Cereal beta-glucan and cardiovascular disease risk reduction in overweight and obese populations: a systematic review and meta-analysis of lipid, blood pressure, and anthropometric parameters

Abstract

Obesity-related dyslipidemia and hypertension are the key drivers of global cardiovascular disease (CVD) mortality. Although the lipid-lowering capacity of beta-glucan has been demonstrated in the general population, evidence regarding its effects on dyslipidemia and hypertension in overweight and obese populations remains limited. As of April 23, 2025, we searched nine databases. Two reviewers independently conducted study screening, data extraction, and bias assessment. We assessed the effects of cereal beta-glucan (CBG) on lipids, blood pressure, and anthropometric parameters in individuals with a body mass index (BMI) ≥25 kg m⁻². This article included 49 eligible studies involving 3854 subjects. Pooled analyses showed that CBG significantly reduced total cholesterol [TC, −0.24 mmol L⁻¹ (−0.31, −0.17), P < 0.001] and low-density lipoprotein cholesterol [LDL-C, −0.19 mmol L⁻¹ (−0.25, −0.13), P < 0.001], particularly with doses ≥3 g day⁻¹ and durations ≥6 weeks. Oat beta-glucan also lowered the systolic blood pressure [SBP, −1.38 mmHg (−2.66, −0.09), P = 0.036]. No effects were observed for triglycerides, high-density lipoprotein cholesterol, diastolic blood pressure, or body weight. BMI results were conflicting owing to publication bias. In conclusion, CBG reduces TC and LDL-C, and oat beta-glucan may modestly lower SBP in overweight and obese populations. These results support evidence-based recommendations for integrating CBG into CVD prevention strategies.

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1. Introduction

Cardiovascular disease (CVD) is the leading cause of mortality and morbidity worldwide, posing a substantial public health burden with approximately 18 million deaths annually.^1,2 As a group of disorders affecting the heart and blood vessels, CVD includes conditions such as coronary heart disease, stroke, arrhythmia, and heart failure, among others. Its high mortality and morbidity rates are driven by a constellation of well-established risk factors, encompassing both non-modifiable factors (age and genetic predisposition) and modifiable determinants (overweight and obesity, hypertension, dyslipidemia, diabetes mellitus, chronic kidney disease, physical inactivity, unhealthy dietary patterns, and tobacco smoking).³ Therefore, the cornerstone of reducing CVD risk lies in addressing these modifiable factors.

Overweight and obesity represent critical modifiable risk factors, the global prevalence of which has risen dramatically over the past few decades. According to the World Health Organization, the number of overweight (body mass index, BMI ≥25 kg m⁻²) adults surged from 388 million to 2.5 billion, including over 890 million individuals with obesity (BMI ≥30 kg m⁻²).⁴ Prevalent overweight and obesity heighten the CVD risk both directly, as independent factors, and indirectly, by driving adverse structural and functional changes in the heart that exacerbate other risk factors such as hypertension and dyslipidemia.^5–7 Specifically, in individuals with overweight or obesity, visceral fat deposition drives atherogenic dyslipidemia by enhancing hepatic secretion of very-low-density lipoproteins, inhibiting the lipoprotein lipase activity, and impairing high-density lipoprotein cholesterol (HDL-C) function.^8,9 Concurrently, visceral fat contributes to hypertension through the chronic activation of the renin–angiotensin–aldosterone system and endothelial dysfunction, the latter of which impairs nitric oxide synthase activity.¹⁰ Moreover, sympathetic overactivation and leptin resistance further potentiate hypertension.¹¹

To address the CVD risk synergistically amplified by obesity, the American College of Cardiology and the American Heart Association strongly recommend lifestyle changes as the foundation for reducing CVD risk, such as dietary intervention.¹² Therefore, dietary components capable of simultaneously improving multiple risk factors are of particular interest. Cereal beta-glucan (CBG), a soluble dietary fiber primarily derived from oats and barley, has been recognized by both the European Commission and the U.S. Food and Drug Administration (FDA) for its cholesterol-lowering efficacy.^13–15 It lowers cholesterol levels by irreversibly binding to bile acids, reducing their reabsorption, and upregulating hepatic low-density lipoprotein cholesterol (LDL-C) receptors, thereby enhancing cholesterol clearance.^16,17 Additionally, in a double-blind randomized controlled trial (RCT) involving hypertensive subjects, beta-glucan (BG) significantly reduced both systolic blood pressure (SBP, −8.3 mmHg, P = 0.008) and diastolic blood pressure (DBP, −3.9 mmHg, P = 0.018).¹⁸ BG may regulate blood pressure by increasing gut microbiota-derived short-chain fatty acids and suppressing NF-κB-mediated inflammation.^19,20 This simultaneous modulation of the lipid profile and blood pressure positions CBG as a promising dietary intervention for obesity-related cardiometabolic disorders.²¹

The lipid-lowering efficacy of BG has been reported in existing meta-analyses, primarily in the general and hypercholesterolemic populations.^22–25 However, evidence concerning the cardiometabolic benefits of BG specifically in overweight and obese populations remains limited. The efficacy of BG intervention may be attenuated in this population due to the distinct pathophysiology of obesity, which includes adipose tissue hypoxia-induced oxidative stress and leptin resistance.^26,27 From a methodological perspective, there is a lack of dose–response analyses in previous meta-analyses, despite their critical role in strengthening causal inference. Furthermore, few meta-analyses have assessed composite lipid-blood pressure outcomes, although such assessments are crucial for a comprehensive understanding of CVD risk management. This study synthesizes evidence from RCTs to evaluate the effects of CBG on lipid profiles, blood pressure, and anthropometric parameters in overweight and obese individuals. The findings will encourage the development of targeted dietary strategies aimed at mitigating CVD risk in this high-risk group.

2. Methods

This article adhered to the Preferred Reporting Items for Systematic Reviews and meta-Analyses (PRISMA) guidelines (Table S1) and was registered in PROSPERO (CRD420251054174).

2.1. Search strategy and study selection

The keywords ‘oats’, ‘barley’, ‘beta-glucan’, ‘lipid profile’, ‘blood pressure’, ‘BMI’ and ‘weight’ were used to search the electronic databases PubMed, Web of Science, Cochrane Library, Embase, Scopus, China National Knowledge Infrastructure, SinoMed, China Science and Technology Journal Database and Wanfang for studies published before April 23, 2025 (Table S2). Medical Subject Headings were used during the search. The reference lists of relevant reviews were manually searched to ensure that all potential studies were included.

The studies considered in the meta-analysis had to match the following criteria: (1) RCTs. (2) Participants with overweight (BMI ≥25 kg m⁻²) or obesity (BMI ≥30 kg m⁻²). (3) Intervention with oat or barley BG. (4) The amount of BG used was indicated or could be estimated from published literature. (5) Studies reporting any of the following outcomes were included: total cholesterol (TC), triglycerides (TG), HDL-C, LDL-C, SBP, DBP, BMI, or body weight. No language restrictions were imposed. Eligibility criteria were established before study initiation.

2.2. Data extraction

Two reviewers independently screened studies for eligibility, with inter-reviewer consistency assessed using Cohen's kappa statistics. Discrepancies were resolved by consensus with a third reviewer. The following study characteristics were extracted using a pre-designed table: first author, year, region, study design, number of subjects, subject characteristics, source of BG, dose, intervention duration and outcome metrics. The lipid results in mg dL⁻¹ were converted to mmol L⁻¹ with conversion factors of 0.02586 (TC, HDL-C, and LDL-C) and 0.01129 (TG). The results were converted from mean ± standard deviation (SD) to mean ± standard error (SE) according to the following formula: , where n represents sample size.

2.3. Quality assessment

The quality of the RCTs was assessed using the Cochrane Risk of Bias Tool 2.0 (RoB 2.0).²⁸ The tool evaluates five domains: randomization process, deviations from intended interventions, missing outcome data, measurement of the outcome, and selection of the reported result. Each domain was rated as Low risk, High risk, or Some Concerns.

2.4. Statistical analysis

Statistical analyses were conducted in Python 3.10 (JupyterLab v4.2.5) using custom scripts (Table S3). P < 0.05 was considered significant. In this study, weighted mean differences (WMD) were pooled using random-effects models to quantify intervention effects on lipids, blood pressure body weight and BMI in treatment-control comparisons.²⁹ The between-study variance (τ²) was calculated using the DerSimonian–Laird estimator and applied to the random-effects weights. The results are presented as [WMD (95% confidence interval), P-value]. For multi-arm trials with varying BG doses as the primary distinction, experimental groups were combined into a single unit for effect size calculation. This prevented the reuse of the control group in analyses. In addition, when both oat and barley BGs were consumed in a single study, they were treated as two trials to minimize trial heterogeneity and obtain more accurate results. The I² statistic was used to quantify the heterogeneity between studies, serving as a quantitative assessment of the inconsistency. Heterogeneity between studies was considered high if I² was greater than 50%. To identify the potential sources of heterogeneity, several prespecified variables were analyzed in subgroups, including study duration (<6 weeks or ≥6 weeks), region (North America, Europe or Other regions), source of BG (oat or barley), dose (<3 g day⁻¹, <6 g day⁻¹ or ≥6 g day⁻¹), BMI (≥25, ≥30), age (<20, <40, <60 or ≥60), study type (parallel or crossover) and degree of hypercholesterolemia (healthy, mild hypercholesterolemia or moderate hypercholesterolemia). Meta-regression analyses were performed to assess the extent to which different variables contributed to heterogeneity in the pooled effect. Dose–response analyses were conducted to understand the relationship between the intake of BG and changes in outcomes in people with abnormal BMI using both linear and nonlinear models. Potential publication bias was assessed using funnel plots and Begg's and Egger's tests.^30–32 We applied the trim-and-fill method to impute missing studies, correct publication bias, and generate more plausible results. To assess the stability of the results, sensitivity analyses were performed to systematically exclude individual studies and recalculate the effect sizes of the remaining studies.

2.5. Grading of evidence

The GRADE system was employed to evaluate the certainty of the evidence, with the GRADE handbook serving as a reference guide.³³ Because the RCTs included in this review had the highest level of evidence, we only considered five downgrading factors: study limitations, inconsistency, indirectness of evidence, imprecision, and reporting bias. The evidence may be upgraded if a significant dose–response relationship is identified. The results were rated as High, Moderate, Low, or Very low.

3. Results

3.1. Characteristics of the included studies

Systematic searches were conducted across nine electronic databases, identifying 2376 records. The screening process showed high inter-rater consistency (Cohen's kappa = 0.852), and 43 records met the inclusion criteria. An additional six studies were identified through manual searches of the reference lists, resulting in the final inclusion of 49 primary studies comprising 52 RCTs with 3854 participants (Fig. 1).

Fig. 1 Flow chart from search to meta-analysis.

Table 1 summarizes the characteristics of the included studies. The trials adopted crossover (17 trials) or parallel (35 trials) designs with blinding methods categorized as single-blind (11 trials), double-blind (26 trials), or unclear (15 trials). Interventions delivered oat (43 trials) or barley BG (9 trials) at 1.35–10.3 g day⁻¹ for 2–12 weeks, with sample sizes ranging from 8 to 367 participants. Publication dates spanned 1991–2023.

Table 1 The study characteristics of the forty-nine included articles

No.	Study	Country	Study design	Masking	Sample (M/F)	Subject state	Mean age (SD)	Mean BMI (SD)	Intervention	Control	Beta-glucan content (g day^−1)	Duration (Weeks)	Wash out (Weeks)
M, male; F, female; SD, standard deviation; MoCH, moderate hypercholesterolemia; MCH, mild hypercholesterolemia; NIDDM, non-insulin-dependent diabetes mellitus; T2DM, Type 2 diabetes mellitus; MS, metabolic syndrome; VFO, visceral fat obesity.
1	Davidson et al., 1991^70	America	Parallel	Single	140 (80/60)	MoCH	52.78	25.37	Oat or oat bran	Starch	3.2	6	—
2	Uusitupa et al., 1992^71	Finland	Parallel	Double	36 (20/16)	MoCH	47.78 (7.78)	26.48 (2.97)	Oat bran	Wheat bran	10.3	8	—
3	Hegsted et al., 1993^72	America	Crossover	Single	11 (10/1)	MCH	37 (10)	26.6 (3.4)	Oat bran	Rice bran	3.8	3	2
4	Pick et al., 1996^73	Canada	Crossover	Unclear	8 (8/0)	NIDDM	46 (2.8)	27.6 (0.6)	Oat bran bread	Wheat bread	9.1	12	0
5	Behall et al., 1997^74	America	Crossover	Single	23 (7/16)	MCH	51.4 (2)	26.8 (1.5)	Oat bran extract	Placebo	8.7	5	0
6	Onning et al., 1999^75	Sweden	Crossover	Double	52 (52/0)	MoCH	62.59 (5.55)	27.17 (3.31)	Oat milk	Rice milk	3.8	5	5
7	Lovegrove et al., 2000^76	Britain	Parallel	Double	62 (31/31)	MoCH	56.55 (9.3)	25.9 (3.46)	Oat concentrate	Wheat bran	3	8	—
8	Davy, Davy, et al., 2002; Davy, Melby, et al., 2002^77,78	America	Parallel	Unclear	36 (36/0)	Healthy	59 (2)	29.4 (0.8)	Oatmeal and oat bran	Wheat flakes	5.5	12	—
9	Kabir et al., 2002^79	France	Crossover	Double	13 (13/0)	T2DM	59 (7.2)	28 (3.6)	Oat bran concentrates	Whole meal bread	3	4	2
10	Pins et al., 2002^80	America	Parallel	Double	88 (45/43)	MCH	47.58 (16.11)	30.86 (4.91)	Oatmeal and oat bran	Wheat	2.8	12	—
11	Kerckhoffs et al., 2003^81	Netherlands	Parallel	Single	48 (21/27)	MCH	51.3 (2.1)	25 (0.65)	Oatmeal cookie bread	Cookie bread	5.9	4	—
			Crossover	Single	25 (10/15)	MCH	53.5 (2.24)	25 (0.71)	Oatmeal orange juice	Wheat-orange juice	5	2	1
12	Lia Amundsen et al., 2003^82	Sweden	Crossover	Single	16 (9/7)	MoCH	57 (7.9)	25.4 (1.9)	Oat bran concentrates	Wheat bread	5	3	2.5
13	Maki et al., 2003^83	America	Crossover	Double	18 (9/9)	Healthy	10.6 (2.12)	27.4 (8.06)	Oat bran	Corn flour	3	4	0
14	Behall et al., 2004, 2006^84,85	America	Crossover	Unclear	25 (7/18)	MCH	47 (12)	30.14 (3.95)	Barley products	Placebo	9	5	0
15	Biörklund et al., 2005^86	Sweden	Parallel	Single	79 (44/35)	MoCH	59 (7)	25.3 (3.2)	Beta-glucan beverage	Placebo beverage	7.2	5	—
16	Mårtensson et al., 2005^87	Sweden	Parallel	Double	56 (26/30)	MCH	55.64 (9.17)	25.26 (3.24)	Oatmeal ferment	Placebo ferment	3.3	5	—
17	Robitaille et al., 2005^88	Canada	Parallel	Unclear	34 (34/0)	Healthy	38.29 (7.47)	29.13 (4.64)	Oatmeal muffin	Placebo muffin	2.3	4	—
18	Keenan et al., 2007^89	America	Parallel	Unclear	155 (75/80)	MS	54.99 (11.16)	29.32 (5.23)	Cornflake and juice	Placebo	4	6	—
19	Maki et al., 2007^90	America	Crossover	Double	27 (27/0)	Healthy	41.7 (8.7)	27.7 (4)	Oatmeal products	Wheat products	5.7	2	2
20	Panahi, 2007^91	Canada	Parallel	Double	105 (49/56)	MoCH	62.2 (8.31)	25.7 (4.16)	Oatmeal cookie	Wheat cookie	6	6	—
21	Queenan et al., 2007^92	America	Parallel	Double	75 (50/25)	MoCH	44.94 (12.81)	≥30	Oat beta-glucan	Glucose	6	6	—
22	Reyna-Villasmil et al., 2007^93	Venezuela	Parallel	Unclear	38 (38/0)	MCH	59.8 (3.7)	28.3 (3.7)	Oatmeal bread	Whole meal bread	6	8	—
23	Biörklund et al., 2008^94	Sweden	Parallel	Single	43 (19/24)	MoCH	58 (8.2)	25 (3.1)	Oat beta-glucan soup	Placebo soup	4	5	—
24	Jenkins et al., 2008^95	Cananda	Crossover	Double	28	MCH	62 (5.29)	26.5 (3.17)	Oat bran bread	Strawberry	2	4	2
25	Shimizu et al., 2008^96	Japan	Parallel	Double	39 (39/0)	MoCH	41.48 (8.51)	25.33 (2.6)	Pearl barley and rice	Rice	7	12	—
26	Smith et al., 2008^97	America	Parallel	Double	90 (26/64)	MCH	44.6 (13.51)	26.35 (3.73)	Dietary supplement	Placebo	6	6	—
27	Liatis et al., 2009^98	Greece	Parallel	Double	41 (23/18)	T2DM	62.98 (9.01)	28.47 (4.31)	Oat bread	Wheat bread	3	3	—
28	Maki et al., 2009^99	America	Parallel	Unclear	144 (31/113)	MCH	49.01 (10.13)	32.1 (4.64)	Oatmeal products	Low-fiber food	3	12	—
29	Beck et al., 2010^100	Australia	Parallel	Unclear	56 (56/0)	Healthy	37.4 (5.64)	29.28 (2.16)	Oatmeal products	Placebo	5	12	—
30	Cugnet-Anceau et al., 2010^101	France	Parallel	Double	53 (21/32)	T2DM	61.86 (8.51)	29.82 (4.06)	Oat beta-glucan soup	Placebo soup	3.5	8	—
31	Wolever et al., 2010; Wolever et al., 2011^102,103	Canada	Parallel	Double	367 (161/206)	MCH	52.11 (9.18)	27.52 (4.21)	Ready-to-eat cereal	Wheat bran	3.5	4	—
32	Charlton et al., 2012^104	Australia	Parallel	Single	87 (41/46)	MCH	51.26 (10.24)	27.21 (4.01)	Oat porridge	Placebo	2.35	6	—
33	Ma et al., 2013^105	China	Parallel	Single	186 (79/107)	T2DM/MS	59.38 (6.01)	26.67 (2.87)	Oatmeal products	Placebo	7.5	4	—
34	Thongoun et al., 2013^106	Thailand	Crossover	Unclear	24 (2/22)	MoCH	51.04 (6.87)	26.78 (5.81)	Oatmeal	Rice porridge	3	4	1
35	Johansson-Persson et al., 2014^107	Sweden	Crossover	Unclear	25 (12/13)	MCH	58.6 (5.5)	26.6 (2.5)	Bread roll beverage	Placebo	2.8	5	3
36	Momenizadeh et al., 2014; Tabesh et al., 2014^108,109	Iran	Parallel	Single	60 (21/39)	MCH	51.12 (9.31)	28.96 (4.42)	Oat bran bread	Wheat bread	6	4	—
37	Connolly et al., 2016^110	Britain	Crossover	Double	30 (11/19)	MCH	42	26.4 (5.7)	Oatmeal	Cornflake	1.35	6	4
38	Li et al., 2016^111	China	Parallel	Unclear	211 (108/103)	T2DM	59.37 (6.08)	26.52 (2.27)	Oatmeal products	Regular diet	4	4	—
39	Wang et al., 2016^112	Canada	Crossover	Unclear	30 (12/18)	MCH	59 (4)	28.5 (1.4)	Breakfast with barley	Regular breakfast	3.7	5	4
40	Aoe et al., 2017^113	Japan	Parallel	Double	100 (56/44)	VFO	47.85 (8.96)	27.6 (2.8)	Barley and rice	Rice	4.4	12	—
41	Carneiro de Souza Leao et al., 2019^114	Brazil	Parallel	Unclear	142 (38/104)	MS	47.6 (12.5)	34.47 (5.59)	Oat bran	Low-calorie diet	3	6	—
42	Velikonja et al., 2019^115	Slovenia	Parallel	Double	43 (10/33)	MS	52.21 (7.74)	≥25	Barley bread	Placebo bread	6	4	—
43	Cicero et al., 2020^116	Italy	Crossover	Double	80 (34/46)	MCH	52.3 (4.4)	≥25	Oatmeal	Placebo	3	8	4
44	Ferguson et al., 2020^117	Australia	Parallel	Double	36 (16/20)	MoCH	55.59 (2.85)	28.15 (0.87)	Oat cookies	Placebo cookies	3	6	—
45	Pino et al., 2021^118	Chile	Parallel	Double	37 (9/28)	T2DM	50.87 (5.37)	33.64 (6.13)	Oat beta-glucan	Cellulose	5	12	—
46	Wolever et al., 2021^119	Canada	Parallel	Double	191 (72/119)	MCH	47.6 (11.4)	27.9 (4.55)	Oatmeal beverage	Rice beverage	3	4	—
47	Nie, 2021^120	China	Parallel	Double	109 (50/59)	T2DM	49.64 (10.04)	25.56 (3.46)	Oat beta-glucan	Lotus root powder	3	12	—
48	Cai et al., 2022^121	China	Parallel	Unclear	98 (59/39)	T2DM	56.19 (7.46)	25.76 (3.24)	Oatmeal	Regular diet	5.7	12	—
49	Rioux-Labrecque et al., 2023^122	Canada	Parallel	Double	263 (94/169)	MoCH	60.1 (9.96)	≥25	Oat beta-glucan	Placebo	3.5	12	—

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3.2. Risk of bias assessment

Fig. 2, Fig. S1 and Table S4 present the risk of bias evaluation using RoB 2.0 for included studies. Ten studies (20.4%) were rated as Some Concerns for inadequate randomization description, seven (14.3%) for insufficient blinding implementation or inappropriate analytical methods, and five (10.2%) for missing outcomes. The selection of data and reporting of results also exhibited Some Concerns. In summary, 31 studies (63.3%) were rated as Low risk, 18 (36.7%) as Some Concerns, and none as High risk.

Fig. 2 Results of risk of bias assessment using the Cochrane Risk of Bias Tool 2.0.

3.3. Pooled effects and subgroup effects of CBG consumption on lipid profile

51 RCTs involving 3712 participants assessed TC (Fig. 3A). The pooled analysis revealed a significant reduction in TC levels [−0.24 mmol L⁻¹ (−0.31, −0.17), P < 0.001], with moderate heterogeneity [I² = 44.0% (43.0%, 44.9%), P < 0.001]. In contrast, stratification by dose (Fig. S3) revealed no significant effect for intakes below 3 g day⁻¹ [−0.27 mmol L⁻¹ (−0.57, 0.03), P = 0.077]. No benefit was observed in the under-40 subgroup, while all other subgroups showed significant TC reduction.

Fig. 3 Forest plot on the pooled effect of cereal-derived beta-glucan intake on TC (A), TG (B), HDL-C (C) and LDL-C (D).

For TG levels (Fig. 3B), the overall pooled analysis of 49 trials (n = 3644) showed no effect [−0.01 mmol L⁻¹ (−0.06, 0.03), P = 0.591], with minimal heterogeneity [I² = 6.2% (0%, 14.8%), P = 0.35]. However, exploratory subgroup analyses identified a marginal TG reduction [−0.14 mmol L⁻¹ (−0.27, −0.01), P = 0.041] for low-dose interventions (<3 g day⁻¹).

The pooled analysis of HDL-C from 47 trials (n = 3541) showed no significant pooled effect [−0.02 mmol L⁻¹ (−0.04, 0.00), P = 0.084]. However, a significant reduction of −0.03 mmol L⁻¹ (95% CI: −0.05, −0.01) in HDL-C was observed across four subgroups, with P-values ranging from 0.002 to 0.010. These subgroups were: moderate-dose regimens (≥3 g day⁻¹ but <6 g day⁻¹), shorter interventions (<6 weeks), younger participants (<40 years), and cohorts with mild hypercholesterolemia.

Analysis of LDL-C (Fig. 3D) across 49 trials (n = 3691) demonstrated a marked reduction [−0.19 mmol L⁻¹ (−0.25, −0.13), P < 0.001] despite moderate heterogeneity [I² = 49.7% (49%, 50.5%), P < 0.001]. Stratification by population characteristics identified two non-responsive subgroups: younger adults <40 years [−0.16 mmol L⁻¹ (−0.35, 0.04), P = 0.125] and normocholesterolemic individuals [−0.19 mmol L⁻¹ (−0.38, 0.01), P = 0.061]. All the other subgroups achieved a significant LDL-C reduction.

3.4. Pooled effects and subgroup effects of CBG consumption on blood pressure

The pooled analysis of SBP (Fig. 4A) from 17 trials (n = 1482) indicated a non-significant reducing trend [−1.10 mmHg (−2.28, 0.07), P = 0.066]. Oat beta glucan (OBG) interventions demonstrated modest, but statistically significant SBP decreases [−1.38 mmHg (−2.66, −0.090), P = 0.036]. DBP analysis across 16 trials (n = 1420) showed no significant effect [−0.03 mmHg (−0.93, 0.87), P = 0.944], with no subgroup variations detected (Fig. 4B).

Fig. 4 Forest plot on the pooled effect of cereal-derived beta-glucan intake on SBP (A), DBP (B), weight (C) and BMI (D).

3.5. Pooled effects and subgroup effects of CBG consumption on anthropometric parameters

The pooled analysis of body weight (Fig. 4C) across 28 trials (n = 2440) showed no significant intervention effect [0.03 kg (−0.19, 0.24), P = 0.817]. The pooled effect of BMI (Fig. 4D, 20 trials, n = 1388) showed a small but significant increase [0.51 kg m⁻² (0.16, 0.85), P = 0.004], which was primarily observed in subgroups including those with OBG interventions, moderate-dose regimens (≥3 g day⁻¹ but <6 g day⁻¹), longer durations (≥6 weeks), middle-aged (40–60 years), overweight populations and parallel-design studies. Detailed results of the pooled effects and subgroup analyses are available in Tables S5–S12 and Fig. S2–S65.

3.6. Meta-regression

Meta-regression analyses were conducted to explore the influence of various covariates on all primary outcomes. For TC, interventions ≥6 weeks showed significantly (P = 0.012) greater reductions [25 trials, n = 2,140, −0.32 mmol L⁻¹ (−0.43, −0.20), P < 0.001] compared to <6 weeks interventions [26 trials, n = 1,572, −0.15 mmol L⁻¹ (−0.21, −0.08), P < 0.001]. Meta-regression revealed significant regional differences in LDL-C reduction, with Europe showing distinct effects from both North America (P = 0.028) and other regions (P = 0.012). No covariates examined through meta-regression showed a significant association with the effect on SBP. BMI was the most complex variable, and the results revealed significant differences in the effects of supplement doses, intervention times, hypercholesterolemic conditions, and baseline BMI. Meta-regression analyses for TG, HDL-C, DBP, and body weight are presented in Tables S13–S21.

3.7. Sensitivity analysis

Sensitivity analysis was performed by sequentially excluding individual studies and recalculating the pooled estimates for the remaining trials. Sensitivity analyses showed that the exclusion of any of the studies did not significantly affect the pooled effects of TC (Fig. 5A), TG, LDL-C (Fig. 5C), SBP, DBP, or weight (Fig. S66–S69). For HDL-C (Fig. 5B), excluding studies by Eleanor, Reyna, or Robitaille led to significant reductions. Similarly, the exclusion of studies by Wolever, Smith, Charlton, or Behall resulted in a significant pooled reduction for SBP (Fig. 5D). For detailed results, see Fig. S70–S76.

Fig. 5 Sensitivity analysis to assess the stability of the effect of cereal-derived beta-glucan consumption. Sensitivity analysis of the included studies on TC (A), HDL-C (B), LDL-C (C) and SBP (D).

3.8. Dose–response analysis

Dose analysis results showed no significant linear or nonlinear dose relationships for TG, HDL-C, DBP, or body weight (Fig. S77–S80). A significant linear dose–response relationship was observed for both TC (WMD: −0.050 × dose, P < 0.001; Fig. 6A) and LDL-C (WMD: −0.042 × dose, P < 0.001; Fig. 6B). For SBP, a marginally significant linear relationship was found (WMD: −0.278 × dose, P = 0.056; Fig. 6D), which is consistent with the overall pooled effect. BMI (Fig. 6C) showed a significant quadratic nonlinear dose response (WMD: 0.358 × dose − 0.045 × dose², P_dose = 0.001, P_dose² = 0.005).

Fig. 6 Dose–response analysis of cereal-derived beta-glucan consumption. Dose–response analysis of the included studies on TC (A), LDL-C (B), SBP (C) and BMI (D).

3.9. Publication bias

Funnel plot asymmetry indicated a potential publication bias for TC (Fig. 7A) and LDL-C (Fig. 7B), whereas no evidence of bias was observed for the other outcomes (Fig. 7C, D and Fig. S81–S84).

Fig. 7 Funnel plots to test for publication bias of TC (A), LDL-C (B), HDL-C (C), and BMI (D). Funnel plot of HDL-C for the trim-and-fill method used to correct for publication bias (E). Funnel plot of BMI for the trim-and-fill method used to correct for publication bias (F).

Begg's test indicated no significant publication bias for any indicators. However, Egger's test suggested a significant publication bias for HDL-C (P = 0.0088) and BMI (P = 0.0015), with no evidence of bias for other indicators (Table 2). To correct for publication bias, we applied a trim-and-fill method for HDL-C (Fig. 7E). After adding 20 possible trials, the pooled effect of HDL-C changed from no effect to a significant increase [69 trials, 0.05 mmol L⁻¹ (0.01, 0.03), P < 0.001]. The trim-and-fill method of BMI (Fig. 7F) showed that it shifted the pooled effect from an increase in BMI to a decrease [28 trials, −0.66 kg m⁻² (−0.99, −0.33), P < 0.001] after the addition of eight potential trials.

Table 2 Summary of the results of Begg's test and Egger's test

Indicators	P-Value for Begg's test	P-Value for Egger's test
TC, total cholesterol; TG, triglycerides; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; SBP, systolic blood pressure; DBP, diastolic blood pressure; BMI, body mass index.
TC	0.2140	0.2217
TG	0.2927	0.3698
HDL-C	0.4284	0.0088
LDL-C	0.8294	0.6202
SBP	0.9671	0.1862
DBP	0.1206	0.3439
Weight	0.5015	0.8612
BMI	0.2086	0.0015

---

3.10. Heterogeneity exploration

Moderate heterogeneity was detected for both TC [I² = 44.0% (43.0%, 44.9%), P < 0.001] and LDL-C [I² = 49.7% (49%, 50.5%), P < 0.001]. To explore the sources of heterogeneity, we combined subgroup analysis with meta-regression. The results suggested that the intervention duration was the primary source of heterogeneity for TC. Meta-regression revealed that CBG intake exceeding 6 weeks yielded significantly superior TC-lowering effects compared to durations shorter than 6 weeks (P = 0.012). For LDL-C, the geographical region of the trials constituted the main source of heterogeneity, with studies conducted in Europe demonstrating the most pronounced LDL-C reduction compared to those in North America (P = 0.028) or other regions (P = 0.012). The remaining indicators showed minor or no heterogeneity.

3.11. Quality of evidence

The results of the GRADE quality assessment are detailed in Table S22. Key considerations for evidence downgrading included the following: (1) Study restrictions: The quality of the studies included was high, no downgrading was required. (2) Inconsistency: TC (I² = 44.0%) and LDL-C (I² = 49.7%) had moderate heterogeneity but were still interpretable. (3) Indirectness: All endpoints provided direct clinical evidence without requiring a downgrade. (4) Imprecision: TG, HDL-C, SBP, DBP, and weight were downgraded owing to their large confidence intervals. (5) Reporting bias: Significant bias was detected for HDL-C and BMI, warranting downgrades. The BMI was further downgraded as the trim-and-fill adjustment reversed the pooled effect direction. The evidence was upgraded for TC, LDL-C, and BMI based on the identified dose–response gradients. The final GRADE classifications were High for TC and LDL-C, Moderate for TG, SBP, DBP, BMI, and weight, and Low for HDL-C.

4. Discussion

This meta-analysis, focused on an overweight and obese population, aims to comprehensively assess the effects of CBG on lipid profiles, blood pressure, weight, and BMI for CVD risk. This pooled analysis of 52 RCTs (n = 3854) demonstrates that CBG significantly reduces TC (−0.24 mmol L⁻¹) and LDL-C (−0.19 mmol L⁻¹) in overweight and obese populations, with OBG additionally providing modest SBP reduction (−1.38 mmHg). However, CBG showed no significant effect on TG, HDL-C, DBP, or body weight. The inconsistent BMI findings initially suggested an increase (0.51 kg m⁻², P = 0.004) but demonstrated a decrease (−0.66 kg m⁻², P < 0.001) after trim-and-fill adjustment.

Dyslipidemia commonly coexists with obesity and drives coronary artery calcification and plaque vulnerability.³⁴ Previous meta-analyses of RCTs have established that BG reduces TC and LDL-C in the general population and in individuals with hypercholesterolemia.^35–38 This meta-analysis further confirms its lipid-lowering effects in overweight and obese populations, which suggests that the lipid-lowering efficacy of BG is stable and robust. The hypolipidemic effect of BG can be explained by hindering intestinal bile acid reabsorption, thereby increasing bile acid excretion and stimulating hepatic LDL-C receptor expression to accelerate LDL-C clearance.^39,40 In addition, BG has been proven to reduce the rate of intestinal absorption of glucose, thereby lowering insulin levels and inhibiting cholesterol synthesis.^41,42 It is controversial whether BG may inhibit cholesterol synthesis in the liver by producing propionate through colonic fermentation.⁴³

The U.S. FDA states that ≥3 g day⁻¹ of oat or barley BG is effective in lowering TC and LDL-C.¹⁴ In the stratified analysis by the dose subgroup, the <3 g d⁻¹ BG subgroup failed to reduce TC (−0.27 mmol L⁻¹, P = 0.077) but did reduce LDL-C (−0.14 mmol L⁻¹, P = 0.04). While the effect appears modest at first glance, its actual clinical benefits are considerable. According to U.S. data from 1980–2000, a 0.34 mmol L⁻¹ reduction in TC prevented or delayed 82 830 deaths (24.2%) from coronary heart disease.⁴⁴ Although the pooled WMD was smaller than 0.34 mmol L⁻¹, interventions with a duration ≥6 weeks (−0.32 mmol L⁻¹, P < 0.001) approached this magnitude and also significantly reduced the risk of CVD. Supported by experimental, genetic, and epidemiological data, elevated LDL-C levels are a primary causal factor in the pathogenesis of coronary heart disease.⁴⁵ Any reduction in LDL-C is associated with a decrease in CVD events, particularly coronary heart disease.⁴⁶ Additionally, dose–response analyses revealed that, for each 1 g day⁻¹ increase in BG intake, TC and LDL-C decreased by 0.05 mmol L⁻¹ and 0.042 mmol L⁻¹, respectively, indicating that a better hypolipidemic effect is achievable at a certain dosage. It is noteworthy that the maximum BG intake in this study was 10.3 g day⁻¹, with only 7 randomized controlled trials (14%) exceeding 7 g day⁻¹. Whether the lipid-lowering effects of high-dose BG intake satisfy the aforementioned linear relationship remains to be proved by future specific trials.

The results for TC and LDL-C were affected by moderate heterogeneity, with I² values of 44.0% and 49.7%, respectively. Although the primary sources of heterogeneity were identified through combined subgroup analyses and meta-regression, it must be acknowledged that heterogeneity arising from confounding factors in different control groups (e.g., wheat bread, starch, BG-free beverages) is unavoidable. Heterogeneity is a common challenge in meta-analyses that can be mitigated, though not eliminated, through rigorous and specific inclusion criteria. Llanaj et al. synthesized 12 studies examining the effects of oat supplementation on cardiovascular disease risk. They found heterogeneity as high as 96.1% for TC and 72.6% for LDL-C. This was largely due to their relatively broad inclusion criteria, which only restricted studies to human trials and interventions involving oats or oat extracts.³⁸ In contrast, our screening criteria that required both overweight and obese subjects and obtainable or estimable doses of BG were advantageous. Establishing reasonable subgroups can help identify sources of heterogeneity and acquire deeper insights. For instance, trials conducted in Europe (−0.30 mmol L⁻¹) may provide a greater reduction in LDL-C compared to those in North America (−0.15 mmol L⁻¹) or other regions (−0.17 mmol L⁻¹). This is associated with their Mediterranean dietary pattern, where the intake of olive oil and fish aligns with the AHA/ACC lifestyle management guidelines for CVD risk reduction—specifically, increasing unsaturated fatty acid intake to lower LDL-C.^47,48

As expected, BG was ineffective for TG (−0.01 mmol L⁻¹) and HDL-C (−0.02 mmol L⁻¹), as most previous meta-analyses had similar results.^49–51 A meta-analysis of OBG, which included 13 trials and 935 hypercholesterolemic patients, demonstrated no effect on TG (−0.04 mmol L⁻¹) or HDL-C (0.00 mmol L⁻¹), indicating that the modulatory effects on TG and HDL-C are outside the main scope of action of BG.⁵² Although HDL-C facilitates reverse cholesterol transport and is thus termed ‘good’ cholesterol, most contemporary studies, including Mendelian randomization analyses, have not supported a direct causal role for HDL-C in reducing CVD events.^53–55

Recently, dietary fiber and whole grains have been hot topics for dietary interventions to regulate hypertension, but there has been much controversy regarding the blood pressure-lowering effects of BG. A meta-analysis of 22 RCTs found that soluble fiber significantly reduced SBP (−1.59 mmHg, P = 0.006), and its subgroup analysis revealed that the effect was primarily driven by psyllium (−2.39 mmHg, P = 0.04), whereas BG supplementation (8 RCTs) showed no significant effect.⁵⁶ In contrast, another meta-analysis from 2015 reported that BG supplementation at a median dose of 4 g day⁻¹ significantly reduced both SBP (−2.9 mmHg) and DBP (−1.5 mmHg), despite the overall analysis of various fibers showing no significant effect.⁵⁷ The superior blood pressure reduction observed may be attributable to the smaller sample size (SBP: 5, DBP: 4), making the magnitude of the effect worth careful consideration. Methodologically, small sample sizes and high heterogeneity may amplify positive signals, while selective reporting of results and unpublished negative trials could also compromise the findings. Given the limited number of included studies, these risks warrant particular attention. Additionally, trials on hypertensive patients may have generated greater decreases in blood pressure compared to other populations.⁵⁸ This could also explain why our results were relatively minor, as our study did not include hypertensive subjects. Dietary intake during the trial period may also influence blood pressure outcomes; however, most studies lack explicit dietary restrictions. It has been shown that reducing salt intake lowers blood pressure.⁵⁹ Better management of salt intake could optimize SBP-lowering outcomes, with epidemiological models indicating that a 2 mmHg decrease in SBP could reduce stroke mortality by 10% and mortality from other vascular causes by 7%.⁶⁰ In summary, a daily intake of over 3 grams of OBG combined with reduced salt consumption may represent a viable approach for managing hypertension and preventing CVD.

Despite the satiety provided by BG, no weight loss occurred, underscoring the importance of calorie restriction.⁶¹ The homogeneity of subgroup analyses and robustness of sensitivity analyses indicate that the results seem credible. Paradoxically, although BMI serves as a population-level index reflecting weight changes in height-stable adults and should logically align with body weight trends, the pooled analysis demonstrated an increase in BMI (0.51 kg m⁻², P = 0.004). Unexpectedly, after significant publication bias was detected in the Egger test (P = 0.0015), the trim-and-fill adjustment result reversed to a decrease (−0.66 kg m⁻²). This inconsistency mirrors the conflicting findings of previous meta-analyses. Rahmani et al. reported significant BMI reductions (−0.62 kg m⁻², I² = 0%) by pooling the 7 RCTs, but publication bias was present (P = 0.01 for Egger's test).⁶² Thompson et al. also reported a reduction in BMI (−0.84 kg m⁻²) with substantial heterogeneity (I² = 96%).⁶³ In contrast, Jovanovski et al. found that BG had no effect on BMI (−0.09 kg m⁻²) in a larger subgroup analysis of viscous fiber trials.⁶⁴ The lack of a consistent and reliable effect on BMI across studies suggests that the observed fluctuations may stem from methodological heterogeneity or confounding factors rather than a true biological effect of BG. Given the inability of BMI to distinguish fat from lean mass, future research should directly assess the effect of BG on visceral adiposity, as visceral fat is a more reliable predictor of CVD risk than BMI.⁶⁵ It is noteworthy that physical activity and BMI are inversely correlated. As a primordial prevention strategy, increasing physical activity is applicable to individuals of all ages and should remain a top priority in CVD prevention efforts.⁶⁶

While this analysis enhances our understanding of the role of CBG in obesity interventions for CVD risk, its limitations must be acknowledged. First, the reliability of estimates of barley BG is constrained by the paucity of studies (9/52 studies). Second, the short intervention duration may lead to an underestimation of long-term effects on blood lipids, blood pressure, and body weight. Third, heterogeneity in blinding and risk of bias (36.7% concern) and incomplete control of the diet and lifestyle of participants introduce potential confounding factors. Fourth, despite prespecified subgroup analyses, the absence of stratification by BG molecular weight or delivery matrix hindered deeper mechanistic insights. Finally, the lack of data on indicators such as body fat percentage or waist circumference complicates the analysis of BG's true impact on visceral fat.

Despite these limitations, this meta-analysis supports the inclusion of CBG (≥3 g day⁻¹) in dietary guidelines for overweight and obese populations, particularly those with hypercholesterolemia or elevated SBP. These findings are consistent with the European Society of Cardiology's emphasis on the role of functional foods in primary CVD prevention.⁶⁷ Future research should prioritize long-term, large-scale clinical trials with durations exceeding one year, with a particular focus on investigating the effects of barley BG on blood lipids and blood pressure. To improve the accuracy of body composition assessment, we recommend minimizing the use of BMI as a primary endpoint and instead adopting imaging techniques such as magnetic resonance imaging to directly quantify changes in visceral adipose tissue. Furthermore, given the enhanced lipid-lowering effects observed with sterol-statin combination therapy, exploring potential synergistic mechanisms between BG and statins represents a promising direction for advancing CVD risk management.^68,69 Prior to conducting meta-analyses, pre-specifying well-defined subgroups and detailed selection criteria will strengthen methodological rigor and interpretability. We believe that addressing the evidence gap through rigorously designed long-term trials will help elucidate the potential of BG in alleviating the global burden of CVD.

5. Conclusion

In conclusion, this meta-analysis of 52 RCTs involving 3854 overweight and obese individuals demonstrates that CBG holds significant potential for CVD management. For overweight and obese individuals with dyslipidemia, consumption of CBG-containing products probably reduces TC and LDL-C levels compared to usual diets, while probably having little effect on HDL-C and TG. Additionally, OBG-containing products may reduce SBP. These findings support recommending CBG-rich foods, particularly for overweight and obese individuals with hypercholesterolemia or hypertension, to mitigate CVD by improving lipid profiles and blood pressure. Nevertheless, future high-quality trials with extended durations and higher doses are warranted to further validate the efficacy of CBG, especially regarding body weight and SBP.

Author contributions

Ruihao Zheng: conceptualization, data curation, formal analysis, investigation, methodology, software, validation and writing – original draft; Hui Zhang: investigation, funding acquisition, project administration, resources, validation and writing – review & editing; Hongying Hua: formal analysis and supervision; Yan Zhang: formal analysis and supervision; Chengming Jin: investigation and supervision; Yijun Zhou: investigation and data curation; Xiaowei Shi: methodology and data curation; Ling Zhu: conceptualization, investigation, funding acquisition, project administration, resources, software and writing – review & editing.

Conflicts of interest

The authors declare no competing financial interest.

Abbreviations

CVD	Cardiovascular disease
CBG	Cereal beta-glucan
OBG	Oat beta-glucan
BG	Beta-glucan
RCT	Randomized controlled trial
SE	Standard error
SD	Standard deviation
RoB 2.0	Risk of bias tool 2.0
PRISMA	Preferred reporting items for systematic review and meta-analysis
WMD	Weighted mean difference
TC	Total cholesterol
TG	Triglycerides
HDL-C	High-density lipoprotein cholesterol
LDL-C	Low-density lipoprotein cholesterol
SBP	Systolic blood pressure
DBP	Diastolic blood pressure
BMI	Body mass index

Data availability

The data supporting the findings of this meta-analysis are available from the corresponding author upon reasonable request. This pertains specifically to the raw data extracted from the published studies included in our analysis. All other generated materials are comprehensively provided within the supplementary information (SI). Supplementary information: the search strategy, all analytical code, risk of bias assessment results, pooled effect estimates, subgroup and meta-regression results, GRADE assessments, as well as results from sensitivity, dose-response, and funnel plot analyses. See DOI: https://doi.org/10.1039/d5fo03598h.

Acknowledgements

This research was financially supported by the “Key R&D Program of Shandong Province, China (2024CXGC010918)” and the “Open Program of Shandong Key Laboratory of Food Resources and Healthy Food Development (ZY202502)”.

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