAUCs) for all parameters tested between the three study groups over 3 h after the intake of the test meals are shown in Table 6. In general, the iAUCs were similar between the three groups in all tested biomarkers (all p’s > 0.05). Only TAC iAUC differed significantly between groups (p = 0.03), with higher values observed in the intervention groups compared to placebo (Table 6, Figure 2b).

Table 6. The incremental Areas Under Curve (iAUCs) in the three study groups, for biochemical parameters tested over 3 h after the intake of the test meals.

Biochemical and Immunological Markers
		iAUC (95% CI)	p Value
Total cholesterol (mg/dL × h)	Placebo	29,530 (27,605.65; 31,454.80)	0.31
	Intervention I	30,410 (28,440.34; 32,376.47)
	Intervention II	30,420 (28,268.75; 32,561.69)
Glucose (mg/dL × h)	Placebo	15,320 (14,312.59; 16,835.73)	0.52
	Intervention I	14,910 (14,231.14; 15,680.51)
	Intervention II	14,760 (13,597.14; 15,869.51)
HDL-cholesterol (mg/dL × h)	Placebo	11,140 (10,181.26; 12,202.06)	0.36
	Intervention I	11,480 (10,395.13; 12,456.53)
	Intervention II	11,420 (10,453.45; 12,526.54)
LDL-cholesterol (mg/dL × h)	Placebo	16,160 (13,977.73; 17,413.15)	0.68
	Intervention I	16,390 (13,965.57; 17,892.98)
	Intervention II	16,520 (13,619.98; 18,064.78)
Triglycerides (mg/dL × h)	Placebo	17,890 (14,792.54; 19,905.79)	0.86
	Intervention I	18,330 (13,330.65; 20,029.34)
	Intervention II	17,800 (14,598.50; 20,666.49)
CRP (mg/dL × h)	Placebo	62.95 (58.62; 74.90)	0.34
	Intervention I	41.75 (21.91; 53.49)
	Intervention II	40.06 (21.50; 54.91)
Uric Acid (mg/dL × h)	Placebo	842.82 (736.20; 962.01)	0.33
	Intervention I	876.61 (748.85; 998.59)
	Intervention II	872.72 (777.23; 1006.52)
Insulin (μIU/mL × h)	Placebo	4210 (2847.68; 5570.43)	0.41
	Intervention I	3680 (2960.64; 4404.57)
	Intervention II	3610 (2730.61; 4479.71)
Oxidative stress markers
Total plasma Antioxidant Capacity (TAC) (mmol/L × h)	Placebo	667.11 (614.98; 736.00)	0.03 *
	Intervention I	692.30 (638.63; 758.87)
	Intervention II	744.52 (676.16; 850.12)
dRom (index × h)	Placebo	5040 (4734.03; 5724.40)	0.31
	Intervention I	5360 (4729.69; 6039.97)
	Intervention II	5610 (5002.91; 6371.53)
PAT (index × h)	Placebo	767,861 (717,723.41; 840,154.58)	0.87
	Intervention I	751,046 (707,431.05; 831,182.50)
	Intervention II	763,720 (718,126.19; 798,311.80)
Oxidative stress status (OSI index × h)	Placebo	13,360 (11,320.52; 18,049.47)	0.43
	Intervention I	14,510 (10,513.50; 19,106.49)
	Intervention II	16,120 (12,667.44; 20,122.55)

* refers to statistically significant results.

4. Discussion

Dairy products are a good source of nutrients, making them a convenient way to meet daily nutritional needs, and they are considered a convenient vehicle for incorporating bioactive compounds and developing functional foods [52]. This has contributed to the growth of the functional dairy market, with numerous products introduced to meet consumer demand. These include fortified milk or milk substitutes, yogurt with added probiotics, and several types of cheese enriched with antioxidants [53]. In parallel, there has been a trend towards the consumption of functional foods that are perceived as healthier and more natural, especially among the younger population [54]. This can be attributed to the fact that younger people are more health-conscious and, as a result, are more interested in functional foods that support particular health goals or address specific health concerns such as weight loss, gut health, and immunity support [55]. Further research is needed to determine the optimal doses and potential benefits of the functional ingredients in dairy products.

The present study tested the hypothesis that yogurt enriched with L. rhamnosus OLXAL-1 or yogurt enriched with this microorganism and antioxidants may have a positive effect on postprandial lipemia, glycemia, and the antioxidant profile in healthy participants. A statistically significant increase in plasma TAC was observed; however, this was not accompanied by consistent changes in other oxidative stress markers, suggesting a limited physiological impact. In the case of probiotic yogurt intervention, there was no significant improvement in plasma antioxidant capacity, in contrast to studies suggesting that probiotic bacteria may enhance the antioxidant properties of yogurt [56]. Regarding other oxidative stress biomarkers, no statistically significant differences in the biomarkers of pro-oxidant status, antioxidant potential, and oxidative stress were noted between the intervention and placebo groups.

The present study also examined whether probiotic-enriched yogurt could influence blood glucose levels to control blood glucose [57,58,59] and diabetic complications [60]. Moreover, the addition of plant-derived products rich in antioxidant and phenolic compounds into probiotic yogurts has a greater effect on postprandial glycemic control in both animal [61] and human studies [13,41,57]. To date, most studies that have found beneficial effects of probiotics and/or antioxidants on the human glycemic response have been long-term clinical trials, with a duration of at least four weeks [28,62,63,64]. In the present short-term study, although no significant differences were observed in 2 h postprandial glucose levels, a transient trend was noted, with higher glucose levels at 30 min in the control group followed by a greater reduction at 1.5 h. Regarding probiotics, this finding may be due to the study design, which was an acute dietary intervention, likely due to the acute study design, which may not allow sufficient time for microbiota-mediated effects, given that no microbiome-related biomarkers were assessed [57,65]. In contrast, other long-term studies did not find a significant reduction in 2 h postprandial glucose levels after the administration of probiotics compared to placebo [66], and such findings may be strain-specific [29]. Regarding antioxidants, a proposed mechanism for glycemic control is the ability to inhibit carbohydrate-hydrolyzing enzymes, such as pancreatic α-amylase and α-glucosidase, and in vitro studies have shown that the addition of phenolic compounds from medicinal plants to yogurt could be an effective treatment for postprandial hyperglycemia [67,68]; however, such mechanisms were not evaluated in the present clinical study. In addition, Helal and Tagliazucchi (2018) showed that a yogurt matrix could enhance the gastrointestinal stability and bioaccessibility of polyphenols [69]. In contrast, the results of the present study are in agreement with previous acute dietary interventions that did not find additional benefits in postprandial glucose levels after the inclusion of antioxidant extracts [70,71,72].

Additionally, the inclusion of metabolically healthy participants may have limited the ability to detect measurable changes, as such populations typically exhibit high metabolic flexibility and lower responsiveness to nutritional interventions. Previous studies reported significant effects of probiotics and/or antioxidants on the reduction in glucose in participants with diabetes [73] but not in healthy people [74]. This likely reflects the metabolic flexibility of healthy individuals in maintaining homoeostatic control [75]. Continuing the examination of the postprandial glycemic response, serum insulin levels were not significantly different among the groups, likely due to the identical meal composition across groups [76,77,78]. However, there was a significant time effect, with an increase from baseline to 30 min followed by a significant decrease from 30 min to each remaining time point. Postprandial insulin tends to mirror postprandial glucose, with an increase in glucose within 1 h and subsequent insulin production, followed by a return to normal values at 2 and 3 h after meal consumption in healthy subjects [77,79,80,81]. Phenolic compounds have been shown to influence insulin sensitivity [82,83]. There is evidence that polyphenolic compounds can enhance insulin secretion from pancreatic β-cells and thus attenuate postprandial glycemia in healthy subjects [84]. In contrast, Skulas-Ray et al. (2011) found that the addition of plant-derived phenolic compounds at high concentrations could lead to reductions in postprandial insulin levels [85], possibly due to an improvement in insulin sensitivity by polyphenols and the reduced insulin requirement to achieve glucose homoeostasis [85,86]. In long-term probiotic studies, fasting is the primary measurement of insulin levels. There are studies that found no statistically significant mean difference between the probiotic and control groups [73,87] whereas others have reported significantly reduced fasting insulin serum levels after probiotic administration [62,63,88,89]. These findings suggest that, in an acute setting, there is no significant effect on postprandial insulinemia by the ingestion of yogurt enriched with probiotics or probiotics plus antioxidants compared to conventional yogurt. The effect of probiotics and antioxidants on insulin secretion could be dependent on the dose and origin of polyphenols, the dose, strain, and number of probiotic species used, the duration of the study, the number and health state of the participants, and the control group.

Regarding postprandial lipemia biomarkers, no significant differences were observed in postprandial TC, TG, HDL-C, and LDL-C responses between the intervention and placebo groups or between the intervention groups. Dietary short- or medium-chain fatty acids have a limited effect on postprandial lipemia because they directly enter the general circulation via the portal route, instead of via chylomicron secretion [90]. This may be explained by the similarity in fatty acid composition and total fat content among the three meals [91]. This may also reflect that the administered dose and acute exposure were insufficient to elicit measurable changes in postprandial lipemia. In addition, the dairy fats provided in the present study, as the only source of saturated fatty acids, contain significant amounts of short- and medium-chain fatty acids. However, many studies have assumed that the biological role of polyphenols is dose-dependent and varies between different polyphenol sources [90]. Research has examined the consumption of a high-fat meal alongside some source of polyphenols, with positive results in animal studies [92], while high variability has been observed in clinical trials [17,18,93,94]. In the present study, the intervention product high in antioxidants did not appear to have any effect on the lipid profile of the volunteers, which can also be attributed to the dose of the antioxidant compound in the final product.

Similarly, in the probiotic intervention, no acute effect was observed on postprandial lipemia, as there was no significant difference in the biomarkers under examination. However, long-term studies (8–12 weeks) have shown favorable effects of daily probiotic intake on TC, LDL-C, HDL-C, and TG lipid biomarkers [95]. Their effects on postprandial lipemia indices appear to be based on several cholesterol-lowering mechanisms, including enzymatic deconjugation of bile acids by bacterial hydrolases, cholesterol binding by small intestine probiotics leading to incorporation of cholesterol in cell membranes of probiotics, and decreased concentration of cholesteryl esters in LDL particles [96]. Serum uric acid levels reflect hyperuricemia, a condition characterized by high serum uric acid concentrations, [97]. Uric acid is generated as a product of purine metabolism, and is considered to be a component of metabolic syndrome and is associated with metabolic syndrome risk factors such as hypertension, elevated triglyceride levels, and insulin resistance [98,99]. Probiotic supplements containing uricolytic bacteria have been proposed to lower serum uric acid levels in hyperuricemic animals, with beneficial consequences on blood pressure [100]. Moreover, research data indicate that regular long-term intake of probiotic yogurt results in a significant decrease in serum uric acid levels [101,102].

The results of the present study did not show a positive association between the consumption of probiotic yogurt and reduction in uric acid, likely due to the acute study design. Specifically, an increase in plasma uric acid after administration of phenol-rich foods/beverages has already been demonstrated and could be ascribed to the interaction of phenols with the secretion and reabsorption of uric acid [103]. The fact that there was no increase in uric acid levels in the present study may be due to the coexistence of probiotics and polyphenols in the intervention product.

Moreover, serum C-reactive protein (CRP) has also been examined as a marker of systemic inflammation, which is elevated in the presence of chronic conditions, including cardiovascular diseases, type 2 diabetes, and metabolic syndrome [104]. Probiotics are suggested for the reduction in inflammation biomarkers as CRP, possibly due to their effects on increasing glutathione (GSH) levels and scavenging superoxide and hydroxyl radicals, decreasing the expression of interleukin-6 (IL-6) in adipocytes, and decreasing adiposity [105,106]. Moreover, dietary antioxidant consumption may affect CRP through oxidative stress and inflammation reduction; however, no significant reduction has been detected in healthy individuals [107,108]. In the present study, CRP levels did not differ among the groups or among the time points of each group, as expected in an acute study with healthy participants.

Continuous experimental findings consistently suggest a potential correlation between gut flora-generated metabolites and disease-associated risk factors [109]. Trimethylamine-N-oxide (TMAO), which is mainly formed from nutritional substrates by intestinal microflora in the colon, has been associated with increased risk of cardiovascular disease (CVD) [110]. According to recent data, a short-term, high-fat diet does not increase fasting plasma TMAO concentrations but appears to increase postprandial TMAO concentrations in response to a high-fat meal [111]. A pilot study with a similar design in healthy volunteers examined the effect of probiotic supplementation on postprandial TMAO concentrations without identifying statistically significant differences between the intervention and control groups [112]. These results agree with those of the present study. Moreover, the current knowledge on the available strategies to reduce TMAO formation is discussed, highlighting the potential of phytochemicals, like antioxidants, to reduce TMAO formation by modulating gut microbiota composition [113]. However, this pathway remains speculative in the context of the present study due to the absence of microbiome analysis. There is insufficient literature on the effect of antioxidants on postprandial concentrations; however, a short-term study on antioxidants showed no effect on TMAO in healthy volunteers [114]. In the present study, TMAO levels did not differ among the groups or among the time points of each group.

Finally, there are some limitations of the current study that should be considered. First, there was a lack of dietary control over the participants’ background diet during the study. Second, the sufficiency of nutrient intake and status in our population may have also affected the main study outcomes, as it is difficult to change the postprandial state in subjects who are already well nourished [115]. Thirdly, the acute design of the study did not allow the probiotic ingredients to differentiate the volunteers’ gut microbiota [116], so that the desired changes would be detectable in the blood biomarkers. Furthermore, no mechanistic biomarkers (e.g., enzyme activity) or microbiome analyses were performed, limiting the ability to support mechanistic interpretations such as α-glucosidase inhibition or gut microbiota modulation. Finally, a larger sample size could have improved the accuracy and strength of the observed effect sizes. In addition, the mechanistic framework remains limited, particularly given the acute design, especially given the acute design, which is inherently unsuitable for microbiota-mediated effects. Finally, the use of healthy young participants with normal biomarkers limits the ability to detect intervention effects. Importantly, the absence of significant findings across most endpoints should be interpreted with caution, as it may reflect limitations related to the intervention dose, the acute nature of the study, and the selection of a metabolically healthy population, rather than a true lack of biological effect.

5. Conclusions

In summary, the study identified a modest increase in plasma TAC following the combined intervention; however, this isolated finding, in the absence of changes in other markers, suggests limited overall impact on oxidative status. No further changes were detected in either intervention group in the postprandial state, likely due to the acute study design. The effects of probiotics may be more evident in long-term interventions, as opposed to a postprandial state study.