Effects of multistrain probiotics on metabolic biomarkers, inflammation, and antioxidant system in patients with type 2 diabetes mellitus: a prospective controlled trial in Türkiye

Abstract

Background

Recent evidence suggests that the gut microbiota plays a crucial role in the development and progression type 2 diabetes mellitus (T2DM). This study aimed to investigate the effects of multi-strain probiotic supplementation on glucose and lipid metabolism, inflammation, and antioxidant system in patients with T2DM.

Methods

This prospective controlled clinical trial was conducted at Ege University Faculty of Medicine from July 2020 to June 2023. Participants aged 35–65 years diagnosed with T2DM were allocated sequentially according to order of presentation to either probiotic group (n = 39) or control group (n = 38). The intervention group received a multi-strain probiotic supplement containing Lactobacillus acidophilus, Lactobacillus rhamnosus, Bifidobacterium lactis and Lactobacillus paracasei, twice daily for 12 weeks, while the control group continued standard care. This study evaluated anthropometric measurements, eating attitudes, dietary frequency, quality of life, and physical activity. Biochemical analyses included glycemic control, lipid profiles, inflammation markers (high-sensitivity C-Reactive Protein, Ceruloplasmin), and oxidative stress markers (Malondialdehyde, Glutathione).

Results

Seventy-seven of the 80 participants completed the study (mean age 55.38 ± 6.40 years, 42.86% female). After 12 weeks a significant within-group decrease in fasting blood glucose (FBG), postprandial blood glucose (PPBG), HbA1c, LDL-C, Non-HDL-C and malondialdehyde in the probiotic group compared to the control group (p < 0.001, p = 0.003, p = 0.004, p = 0.044, p = 0.034, p = 0.001). No significant changes were observed in inflammatory markers or oxidative stress markers’ levels. Anthropometric parameters remained unchanged in both groups. In a two-group comparison, three-month multi-strain probiotic supplementation statistically significantly improved only PPBG in patients with T2DM (p = 0.038).

Conclusions

Three months of multi-strain probiotic supplementation led to a significant reduction in PPBG levels among patients with T2DM, while FBG, HbA1c, lipid profile parameters, and inflammatory and oxidative stress markers showed improvements that did not reach statistical significance.

Clinical trial registration

ClinialTrials.gov registration number: NCT07330388. Registered on 23 December 2025. https://clinicaltrials.gov/search?term=NCT07330388.

Introduction

Type 2 diabetes mellitus (T2DM) prevalence has increased alarmingly due to obesity and sedentary lifestyle trends [1, 2]. T2DM causes end-stage renal failure, blindness, and lower limb amputations through progressive atherosclerosis, leading to threefold increased cardiovascular events and twofold increased cerebrovascular events [3, 4].

Recent data suggest that the gut microbiota plays a key role in development and progression of T2DM. This complex ecosystem of at least 10¹⁴ different bacteria affects energy homeostasis through the microorganism-gut-brain axis [5, 6]. Gut microbiota changes can alter enteroendocrine signals and gut hormones that regulate β-cell function, insulin secretion, and energy homeostasis [7]. The gut microbiota can affect the host's inflammation response and energy metabolism, in other words, alteration of the gut microbiota can affect glucose and lipid metabolism and insulin action. One of the most effective methods of maintaining the balance of the gut microbiota is the use of probiotics, defined as live microorganisms that, when given in adequate amounts, show host-specific benefit [8]. Recently, a growing number of studies have found that probiotics can alter gut flora, improve total cholesterol (Total-C) and Low-Density Lipoprotein Cholesterol (LDL-C) levels [9–11], and reduce blood glucose levels and insulin resistance [12, 13]. Nowadays, oxidative stress is also suggested to be a mechanism underlying diabetes and its complications. Antioxidant mechanisms of probiotics include scavenging of reactive oxygen species, metal ion chelation, enzyme inhibition, reduction activity, and inhibition of ascorbate autoxidation [14].

Modification of gut microflora with probiotics may be seen as a novel tool for the regulation of glucose metabolism and improvement of oxidative stress in T2DM. Increased severity of beta cell dysfunction was positively correlated with increased high-sensitivity C-Reactive Protein (hs-CRP) concentrations [15].

Although T2DM is recognized as a chronic inflammatory disease, the role of probiotics and prebiotics in its management remains insufficiently explored, and existing evidence regarding their overall efficacy in health and disease is inconsistent [16].

Regulation of gut microbiota through dietary interventions (e.g., probiotic intake) may be beneficial in reducing inflammation [hs-CRP and Ceruloplasmin (Cp)] and oxidative stress [Glutathione (GSH) and Malondialdehyde (MDA)], as well as regulating glucose and lipid metabolism in T2DM. The aim of this study was to investigate the effects of probiotics on glucose and lipid metabolism, inflammation and antioxidant system in patients with T2DM.

Materials and methods

Trial design

This was a single center, prospective controlled study conducted at the Internal Medicine Outpatient Clinic of Ege University Faculty of Medicine in Türkiye between July 2020 and June 2023. The study included a screening visit, a baseline visit (D0) and one follow-up visit during a 60-days supplementation period. The primary outcomes of the study were defined as the effects of the intervention on inflammatory markers, specifically focusing on a significant decrease in hs-CRP and Cp levels, and oxidative stress markers, including a significant reduction in MDA levels and a significant increase in reduced GSH levels. Secondary outcomes included assessing the glucose metabolism and lipid profile changes, which specifically targeted a significant decrease in Fasting Blood Glucose (FBG), Postprandial Blood Glucose (PPBG), and Glycated Hemoglobin (HbA1c), alongside a reduction in LDL-C and Triglyceride (TG) levels, and an increase in High-Density Lipoprotein Cholesterol (HDL-C) levels.

The study adhered to CONSORT guidelines. Also, the study was registered in ClinicalTrials.gov with a registration number (NCT07330388) on 2025/12/23.

Trial participants

Power analysis was performed and the sample size was calculated as 79 with a statistical power of 80% and an effect size of 0.35. Since two groups were to be studied, 80 patients were planned to participate in the study to equalize the number of participants.

This study included adults aged 35–65 years who were diagnosed with T2DM according to American Diabetes Association guidelines, who volunteered to participate in this study. Exclusion criteria were as follows: Use of any systemic antibiotics, multivitamins, minerals, herbal medicines, prebiotic, probiotic and postbiotic supplements in the last 3–6 months, having a diagnosis of any inflammatory bowel disease, severe renal dysfunction or hepatic dysfunction, immunodeficiency diseases, acute infection, rheumatoid arthritis, cancer history, history of alcohol abuse or drug dependence, pregnant or lactating women. Dietary habits, physical activity, and glucose-lowering therapy of the participants were not intervened during the study. Participants were asked not to change their habitual diet, physical activity levels, or antidiabetic treatment during the study period. These variables were assessed using validated tools (FFQ and IPAQ), allowing us to confirm that there were no significant differences between groups at baseline or during the study.

Participants were allocated sequentially according to order of presentation (alternate allocation). According to the order of presentation to the outpatient clinic, the first patient who met the inclusion criteria was included in the probiotic group and the second patient who presented to the outpatient clinic was included in the control group. Patients in the intervention group were given a probiotic supplement containing Lactobacillus acidophilus, Lactobacillus rhamnosus, Bifidobacterium lactis and Lactobacillus paracasei, each containing 1.25 × 10⁹ CFU per doselive microorganisms, without vitamins and minerals, twice a day in addition to their current treatment for 12 weeks (Probiotic group). Participants in the probiotic group were given detailed information regarding dosage and were regularly monitored during outpatient visits. Compliance was monitored through patient self-reporting at follow-up visits, direct questioning about missed doses, and recording any interruptions due to adverse events. Participants were instructed to maintain consistent intake throughout the study period, and no serious adverse events requiring interruption were reported. Participants with self-reported adherence below 80% were considered non-compliant; however, no participants met this criterion. Patients in the control group were not given any additional treatment and were allowed to continue their routine antidiabetic treatment for 12 weeks (Control group). Participants taking probiotics were instructed to discontinue probiotic intake if they experienced any serious adverse events requiring intervention. The probiotic product was supplied without placebo equivalent, and due to COVID-19-related supply limitations no identical-appearing placebo powder could be obtained. Sociodemographic data and medical data were recorded for all participants.

Anthropometric measurements

Body weight, height, waist (WC) and hip circumferences (HC) were collected following the standardized procedures recommended in the World Health Organization Expert Committee Guidelines for physical examination at baseline and after 12 weeks [17]. Participant's weight (kg) was measured by the bioelectrical impedance analysis (Tanita MC-780). Participants were asked to take off their shoes, and heavy outer clothes before the measurement. Height (cm) was measured in a standing position by an inelastic tape measure to the nearest 0.5 cm. WC was measured using a non-stretchable tape with an accuracy of 0.1 cm at the midpoint between the lower margin of the last rib and the iliac crest, while HC was measured with the same type of tape (precision 0.1 cm) placed horizontally around the widest part of the buttocks with the participant standing upright. Then, body mass index (BMI) (kg/m²) and waist-to-hip ratio (WHR) (WC/HC) were calculated.

A Food Frequency Questionnaire (FFQ)

A Food Frequency Questionnaire was employed to determine how often specific food groups or individual food items were consumed. The frequency of consumption was recorded on a daily, weekly, biweekly, or monthly basis, providing insight into participants’ overall dietary patterns. The FFQ is a commonly used tool to assess the relationship between dietary habits and health outcomes and can be adapted in various ways depending on the researcher’s objectives. Food items can be listed individually or grouped according to categories such as full-fat, low-fat, or fat-free options [18]. The FFQ consisted of 68 individual food items clustered into five main food groups. In this study, the FFQ included five main food groups: dairy products, meat-egg-legumes, fruits and vegetables, cereals, fats and sweets, and beverages. Participants were asked to indicate how often they consumed each item (e.g., 1–2 times daily, 1–3 times weekly, 4–6 times weekly, once every two weeks, once monthly, or never), which enabled the calculation of their food consumption frequency over the past month.

The International Physical Activity Questionnaire (IPAQ)

When applying the IPAQ, participants were asked about their vigorous physical activity, moderate physical activity, and walking duration in the last seven days. Additionally, the number of days per week they performed these activities and time spent sitting were determined [19]. The Turkish validity and reliability study of the questionnaire was conducted by Öztürk [20]. Scoring was calculated as Metabolic Equivalent of Task (MET) scores from the sum of duration and frequency of moderate activity, vigorous activity, and walking [21]. Categories:

• Category I: Inactive individuals (< 600 MET-min/week)

• Category II: Minimum active individuals (600–3000 MET-min/week)

• Category III: Very active individuals (> 3000 MET-min/week)

Ferrans and powers quality of life index^© diabetes version- III

The scale was developed by Ferrans and Powers to measure quality of life in diabetic patients, consisting of 34 satisfaction and 34 importance items. It includes 5 subscales: Total quality of life, Health and Functioning, Social and Economic, Psychological/Spiritual, and Family scores [22]. Higher scores indicate better quality of life. The Turkish validity study was conducted by Ozer [23].

Eating Attitudes Test (EAT-26)

The EAT-26 was developed for Garner et al. [24]. The test consists of 26 items, and the total score is between 0–53. A score of 20 and above is defined as “abnormal eating behavior” and a score less than 20 is defined as “normal eating behavior”. The Turkish validity and reliability study was conducted by Erguney-Okumus and Sertel-Berk [25].

Biochemical measurements

Fasting blood samples were collected from the participants at baseline and week 12, following overnight fasting and after 2 h of satiety. FBG, PPBG, HbA1c, Total-C, LDL-C, HDL-C, TG and hs-CRP were measured using an autoanalyzer. Serum MDA levels were determined by the thiobarbituric acid method [26], GSH levels by enzymatic recycling method [27] and Cp levels by colorimetric method [27] using a microplate reader.

Statistical analysis

Statistical analysis was performed using SPSS 25.0 statistical package program. Data from all patients who completed the study were included in the statistical analysis. For descriptive findings, categorical variables were presented as numbers and percentages, and continuous variables were presented as mean, standard deviation, minimum value and maximum value. Kolmogorov–Smirnov normality tests were used to determine whether the data showed normal distribution. Pearson chi-square test was used to analyse categorical variables. Parametric tests (t test in independent groups, t test in dependent groups) were used in the analysis of normally distributed data, and non-parametric tests (Mann–Whitney U test, Wilcoxon Signed Rank Test) were used in the analysis of non-normally distributed data. Statistical significance level was accepted as p < 0.05.

To control for multiplicity, outcome families were pre-specified and the Benjamini–Hochberg false discovery rate (FDR) procedure was applied at q = 0.05 within each family (primary: hs-CRP, ceruloplasmin, MDA, GSH; secondary: FBG, PPBG, HbA1c, LDL-C, HDL-C, TG, Non–HDL-C, Total-C; anthropometrics: BMI, WHR). 95% confidence intervals (CIs) for all estimates were reported. Between-group differences at 12 weeks are presented as mean differences with 95% CIs using Welch’s approach; within-group changes are summarized with paired comparisons.

Ethical approval

This study was approved by the Clinical Research Ethics Committee of Ege University, Faculty of Medicine (Date: 17.04.2018, Number: 18–4.1/67) and conducted in accordance with the principles of the Declaration of Helsinki. Informed consent was obtained from all participants or their legal representatives before the trial commencement.

Results

A total of 145 subjects were screened for eligibility; of these, 43 did not meet the inclusion criteria, and 22 declined to participate. Of the 80 participants; 40 were allocated sequentially to the probiotic group and 40 were allocated to the control group. The per protocol (PP) population consisted of 80 subjects. In the probiotic group, 39 subjects completed the study, while in the control group, 38 participants completed the study (Total n = 77). The reasons for exclusion from the PP population were one of the following: failure to attend final follow-up visits (n = 3) (Fig. 1). The mean age was 55.38 ± 6.40 years and 42.86% were female. It was confirmed that baseline characteristics were balanced in both groups, except for a higher rate of education in the probiotic group (Table 1).

Fig. 1.

The CONSORT diagram shows the participants' flow through each stage of the clinical trial

Table 1.

Baseline sociodemographic and medical characteristics of the participants in each group

Characteristics	Probiotic group (n = 39)	Control group (n = 38)	p
Age, year (mean ± SD)	54.92 ± 6.17	55.84 ± 6.68	0.510
Gender (n, %)			0.494
Female	15 (38.5)	18 (47.4)
Male	24 (61.5)	20 (52.6)
Education level, (n, %)			0.026
Illiterate	2 (5.1)	0
Literate	0	1 (2.6)
Primary school graduate	6 (15.4)	18 (47.4)
Middle school	4 (10.3)	1 (2.6)
High school graduate	9 (23.1)	7 (18.4)
University and above	18 (46.2)	11 (28.9)
Diabetes duration, months, (mean ± SD)	130.74 ± 99.73	107.66 ± 78.04	0.400
Smoking, (n, %)			0.635
Non-smoker	25 (64.1)	28 (73.7)
Ex-smoker	5 (12.8)	3 (7.9)
Smoker	9 (23.1)	7 (18.4)
Alcohol consumption, (n, %)			0.665
None	36 (92.3)	36 (94.7)
Yes	3 (7.7)	2 (5.3)
Hypertension, (n, %)			1.00
Yes	24 (61.5)	23 (60.5)
No	15 (38.5)	15 (39.5)
Coronary artery disease, (n, %)			0.675
Yes	2 (5.1)	3 (7.9)
No	37 (94.9)	35 (92.1)
Hyperlipidemia, (n, %)			1.00
Yes	25 (64.1)	24 (63.2)
No	14 (35.9)	14 (36.8)
Retinopathy, (n, %)			0.200
Yes	5 (12.8)	1 (2.6)
No	34 (87.2)	37 (97.4)
Nephropathy, (n, %)			0.564
Yes	4 (10.3)	4 (10.5)
No	35 (89.7)	34 (89.5)
Neuropathy, (n, %)			1.00
Yes	3 (7.7)	2 (5.3)
No	36 (92.3)	36 (97.4)
Eating Attitude Disorder, (n, %)			0.575
Yes	13 (33.3)	15 (39.5)
No	26 (66.7)	23 (60.5)
Quality of life, (mean ± SD)
Total quality of life score	23.82 ± 3.20	22.89 ± 3.24	0.252
Health and functionality subcategory score	22.91 ± 3.90	21.60 ± 4.43	0.233
Social and economic subcategory score	22.19 ± 4.35	21.84 ± 4.02	0.537
Psychological and belief subcategory score	25.18 ± 4.20	24.22 ± 4.43	0.300
Family subcategory score	27.02 ± 3.07	27.02 ± 2.86	0.975
Physical activity level, (n, %)			0.572
Inactive	9 (23.1)	8 (21.1)
Minimum active	29 (74.4)	27 (71.1)
Very active	1 (2.6)	3 (7.9)

SD Standard deviation

Values in bold indicate statistical significance (p < 0.05)

There were no statistically significant differences between the probiotic and control groups across the main food groups assessed (dairy products; meat, eggs, and legumes; fruits and vegetables; bread and cereals; fats, sweets, and beverages) (p > 0.05 for all comparisons).

No serious adverse reactions were reported in patients in the probiotic group throughout the study. The type and dosage of medications used by all patients during the study were not changed.

After 12 weeks, a significant intragroup decrease was seen in FBG, PPBG, HbA1c, LDL-C, Non-HDL-C and MDA levels in the probiotic group compared to the control group (p < 0.001, p = 0.003, p = 0.004, p = 0.044, p = 0.034, p = 0.001) (Table 2).

Table 2.

Intra-group changes of variable throughout study in probiotic and control group

Characteristics	Probiotic group (n = 39)			Control group (n = 38)
	Baseline	After the 12 weeks	p	Baseline	After the 12 weeks	p
BMI	30.76 ± 4.68	30.87 ± 5.07	0.650	31.10 ± 3.83	30.39 ± 6.10	0.428
WHR	0.96 ± 0.07	0.96 ± 0.08	0.518	0.97 ± 0.07	0.97 ± 0.6	0.865
FBG	138.49 ± 44.86	119.41 ± 27.17	< 0.001	131.26 ± 41.54	132.16 ± 29.90	0.653
PPBG	170.00 ± 53.44	148.69 ± 30.73	0.003	179.68 ± 57.55	170.18 ± 43.83	0.380
HbA1c	7.36 ± 1.19	6.80 ± 1.04	0.004	7.25 ± 1.36	7.09 ± 1.04	0.343
Total-C	200.38 ± 48.68	190.97 ± 44.03	0.061	195.18 ± 42.50	187.76 ± 41.93	0.107
TG	196.74 ± 130.31	167.85 ± 68.99	0.267	191.50 ± 80.16	179.66 ± 75.31	0.192
HDL-C	46.36 ± 11.76	48.26 ± 12.07	0.261	44.00 ± 9.67	44.95 ± 9.99	0.358
LDL-C	116.95 ± 42.46	109.05 ± 38.44	0.044	113.18 ± 36.07	106.87 ± 37.86	0.122
Non-HDL-C	154.08 ± 48.75	142.71 ± 41.26	0.034	150.16 ± 40.46	142.37 ± 41.65	0.126
hs-CRP	0.26 ± 0.22	0.30 ± 0.37	0.868	0.34 ± 0.37	0.37 ± 0.34	0.338
Cp	23.16 ± 13.95	21.72 ± 14.33	0.372	23.04 ± 18.44	25.37 ± 19.30	0.064
MDA	29.34 ± 16.60	20.94 ± 14.39	0.001	29.32 ± 14.88	28.07 ± 21.19	0.092
GSH	47.13 ± 15.64	46.75 ± 15.68	0.919	39.49 ± 11.88	46.35 ± 20.53	0.098

Abbreviations: BMI Body Mass Index, FBG Fasting Blood Glucose, GSH Glutathione, HDL-C High-Density Lipoprotein Cholesterol, hs-CRP High-Sensitivity C-Reactive Protein, Cp Ceruloplasmin, LDL-C Low-Density Lipoprotein Cholesterol, MDA Malondialdehyde, Non-HDL-C Non–High-Density Lipoprotein Cholesterol, PPBG Postprandial Blood Glucose, Total-C Total Cholesterol, TG Triglyceride, WHR Waist Hip Ratio

Values in bold indicate statistical significance (p < 0.05).

A statistically non-significant within-group increase in serum HDL-C levels was found in both the probiotic group and the control group (Table 2).

Within-group comparisons determined that there was no statistically significant change in weight, BMI, WHR in both the control group and the probiotic group after 12 weeks (Table 2).

However, comparisons between the two groups after 12 weeks showed that there was no statistically significant change in anthropometric measurements and biochemical data except PPBG levels (p = 0.038) (Table 3).

Table 3.

Comparison of anthropometric and biochemical measurement data of the study groups after 12 weeks

Characteristics	Probiotic group (n = 39)	Control group (n = 38)	p	95% CI (Probiotic − Control)	BH–FDR p
BMI	30.87 ± 5.07	30.39 ± 6.10	0.775	− 2.07 to 3.03	0.775
WHR	0.96 ± 0.07	0.97 ± 0.06	0.481	− 0.04 to 0.02	0.962
FBG	119.41 ± 27.17	132.15 ± 29.90	0.056	− 25.7 to 0.2	0.224
PPBG	148.69 ± 30.73	170.18 ± 43.82	0.038	− 38.8 to − 4.2	0.224
HbA1c	6.83 ± 1.04	7.09 ± 1.04	0.131	− 0.73 to 0.21	0.349
Total-C	190.97 ± 44.02	187.76 ± 41.93	0.744	− 16.3 to 22.7	0.917
TG	167.84 ± 68.99	179.66 ± 75.31	0.558	− 44.6 to 21.0	0.893
HDL-C	48.25 ± 12.07	44.95 ± 9.99	0.213	− 1.7 to 8.3	0.426
LDL-C	109.05 ± 38.43	106.86 ± 37.85	0.802	− 15.1 to 19.5	0.917
Non-HDL-C	142.71 ± 41.26	142.36 ± 41.64	0.971	− 18.5 to 19.2	0.971
hs-CRP	0.29 ± 0.37	0.37 ± 0.34	0.164	− 0.24 to 0.08	0.392
Cp	21.72 ± 14.33	25.37 ± 19.29	0.548	− 11.39 to 4.09	0.548
MDA	20.94 ± 14.39	28.07 ± 21.19	0.196	− 15.39 to 1.13	0.392
GSH	46.75 ± 15.68	46.35 ± 20.53	0.409	− 7.92 to 8.72	0.545

Abbreviations: BMI Body Mass Index, BH-FDR Benjamini–Hochberg Corrections and False Discovery Rates, CI Confidence Interval, Cp Ceruloplasmin, FBG Fasting Blood Glucose, GSH Glutathione, HDL-C High-Density Lipoprotein Cholesterol, hs-CRP High-Sensitivity C-Reactive Protein, LDL-C Low-Density Lipoprotein Cholesterol, MDA Malondialdehyde, Non-HDL-C Non–High-Density Lipoprotein Cholesterol, PPBG Postprandial Blood Glucose, Total-C Total Cholesterol, TG Triglyceride, WHR Waist Hip Ratio

Values in bold indicate statistical significance (p < 0.05).

After FDR control (q = 0.05), the within-group reductions in FBG, PPBG and HbA1c in the probiotic arm and the decrease in MDA remained statistically significant, whereas within-group lipid changes did not. At 12 weeks, the between-group PPBG difference was no longer significant after FDR adjustment; however, the mean difference favored probiotics with a 95% CI excluding zero. Adjusted p-values and 95% CIs are provided in Table 3.

Discussion

In the literature, there are many studies showing that probiotic use may affect glucose, lipid metabolism, inflammation, and oxidation process by balancing intestinal microbiota [28]. The present study is, to our knowledge, the first to demonstrate the effects of a multistrain probiotic supplement given over 3-months in the Turkish T2DM population, using inflammation and antioxidant system as the primary endpoint. While the present study is not the first interventional study undertaken on the effects of probiotics in patients with T2DM, which highlighted cardiometabolic benefits in the probiotic group from baseline to 12 weeks. The results of this study revealed that supplementation with probiotics for 12 weeks significantly reduced FBG, PPBG, HbA1c, LDL-C, non-HDL-C, and MDA in the probiotic group compared to the control group in within-group comparisons. However, comparisons between the two groups after 12 weeks revealed a significant reduction in FBG only in the probiotic group.

In the present study, no effect of probiotic use on weight loss was observed. Other studies have reported changes in weight, but these usually occurred when probiotics were taken in combination with a hypocaloric diet and/or the use of bioactive compounds [29]; no special diet or bioactive compounds were used in this study.

Some studies have shown that probiotics reduce FBG, PPBG, LDL-C, TG levels and increase HDL cholesterol levels [9–13]. Although not the main focus of this study, the effects of probiotics on glycemic control are also noteworthy. In this study, significant decreases in PPBG (p < 0.038) levels were observed in the probiotic group.

The intervention period of studies evaluating the effects of probiotic use on metabolism in diabetic patients was usually 4–8 weeks. In these studies, FBG, PPBG, insulin, HbA1c, lipid profile and oxidative stress and/or inflammation markers were generally evaluated. Probiotics used in clinical trials were L. acidophilus, Lactobacillus sporogenes, Bifidobacterium bifidum and B. lactis, used alone or in combination [28]. In this study, the probiotic group was given a probiotic supplement consisting of Lactobacillus acidophilus, Lactobacillus rhamnosus, Bifidobacterium lactis, and Lactobacillus paracasei, without vitamins or minerals. In addition, only a few studies have assessed nutrient intake qualitatively and quantitatively, which may affect the interpretation of results. In this study, participants were not given a special diet or exercise program; they were only warned not to consume foods containing probiotics.

In this study, probiotics led to significant improvements in glycemic control. While prior research on their effects in T2DM patients has shown mixed outcomes, a recent meta-analyse suggest a modest but greater impact with multi-strain formulations [30]. A recent RCT found no evidence that a 6-week probiotic supplementation improves short-term glycemic control. On the contrary, there was limited evidence suggesting these strains may have a negative impact on glucose homeostasis [31]. Chaithanya et al. found a decrease in PPBG levels in the probiotic group, similar to the present study [32].

Three different meta-analyses over the last five years suggest that probiotic supplementation has a small but significant effect on glycaemic indices [33–35]. However, the magnitude of effect appears to be modulated by baseline BMI, probiotic strain, dosage, duration of intervention, and ethnic background. In this study, consistent with the results of these meta-analyses, a significant decrease in PPBG levels was found in the probiotic group compared to the control group. The present study supports the idea that probiotics improve glycemic control in T2DM patients.

In the meta-analysis by G. Li et al., subgroup analysis found that the effect of probiotics on FBG was greater in individuals with higher baseline BMI (≥ 30.0 kg/m²). Similar to this meta-analysis, the participants in this study had a BMI ≥ 30.0 kg/m2 at baseline. The use of probiotics may be a promising adjuvant therapy for glycemic control in patients with T2DM.

In the study by Andreasen et al., consistent with this study, did blood glucose metabolism parameters decrease more in patients receiving probiotic therapy in addition to their treatment compared to the control group, but this decrease was not statistically significant [36]. In this study, PPBG levels decreased statistically significantly compared to the control group. We cannot exclude the possibility that the difference in PPBG changes between the two groups in this study was due to an unknown bias, leading to a false-positive result. However, the absence of any significant confounding factors in this study suggest that this possibility is unlikely.

Although reductions within-group FBG and PPBG were statistically significant, mean values did not achieve ADA glycemic targets (FBG < 100 mg/dL; PPBG < 140 mg/dL). The observed reductions (−19 mg/dL for FBG and −21 mg/dL for PPBG) may still be clinically relevant, as even modest improvements in glycemic variability and postprandial excursions can lower long-term cardiometabolic risk. However, the magnitude of change suggests that multistrain probiotics should be viewed as an adjunct to, not a replacement for, standard glycemic therapy.

In the present study, in within-group comparisons for the probiotic group, more pronounced decreases in Total-C, TG, LDL-C, and non-HDL-C levels were observed compared to the control group. In particular, the decreases in LDL-C and non-HDL-C levels were found to be statistically significant (p = 0.044 and p = 0.034, respectively). Although an increase in HDL-C was observed within the group, this change was not statistically significant. These findings are consistent with studies in the literature showing the positive effects of probiotics on lipid metabolism. Begley et al. demonstrated that probiotics could affect cholesterol metabolism by regulating the intestinal microbiota [9]. Similarly, two studies have shown that probiotic supplementation can lower Total-C and LDL-C levels [10, 11].

Tonucci et al. reported a decrease in lipid levels in patients using probiotics for T2DM. In this study, a within-group decrease in lipid parameters was also found the probiotic group after 12 weeks [37]. RCTs have demonstrated that a six-week intake of probiotic yogurt can lead to a significant reduction in Total-C [38, 39]. However, data supporting lipid profile improvement in individuals with diabetes remain scarce [40, 41]. A meta-analysis reported that 4–8 weeks of probiotic treatment led to a 4% reduction in Total-C and a 5% reduction in LDL-C in healthy individuals [42]. Another meta-analysis also showed that a significant LDL-C reduction was observed with the use of probiotics [43]. In a RCT conducted in Saudi Arabia, a significant reduction in glucose, TG, Total-C levels were observed in the probiotic group [16]

Although several studies have explored the effects of various probiotic strains on hyperglycemia and lipid profiles in patients with T2DM, the results remain inconsistent. Feizollahzadeh et al. found no significant effect on FBG but reported improvements in lipid parameters, including increased HDL and decreased LDL levels in the probiotic group [44]. Another study observed a similar trend for triglycerides, though not statistically significant [45]. In contrast, this study demonstrated non-significant changes in TG, LDL, and HDL levels in both groups, suggesting a limited effect of probiotics on lipid metabolism in this context.

The cholesterol-lowering mechanisms of probiotics may include: (1) probiotic bacteria reducing cholesterol absorption by binding cholesterol to cell membranes or taking it into the cell, (2) increasing cholesterol elimination by disrupting the enterohepatic circulation of bile salts through deconjugation of bile salts, and (3) reducing cholesterol synthesis in the liver through production of short-chain fatty acids (SCFAs) [9]. The significant decrease in non-HDL-C levels is important in terms of reducing cardiovascular disease risk, as non-HDL-C includes all atherogenic lipoprotein particles.

The mechanisms underlying the observed metabolic improvements can be explained by the multifaceted effects of probiotics on the gut-host axis. Literature suggests that Lactobacillus and Bifidobacterium strains increase the production of SCFAs, triggering the release of glucagon-like peptide-1 (GLP-1), which contributes to postprandial glycemia control via this pathway [46]. Furthermore, these strains are known to stabilize 'tight junction' proteins in the intestinal epithelium, thereby suppressing metabolic endotoxemia and associated chronic inflammation [46]. While changes in microbiota composition were not directly measured in the present study, the improvements in PPBG and lipid parameters may reflect the potential regulatory effect of this probiotic formulation on gut barrier integrity and enterohormonal signaling pathways.

Studies have shown that L. acidophilus reduces MDA in diabetic rats [47], and probiotic yogurt also reduces MDA compared to conventional yogurt [48]. MDA serves as a lipid peroxidation marker associated with diabetic complications, and Lin and Yen demonstrated various antioxidant mechanisms of probiotics [14].

A meta-analysis demonstrated a significant difference in GSH levels between probiotic and control groups; however, considerable statistical heterogeneity was observed, and its sources could not be fully explained [49]. Another meta-analysis showed that 12 weeks of probiotic supplementation in patients with T2DM significantly decreased serum hs-CRP and MDA levels while increasing oxidative markers such as total antioxidant capacity (TAC), nitric oxide, and GSH [50]. In this study, a statistically non-significant within-group decrease in hs-CRP and MDA levels was observed in the probiotic group, and contrary to expectations, a slight within-group decrease in GSH levels was observed in the probiotic group, while a within-group increase was observed in the control group, but these changes were not statistically significant. This unexpected result may be explained by various factors including dietary and lifestyle influences, insufficient study duration to detect significant changes in glutathione metabolism, or the specific probiotic strain composition affecting glutathione pathways.

Dyslipidemia, reduced insulin sensitivity, oxidative stress, and systemic inflammation are common in patients with diabetes and contribute to complications such as increased risk of coronary artery disease [51], diabetic retinopathy, neuropathy, nephropathy, and hypertension [52]. The effect of probiotics on high-sensitivity C-reactive protein (hs-CRP), a marker of systemic inflammation, remains controversial. While some studies have reported non-significant reductions in hs-CRP following probiotic supplementation, [45, 53, 54] Asemi et al. found a significant decrease after 8 weeks of probiotic intake [55]. In another study, all inflammatory markers (TNFα, IL-6 and C-reactive protein) showed a significant decrease in the probiotic group, while no significant change was observed in the placebo group [16]. Since Pfützner et al. [15] demonstrated a positive correlation between beta cell dysfunction severity and high hs-CRP concentrations, inflammation control remains important in diabetic patients. In our study, hs-CRP levels showed a non-significant increase in both groups after 12 weeks, suggesting a lack of anti-inflammatory effect. This may be attributed to relatively low baseline hs-CRP levels in our study population, the specific strain composition and dosage used, or an insufficient study duration to detect changes in chronic inflammatory markers.

As an acute phase protein and antioxidant enzyme, elevated Cp levels in diabetic patients indicate increased oxidative stress and inflammation. A literature search revealed no studies demonstrating the effect of probiotic use on serum Cp levels in patients with T2DM. However, given the anti-inflammatory effects of probiotics, a decrease in Cp levels in patients using probiotics is expected. In this study also found a greater decrease in Cp levels in the probiotic group compared to the control group, but this difference was not statistically significant. Larger sample sizes and longer intervention durations are needed in this regard.

Limitations and strengths of the study

The present study has several limitations. Firstly, participants were asked not to change their usual diet, physical activity level, or antidiabetic treatment during the study period. These variables were assessed using validated tools (FFQ and IPAQ), confirming that there were no significant differences between groups at baseline or during the study. However, although these factors were monitored, they could not be controlled for in a way that would significantly affect the results. Because the FFQ assessed consumption frequency rather than quantitative nutrient intake, we were unable to calculate caloric or macronutrient values. Due to operational constraints during the COVID-19 pandemic, semi-quantitative FFQ or 24-h dietary recalls could not be applied. Secondly, we did not assess gut microbiota composition directly, limiting our understanding of probiotic mechanisms. Due to operational constraints and logistical challenges during the COVID-19 pandemic (the period during which the trial was conducted) fecal sample collection and microbiome profiling (such as 16S rRNA or metagenomic sequencing) were not feasible. This prevents us from defining the specific taxonomic shifts occurring in the patients' gut flora following supplementation. Thirdly, A placebo powder identical in appearance was not available from the manufacturer during the COVID-19 period; therefore, the control group continued standard care without additional supplementation. Fourthly, multivariate analysis was not performed due to limited sample size and the risk of overfitting. Baseline characteristics were balanced between groups, reducing major confounding. Additionally, computer-generated randomization was not used, and patient recruitment was delayed due to the COVID-19 pandemic.

Despite these limitations, this study has notable strengths including its prospective controlled design, high patient compliance, and comprehensive evaluation of metabolic parameters. To our knowledge, this is among the first studies to comprehensively assess probiotic effects on lipid profile, oxidative stress, and inflammation in T2DM patients. The 12-week intervention period, longer than most comparable studies (4–8 weeks), enhances the significance of our positive findings.

Conclusion

In conclusion, three months of multi-strain probiotic supplementation significantly reduced PPBG levels in T2DM patients, as well as FBG, HbA1c levels, lipid metabolism parameters, and inflammatory markers and oxidative stress markers, although not statistically significantly. The findings of this study suggest that probiotic supplementation, in addition to standard therapy, may provide metabolic benefits in T2DM patients. However, larger-scale studies with microbiome profiling, dietary monitoring, and longer follow-up are needed to confirm these findings and elucidate the underlying mechanisms.

Abbreviations

T2DM

Type 2 diabetes mellitus

WC

Waist circumference

HC

Hip circumference

BMI

Body mass index

WHR

Waist-to-hip ratio

FFQ

A Food Frequency Questionnaire

IPAQ

The International Physical Activity Questionnaire

MET

Metabolic Equivalent of Task

EAT-26

Eating Attitudes Test

FBG

Fasting blood glucose

PPBG

Postprandial blood glucose

Total-C

Total cholesterol

LDL-C

LDL cholesterol

HDL-C

HDL cholesterol

TG

Triglyceride

hs-CRP

High-sensitivity C-Reactive Protein

MDA

Malondialdehyde

Cp

Ceruloplasmin

GSH

Glutathione

HOMA-IR

Homeostasis model of assessment-insulin resistance

Authors’ contributions

AK, OY, NT, OZ and IYŞ generated the study idea and designed the protocol. AK and OZ contributed to data collection and/or analysis of study outcomes. AK and OZ performed and interpreted the data analysis and drafted the manuscript. AK, NT, OY, IYŞ, and OZ reviewed the manuscript providing substantial academic and/or clinical input. All authors approved the final manuscript. AK is the guarantor of the work and as such had full access to all the data and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Funding

This study was funded and supported by Ege University Scientific Research Projects Unit (Number: TGA-2020–20200).

Data availability

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

Declarations

Ethics approval and consent to participate

The study was completed in compliance with the guidelines of the Helsinki Declaration. The study protocol was reviewed and approved by the Clinical Research Ethics Committee of Ege University Faculty of Medicine (Date: 17.04.2018, Number: 18–4.1/67). Approval for data collection from the administration of hospital has been acquired. All participants reviewed and signed an informed consent form before participation.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.