Quantitative assessment of the effects of sumac (Rhus Coriaria) supplementation on cardiovascular disease risk factors: evidence from a meta-analysis of randomized controlled trials

Abstract

Background

Cardiovascular disease (CVD) is a pressing public health issue worldwide. Accordingly, primary and secondary CVD prevention are essential. Many clinical trials have investigated the effects of sumac (Rhus Coriaria) supplementation on CVD risk factors (CVDRFs). However, these studies have yielded contradictory findings. This study aimed to conduct a systematic review and meta-analysis of randomized controlled trials (RCTs) to comprehensively assess the impact of sumac supplementation on CVDRFs in human subjects.

Methods and materials

We searched the MEDLINE/PubMed, EMBASE, CENTRAL, and Web of Science to identify the relevant studies in any language until March 2025. RCTs that investigated the impact of sumac supplementation compared no sumac consumption or placebo capsule interventions or consumed substitutions containing no sumac on CVD outcomes in adults at least 2 weeks were included for data synthesis. The primary outcomes were the mean difference in lipid profiles, blood pressure, glycemic control, and anthropometric indices. Secondary outcomes were the mean difference in inflammatory and oxidative stress markers. The quality of the included trials was assessed using the Cochrane Risk-of-Bias tool. Effect sizes were calculated using a random effect model and presented as weighted mean differences and 95% confidence intervals (CIs), while the I² index was utilized to assess between-study heterogeneity. To explore the potential sources of heterogeneity, subgroup and meta regression analyses were evaluated. Additionally, publication bias and sensitivity analyses were conducted. Finally, the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach was employed to evaluate the quality of evidence.

Results

A total of 17 trials (comprising 18 treatment arms) with 1170 participants were included in our analysis. The findings revealed statistically significant effects of sumac consumption on various CVDRFs, including low-density lipoprotein cholesterol (-9.62 mg/dL; 95% CI= -14.59 to -4.65), total cholesterol (-9.47 mg/dL; 95% CI= -15.92 to -3.02), triglycerides (-8.96 mg/dL; 95% CI= -16.19 to -1.73), high-density lipoprotein cholesterol (2.95 mg/dL; 95% CI = 0.66 to 5.25), diastolic blood pressure ( -2.87 mmHg; 95% CI= -4.23 to -1.51), insulin (-1.68 µU/mL; 95% CI: -3.26 to -0.09), homeostatic model assessment of insulin resistance ( -0.87; 95% CI: -1.61 to -0.14), body weight ( -1.03 kg; 95% CI= -1.89 to -0.17), body mass index (WMD = -0.31 kg/m; 95% CI= -0.55 to -0.07), waist circumference (-0.59 cm; 95% CI= -1.06 to -0.12), malondialdehyde (0.84 µmol/L; 95% CI = 0.38 to 1.30), and total antioxidant capacity ( -0.83 µU/L; 95% CI = -1.10 to -0.56). However, no significant effects were observed for other analyzed CVDRFs, such as systolic blood pressure, fasting blood glucose, quantitative insulin sensitivity check index, hip circumference, waist-to-hip ratio and hypersensitive C-reactive protein. These results were stable in sensitivity analysis, and no significant publication bias was detected. The GRADE profile for sumac supplementation evaluated the certainty of the outcomes and indicated that the quality of evidence was was rated as very low to high across all outcomes.

Conclusion

The findings of our present study suggest that sumac supplementation may have potential benefits in improving various CVDRFs, such as blood lipid levels, blood glucose control, weight management, and oxidative stress markers. Therefore, integrating sumac could be explored as a complementary dietary approach to improve the overall cardiometabolic health. However, these results warrant cautious interpretation, as the findings were derived predominantly from Iranian populations, and significant heterogeneity was observed across trials for different outcomes. Moreover, our pooled results were based on unadjusted estimates, the precise effect of sumac on CVDRFs could be impacted by various confounders. Further large-scale and high-quality RCTs with longer duration are required to confirm these results.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12906-025-05213-1.

Introduction

Cardiovascular disease (CVD) is a major public health concern that is closely linked to morbidities, disabilities, and mortalities worldwide [1, 2]. Currently, there is a growing global aging population, with a concurrent rise in the prevalence of cardiovascular disease (CVD). This increase in CVD has a substantial impact on public health and places a significant financial burden on society. It is reported that CVD accounted for over 17.9 million deaths worldwide in 2019, and it is projected that the number of CVD related fatalities will surpass 23.6 million by 2030 [3]. Especially in lower- and middle-income countries, the rate of CVD death was approximately three times higher compared to countries with high incomes [4]. Hypertension, inadequate glycemic control, obesity, and dyslipidemia are notable factors associated with CVD [5, 6]. Research has indicated that effectively managing modifiable risk factors associated with cardiovascular disease (CVDRFs) can significantly reduce the likelihood of developing CVD and mitigate potential complications [7, 8]. Accordingly, primary and secondary CVD prevention are crucial. A wealth of evidence indicates that natural dietary products and nutraceuticals, such as consumption of Nigella sativa or Curcumin or ginger or Ganoderma lucidum, play a significant role in CVD prevention and management due to their anti-inflammatory and antioxidant properties [9–11].

Rhus Coriaria, known more commonly as sumac, exhibits a wide distribution across Africa, South Eastern Anatolia, the Mediterranean region, and Western Asia [12]. Sumac, a member of the Anacardiaceae family, is abundant in bioactive compounds and medicinally valuable phytochemical constituents. These bioactive compounds include flavonoids, flavones, phenolic acids, hydrolysable tannins, quercetin, and anthocyanin, making it a highly valuable resource [13, 14]. Prior research has demonstrated a range of advantageous effects associated with sumac supplementation, encompassing antilipidemic, antifibrogenic, anti-inflammatory, antidiabetic, antioxidant, anti-ischemic, antithrombotic, anti-hypertensive, and hypoglycemic properties [15–17]. Moreover, sumac may potentially safeguard against liver damage by means of its ability to scavenge free radicals [18]. Base on the powerful antioxidant and anti-inflammatory capacities of sumac, several randomized controlled trials (RCTs) have assessed the impact of sumac supplementation on different CVDRFs, including body weight, inflammatory, hypertension, oxidative stress, hypoglycemic and hyperlipidemia [19–23]. However, the variability in sample sizes, dosages of supplementation, and the heterogeneity of participants’ baseline health statuses have contributed to inconsistent findings. In order to provide the most up-to-date and compelling evidence, we conducted a comprehensive systematic review and meta-analysis of RCTs involving human subjects. The present meta-analysis specifically focused on examining the effects of sumac consumption on various parameters, including blood lipids, blood glucose, insulin, blood pressure (BP), body weight, as well as markers of inflammation and oxidative stress. Our present study may offer valuable insights into the potential clinical effectiveness of sumac in CVD and also suggests areas for future research.

Materials and methods

Approach for conducting literature search

The authors (H.H. and B.H.) conducted a keyword-based search in several databases, including MEDLINE/PubMed, EMBASE, Cochrane Central Register of Controlled Trials (CENTRAL), and Web of Science. The search spanned from the inception of these databases to March 2025. We gathered all relevant RCTs that examined the clinical efficacy of sumac in managing CVDRFs in human subjects. A thorough and inclusive search strategy was devised, employing both Medical Subject Headings (MeSH) and free-text terms, as outlined in Supplementary Table S1.

Criteria for study inclusion and exclusion

Following a thorough review of titles, abstracts, and full texts, the selection of eligible studies was guided by the following inclusion and exclusion criteria. These criteria were formulated using the Participants, Intervention, Comparators, Outcomes, and Study design (PICOS) approach (Supplementary Table S2).

Data extraction in included trials

To ensure accuracy and reliability, two researchers (Y.C. and B.H.) were assigned the responsibility of retrieving the data. In cases where the same patients were noted in different publications with varying reported outcomes, all relevant publications were included. However, for calculation purposes, the sample size from these publications was considered only once. Any discrepancies that arose during the data retrieval process were addressed by another investigator (H.H.). The following information was extracted from the included study:

• Publication data: First author’s name, publication year, and country where the trial was conducted;

• Participants’ characteristics: Mean age, mean BMI, underlying health condition, and gender distribution;

• Study characteristics: Number of enrolled patients, sumac dosage and formulation, type of control treatment, intervention duration, and details of the study design;

• Outcomes assessed: The primary outcomes in this study encompassed total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), triglycerides (TG), apolipoprotein A1 (Apo-A1), apolipoprotein B (Apo-B), systolic blood pressure (SBP), diastolic blood pressure (DBP), fasting blood glucose (FBG), insulin, hemoglobin A1c (HbA1c), homeostatic model assessment of insulin resistance (HOMA-IR), quantitative insulin sensitivity check index (QUICKI), body mass index (BMI), body weight (BW), waist circumference (WC), hip circumference (HC), and waist-to-hip ratio (WHR). Secondary outcomes were malondialdehyde (MDA), total antioxidant capacity (TAC), and hypersensitive C-reactive protein (hs-CRP). Mean values and standard deviations (SDs) of the main outcomes at baseline, post-treatment, or changes between baseline and post-treatment were obtained. Additionally, information regarding serious adverse events (AEs) was also gathered. Whenever SDs were not directly available, the estimation of SDs using the standard error (SE) or 95% confidence intervals (CIs). We converted SE to SD using a standard equation (SD = SE×√n). For studies for which the lipid profiles were reported as mean changes and associated 95% confidence intervals (CIs), the CIs were converted into SD using √n×(UCI-LCI)/3.92, where n is the sample size and UCI and LCI are upper and lower confidence intervals, respectively. We also sought the supplementary files of the included trials or contacted the corresponding authors to verify the extracted data and to request any missing data.

Evaluation of risk of bias in included trials

To evaluate the quality of the included RCTs, we employed the Cochrane risk-of-bias tool. Following the guidelines outlined in the Cochrane Handbook [24]. A qualitative assessment (no, yes, or unclear) was assigned to seven domains. Based on these assessments, the risk of bias for each included study was categorized as “uncertain”, “high risk”, or “low risk”. This comprehensive approach ensured a thorough evaluation of the quality of the included trials.

Synthesis and analysis of data

For the synthesis and analysis of data, we employed the DerSimonian and Laird random-effects model to estimate the weighted mean difference (WMD) and calculate the 95% confidence interval (CI) for continuous outcomes. To assess the presence of heterogeneity across trials, we utilized both the I² index and the chi-square test. A moderate to high level of heterogeneity was indicated by an I² score of ≥ 50% [25]. Sensitivity analyses were conducted to evaluate their impact on the overall results by systematically excluding individual studies. Furthermore, a pre-defined subgroup analysis was carried out, focusing on potential sources of heterogeneity when a sufficient number of studies (n ≥ 5) were available. These subgroup analyses investigated factors such as baseline BMI (< 28 kg/m² vs. ≥28 kg/m²), study design (crossover design vs. parallel design), intervention duration (< 8 weeks vs. ≥8 weeks), and sumac dose (< 3 g/d vs. ≥3 g/d). To assess the presence of publication bias, Begg’s rank correlation, Egger’s weighted regression statistics, and funnel plot analyses were employed [26, 27]. Additionally, in order to explore the relationship between the duration and dosage of sumac supplementation and potential sources of heterogeneity, a random-effects meta-regression analysis was conducted using the restricted maximum likelihood (REML) method [28]. The quality of evidence for each CVDRF was meticulously evaluated using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach. This approach classified the quality of evidence as very low, low, moderate, or high [29]. To present a concise summary of findings, a summary of findings table was created using GRADEpro software. Statistical analyses were conducted using RevMan (v12.0) and STATA (v5.0). All data analyses were two-sided, and statistical significance was defined as a p value < 0.05.

Results

Trials selection

Following the designated search strategy, a comprehensive search across various databases initially yielded a total of 768 records. Through citation chaining, an additional 6 studies were also included. A duplication analysis (n = 163) was conducted, resulting in the exclusion of 580 publications based on the review of titles and abstracts. During the full-text review, we further excluded three reviews, four meta-analyses and systematic reviews, one non-English language publication, two studies with insufficient data for the outcomes of interest, two studies that did not report the desired outcomes, one study that was not a RCT, and one study that utilized a mixture of sumac and other nutrients. Ultimately, the current meta-analysis included a total of 17 publications (18 treatment arms) [19–23, 30–41]. A comprehensive visual representation of the study selection process was provided in Fig. 1.

Fig. 1.

Flow diagram of study selection. Adapted from the PRISMA guidelines. # The work conducted by Alahnoori et al. was separated into 2 treatment arms

Subjects and study characteristics

Table 1 provides an overview of the study characteristics and key participants’ information. The number of participants in each trial varied from 30 to 172, resulting in a total of 1170 individuals included for data pooling.

Table 1.

Characteristics of study populations, type of interventions, and study designs in the included trials

Study ID		Years	Study design1	Country	Sample size	Sex (M/F)1	Study Population	Mean age2	BMI2	Intervention form		Dose (g/d)	Duration (week)	Outcomes reported1
										Treatment Group	Control Group
Ardalani et al. [33]	2016		R, DB, PC, P	Iran	80	38/42	Patients with Hypertension	58.64	30.98	Sumac capsules + captopril	Placebo capsules + captopril	1	8	BMI, SBP, DBP
Asgary et al. [19]	2018		R, TB, PC, C	Iran	30	9/21	Patients with Hyperlipidemia	47.39	27.58	Sumac capsule	Placebo	1	8	WC, BW, HC, BMI, SBP, DBP, TC, TG, LDL-C, HDL-C, FBG
Hajmohammadi et al. [35]	2018		R, DB, PC, P	Iran	70	28/42	Patients with Hyperlipidemia	43.92	28.00	Sumac capsule	Placebo	1	6	TC, TG, LDL-C, HDL-C, Apo-A1, Apo-B
Hariri et al. [20]	2020		R, DB, PC, P	Iran	62	0/62	Overweight patients with depression	43.11	31.82	Sumac powder capsules	Placebo	3	12	BW, BMI, WC, HC, WHR
Heydari et al. [21]	2019		R, DB, PC, P	Iran	50	28/21	Patients with overweight or obese	44.14	30.14	Sumac powder capsules	Placebo	1	6	BW, WC, HC, BMI, WHR, FBG, Insulin, HOMA
Hariri et al. [36]	2023		R, DB, PC, P	Iran	60	0/60	Overweight/obese women with depression	44.01	31.61	Sumac powder capsules	Placebo	3	12	FBG, Insulin, HOMA-IR, QUICKI
Kazemi et al. [37]	2020		R, DB, PC, P	Iran	80	34/46	Patients with non-alcoholic fatty liver disease	41.6	27.59	Sumac powder capsules	Placebo	2	12	BW, BMI, WC, FBG, insulin, HbA1c, HOMA-IR, QUICKI, hs-CRP, MDA
Rahideh et al. [38]	2014		R, DB, PC, P	Iran	41	16/25	Patients With Type 2 diabetes	46.8	29.5	Sumac powder capsules	Placebo	3	12	BW, BMI, WC, FBG, Insulin, HOMA-IR, hs-CRP, MDA
Sabzghabaee et al. [40]	2014		R, TB, PC, P	Iran	72	23/39	Patients With Hyperlipidemia	14.4	25.5	Sumac powder capsules	Placebo	1.5	4	TC, TG, LDL-C, HDL-C
Shidfar et al. [41]	2014		R, DB, PC, P	Iran	41	16/25	Patients With Type 2 diabetes	46.8	29.5	Sumac powder capsules	Placebo	3	12	FBG, Apo B, ApoA-I, HbA1c, TAC
Ardakani et al.	2017		R, DB, PC, P	Iran	58	NA	Patients With Type 2 diabetes	51.96	28.88	Sumac powder capsules	Placebo	6	12	FBG, HbA1, insulin
Rouhi-Boroujeni et al. [39]	2016		R, DB, P	Iran	172	94/78	Hypercholesterolemia	57.48	26.13	Lovastatin + Sumac group	Lovastatin	1	12	LDL
Alahnoori et al. [31]	2022		R, DB, PC, P	Iran	105	60/45	Hemodialysis	NA	23.35	Sumac powder capsules	Placebo	2 3	12	TC, TG, LDL-C, HDL-C, BMI
Ehsani et al. [34]	2022		R, DB, PC, P	Iran	80	46/34	Patients with non-alcoholic fatty liver disease	41.6	27.6	Sumac powder capsules	Placebo	2	12	BW, BMI, WHR, SBP, DBP, TC, TG, LDL-C, HDL-C
Afandak et al. [30]	2023		R, DB, PC, P	Iran	75	0/75	Patients with polycystic ovary syndrome	24.5	25.69	Sumac powder capsules	Placebo (starch)	3	12	BW, BMI, WC, HC, SBP, DBP, FBG, Insulin, TC, TG, LDL-C, HDL-C, hs-CRP
Hajhashemy et al. [22]	2023		R, TB, PC, C	Iran	47	9/38	Patients with metabolic syndrome	58.7	31.6	Sumac capsule	Placebo	1	6	FBG, Isulin, HOMA-IR, QUICKI, MDA, TAC
Mirenayat et al. [23]	2023		R, TB, PC, C	Iran	47	9/38	Patients with metabolic syndrome	58.7	31.6	Sumac capsule	Placebo	1	6	BW, BMI, WC, SBP, DBP, FBG, TC, TG, HDL-C, LDL-C

¹NA Not available, M Male, F Female, R Randomized, PC Placebo-controlled, DB Double-blind, TB Triple-blind, P Parallel, C Crossover, TC Total cholesterol, LDL-C Low-density lipoprotein cholesterol, HDL-C High-density lipoprotein cholesterol, TG Triglycerides, SBP Systolic blood pressure, DBP Diastolic blood pressure, FBG Fasting blood glucose, HOMA-IR Homeostatic model assessment of insulin resistance, BW Body weight, BMI Body Mass Index, WC Waist circumference, HC Hip circumference, WHR Waist-hip ratio, hs-CRP hypersensitive C-reactive protein, Apo-A1 Apolipoprotein-A1, Apo-B Apolipoprotein-B

²Values for age and BMI are expressed as mean unless otherwise stated

• Details of the publications: The publications spanned from 2014 to 2023 in terms of their respective publication dates. All enrolled studies were conducted in Iran.

• Subjects’ characteristics: Among the included studies, three focused exclusively on female subjects, while thirteen included participants of both genders. However, one study did not provide information regarding the gender composition. The age range of the participants ranged from 14.4 to 58.7 years, with BMI values ranging between 23.35 and 31.82 kg/m². The trials encompassed participants with diverse conditions, such as type 2 diabetes (n = 3) [32, 38, 41], non-alcoholic fatty liver disease (n = 2) [34, 37], metabolic syndrome (n = 2) [22, 23], depression (n = 2) [20, 36], polycystic ovary syndrome (n = 1) [30], hypercholesterolemia (n = 1) [39], hyperlipidemia (n = 3) [19, 35, 40], overweight or obesity (n = 1) [21], hypertension (n = 1) [33], and hemodialysis (n = 1) [31].

• Study characteristics: The dosage of sumac varied from 1 to 6 g/day in the administered interventions. The intervention duration ranged from 4 to 12 weeks. Among the included trials, three were designed as crossover studies [19, 22, 23], while fourteen were utilized a parallel design [20, 21, 30–41].

• Outcome measurement: In these enrolled studies, nine trials explored the influence of sumac on lipid profile, nine trials on glycemic indices, five trials on BP, eleven trials on body composition, three trials on oxidative stress and inflammation markers.

• Trial quality evaluation: Fig. 2 presents an overview of the risk evaluation. Out of the studies, seven were identified as having a significant risk of bias [20, 33–37, 39], while ten had an undetermined level of risk [19, 21–23, 30–32, 38, 40, 41].

Fig. 2.

Quality assessment of the included studies. (A) Risk of bias summary: review authors' judgments about each risk of bias item for each included study. Designating an item as“Question mark”denoted an unclear or unknown risk of bias; designating an item as “negative sign” denoted a high risk of bias; designating an item as“positive sign”denoted a low risk of bias. (B) Risk of bias graph: review authors' judgments about each risk of bias item presented as percentages across all included studies

Quantitative assessment of the effects of Sumac supplementation on lipid profile

Utilizing a random-effects model, the combined results indicated a significant reduction in TC (WMD = −9.47 mg/dL; 95% CI= −15.92 to −3.02; p = 0.004; I² = 29%), LDL-C (WMD = −9.62 mg/dL; 95% CI= −14.59 to −4.65; p = 0.0001; I² = 36%) and TG levels (WMD= −8.96 mg/dL; 95% CI= −16.19 to −1.73; p = 0.02; I² = 0%) following sumac supplementation. Moreover, the overall analysis also showed that sumac significantly increase the levels of HDL-C (WMD = 2.95 mg/dL; 95% CI = 0.66 to 5.25; p = 0.01; I² = 39%) and Apo-A1 (WMD = 19.22 mg/dL; 95% CI = 11.60 to 26.85; p = 0.0001; I² = 0%). However, no significant effect was found in Apo-B (WMD= −5.91 mg/dL; 95% CI= −21.56 to 9.74; p = 0.46; I² = 55%). Figure 3 displays a forest plot illustrating the impact of sumac on lipid profile.

Fig. 3.

Forest plot evaluating the effect of sumac on blood lipids with a random effects model. (A) Forest plot of total cholesterol (TC); (B) Forest plot of low-density lipoprotein cholesterol (LDL-C); (C) Forest plot of high-density lipoprotein cholesterol (HDL-C); (D) Forest plot of triglycerides (TG); (E) Forest plot of Apo-A1; (F) Forest plot of Apo-B. WMD, weighted mean difference; CI, confidence interval

Subgroup analyses were conducted to further investigate the impact of sumac on lipid profile parameters (Supplemental Table S3). Among subjects who consumed sumac for more than 8 weeks, significant effects were observed on TC (p = 0.02), LDL-C (p = 0.002), and TG (p = 0.04). Conversely, individuals who consumed sumac for less than or equal to 8 weeks experienced an increase in HDL-C (p = 0.0001) with statistical significance. Moreover, trials with a daily sumac dose of less than 3 g/d demonstrated significant reductions in TC (p = 0.005) and LDL-C (p = 0.0004) after sumac supplementation. When considering baseline BMI, subjects with a BMI lower than 28 kg/m² showed significant decreases in TC (p = 0.02), LDL-C (p = 0.04), and TG (p = 0.04) following sumac supplementation. However, those with a baseline BMI of 28 kg/m² or higher exhibited a significant increase in HDL-C (p = 0.03). Finally, trials with a parallel design, sumac supplementation had notable effects on TC (p = 0.04), LDL-C (p = 0.001), HDL-C (p = 0.009), and TG (p = 0.04).

Quantitative assessment of the effects of Sumac supplementation on blood pressure

The intervention of sumac did not yield a statistically significant impact on SBP (WMD= −3.08 mmHg; 95% CI= −15.49 to 9.25; p = 0.62). However, significant reductions in DBP levels were observed with sumac supplementation (WMD= −2.87 mmHg; 95% CI= −4.23 to −1.51; p = 0.0001) (Fig. 4). It is important to note that a notable level of heterogeneity was identified for SBP (I² = 99%).

Fig. 4.

Forest plot evaluating the effect of sumac on blood pressure with a random effects model. (A) Forest plot of systolic blood pressure (SBP); (B) Forest plot of diastolic blood pressure (DBP). WMD, weighted mean difference; CI, confidence interval

Additionally, subgroup analyses were examined to investigate the impacts of sumac on BP within specific subgroups (Supplemental Table S4). Significant changes in DBP were detected among subjects with BMI ≥ 28 kg/m² (p = 0.0001), sumac dose ≥ 3 g/d (p = 0.02) or <3 g/d (p = 0.001), trials with a parallel design (p = 0.0001), intervention duration ≤ 8 weeks (p = 0.006), or >8 weeks (p = 0.006). However, the subgroup analyses based on study design, intervention duration, mean BMI, and sumac dose did not reveal any statistically significant impacts of sumac on SBP.

Quantitative assessment of the effects of Sumac supplementation on glucose management, insulin levels and sensitivity

Sumac exhibited inhibitory effects on insulin (WMD = −1.68 µU/mL; 95% CI: −3.26 to −0.09; p = 0.04; I² = 80%) and HOMA-IR (WMD = −0.87; 95% CI: −1.61 to −0.14; p = 0.02; I² = 84%). Nonetheless, there were no notable variations in FBG (WMD = −9.74 mg/dL; 95% CI: −25.12 to 5.64; p = 0.21; I² = 98%), QUICKI (WMD = 0.01; 95% CI: −0.01 to 0.04; p = 0.34; I² = 91%) and HbA1c (WMD = −0.48; 95% CI: −1.01 to 0.05; p = 0.07; I² = 0%) (Fig. 5).

Fig. 5.

Forest plot evaluating the effect of sumac on glucose management, insulin levels and sensitivity with a random effects model. (A) Forest plot of fasting blood glucose (FBG); (B) Forest plot of insulin; (C) Forest plot of homeostatic model assessment of insulin resistance (HOMA-IR); (D) Forest plot of HbA1c. WMD, weighted mean difference; CI, confidence interval

Subgroup analyses were examined to investigate the impacts of sumac on glucose management, insulin levels and sensitivity within specific subgroups. Significant reductions in insulin and HOMA-IR were detected in trials with a parallel design, sumac dose ≥ 3 g/d, and intervention durations >8 weeks. In addition, significant change in HOMA-IR was detected among subjects with BMI <28 kg/m². (Supplemental Table S5).

Quantitative assessment of the effects of Sumac supplementation on body weight and body composition

In contrast to the control group, the consumption of sumac had an impact on BW (WMD = −1.03 kg; 95% CI= −1.89 to −0.17; p = 0.02; I² = 46%), BMI (WMD = −0.31 kg/m2; 95% CI= −0.55 to −0.07; p = 0.01; I² = 29%), and WC (WMD = −0.59 cm; 95% CI= −1.06 to −0.12; p = 0.01; I² = 0%). However, HC (WMD = −0.90 cm; 95% CI= −2.07 to 0.27; p = 0.13; I² = 60%) and WHR (WMD = −0.00; 95% CI= −0.02 to 0.01; p = 0.54; I² = 0%) did not exhibit significant differences (Fig. 6). Subgroup analysis revealed that sumac significantly reduced body weight and BMI in subjects with a BMI ≥ 28 kg/m², trials with a parallel design, sumac doses of less than 3 g/d or greater than or equal to 3 g/d, and intervention durations ≤ 8 weeks or >8 weeks. Moreover, a significant change in WC was observed in trials with a parallel design, sumac doses ≥ 3 g/d, and intervention durations >8 weeks (Supplemental Table S6).

Fig. 6.

Forest plot evaluating the effect of sumac on body weight and body composition. (A) Forest plot of body weight (BW); (B) Forest plot of body mass index (BMI); (C) Forest plot of waist circumference (WC); (D) Forest plot of waist-to-hip ratio (WHR). WMD, weighted mean difference; CI, confidence interval

Quantitative assessment of the effects of Sumac supplementation on inflammatory and oxidative stress markers

We also analyzed the influence of sumac ingestion on inflammatory and oxidative stress markers, including hs-CRP, MDA and TAC. Compared with the control, sumac significantly affected TAC (WMD = 0.84 µmol/L; 95% CI = 0.38 to 1.30; p = 0.0003; I² = 0%) and MDA (WMD = −0.83 µU/L; 95% CI = −1.10 to −0.56; p = 0.0001; I² = 0%). However, sumac did not affect hs-CRP (WMD = −1.32 mg/dL; 95% CI= −3.71 to 1.07; p = 0.28; I² = 78%) (Table 2).

Table 2.

Effect of Sumac consumption on inflammatory and oxidative stress factors in human subjects

Outcomes	No. of trials	No. of patients	WMD (95% CI)	P Value	I2, %	P Value of Heterogeneity
MDA	3	168	−0.83 (−1.10 to −0.56)	0.0001*	0	0.41
TAC	2	88	0.84 (0.38 to 1.30)	0.0003*	0	0.60
hs-CRP	3	196	−1.32 (−3.71 to 1.07)	0.28	78	0.01

MDA Malondialdehyde, TAC Total antioxidant capacity, hs-CRP hypersensitive C-reactive protein

*Indicates a significant result

Adverse events evaluation

The majority of trials reported no significant serious AEs following the consumption of sumac. Among the studies included in our analysis, there were three studies which reported AEs over the duration of the study, involving a total of six patients [30, 33, 35]. Three patients in the sumac group experienced mild generalized pruritus, drowsiness, and heartburn, while three patients in the placebo group reported mild reflux, constipation, and dyspepsia.

Sensitivity analyses

To ensure the reliability of the findings, sensitivity analyses were performed. The pooled effect sizes for TC, LDL-C, Apo-a, Apo-B, SBP, DBP, QUICKI, HC, and WHR remained consistent and were not influenced by any individual study. However, upon excluding the studies conducted by Ehsani et al. [34], the effect of sumac on HDL-C exhibited a WMD of 2.27 mg/dL, with a 95% CI of−0.07 to 5.62 (p = 0.06). Similarly, excluding the study by Afandak et al. [30] changed the pooled effect of sumac on TG, resulting in a WMD of−10.32 mg/dL, with a 95% CI of−21.55 to 0.91 (p = 0.07). Excluding the study conducted by Heydari et al. [21] led to alterations in the overall impact of sumac on BW (WMD=−1.19 kg; 95% CI=−2.65 to 0.28; p = 0.11) and BMI (WMD:−0.32; 95% CI:−0.66 to 0.02; p = 0.06). Moreover, excluding the studies conducted by Hariri et al. [36], Kazemi et al. [37], Heydari et al. [21], and Rahideh et al. [38] led to alterations in the pooled results of sumac on insulin and HOMA-IR. Removing the study by Rahideh et al. also led to alterations on FBG to a WMD of−3.05 mg/dL, with a 95% CI of−5.68 to−0.42 (p = 0.02) (Supplemental Figure S1).

Meta-regression analysis

Given that the trials included in our analysis utilized various doses and durations of sumac supplementation, we further conducted a meta-regression analysis to explore the potential linear relationship between the dosage and duration of consuming sumac and CVDRFs (Supplemental Table S7). Our analysis did not find any significant associations between the duration of supplementation and the impact of sumac on TC, LDL-C, HDL-C, TG, SBP, DBP, FBG, Insulin, HOMA-IR, BMI, WC, and WHR. Similarly, there were no significant correlations observed between the dose of sumac intervention and changes in TC, LDL-C, HDL-C, TG, SBP, DBP, Insulin, FBG, HOMA-IR, BMI, WC, and WHR. However, the meta-regression analysis revealed that higher doses of sumac supplementation were significantly associated with greater reductions in BW (coefficient: −1.05; 95% CI: −1.845 to −0.252; p = 0.018), as well as longer duration of sumac supplementation (coefficient: −0.31; 95% CI: −0.566 to −0.052; p = 0.026). Due to limited trials (n ≤ 5), we did not conduct a meta-regression analysis for Apo-A1, Apo-B, QUICKI, HbA1c, HC, hs-CRP, TAC, and MDA.

GRADE evidence profile assessment

Supplemental Table S8 displays the GRADE profiles for the clinical evidence regarding the impact of sumac supplementation on BP, blood lipids, glycemic indices, inflammatory and oxidative stress markers, and BW management. Based on the GRADE Working Group’s assessment, the level of evidence quality was assessed as very low for SBP, FBG; low for Insulin, HOMA-IR, QUICKI, and hs-CRP, moderate for Apo-B, and HC; and high for TC, TG, HDL-C, LDL-C, ApoA-I, DBP, HbA1c, BMI, BW, WC, WHR, MDA and TAC.

Publication bias assessment

According to Egger’s regression test, there was no substantial indication of publication bias found for TC, LDL-C, HDL-C, TG, SBP, DBP, FBG, Insulin, HOMA-IR, BW, BMI, WC, and WHR (p > 0.05). However, the presence of a notable publication bias were identified for SBP (p = 0.021) and FBG (p = 0.003). Figure 7 illustrates the results of the funnel plots, while Supplemental Table S9 presents the findings of the Egger and Begg tests. Due to the limited number of the included studies (n ≤ 5), we did not assess the publication bias for Apo-A1, Apo-B, QUICKI, HbA1c, HC, hs-CRP, TAC, and MDA.

Fig. 7.

Funnel plot evaluating the effect of sumac on analyzed CVDRFs. (A) TC; (B) LDL-C; (C) HDL-C; (D) TG; (E) SBP; (F) DBP; (G)FBG; (H) Insulin; (I) HOMA-IR; (J) BW; (K) BMI; (L)WC; (M) WHR.Funnel plot evaluating the effect of sumac on analyzed CVDRFs

Discussion

Health-conscious lifestyle, encompassing specific food groups and nutrients dietary practices, is a key element in effectively preventing and managing CVD. This approach is universally endorsed by CVD prevention guidelines, highlighting its essential role in both primary and secondary prevention strategies. Herbal compounds or natural products have shown promise in managing CVD risk factors, with some demonstrating positive effects. A recent systematic review and meta-analysis conducted by Jafari et al. highlight the potential benefits of Ganoderma lucidum supplementation in improving key health related indices, particularly BMI, creatinine levels, glutathione peroxidase, and heart rate [42]. Another meta-analysis shows that Nigella sativa supplementation significantly improves cardiovascular risk factors, including body composition, blood pressure, glycemic control, lipid profile, liver function, inflammation, and oxidative stress [9]. Sumac possesses a wealth of biologically active compounds and holds potential as an agent for modulating and treating CVD and the related complications. Numerous investigations have demonstrated that sumac can improve hypertension, adiposity, and glucose and lipid metabolism disorder. However, varying outcomes have emerged from disparate clinical trials. Here, we present a comprehensive overview of scientific evidence derived from RCTs concerning the beneficial impact of sumac on biomarkers linked to the risk of CVD. In total, 17 trials (18 treatment arms) involving 1170 individuals were included for statistical pooling. Our findings indicate that sumac positively influences TC, LDL-C, HDL-C, TG, apoA-I, DBP, Insulin, HOMA-IR, BW, BMI, WC, MAD, and TAC. However, there were no notable alterations observed in ApoB, SBP, FBG, HbA1c, QUICKI, HC, WHR, and hs-CRP levels when comparing the sumac supplementation group with the control group. Sumac exhibited a positive tolerability profile, with no significant variance in adverse events between the sumac and control groups. Hence, consuming sumac might be a beneficial dietary strategy for regulating hyperinsulinemia, hyperglycemia, body weight and hypercholesterolemia, especially in special subgroup groups of population. The results of our study offer perspectives that could be incorporated into dietary approaches focused on averting atherosclerosis progression and enhancing cardiovascular well-being in individuals.

Dyslipidemia, an important contributor to the advancement of CVD, is marked by disruptions in lipid balance. The pathogenesis of CVD is strongly linked to disturbances in lipoprotein and lipid metabolism [43]. A study conducted in US population revealed that with a mere 10% improvement in the rate of hyperlipidemia treatment could potentially prevent approximately 8000 deaths per year [44]. Lowering TC, TG, and LDL-C while raising HDL-C levels are effective approaches for protecting against both primary and secondary CVD. Observational epidemiological evidence suggests that a 1% reduction in serum cholesterol is strongly associated with a 3% decrease in the risk of cardiovascular disease (CVD) [45, 46]. Importantly, LDL-C not only does so safely but also leads to significant further reductions in the incidence of heart attacks, revascularization procedures, and ischemic strokes. Specifically, each 1.0 mmol/L reduction in LDL cholesterol reduces the annual rate of these major vascular events by just over 20% [47]. The impact of sumac on lipid profile has been summarized in two preceding systematic reviews and meta-analyses [48, 49]. In the meta-analysis conducted by Akbari-Fakhrabadi et al. [48], which incorporated three randomized controlled trials (RCTs), the findings suggested that no detectable effects of sumac supplementation on lipid profile improvement. Insufficient eligible RCTs have hindered the establishment of definitive conclusions as reported in this meta-analysis. Bahari et al. further conducted another meta-analysis [49], which reported that sumac supplementation has been associated with reductions in TC, TG and LDL-C. Moreover, a noteworthy increase in HDL-C levels was observed after sumac supplementation. Herein, our findings also support the notion that sumac has the potential to modify dyslipidemia, thereby influencing the development of atherosclerosis and demonstrating its effectiveness in lowering lipid levels. Our analyses for the outcomes of lipid with sumac consumption were similar to those of Bahari et al. However, a recent letter to the editor raises important concerns about methodological issues in this meta-analysis [50]. To further confirm the robustness of the results, we conducted a sensitivity analysis. Exclusion of these Trials with a high risk of bias from the overall analysis did not meaningfully change the magnitude or direction of the summary effect of sumac on CVDRFs [31, 39]. In addition, lipoprotein, such as apoA-I and apoB, were also systematically integrated following sumac supplementation, which had not been evaluated in previous systematic reviews and meta-analyses. The exact mechanisms behind the lipid-regulating effects of sumac remain unclear. The active compounds of sumac, such as phenolic acids and flavonoids like quercetin, have been associated with the reduction of cholesterol levels [51]. The polyphenolic components found in sumac have been shown to effectively inhibit lipid absorption in the gastrointestinal tract and enhance the excretion of bile acids, leading to a reduction in lipid levels [52]. Sumac’s powerful antioxidant properties and ability to scavenge free radicals also help prevent lipid peroxidation. Additionally, it inhibits xanthine oxidase, which may contribute to its ability to lower serum cholesterol levels [53]. By inhibiting HMG-CoA reductase, sumac encourages the upregulation of peroxisome proliferator-activated receptor alpha (PPAR-α) and the synthesis of Apo A−1 in the liver [54]. This mechanism contributes to the promotion of HDL-C production, thereby exerting a positive effect on lipid profile improvement.

The American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines for the high BP in adults emphasize the importance of aggressive BP management, highlighting that even modest reductions are considered beneficial [55]. This latest meta-analysis demonstrates that BP reduction provides cardiovascular benefits regardless of baseline BP levels, even in individuals without hypertension, underscoring the universal applicability of BP management [56]. Sumac extract possesses abundant natural antioxidants, which can effectively combat oxidative stress. The presence of antioxidative phenolic compounds in sumac, such as tannins and flavonoids, can exert a beneficial influence on BP. Sumac can promote vasorelaxation by activating the COX pathway and endothelial nitric oxide synthase. It also inhibits TNF-α and acts as a scavenger for free radicals and reactive oxygen species, thus enhancing its antioxidant properties [57]. Our findings indicate that sumac positively influences DBP, However, sumac did not significantly affect the measures of SBP. Sumac is abundant in phenolic antioxidants, including tannins and flavonoids compounds well-documented to exert beneficial effects on blood pressure. Specifically, two key flavonoids, quercetin and apigenin, positively regulate blood pressure by enhancing the expression of angiotensin-converting enzyme−2 (ACE−2) in the kidneys [58, 59]. Collectively, these effects provide a mechanistic rationale for the hypotensive properties attributed to sumac.

Furthermore, our study revealed a statistically significant decrease in insulin levels and HOMA-IR among participants treated with sumac. Interestingly, these findings seem to contradict the results of a meta-analysis conducted by Mohit et al. [60], which reported no significant effects on any glycemic indices following the supplementation of sumac powder. Another meta-analysis, conducted by Ghafouri et al. [61], examined data from 4 RCTs. The findings of this analysis showed that the consumption of sumac led to a significant reduction in FBG levels, as well as insulin and HOMA-IR indices. Using the previous meta-analysis as a base, we included four other recent RCTs [30, 32, 36, 41]. As a result, our recent meta-analysis represents a comprehensive and thorough investigation that enhances and solidifies the conclusions drawn from previous studies. Our study provides the most up-to-date and robust findings, contributing to a better understanding of the effects of sumac intake on relevant parameters. Sumac has been shown to regulate the key proteins involved in AMPK and PI3K/Akt signaling pathways, thereby enhancing insulin activity and promoting glucose utilization, ultimately reducing insulin resistance [62]. Additionally, sumac contains flavonoids and phenolic acids which have potential benefits in reducing oxidative stress and inflammation, improving insulin production, and facilitating glucose uptake [63]. Studies also suggest that quercetin and gallic acids found in sumac can hinder the absorption of glucose in the intestines [64].

There is growing evidence suggests that inhibiting pancreatic lipase can effectively reduce the absorption of dietary fats, making it a promising approach to prevent and manage obesity. Sumac has demonstrated potent inhibition of pancreatic lipase activity, thereby potentially reducing both fat absorption and calorie intake [65]. The current study examined the impact of sumac supplementation on body weight and composition. Our findings indicate that sumac supplementation influences BW, BMI, and WC.

Oxidative stress is one of the most important factors driving atherosclerosis and its complications. Several in vitro and animal experiments indicated that sumac possesses strong antioxidant properties which protects humans against oxidative DNA-damage [66]. High levels of plasma hs-CRP have been linked to obesity, insulin resistance (IR), and high blood glucose levels, indicating that IR, type 2 diabetes, and ischemic heart disease (IHD) may result from the persistent acute phase response [67]. After consuming sumac, we observed significant alterations in the levels of MDA and TAC. However, sumac supplementation had no significant effect on hs-CRP.

Our present meta-analysis represents the latest, comprehensive, and rigorous comparative study, which has effectively refined and reinforced the conclusions drawn from previous investigations [68–70]. We conducted the criteria of GRADE to evaluate the overall body of clinical evidence and the recommendation’s strength for each outcome, different from previous studies. Moreover, we also used REML-based dose-response analysis to find an optimal duration and dosage for sumac intervention. Our study has yielded promising results, it is important to acknowledge the presence of certain limitations. Firstly, the metabolic response to dietary intervention can be influenced by factors such as ethnic background and dietary habits. It is crucial to acknowledge that the studies analyzed in our research were conducted exclusively in Iran, which may limit the generalizability of these findings to populations in other countries. Secondly, we observed significant heterogeneity among the trials for various outcomes, including SBP (I² = 99%), FBG (I² = 98%), Insulin (I² = 80%), HOMA-IR (I² = 84%), QUICKI (I² = 91%), hs-CRP (I² = 78%), Apo-B (I² = 55%), and HC (I² = 60%) outcomes. The heterogeneity observed in the results can be attributed to several potential factors, including differences in the quality of studies, variations in the dosage of sumac used, diverse health statuses, variations in dietary habits among participants, as well as individual characteristics such as age, gender, and baseline BMI. Despite attempts to address this through sensitivity analysis, meta-regression, and subgroup analysis, the sources of heterogeneity could not be fully resolved. Thirdly, the number of studies available for some CVD markers, such as Apo-A1, Apo-B, MDA, TAC, hs-CRP, HC, HbA1c, and QUICKI, were limited, leading to inconclusive findings. Therefore, caution should be exercised when interpreting these results. To obtain definitive conclusions, it is imperative to conduct additional large-scale, well-designed clinical studies of high quality. Finally, although the included studies were designed as RCTs, there was inconsistent reporting regarding allocation concealment, randomization effectiveness, and participant withdrawal information in our quality evaluation. This suggests that the present research could generate a certain impact on the confidence in the estimated effects.

Conclusions

Our present meta-analysis of RCTs reveals that the addition of sumac to the diet can lead to significant improvements in several cardiovascular disease risk factors. Specifically, sumac supplementation has been shown to improve TC, LDL-C, HDL-C, TG, apoA-I, DBP, Insulin, HOMA-IR, BW, BMI, WC, MAD, and TAC. However, our analysis did not find any beneficial effects of sumac supplementation on ApoB, SBP, FBG, HbA1c, QUICKI, HC, WHR, or hs-CRP. It should be noted that the observed changes in CVD biomarkers were influenced by factors such as the design of the clinical trials, dosage of the sumac supplement, baseline BMI, and duration of the intervention. The findings of this study have significant implications for healthcare professionals and the general public, as they contribute to a better understanding of the potential cardiovascular disease preventive benefits associated with the consumption of sumac. Future research ought to prioritize large-scale, multi-center RCTs that enroll diverse populations spanning varied geographic and cultural contexts. To ensure comparability and reproducibility, these trials must adopt standardized methodologies for intervention protocols, outcome assessments, and reporting criteria.

Authors’ contributions

H. H.: Conception and design of the study, acquisition of data, analysis and interpretation of data, drafted the manuscript, discussed the idea of the meta-analysis, submitted the paper.; B. H. and R. P.: Completed the database searches and selected, reviewed the articles and extracted the data; G.Z. and Y.C.: reviewed and extracted the data, and performed the data analyses; and all authors: read and approved the final version of the manuscript.

Funding

None.

Data availability

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.