Postbiotics as a novel intervention for obesity management and improving metabolic parameters: a systematic review and meta-analysis of animal studies

Abstract

Background

Recently, it has been reported that consuming specific postbiotics may be effective in managing obesity. Therefore, the present study aimed to systematically assess the effect of postbiotics consumption on weight management, dietary intake, and metabolic indices in animal models of obesity.

Method

The search was conducted using the PubMed, Scopus, Web of Science, and Google Scholar databases for relevant articles published in English up to May 2025. The risk of bias for included studies was evaluated by the OHAT tool. The STATA 17 software, using the random-effect model, was employed to combine the results. The heterogeneity of included studies was evaluated using Cochrane’s Q and I-square tests. The subgroup and sensitivity analyses were also performed.

Result

Forty animal studies were included in this meta-analysis. Accordingly, supplementation with different forms of postbiotics was accompanied by a significant decrease in the body weight, body fat, and serum levels of LDL-C, TG, TC, FBG, insulin, and HOMA-IR (P = 0.001, 0.040, < 0.001, < 0.001, < 0.001, < 0.001, < 0.001, 0.014 respectively) and significant increase of serum HDL-C level (P = 0.011). However, no significant change was observed in food intake (P = 0.497). Upon the subgroup analyses, SCFAs as a postbiotic supplement, the treatment dose of ≥ 100 mg/kg, and intervention duration ≥ 8 weeks were more effective in improving body weight and fat mass and FBG, HOMA-IR, and HDL-C levels; reducing FBG and HOMA-IR levels; and lowering serum FBG levels, respectively.

Conclusion

Overall, the intake of postbiotics demonstrated potential anti-obesity effects and metabolic improvements through gut microbiota modulations, decreasing obesity-related inflammation, reducing adipogenesis, and improving lipid metabolism. However, future well-designed clinical trials are required to determine their safety, efficacy, and health benefits on humans and confirm this preliminary evidence.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-025-06927-z.

Introduction

The worldwide pandemic of obesity has been considered the primary health issue in recent years, and it has been estimated that nearly 57.8% of adults worldwide will be overweight or obese by 2030 [1]. Obesity is a low-grade inflammatory status defined by extra body fat accumulation and is linked to several chronic diseases, like type 2 diabetes (T2DM), cardiovascular diseases (CVD), and cancers [2, 3]. It is a multifactorial disease affected by genetic-environmental interactions [2]. Moreover, gut microbiota dysbiosis has recently been considered a crucial etiologic factor [2]. Therefore, altering the gut microbiome using probiotics, prebiotics, or postbiotics is a promising treatment strategy for controlling the host’s metabolism and obesity [4].

Postbiotics are Metabolites or non-viable bacterial compounds originating from probiotic microorganisms [5]. Generally, extracellular polysaccharides, components of the cell wall, and microbial metabolites of proteins and carbohydrates, such as short-chain fatty acids (SCFAs), vitamins, enzymes, and teichoic acid, are among the most important bioactive compounds considered postbiotics [6–8]. Since postbiotics are more stable and can be handled, stored, and transported more affordably than probiotics, they offer some advantages over probiotics [9]. Furthermore, it has been reported that postbiotics have a higher signaling capability for different organs and tissues, improved absorption, and the ability to initiate several biological reactions that are advantageous to the user [10].

Recent studies have reported that postbiotics can serve as a new dietary intervention for preventing and treating metabolic diseases and obesity [11, 12]. Overall, among various postbiotics, short-chain fatty acids (SCFAs), cell fragments, and heat-killed probiotics are considered to have the most significant anti-obesity effects. Moreover, muramyl dipeptide (MDP), lipoteichoic acid (LTA), S-layer proteins (SLPs), and exopolysaccharide (EPS) have been identified as major anti-obesity factors among the cell wall components [5]. SCFA production is often reduced in gut dysbiosis associated with obesity [13], but supplementation with postbiotics helps replenish SCFA levels directly or by modifying the gut microbiome and fermentation [14, 15].

The anti-obesity effects of postbiotics can be through various mechanisms, including increasing energy expenditure and insulin sensitivity, inhibiting adipogenesis, modifying lipid metabolism, and having anorexic effects, as well as decreasing food intake [5, 14–16]. Obesity is associated with reduced AMP-activated protein kinase (AMPK) activity, which leads to metabolic dysfunction [13]. In contrast, postbiotics (e.g., short-chain fatty acids) can activate the AMPK enzyme and modulate specific metabolic pathways [7, 16]. Activation of AMPK encourages fatty acid oxidation and inhibits lipogenesis by the phosphorylation of ACC (acetyl-CoA carboxylase) [17]. It also enhances glucose uptake by facilitating glucose transporter 4 (GLUT4) translocation to the cell surface in adipose tissue and skeletal muscle [18]. Additionally, AMPK can inhibit SREBP activity, resulting in reduced lipid biosynthesis and accumulation in adipose tissues and the liver. It also increases the activity of Peroxisome Proliferator-Activated Receptor Gamma Coactivator 1-alpha (PGC-1α), which enhances mitochondrial activity and energy expenditure [19]. Moreover, AMPK prevents inflammation by stopping NF-κB and Mechanistic target of Rapamycin (mTOR) pathways [20]. In the hypothalamus, AMPK reduces appetite by inhibiting the activity of orexigenic Agouti-Related Protein/ Neuropeptide Y (AgRP/NPY) neurons and increasing the levels of anorexigenic Proopiomelanocortin (POMC) [21].

It was also reported that SCFAs (specifically propionate) can stimulate Free Fatty Acid Receptor 2/ Free Fatty Acid Receptor 3 (FFAR2/FFAR3) receptors in the gut, release GLP-1 and PYY, and promote satiety. Meanwhile, acetate passes through the blood–brain barrier and can increase satiety by signaling in the hypothalamus [22, 23].

A recent systematic review by Eslami et al. reported the beneficial effects of postbiotic intake on body weight and fat percentage, food intake, and lipids and glucose profiles in HFD-obese animals; however, they did not conduct a meta-analysis [24]. Therefore, to the best of our knowledge, this is the first meta-analysis to assess the effects of postbiotics administration on metabolic factors, obesity management, and dietary intake based on the results of animal studies.

Materials and methods

This systematic review and meta-analysis followed the preferred reporting items for systematic reviews and Meta-analysis (PRISMA) guidelines (supplementary Table 2) [25].

Literature search strategies

The search was conducted using PubMed, Scopus, Web of Science, and Google Scholar databases for relevant articles published in English up to May 2025. The current study assessed the anti-obesity effects of different postbiotics, including cell-free supernatant, heat-killed bacteria, surface layer protein, bacteria lysates, extracellular vesicles, cell-free extract, and Muramyl dipeptide. A systematic search approach was performed using the following Mesh terms: (“Postbiotic” [Title/Abstract] OR “Postbiotics” [Title/Abstract] OR “post biotic” [Title/Abstract] OR “post biotics” [Title/Abstract] OR “post-biotic” [Title/Abstract] OR “post-biotics” [Title/Abstract] OR “Short chain fatty acids” [Title/Abstract] OR “SCFAs” [Title/Abstract] OR “Extracellular Vesicles” [MeSH Terms] OR “Extracellular Vesicles” [Title/Abstract] OR “muramyl dipeptide” [Title/Abstract] OR “surface layer protein” [Title/Abstract] OR “heat-killed lactobacillus” [Title/Abstract] OR “heat-killed bifidobacterium” [Title/Abstract]) AND (“obesity” [MeSH Terms] OR “obesity” [Title/Abstract] OR “Overweight” [MeSH Terms] OR “Overweight” [Title/Abstract] OR “Obese” [Title/Abstract] OR “Anthropometric” [Title/Abstract] OR “body weight” [MeSH Terms] OR “body weight” [Title/Abstract] OR “gut microbiome” [Title/Abstract] OR “Appetite” [MeSH Terms] OR “Appetite” [Title/Abstract] OR “Body fat” [Title/Abstract] OR “Adipose tissue” [MeSH Terms] OR “Adipose tissue” [Title/Abstract] OR “Body Composition” [MeSH Terms] OR “Body Composition” [Title/Abstract] OR “fat mass” [Title/Abstract] OR “lipid profile” [Title/Abstract] OR “lipid profiles” [Title/Abstract] OR “Lipids profile” [Title/Abstract] OR “glycem*” [Title/Abstract]). All articles were managed using EndNote software (version 21.5; Clarivate Analytics).

Study selection, inclusion, and exclusion criteria

The PICO criteria that determined the inclusion and exclusion criteria in our research consisted of overweight or obese adults or animals as the population, postbiotics in different forms as the intervention, and anthropometric indices, dietary intake, and metabolic factors as outcomes. During the screening process, randomized clinical trials were excluded due to their highly heterogeneous designs, populations, interventions, doses, and durations. Moreover, irrelevant studies, including observational studies, reviews, case reports, case series, and studies that did not report relevant outcomes, were excluded from the study.

Therefore, experimental studies on high-fat-diet (HFD) induced obesity that examined just the effects of postbiotics (not in combination with other interventions like prebiotics or nutrients) and had a control group for comparison were included. It should be mentioned that animal models other than mice, rats, non-human primates, and rabbits were excluded. Finally, the full texts of relevant studies published in English were assessed. Figure 1 illustrates the literature search and the screening processes.

Fig. 1.

Literature search and the screening processes

Data extraction

The extraction included details such as the first author, publication date, country, age and sex of the animals, type of animal model, intervention form, number of animals, treatment method, dosage, duration, and outcome measurements. Two independent researchers (ZN, VEA) screened the titles and abstracts of the chosen studies. Any researcher disagreements were resolved through discussions or consulting with a third researcher (RMG).

The primary outcome measured was body weight (BW), along with dietary intake and body fat. Secondary outcomes included metabolic markers, such as low-density lipoprotein (LDL), high-density lipoprotein (HDL), triglyceride (TG), total cholesterol (TC), fasting blood glucose (FBG), insulin levels and Homeostasis Model Assessment for Insulin Resistance (HOMA-IR).

Quality assessment

To assess the quality of the included studies, we used the Office of Health Assessment and Translation (OHAT 2015) tool [26]. The OHAT’s tier system categorizes each study’s overall risk of bias to compare the internal validity through the evidence base. Tier 1 studies are characterized as having a low or probably low risk of bias, whereas Tier 3 studies are classified as having a high or probably high risk of bias. Tier 2 studies do not meet the criteria of either Tier 1 or Tier 3. Most studies were found to have a low risk of bias (Supplementary Table 4). Furthermore, the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) tool was used to assess the quality of evidence in our review [27] (Supplementary Table 5).

Data synthesis and statistical analysis

The effect size of postbiotic consumption on the aforementioned parameters was calculated using the change score method. The mean difference between the final and initial values for each group determined the mean change in each group. To determine the SD changes, the formula employed was: SD = square root [(SD pre-treatment)2 (SD post-treatment)2—(2R × SD pre-treatment × SD post-treatment)], with a correlation coefficient (R-value) of 0.8 [28]. When an SEM or SE was mentioned instead of SD, the SD was computed using the formula SD = SEM √n (where n = sample size in each group). The random-effects model was used to synthesize the results of the included studies, presenting weighted mean differences (WMD) along with 95% confidence intervals (CI). The heterogeneity of included studies was assessed using Cochrane’s Q test (with significant P value < 0.05) and I-square test (I² over 50 percent indicating significant heterogeneity).

A subgroup analysis was performed based on sample size, animal age, postbiotic types, intervention dose, treatment method, animal strain, and study duration to control heterogeneity. The sensitivity analysis assessed the influence of each study on the total effect size by excluding each research study. The funnel plot and Egger or Begg tests were used to evaluate publication bias. The trim and fill method was used to compensate for the effects of publication bias. If the result changes after the test, it means that the asymmetry is due to a small study effect. This meta-analysis was performed using STATA 17.0 software (StataCorp, College Station, TX), with statistical significance defined as P < 0.05. We also conducted a narrative analysis when we could not demonstrate the findings of the included articles in a meta-analysis; instead of a statistical (quantitative) synthesis, we provided summaries of intervention effects for each study by computing standardized mean differences.

Result

The flowchart of the study is illustrated in Fig. 1. Of the 25,865 identified studies, 11,603 articles were detected as duplicates and omitted. Then, 14,262 articles were screened based on title and abstract, and 14,145 articles were excluded. These excluded articles were either review articles, irrelevant studies, conference papers, or not full text available. Finally, 40 animal studies were involved in this systematic review and meta-analysis; 32 studies examined the effect of postbiotics on anthropometric parameters, 25 articles focused on lipid profiles, 18 studies investigated glycemic indices, and 7 studies assessed food intake.

Study characteristics

The general features of the included studies are illustrated in Table 1. These articles were published between 2009 and 2025 in South Korea, Iran, China, Japan, Canada, the United States, Saudi Arabia, Taiwan, Egypt, and Spain. Study duration ranged from 2 to 16 weeks. The postbiotics administered as an intervention predominantly included heat-killed bacteria, short-chain fatty acids, cell-free extracts or cell-free supernatants of bacteria, extracellular vesicles (EVs) of bacteria, surface layer proteins, and Muramyl dipeptide. The dosage of intervention in most studies varied. Study design in 39 studies was parallel. Thirty-five studies were conducted on males, one was performed on males and females, and in four studies, the gender of the animals was not mentioned. The sample size in eligible studies varied from 12 to 90. For the meta-analysis, 691 experimental animals were used, with 346 randomized to the postbiotic group and 345 to the control group. The mean age of the animals fluctuated from 3 to 46 weeks. The experimental animals enrolled in these studies were obese, induced by a high-fat diet (HFD).

Table 1.

Characteristics of the included studies

First author Year	Country	Animals	Age of animals	Sample size	Intervention treatment method	Treatment type and dose	Treatment duration	outcomes
Heat-killed probiotics
Yoon et al. (2024)	South Korea	Male C57BL/6J mice	4 weeks	60	oral	heat killed Lactobacillus plantarum (LPHK) 120 mg /kg BW	8 weeks	BW- Body fat - LDL-, HDL + , TC-, TG-
Shiu et el (2024)	Taiwan	Male C57BL/6J mice	6 weeks	36	oral gavage	Heat-killed Lactobacillus gasseri HM1 (HM1) 0.2 mL/day	6 weeks (after 13 weeks HFD)	BW* LDL* HDL* TC* TG* FBG*
Kallassy et al. (2023)	USA	Male C57BL/6J mice	46 weeks	23	oral gavage	killed cell Faecalibacterium prausnitzii (FPZ-S) 1 mg/day	28 days (4 weeks)	FBG-
Jang et al. (2023)	korea	Male C57BL/6 mice	6 weeks	24	Oral gavage	heat-killed Lactiplantibacillus plantarum K8 (K8HK) 1 × 10^9 CFU/mL	12 weeks	BW -
Lee et al. (2022)	korea	Male Sprague–Dawley rats	3 weeks	24	Oral gavage	heat-killed Enterococcus faecalis (EF-2001) 30 mg/kg	6 weeks	BW - HDL* TG- TC-
Yoshitake et al. 2021	Japan	C57BL/6 J mice	6 weeks	90	oral	Heat-killed Lactobacillus plantarum L-137 (HK L-137) 0.002% HK L-137	20 weeks	Food intake * TG* TC -, FBG -, Insulin*
Short chain fatty acids
Jiao. A (2021)	china	Male C57BL/6J mice	4 weeks	48	oral	sodium acetate 5% (w/w)	5 weeks	BW *, LDL*, HDL *, TG -, TC*, FBG *, insulin*
Jiao. B (2021)	china	Male C57BL/6J mice	4 weeks	48	oral	sodium propionate 5% (w/w)	5 weeks	BW *, LDL *, HDL *, TG *, TC *, FBG *, insulin*
Jiao (2021)	china	Male C57BL/6J mice	4 weeks	48	oral	sodium butyrate 5% (w/w)	5 weeks	BW *, LDL *, HDL *, TG -, TC *, FBG *, insulin*
Fang et al. (2019)	china	Male C57BL/6J mice	4–5 weeks	24	oral	sodium butyrate 11 gr	12 weeks	BW - Food intake *
Weitkunat et al. (2017)	Germany	Male C57BL/6JRj mice	8 weeks	70	oral	5% SCFA with different Ac:Pr high acetate (HAc; 10:1 Ac:Pr)	30 weeks	BW - Food intake* Body fat - TC*
Weitkunat et al. (2017)	Germany	Male C57BL/6JRj mice	8 weeks	70	oral	5% SCFA with different Ac:Pr high propionate diet (HPr; 1:2.5 Ac:Pr)	30 weeks	BW - Food intake* Body fat - TC *
Hong et al. (2016)	china	Male C57BL/6J mice	3 weeks	24	oral gavage	sodium butyrate 80 mg	8 weeks	BW- LDL*, HDL*, TG *, TC*, FBG-, insulin-
Lu Y et al. (2016)	china	Male C57BL/6 J mice	3–4 weeks	60	oral	sodium acetate 5% (w/w)	16 weeks	BW - TG - TC - FBG * Insulin *
Lu Y et al. (2016)	china	Male C57BL/6 J mice	3–4 weeks	60	oral	sodium propionate 5% (w/w)	16 weeks	BW - TG - TC - FBG * Insulin *
Lu Y et al. (2016)	china	Male C57BL/6 J mice	3–4 weeks	60	oral	sodium butyrate 5% (w/w)	16 weeks	BW - TG - TC - FBG * Insulin *
Lu Y et al. (2016)	china	Male C57BL/6 J mice	3–4 weeks	60	oral	SCFAs (mixture of acetate,propionate, butyrate) (ratio at 3:1:1) 5% (w/w)	16 weeks	BW - TG - TC * FBG * Insulin *
V. Lin et al. (2012)	USA	C57BL/6N mice	3 months	24	oral	sodium butyrate 500 mg/ kg	4 weeks	BW - Insulin -
V. Lin et al. (2012)	USA	C57BL/6N mice	3 months	24	oral	sodium propionate 430 mg/ kg	4 weeks	BW - Insulin -
V. Lin et al. (2012)	USA	C57BL/6N mice	3 months	24	oral	sodium acetate 370 mg/kg	4 weeks	BW * Insulin *
Gao et al. (2009)	USA	Male C57BL/6 mice	4 weeks	20	oral	Sodium butyrate 5 gr/kg/ day	10 weeks	BW - FBG - Insulin - HOMA-IR -
Bacterial components
wu et al. (2025)	china	Male C57BL/6 J mice	6 weeks	80	Oral gavage	Lacticaseibacillus paracasei K56 postbiotics (DLP) 1 × 10^8 CFU/mL (referred to as Viabe LP)	9 weeks	BW- HDL +  LDL - FBG * Insulin*
Shin et al. (2024)	South Korea	Male C57BL/6 J mice	5 weeks	24	Oral gavage	Bacillus velezensis KMU01 cell-free supernatant (CFS-B.vele) 114 mg/kg	13 weeks	BW- Food intake * LDL-, HDL + , TC-, TG-
Chen et al. (2023)	China	Male C57BL/6 J mice	8 weeks	40	oral	Bacillus coagulans JA845 postbiotics 1.0 × 10^9 CFU/day	16 weeks	BW- LDL-, HDL + , TC-, TG- HOMA-IR-
Melo et al. (2023)	Spain	Male Wistar rats	8–9 weeks	34	Oral gavage	Lacticaseibacillus rhamnosus GG postbiotics 10^9 CFU/day	6 weeks	HDL* TC-
Rahman et al. (2022)	South Korea	Male C57BL/6 mice	6 weeks	56	oral gavage	cell-free supernatants of Bifidobacterium longum DS0950 (BS DS0950) 30 ml/ day	7 weeks	BW-
Seo et al. (2022)	South Korea	Male C57BL/6 mice	5 weeks	50	oral	surface layer protein (SLP) 120 mg/Kg	6 weeks	BW- TG- LDL* TC *
Lim et al. (2022)	South Korea	Male C57BL/6 J mice	6 weeks	28	Oral gavage	lactic acid bacteria lysates (LAB-P) 100 mg/kg	14 weeks	BW- LDL-, HDL*, TC-, TG*
Osman et al. (2021)	Egypt	Male Albino Wistar rats	2 months	40	oral	cell-free extract Lactobacillus paracasei 200 mg/ kg	9 weeks	BW- LDL-, HDL + , TC-, TG-
Ashrafian et al. (2021)	Iran	male C57BL/6 mice	8 weeks	35	oral	Extracellular vesicles (EVs) of Akkermansia. muciniphila 10 µg protein/200 µl	5 weeks	BW-
Lee et al. (2021)	South Korea	C57BL/6J mice	NR	21	oral	Lactobacillus plantarum L-14 extract (L-14) 250 mg/kg/day	7 weeks	BW- Food intake * LDL-, HDL +  TC-, TG- FBG- Insulin-
Kim E. et al. (2021)	South Korea	Male C57BL/6J mice	4 weeks	30	oral	Lentilactobacillus kefiri surface layer proteins; DH5 SLP (SDH5) 120 mg/ kg BW	6 weeks	BW- Body fat - LDL- HDL* TC- TG- FBG*
Williams et al. (2020)	Canada	Male & female C57BL/6 mice	6–7 weeks	38	intraperitoneal injections	Muramyl dipeptide (MDP) MDP (5 mg/kg)/ day)	100 days	FBG- Insulin*
Cavallari et al. (2017)	Canada	Male C3H/HeJ mice	8–10 weeks	22	Intraperitoneal injection	Muramyl Dipeptide (MDP) for 3 days + bacterial lipopolysaccharide (LPS) acute challenge MDP (100 µg) + LPS (0.1–0.2 mg/kg)	5 weeks HFD + 3 days MDP Injection + acute challenge of LPS	HOMA-IR-
Zhang et al. (2016)	china	Male C57BL/6J mice	6 weeks	24	intraperitoneal injection	exopolysaccharides (EPS) from LGG (Lactobacillus rhamnosus GG) 50 mg/kg/2 days	2 weeks	BW * TG - FBG*
Other types
Youn et al. (2022)	South Korea	Male C57BL/6 J mice	3 weeks	50	oral	citrus pomace bioconversion material 10 mL/kg	5 weeks	BW- LDL*, HDL*, TC*, TG-
Abdulrahman et al. (2021)	Saudi Arabia	Male Wistar rats	4 weeks	24	intraperitoneal injection	urolithin A 2.5 mg/kg/week	4 weeks	LDL -, HDL + , TG -, TC -
Abdulrahman et al. (2021)	Saudi Arabia	Male Wistar rats	4 weeks	24	intraperitoneal injection	urolithin B 2.5 mg/kg/week	4 weeks	LDL -, HDL + , TG -, TC -
B. Xia et al. (2020)	Canada	Male C57BL/6 J mice	8 weeks	12	Oral gavage	urolithin A 30 mg/kg	10 weeks	BW -, Food intake *, Body fat -, FBG -, insulin -, HOMA-IR -
Kim T. et al. (2018)	Canada	Male C57BL/6N mice	8 weeks	17	oral	Res-FMT (0.4% Resv ad libitum)	2 weeks Resv + HFHS (after 5 weeks HFHS diet)	Insulin*

NR: not-reported, -: parameters that were significantly decreased, + : parameters that were significantly increased, *: no significant effect

Effect of postbiotics on body weight

We found 32 articles in this area. Pooling their results showed a significant decrease in body weight (WMD: − 9.650 (95% CI − 15.259 to − 4.042), P = 0.001)), albeit with high heterogeneity (I² = 99.9%, P_{heterogeneity} < 0.001) (Fig. 2). The results of the sub-group analysis revealed that postbiotics intake had a significant impact on reducing body weight in studies with animal age < 6 weeks (I² = 99.7%), postbiotics types of heat-killed probiotics (I² = 91.0%) and SCFAs (I² = 99.7%), and intervention dose < 100 mg/kg (I² = 95.6%) (Supplementary Table 3). Sensitivity analysis indicated that removing each study did not change the results (Supplementary Fig. 1A). Egger’s test results indicated no publication bias (P = 0.480). However, there was partial asymmetry in the funnel plot. Thus, the trim and fill method was applied by imputing nine studies, and the results did not change, but the effect size was increased (WMD: − 13.305 (95% CI − 13.443 to − 13.168)) (Supplementary Fig. 1B).

Fig. 2.

Forest plot of the effect of postbiotic supplementation on body weight. CI, confidence interval; WMD, weighted mean difference

Effect of postbiotics on food intake

We identified seven articles that evaluated the effects of postbiotic supplementation on food intake. The pooled results of these studies showed no significant impact on food intake following postbiotic intervention (WMD: − 0.036 (95% CI − 0.140 to 0.068), P = 0.497), with low heterogeneity (I² = 0.0%, P_{heterogeneity} = 0.991) (Fig. 3). The result of subgroup analysis indicated that the dose and duration of the intervention, sample size, types of postbiotics, and age of animals did not affect on the obtained effect. Sensitivity analysis indicated that removing each study did not change the results (Supplementary Fig. 2A). Results of Begg’s test showed no publication bias (P = 0.652). However, there was partial asymmetry in the funnel plot. Thus, the trim and fill method was applied by imputing one study, and the results did not change (WMD: − 0.032 (95% CI − 0.135 to 0.071)) (Supplementary Fig. 2B).

Fig. 3.

Forest plot of the effect of postbiotic supplementation on food intake levels. CI, confidence interval; WMD, weighted mean difference

Effect of postbiotics on body fat

Five included studies evaluated the effects of postbiotics on body fat in animals. The combined results showed a significant decrease in body fat percentage (WMD: − 4.550 (95% CI − 8.895 to − 0.202), P = 0.040) after the intervention with a high heterogeneity (I² = 98.8% P_{heterogeneity} < 0.001) (Fig. 4). According to sub-group analysis significant decrease of body fat was seen in studies with sample size ≥ 40 (I² = 0%) and postbiotics types of bacterial components (I² = 0%) and SCFAs (I² = 0%) (Supplementary Table 3). Sensitivity analysis indicated that removing each study did not change the results (Supplementary Fig. 3A). Begg’s test results showed no publication bias (P = 0.624). However, there was partial asymmetry in the funnel plot. Thus, the trim and fill method was conducted that imputing three studies did not alter the results (WMD: − 6.952 (95% CI − 10.188 to − 3.716)) (Supplementary Fig. 3B).

Fig. 4.

Forest plot of the effect of postbiotic supplementation on body fat levels. CI, confidence interval; WMD, weighted mean difference

Effects of postbiotics on lipid profile

Seventeen studies investigated the effects of postbiotic supplementation on serum levels of LDL-C. Pooling their data based on the random-effects model showed that serum LDL-C levels significantly decreased after postbiotic supplementation (WMD: − 14.299 mg/dl (95% CI − 21.773 to − 6.826), P < 0.001) with high heterogeneity among the studies (I² = 98.8%, P_{heterogeneity} < 0.001) (Fig. 5). Moreover, the results of the sub-group analysis demonstrated that postbiotics intake had significant effect on reducing LDL-C levels in the studies on C57BL/6J mice (I² = 91.2%), oral fed (I² = 88.9%), postbiotics type of heat-killed probiotics (I² = 0%), and intervention duration ≥ 8 weeks (I² = 99.5%) (Supplementary Table 3). Sensitivity analysis indicated that removing each study did not change the results (Supplementary Fig. 4A). Based on Egger’s test results, there was no publication bias (P = 0.098). However, there was a partial asymmetry in the funnel plot. Thus, the trim and fill method was used by imputing five studies, and the results did not change (WMD: − 25.381 mg/dl (95% CI − 42.016 to − 8.746)) (Supplementary Fig. 4B).

Fig. 5.

Forest plot of the effect of postbiotic supplementation on LDL-C levels. CI, confidence interval; WMD, weighted mean difference

We found 19 articles about HDL-C and combining their results showed a significant reduction in HDL-C levels after postbiotic supplementation (WMD: 6.013 mg/dl, (95% CI 1.400 to 10.626), P = 0.011)) with a high heterogeneity among the studies (I² = 96.8%, P_{heterogeneity} < 0.001)) (Fig. 6). Subgroup results showed that postbiotic intake had a significant decrease on HDL-C levels in the studies with oral feeding (I² = 89.3%), treatment dose < 100 mg/kg (I² = 66.2%), and supplementation duration ≥ 8 weeks (I² = 85.5%) (Supplementary Table 3). Sensitivity analysis indicated that omitting each study did not change the results (Supplementary Fig. 5A). Egger’s test results indicated no publication bias (P = 0.939). However, there was a partial asymmetry in the funnel plot. Thus, the trim and fill test was conducted that imputing two studies did not alter the results (WMD: 6.798 mg/dl (95% CI 2.995 to 10.601)) (Supplementary Fig. 5B).

Fig. 6.

Forest plot of the effect of postbiotic supplementation on HDL-C levels. CI, confidence interval; WMD, weighted mean difference

Pooling the findings of 23 studies showed that postbiotic intake significantly decreases serum TG levels (WMD: − 26.397 mg/dl, (95% CI − 28.893 to − 23.901), P < 0.001) with high heterogeneity among the studies (I² = 99.9%, P_{heterogeneity} < 0.001)) (Fig. 7). Subgroup analysis showed that postbiotic administration had a significant impact on TG levels reduction in studies with animal age < 6 weeks (I² = 99.9%), oral feeding (I² = 99.9%), postbiotic type of SCFAs (I² = 100%), and dose < 100 mg/kg (I² = 89.3%). (Supplementary Table 3). Upon conducting the sensitivity analysis, removing each study did not have a significant effect on the results (Supplementary Fig. 6A). The Egger’s test results indicated no publication bias (P = 0.505). However, there was partial asymmetry in the funnel plot. Thus, the trim and fill method was applied by imputing four studies, and the results remained unchanged (Supplementary Fig. 6B).

Fig. 7.

Forest plot of the effect of postbiotic supplementation on TG levels. CI, confidence interval; WMD, weighted mean difference

Based on the combined results of 25 articles, postbiotics supplementation had significant effects on decreasing serum TC levels (WMD: − 37.501 mg/dL, (95% CI − 54.621 to − 20.381), P < 0.001) with high heterogeneity among the studies (I² = 99.0%, P_{heterogeneity} < 0.001) (Fig. 8)). Subgroup results showed that postbiotic intake had a significant impact on TC levels in studies on C57BL/6J mice (I² = 96.8%) and oral feeding (I² = 97.1%) (Supplementary Table 3). Sensitivity analysis indicated that removing each study did not change the results (Supplementary Fig. 7A). Egger’s test results showed no publication bias (P = 0.656). However, there was partial asymmetry in the funnel plot. Nevertheless, the trim and fill method, which imputed seven studies, did not alter the results (Supplementary Fig. 7B).

Fig. 8.

Forest plot of the effect of postbiotic supplementation on TC levels. CI, confidence interval; WMD, weighted mean difference

Effect of postbiotics on glycemic markers

The impact of postbiotic supplementation on serum fasting blood glucose (FBG) and insulin levels was assessed in 18 included studies, and HOMA-IR levels in 4 studies. Pooled analysis showed a significant reduction in FBG levels following postbiotic supplementation (WMD: − 15.712 mg/dL, (95% CI − 23.651 to − 7.772), P < 0.001) with high heterogeneity (I² = 92.3% P_{heterogeneity} < 0.001)) (Fig. 9). However, the results of subgroup analysis showed that postbiotic intake significantly reduced serum FBG levels in the studies with sample size ≥ 40 (I² = 0.0%), animal age < 6 weeks (I² = 81.0%), oral feeding (I² = 69.8%), postbiotics type of SCFAs (I² = 82.5%), intervention dose ≥ 100 mg/kg (I² = 92.3%), and treatment duration ≥ 8 weeks (I² = 82.1%) (Supplementary Table 3). Sensitivity analysis indicated that removing each study did not change the results (Supplementary Fig. 8A). Begg’s test results revealed no publication bias (P = 0.404). However, there was partial asymmetry in the funnel plot. Thus, the trim and fill method was applied with three imputed studies that did not change the results (WMD: − 22.587 mg/dl (95% CI − 36.633 to − 8.541)) (Supplementary Fig. 8B).

Fig. 9.

Forest plot of the effect of postbiotic supplementation on FBG levels. CI, confidence interval; WMD, weighted mean difference

Eatheen studies showed a significant reduction in insulin levels after postbiotic administration (WMD: − 0.096 ng/ml (95% CI − 0.149 to − 0.043), P < 0.001) with high heterogeneity (I² = 94.1% P_{heterogeneity} < 0.001)) (Fig. 10). The results of subgroup analysis showed a significant decrease in insulin levels in studies that had sample size < 40 (I² = 92.4%), animal age ≥ 6 weeks (I² = 94.7%), and postbiotics type of heat-killed probiotics (I² = 0%) (Supplementary Table 3). Sensitivity analysis indicated that removing each study did not change the results (Supplementary Fig. 9A). Egger’s test results indicated publication bias (P = 0.001), and there was partial asymmetry in the funnel plot. Thus, the trim and fill method was applied with two studies imputed that did not alter the obtained results (WMD: − 0.853 mg/dl (95% CI − 1.375 to − 0.331)) (Supplementary Fig. 9B).

Fig. 10.

Forest plot of the effect of postbiotic supplementation on insulin levels.CI, confidence interval; WMD, weighted mean difference

The pooled effects of four studies that evaluated the effect of postbiotics on HOMA-IR measurement showed a notable decrease in HOMA-IR after postbiotics supplementation (WMD: − 11.040, (95% CI − 19.855 to − 2.225), P = 0.014) with a high heterogeneity among the studies (I² = 99.4% P_{heterogeneity} < 0.001)) (Fig. 11). Based on the subgroup analysis, the effect of postbiotic intake on decreasing HOMA-IR was significant in the studies with gavage feeding (I² = 0.0%), intraperitoneal injection (I² = 0.0%), postbiotics types of SCFAs (I² = 0%) and bacterial components (I² = 89.1%), and intervention dose ≥ 100 mg/kg (I² = 0.0%) (Supplementary Table 3). Sensitivity analysis indicated that removing each study did not change the results (Supplementary Fig. 10A). Begg’s test results showed no publication bias (P = 1.00). However, there was partial asymmetry in the funnel plot. The trim and fill test was done without imputing any studies; thus, the asymmetry was not due to a deficit of studies (Supplementary Fig. 10B).

Fig. 11.

Forest plot of the effect of postbiotic supplementation on HOMA-IR.CI, confidence interval; WMD, weighted mean difference

We found only one article that investigated the effect of postbiotic intervention on HbA1c percentage in obese animals. Kallassy et al. in 2023 pointed out that the HbA1c percentage was not significantly lower in 23 male C57BL/6J mice treated for 28 days with 1 mg/day of killed cell Faecalibacterium prausnitzii (FPZ-S) compared to the control.

Discussion

To the best of our knowledge, this is the first meta-analysis to assess the effect of postbiotics on obesity management, considering anthropometric measurements, dietary intake, and metabolic parameters. Pooling the information from the included studies revealed that postbiotics originating from different probiotics have beneficial effects on weight management and metabolic health. Generally, the findings of this meta-analysis showed that supplementation with different postbiotics results in a 9g decrease in body weight and a 4.5% reduction in body fat of obese animal models. Furthermore, postbiotics treatment reduced LDL-C by 14 mg/dl, TG by 26 mg/dl, TC by 37 mg/dl, FBG by 15 mg/dl, insulin by 0.096 ng/ml, and HOMA-IR by 11.040. Additionally, a 6 mg/dl increase in serum HDL-C levels was detected following postbiotics intake. However, it had no significant impact on food intake.

Similarly, a recent systematic review by Eslami et al. in 2024 reported that, according to most of their included studies, supplementation with postbiotics had beneficial effects on reducing body weight or body weight gain, as well as body fat, and improvements in lipid and glycemic profiles [24]. Our subgroup analysis also revealed that the use of SCFAs as a postbiotic supplement was more effective in improving FBG and HOMA-IR levels, body weight and fat, and HDL-C levels in comparison with the other types. In addition, the treatment dose of ≥ 100 mg/kg compared to < 100 mg/kg was more beneficial in reducing FBG and HOMA-IR levels, and intervention duration ≥ 8 weeks for lowering serum FBG levels.

A more recent study by Shin et al. demonstrated that administering 114 mg/kg of Bacillus velezensis KMU01 cell-free supernatant (CFS-B.vele) for 13 weeks resulted in a significant reduction in body weight in obese mice [29]. Kim et al. reported that the intake of 120 mg/kg body weight (BW) postbiotic, specifically Lentilactobacillus kefiri surface layer proteins, for 6 weeks significantly reduced adipose tissue weight in obese mice [30]. Moreover, extracellular vesicles (EVs) from Akkermansia muciniphila (A. muciniphila) have been shown to affect body weight and plasma biochemical profiles by modulating fatty acid infiltration, adipocyte size, and lipid metabolism while enhancing glucose tolerance and reducing pro-inflammatory cytokines in the adipose tissue of high-fat diet-challenged mice [31–34]. These EVs can enhance metabolic functions by improving the integrity of the gut barrier and reducing metabolic endotoxemia, a condition associated with obesity [35].

Accordingly, the results of Ashrafian et al. showed that utilizing the EVs extracted from A. muciniphila significantly decreased body weight, fat mass, food intake, blood lipids and glucose levels, and inflammatory markers like Interleukin-6 (IL-6) and the Tumor Necrosis Factor-α (TNF-α) as compared to the control group [31, 32]. Similarly, de Oliveira Melo et al. demonstrated that 6 weeks of supplementation with 10⁹ CFU/day of heat-inactivated Lactobacillus rhamnosus GG in obese Wistar rats resulted in a significant reduction in visceral fat mass but not in food intake [36].

It was also reported that postbiotics can decrease adipogenic factors like the protein levels of Peroxisome proliferator-activated receptor gamma (PPARγ) and CCAAT/enhancer-binding protein alpha (C/EBPα) and their target molecule, fatty acid-binding protein 4 (FABP4), so they may directly act on the mechanism responsible for fat deposition and utilization [37–39].

Overall, postbiotics derived from probiotics can potentially offer a beneficial solution for managing obesity through some default mechanisms (Fig. 12). Heat-killed probiotics exert anti-obesity effects through the immunomodulatory impact of conserved microbial structures (peptidoglycan, lipoteichoic acid), which enhance intestinal barrier integrity and reduce metabolic endotoxemia by suppressing Nuclear Factor Kappa-light-Chain-enhancer of Activated B cells (NF-κB)-mediated inflammation. They also play an essential role in the selective enrichment of SCFA-producing bacteria (e.g. Akkermansia, Bifidobacterium) [7, 40–42].

Fig. 12.

Postbiotics and related metabolic pathways by key molecular players

Short-chain fatty acids (SCFAs), primarily propionate and butyrate, exert anti-obesity effects through multiple mechanisms including activation of AMP. AMPK is a cellular energy-regulating reducing adipogenesis by downregulating PPAR-γ, promoting thermogenesis in brown adipose tissue via Uncoupling protein 1 (UCP1) upregulation, improving insulin sensitivity by enhancing GLUT4 translocation and suppressing NF-κB-mediated inflammation, reinforcing gut barrier integrity, thereby attenuating metabolic endotoxemia [43–47].

Bacterial structural components are another type of postbiotics that have anti-obesity effects through distinct but connected pathways. For instance, Muramyl dipeptide (MDP) is a peptidoglycan fragment that activates Nucleotide-Binding Oligomerization Domain-Containing Protein 2 (NOD2) signaling in immune and epithelial cells, thereby suppressing NF-κB-related inflammation, enhancing gut barrier integrity, and modulating adipocyte lipolysis via SREBP-1c downregulation [5, 48]. Cell-free supernatants (CFS) from probiotic bacteria mediate metabolic benefits primarily via short-chain fatty acids (SCFAs), particularly butyrate and propionate [11, 29].

Bacterial extracellular vesicles (EVs), such as those produced by Akkermansia muciniphila, modulate the gut barrier by producing mucin and upregulating tight junctions [31, 34]. EVs also transport Micro RNAs (miRNAs) and proteins that enhance fatty acid oxidation via PPARα and adipose tissue browning via UCP1, collectively mitigating metabolic inflammation, insulin resistance, and adiposity [49, 50].

Although, to our knowledge, this is the first meta-analysis to evaluate the effects of postbiotics on dietary intake, anthropometric indices, and metabolic factors based on the results of related articles, there are some limitations, including the high heterogeneity of studies in terms of study designs, types of post-biotics, dosage, and intervention duration. Moreover, translating findings from animal models to human obesity research poses challenges due to differences in microbiota, metabolism, and physiology, which complicate the process of drawing accurate conclusions about the efficacy and safety of postbiotics for obesity management in humans. Thus, future clinical trials are needed to confirm this preliminary evidence.

Conclusion

In conclusion, this meta-analysis demonstrated that postbiotics, particularly SCFAs, may be a promising strategy for managing body weight, body fat mass, and serum levels of lipid profiles and glycemic markers. However, further well-designed experimental studies focusing on specific postbiotic types, standardizing dosages, and also clinical trials are required to determine their safety, efficacy, and consequences on humans.

Supplementary Information

Below is the link to the electronic supplementary material.

Abbreviations

EVs

Extracellular vesicles

LDL-C

Low-density lipoprotein

HDL-C

High-density lipoprotein

TG

Triglyceride

TC

Total cholesterol

FBG

Fasting blood glucose

HOMA-IR

Homeostasis model assessment for insulin resistance

FFA

Free fatty acid

IL-6

Interleukin-6

TNF-α

Tumor necrosis factor-α

PYY

Peptide YY

GLP-1

Glucagon-like peptide-1

AMPK

Adenosine monophosphate-activated protein kinase

SREBP-1

Sterol regulatory element-binding protein 1

FAS

Fatty acid synthase

ACC

Acetyl-CoA carboxylase

PPAR-γ

Peroxisome proliferator-activated receptor gamma

C/EBPα

CCAAT/enhancer-binding protein alpha

FABP4

Fatty acid-binding protein 4

PGC-1α

Peroxisome proliferator-activated receptor gamma coactivator 1-alpha

NF-κB

Nuclear factor kappa-light-chain-enhancer of activated B cells

mTOR

Mechanistic target of rapamycin

AgRP/NPY

Agouti-related protein/neuropeptide Y

POMC

Proopiomelanocortin

FFAR2/FFAR3

Free fatty acid receptor 2/free fatty acid receptor 3

UCP1

Uncoupling protein 1

NOD2

Nucleotide-binding oligomerization domain-containing protein 2

miRNA

Micro RNA

Author contributions

ZN and VEA were responsible for the conceptualization and research design, data collection and interpretation, data extraction, as well as drafting and revising the manuscript. LRN helped with data extraction, as well as drafting and revising the manuscript. RMG conducted statistical evaluations, data analysis, and quality review. All authors have reviewed and approved the final manuscript version and assert that it is not published elsewhere and is original, not copied from other documents.

Funding

No funds were obtained from any entities to support the current research.

Data availability

The information supporting the results of this review can be found in our supplementary documents.

Declarations

Competing interests

The authors declare that they have no compting interests.