Cornelian cherry, cardiometabolic health, and dietary intake: a GRADE-assessed systematic review and meta-analysis

Abstract

Background

Given the essential role of cardiometabolic risk factors in the global burden of chronic diseases, this study aimed to assess the potential of cornelian cherry(Cornus mas L.) in improving anthropometric parameters, lipid profile, glycemic indices, liver enzyme levels, and dietary intake of energy and macronutrients.

Methods

We conducted a systematic search of PubMed, Scopus, Web of Science, and the Cochrane Library for eligible randomized controlled trials(RCTs) published up to May 2025, with no restrictions on language or publication date. The weighted mean difference(WMD) and 95% confidence interval(95%CI) for each outcome were determined using a random-effects model. The certainty of the assessments was further examined using the GRADE assessment.

Results

Seven RCTs were included in the final analysis. Pooled effect size showed that Cornus mas L(CM) intake significantly reduced body weight (MD:-0.57 Kg,95%CI:-1.03,-0.12;P = 0.013), body mass index(MD:-0.38 kg/m²,95%CI:-0.52,-0.23;P = 0.001), fat mass (MD:-0.97%,95%CI:-1.53,-0.41;P = 0.001), waist circumference (MD:-1.36 cm,95%CI:-1.80,-0.92;P = 0.001), and hip circumference(MD:-0.95 cm,95%CI:-1.55,-0.36;P = 0.002), triglycerides(MD:-30.6 mg/dl,95%CI:-61.0,-0.14;P = 0.049), fasting blood sugar (FBS) (MD:-5.72 mg/dl,95%CI:-11.25,-0.20;P = 0.042), and hemoglobin A1c(HbA1c)(MD:-0.28,95%CI:-0.35,-0.20;P = 0.001). Additionally, CM increased high-density lipoprotein-cholesterol levels(MD:2.03 mg/dl,95%CI:0.82,3.25;P = 0.001). However, we observed no significant effects of CM intake on total cholesterol levels, low-density lipoprotein-cholesterol, insulin, aspartate aminotransferase, and alanine aminotransferase.

Conclusions

The findings of our meta-analysis indicate that consuming CM can positively affect CMR factors. These benefits include improved anthropometric parameters, including body weight, body mass index, fat mass, waist circumference, and hip circumference, a reduction in glycemic parameters such as FBS and HbA1c, and an improvement in lipid profile. However, the study did not reveal any significant impact of CM consumption on insulin, total cholesterol, and liver enzyme levels.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12986-025-00991-4.

Introduction

Cardiometabolic risk (CMR) encompasses clinical markers that predict chronic diseases like cardiovascular diseases (CVDs), obesity, metabolic syndrome (MetS), and type 2 diabetes (T2D) [1]. Diagnosis of CMR relies on measurable parameters and clinical symptoms such as inappropriate levels of anthropometric variables, abnormal lipid profiles, elevated fasting blood sugar (FBS), high insulin, and elevated hemoglobin A1C (HbA1C), increased liver enzymes, and elevated blood pressure (BP) [2]. Chronic diseases linked to CMR, including CVDs, MetS, obesity, T2D, and dyslipidemia are the leading and rapidly rising causes of global mortality [3–9]. Alongside genetic predisposition, modifiable environmental risk factors like physical inactivity, smoking, alcohol use, and poor diet significantly elevate CMR. Therefore, adopting a healthy lifestyle is critical for CMR risk reduction [10].

Studies suggest that modifying one’s diet [11, 12] and using alternative and herbal medicine [13, 14] can help prevent CMR factors. Also, incorporating fruits and vegetables into the diet is crucial and can significantly aid in managing cardiometabolic disorders [15]. One commonly used medicinal root worldwide is Cornus mas L. (CM), which is an herbal remedy [16]. CM, also known as cornelian cherry, belongs to the Cornaceae family and is found naturally in Europe and the Middle East. In Iran, it is abundant in the northeastern regions [17, 18]. It has been suggested that the bioactive content of CM, such as anthocyanins, bioflavonoids, and vitamin C may have anti-oxidative and anti-inflammatory effects [19, 20], improve liver function [21], show hypocholesterolemia effects [22] and anti-cancer properties [23]. Recently, several randomized controlled trials (RCTs) have showed the beneficial effect of consuming CM on reducing CMR such as abnormal lipid profiles, glycemic indices, and antropometric measurements [24–26]. However, other CMR factors, such as liver enzymes, did not show significant results in some studies [27, 28].

Two recent meta-analysis exploring the impact of CM consumption on the cardio-metabolic factors concluded that such supplementation resulted in notable reductions in triglycerides (TGs), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), elevation in high-density lipoprotein cholesterol (HDL-C), decreasing the anthropometric indices, and improving glycemic control [15, 29]. However, one of these studies only compile findings on the possible effects of CM on metabolic features in individuals with MetS [29]. Also, none of these meta-analyses examined the impact of Cornelian Cherry consumption on energy and macronutrient intake, which could influence cardiometabolic indices. Furthermore, none employed GRADE assessment to evaluate the certainty of evidence.

Therefore, as a comprehensive review and meta-analysis, we have focused on the randomized controlled trials (RCTs) to quantitatively determine the impact of consuming of CM on anthropometrics variables, including body weight (BW), body mass index (BMI), waist/hip circumference (WC/HC), and fat mass (FM), lipid profiles (TGs, TC, LDL-C, and HDL-C), glycemic indices, including FBS, insulin, and HbA1C, homeostatic model assessment of insulin resistance (HOMA-IR), liver enzymes (Alanine aminotransferase (ALT) and Aspartate aminotransferase (AST)). Also, as the first meta-analysis, we compiled data on the effect of CM consumption on energy and macronutrients intake and also used GRADE assessment to evaluate the certainty of evidence.

Materials and methods

We followed the PRISMA statement for reporting this meta-analysis and systematic review [30].

Systematic search.

To identify eligible randomized controlled trials (RCTs), we systematically searched PubMed, Scopus, Web of Science, and the Cochrane Library from inception (October 1939) to May 2025. The complete search strategy, including keywords related to the intervention and study design, is detailed in Supplementary Table 1. Two reviewers independently screened records using predefined eligibility criteria, first by reviewing titles and abstracts, followed by full-text assessment when necessary. In cases of disagreement, consensus was reached through discussion or consulting a third reviewer. Additional sources, including gray literature identified via Google Scholar and reference lists of included studies, were also examined. Only studies with fully published trial reports were included; abstracts without accompanying full texts were excluded. In this study, both reviewers agreed on the inclusion of 7 studies and the exclusion of 6, resulting in perfect agreement. Inter-rater reliability was assessed using Cohen’s kappa statistic, yielding a value of κ = 1.00, indicating complete agreement between reviewers.

Eligibility criteria

We utilized the PICOS (Population, Intervention, Comparator, Outcome, and Study Design) framework to establish our criteria for selecting studies. To qualify for inclusion in our meta-analysis, human intervention studies had to meet the following criteria: (1) The studies had to be RCTs conducted in adults aged 18 years or older. (2) The studies had to evaluate the impact of CM supplementation (CM extract or powder) compared to control group (placebo or no intervention) on anthropometric measures (BW, BMI, FM, WC, and HC), lipid profile (TC, TG, LDL-C, HDL-C), glycemic indices (FBS, serum insulin, homeostatic model assessment of insulin resistance (HOMA-IR), and HbA1C), liver enzymes (ALT and AST), or dietary intake of energy and macronutrients (carbohydrates, protein, and fat). (3) The studies had to provide mean and standard deviation (SD) of change in BW, BMI, FM, WC, HC, TC, TG, LDL-C, HDL-C, FBS, insulin, HOMA-IR, HbA1C, ALT, AST, total energy, carbohydrate, protein, and fat intake through study arms or provide sufficient information to estimate those values. (4) The studies had to disclose the number of participants in each study arm. We excluded quasi-experimental studies, non-randomized trials, and trials conducted in adolescents (age < 18 years) and pregnant or lactating women.

Data extraction

Two reviewers, MN and MHR, conducted a thorough screening of the full texts of eligible trials and extracted the following data independently and in duplicate: author and year, population location, study design, and duration, characteristics of the population (mean age, gender, health status), total sample size, intervention characteristics (type and dose of CM supplementation), comparison groups, and measured outcomes. If there were any discrepancies or disagreements between the two reviewers, they resolved them through discussion.

Risk of bias (quality) assessment

Two reviewers, MN and MHR, conducted independent duplicate assessments of the risk of bias using the Risk of Bias tool version II [31]. This tool evaluates each study based on five domains: the randomization process, deviations from intended intervention, missing outcome data, outcome measurement, and selection bias in reported results. Each domain is rated as low, high, or has some concern about the risk of bias based on their sub-domains. The overall quality of the study was determined based on the following criteria: (1) if all domains were low or only one had some concern risk, a low risk of bias was assigned; (2) if more than two domains had some concern, some concern overall quality was assigned, and (3) if any domain had a high risk of bias, a high risk of bias was assigned.

Statistical analysis

In this meta-analysis, we have reported the results by considering the weighted mean difference (MD) and its corresponding 95% confidence interval (CI) in the change of studied outcomes as the effect size. We have calculated changes from baseline BW, BMI, FM, WC, HC, TC, TG, LDL-C, HDL-C, FBS, insulin, HOMA-IR, HbA1C, ALT, AST, total energy, carbohydrate, protein, and fat intake in each study arm. In cases where mean values and SDs of changes were not reported, we computed these values by employing data obtained from measurements taken before and after the intervention, adhering to the guidelines outlined in the Cochrane Handbook [32] and assuming a constant R value of 0.8 [33]. If the study reports standard errors (SE) instead of standard deviations (SD), the SE values were converted to SD values [34]. If the studies reported medians and interquartile ranges, we calculated the mean using the formula presented by Hozo et al. [35] and calculated SD by dividing interquartile ranges by 1.35 [34]. Finally, trial-specific results were pooled using a random-effects model [36].

We evaluated the possibility of publication bias using Egger’s [37] and Begg’s tests [38] and by examining the funnel plots. The degree of heterogeneity was calculated using the I² statistic, and a χ² test was conducted for homogeneity (with P-heterogeneity > 0.10) [39]. We utilized STATA software version 17.0 for statistical analysis and considered a two-tailed test with a statistically significant level of less than 0.05.

Grading of the evidence

The certainty of evidence was assessed using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) framework [40]. RCTs were initially considered high-certainty evidence, but could be downgraded based on the following factors: risk of bias (as assessed by the Cochrane Risk of Bias tool) [41], inconsistency across study results [42], indirectness of the evidence [43], imprecision of effect estimates [44], and potential publication bias. Conversely, a dose-response relationship or large effect sizes meeting the minimal clinically important difference (MCID) could increase the certainty of evidence. The final level of evidence was categorized as high, moderate, low, or very low.

Results

Study selection

We screened a total of 1,323 publications, of which 132 were duplicates and 1,178 were deemed irrelevant based on title and abstract review. Following this screening, the full texts of 13 articles were assessed. Of these, six were excluded for the following reasons: outcome not appropriately investigated (n = 2), study population limited to children and adolescents (n = 1), irrelevant topic (n = 1), combined interventions (n = 1), and study protocol (n = 1). Ultimately, seven studies met the inclusion criteria for the current meta-analysis (Fig. 1) [24–26, 45–48].

Fig. 1.

Flow diagram of selection of the published studies

Studies characteristics

Table 1 presents the main characteristics of the 7 included studies. These studies were conducted in Iran (n = 5) and Turkey (n = 2) and published between 2015 and 2024. The studies were RCTs and involved 486 participants. The duration of the trials ranged from 6 to 12 weeks, and the participants’ ages ranged from 41 to 53. Five of the studies included both males and females [26, 45–48] and two RCTs were conducted on postmenopausal women [24, 25] with type 2 diabetes [26], non-alcoholic fatty liver disease [25, 46–48], and metabolic-associated fatty liver disease [45].

Table 1.

Demographic characteristics of the included studies

First Author (year)	Location	Study Design	Health status	Gender	Sample size	Duration (week)	Mean age (year)	Intervention		Outcome
								Treatment group	Control group
Soltani (2015)	Iran	RCT	Type 2 diabetic patients	Both	60	6	49	8 capsules of Cornus mas’ extract /day (two equal doses, containing 600 mg of anthocyanins)	Placebo	BMI, TG, FBS, INS, HbA1C, ALT, AST
Gholamrezayi (2019)	Iran	RCT, double-blind	Postmenopausal women	F	84	8	53	900 mg Cornus mas’ extract/day (three 300 mg capsules)	Placebo	BW, BMI, WC, TC, TG, LDL-C, HDL-C, FBS, INS, HOMA-IR,
Sangsefidi* (2021)	Iran	RCT, double-blind	Non-alcoholic fatty liver disease patients	Both	50	12	41	20 ml Cornus mas extract (containing 32 mg of anthocyanins)	Placebo	ALT, AST
Sangouni* (2022)	Iran	RCT, double-blind	Non-alcoholic fatty liver disease patients	Both	50	12	41	20 ml Cornus mas extract (containing 32 mg of anthocyanins)	Placebo	Energy, Carbohydrate, Protein, Fat
Celık (2023)	Turkey	RCT	Non-alcoholic fatty liver disease patients	F	84	12	18–45	20 gr lyophilized dried cornelian cherry powder (containing 237.5 mg of anthocyanins)	No intervention	BW, BMI, FM, WC, HC, TC, TG, LDL-C, HDL-C, FBS, INS, HOMA-IR, HbA1C, Energy, Carbohydrate, Protein, Fat
Yarhosseini* (2023)	Iran	RCT, double-blind	Non-alcoholic fatty liver disease patients	Both	50	12	41	20 ml Cornus mas extract (containing 32 mg of anthocyanins)	Placebo	BW, FM, WC, HC
Bayram (2024)	Turkey	RCT	Metabolic-associated fatty liver disease patients	Both	108	8	44	30 gr lyophilized dried cornelian cherry powder (containing 350 mg of anthocyanins)	No intervention	BW, BMI, FM, WC, HC, TC, TG, LDL-C, HDL-C, FBS, INS, HOMA-IR, HbA1C, ALT, AST, Energy, Carbohydrate, Protein, Fat

Abbreviations: BW, Body Weight; BMI, Body Mass Index; FM, Fat Mass; WC, Waist circumference; HC, Hip circumference; TC, Total Cholesterol; TG, Triglyceride; LDL, Low-Density Lipoprotein-cholesterol; HDL, High-Density Lipoprotein-cholesterol; FBS, Fasting Blood Sugar; INS, serum Insulin; HOMA-IR, Homeostatic Model Assessment for Insulin Resistance; HbA1C, Hemoglobin A1C; ALT, Alanine transaminase; AST, Aspartate transaminase

*Data from the same population was presented in two articles

Meta-analysis on the association of CM with anthropometric indices

Table 2 and Supplementary Figs. 1–5 (SFig. 1–5) show the overall summary estimate of the mean difference (95%CI) for the relationship between CM intake and selected anthropometric indices. Pooled effect size indicated that CM intake significantly reduced BW (MD: -0.57 Kg, 95%CI: -1.03, -0.12; P = 0.013) (SFig. 1), BMI (MD: -0.38 Kg/m², 95%CI: -0.52, -0.23; P = 0.001) (SFig. 2), FM (MD: -0.97%, 95%CI: -1.53, -0.41; P = 0.001) (SFig. 3), WC (MD: -1.36 cm, 95% CI: -1.80, -0.92; P = 0.001) (SFig. 4), HC (MD: -0.95 cm, 95%CI: -1.55, -0.36; P = 0.002) (SFig. 5). Although between-study heterogeneity was non-significant for these anthropometric indices.

Table 2.

Summary of the effect of cornus Mas on selected cardiometabolic risk factors

Outcome	Participants (studies)	Mean difference (95% CI)	P-value	I2 (95% CI)	P-heterogeneity	Eager test	GRADE
BW (kg)	326 (4)	-0.57 (-1.03, -0.12)	0.013	0.00 (0.00, 44.9)	0.730	0.415	Low
BMI (kg/m2)	336 (4)	-0.38 (-0.52, -0.23)	0.001	0.00 (0.00, 39.2)	0.891	0.909	Low
FM (percent)	242 (3)	-0.97 (-1.53, -0.41)	0.001	0.00 (0.00, 30.1)	0.858	0.547	Low
WC (cm)	326 (4)	-1.36 (-1.80, -0.92)	0.001	0.00 (0.00, 52.4)	0.677	0.455	Low
HC (cm)	242 (3)	-0.95 (-1.55, -0.36)	0.002	0.00 (0.00, 0.00)	0.958	0.889	Low
TC (mg/dl)	276 (3)	-7.04 (-15.2, 1.16)	0.093	63.7 (0.00, 90.9)	0.064	0.884	Low
TG (mg/dl)	336 (4)	-30.6 (-61.0, -0.14)	0.049	93.6 (0.00, 98.2)	0.001	0.141	Low
LDL-C (mg/dl)	276 (3)	-15.3 (38.6, 8.02)	0.199	80.0 (0.00, 94.9)	0.007	0.510	Low
HDL-C (mg/dl)	276 (3)	2.03 (0.82, 3.25)	0.001	0.00 (0.00, 44.3)	0.738	0.802	Low
FBS (mg/dl)	336 (4)	-5.72 (-11.2, -0.20)	0.042	90.4 (0.00, 97.5)	0.001	0.086	Very low
INS (uIU/ml)	336 (4)	-1.41 (-3.61, 0.79)	0.209	95.7 (0.00, 98.9)	0.001	0.556	Low
HOMA-IR	276 (3)	-0.83 (-1.91, 0.24)	0.129	96.9 (0.00, 99.3)	0.001	0.169	Low
HbA1C	252 (3)	-0.28 (-0.35, -0.20)	0.001	0.00 (0.00, 50.5)	0.590	0.297	Low
ALT (IU/L)	218 (3)	-3.95 (-12.2, 4.32)	0.349	80.0 (0.00, 94.9)	0.007	0.606	Low
AST (IU/L)	218 (3)	-4.14 (-10.2, 1.88)	0.186	83.1 (0.00, 95.5)	0.001	0.400	Low

Abbreviations: BW, Body Weight; BMI, Body Mass Index; FM, Fat Mass; WC, Waist circumference; HC, Hip circumference; TC, Total Cholesterol; TG, Triglyceride; LDL, Low-Density Lipoprotein-cholesterol; HDL, High-Density Lipoprotein-cholesterol; FBS, Fasting Blood Sugar; INS, serum Insulin; HOMA-IR, Homeostatic Model Assessment for Insulin Resistance; HbA1C, Hemoglobin A1C; ALT, Alanine transaminase; AST, Aspartate transaminase

Meta-analysis on the association of CM with lipid profile

Overall summary estimates of the mean difference (95%CI) for the association of CM intake with lipid profile are shown in Table 2 and Supplementary Figs. 6–9 (SFig. 6–9). The findings indicate that a higher CM intake is associated with lower TG (MD: -30.6 mg/dl, 95%CI: -61.0, -0.14; P = 0.049) (SFig. 7), however, CM intake significantly increased the HDL-C (MD: 2.03 mg/dl, 95% CI: 0.82, 3.25; P = 0.001) (SFig. 9). No significant relationship between CM intake with TC (SFig. 6) and LDL-C (SFig. 8) was observed. We found significant between-study heterogeneity for TGs (P-heterogeneity = 0.001, I² = 93.6%), and LDL-C (P-heterogeneity = 0.007, I² = 80.0%), however, there is no significant heterogeneity for HDL-C and TC.

Meta-analysis on the association of CM with glycemic indices

Table 2 and Supplementary Figs. 10–13 (SFig. 10–13) show the overall summary estimate of the mean difference (95%CI) for the relationship between CM intake and glycemic indices. The findings revealed that CM intake significantly reduced FBS (MD: -5.72 mg/dl, 95%CI: -11.25, -0.20; P = 0.042) (SFig. 10) and HbA1C (MD: -0.28, 95%CI: -0.35, -0.20; P = 0.001) (SFig. 13), however, we did not observe any significant effects of CM intake on insulin (SFig. 11) and HOMA-IR (SFig. 12). Between-study heterogeneity was significant for FBS (P-heterogeneity < 0.001, I² = 90.4%), insulin (P-heterogeneity = 0.001, I² = 95.7%), and HOMA-IR (P-heterogeneity < 0.001, I² = 96.9%).

Meta-analysis on the association of CM with liver enzymes

Overall summary estimates of the mean difference (95%CI) for the association of CM intake with lipid profile are shown in Table 2 and Supplementary Figs. 14–15 (SFig. 14–15). According to the findings, no significant relationship between CM intake and ALT (SFig. 14) and AST (SFig. 15) was observed. Although between-study heterogeneity was significant for ALT (P-heterogeneity = 0.007, I² = 80.0%), and AST (P-heterogeneity = 0.001, I² = 83.1%). Based on Egger’s regression test, no publication bias was found for studies evaluating the effect of CM on selected anthropometric indices and cardiometabolic risk factors.

Meta-analysis on the association of CM with energy and macronutrient intake

Table 3 and Supplementary Figs. 16–19 (SFig. 16–19) provide an overview of the mean difference estimate (95% CI) for the impact of CM on total energy and macronutrient consumption. The analysis results indicate that CM intake positively affected carbohydrate intake (MD: 17.38, 95% CI: 7.25, 27.50 gr/day; P = 0.001) (SFig. 17). However, there is no significant relationship between the intake of CM and energy (SFig. 16), protein (SFig. 18), and fat (SFig. 19). We observed significant heterogeneity between studies regarding the relationship between CM intake and fat intake (P-heterogeneity = 0.016, I² = 75.8%). According to Egger’s regression test, there is a significant publication bias in the relationship between CM-fat intake (P = 0.018).

Table 3.

Summary of the effect of cornus Mas on total energy and macronutrient intake

Outcome	Participants (studies)	Mean difference (95% CI)	P-value	I2 (95% CI)	P-heterogeneity	Eager test	GRADE
Energy (kcal/day)	242 (3)	-12.3 (-157, 132)	0.867	16.2 (0.00, 77.3)	0.303	0.334	Low
Carbohydrate (g/day)	242 (3)	17.3 (7.25, 27.5)	0.001	0.00 (0.00, 69.4)	0.413	0.723	Low
Protein (g/day)	242 (3)	-2.00 (-5.75, 1.74)	0.295	0.00 (0.00, 66.4)	0.449	0.860	Low
Fat (g/day)	242 (3)	-2.95 (-23.7, 17.8)	0.781	75.8 (0.00, 93.8)	0.016	0.018	Very low

Quality assessment and GRADE

Based on the analysis presented in Table 4, it was found that four out of the total number of studies included in the review had a low risk of bias. This represents more than half of the studies analyzed. On the other hand, two studies were found to have a high risk of bias due to their open-label design or the absence of appropriate analytical methods to justify missing values. Additionally, there were some concerns about the risk of bias in one of the studies due to the lack of an appropriate analysis method for missing values.

Table 4.

Risk of bias assessment of included trials using ROBINS-II tool

Study, year	Bias arising from the randomization process	Bias arising from the randomization process	Bias due to deviations from intended intervention	Bias due to missing outcome data	Bias in the measurement of the outcome	Bias in the selection of the reported result	Overall risk of bias
Bayram. 2024	High	Some Concern	High	Low	Low	Some Concern	High
Celik, 2023	Some Concern	Some Concern	High	Some Concern	Some Concern	Low	High
Gholamrezayi, 2019	Low	Some Concern	Some Concern	Low	Low	Low	Some Concern
Yarhosseini,2023	Low	Low	Low	Low	Low	Low	Low
Sangouni, 2022	Low	Low	Low	Low	Low	Low	Low
Sangsefidi, 2021	Low	Low	Low	Low	Low	Low	Low
Soltani, 2015	Low	Low	Low	Low	Low	Low	Low

The results of the GRADE assessment, as shown in Tables 2 and 3 and detailed in Supplementary Table 2, indicate that the certainty of evidence for the outcomes of interest ranged from low to very low. The primary factors contributing to the downgrading of evidence included a high risk of bias and imprecision. Although the effects observed for serum TG levels and carbohydrate intake were clinically significant, the wide and inconclusive 95% confidence intervals diminished the overall certainty of these findings. Additionally, evidence of publication bias in the analyses of serum FBS and fat intake further contributed to their classification as very low certainty of evidence.

Discussion

Our systematic review and meta-analysis of clinical studies suggested that intake of CM may reduce risk of cardiometabolic disorders. The pooled results demonstrated significant improvements in glycemic control (reduced FBS and HbA1C), anthropometric measures (BW, BMI, FM, WC, HC), and lipid profiles (decreased TGs, increased HDL-C). On the other hand, CM intake did not have significant effects on TC, insulin, HOMA-IR, ALT, and AST levels. Moreover, we did not detect any publication bias among the studies included in the analysis. Furthermore, there is no evidence of heterogeneity among the included studies in these parameters.

Our study results are consistent with and support the findings recently reported by two meta-analyses regarding the benefits of CM consumption in improving cardio-metabolic health [15, 29]. While our study design diverged from recent investigations in several key aspects, including scope comprehensiveness, investigated outcomes, analytical methodology, and number of included study, these differences provide complementary insights into the existing body of evidence. In a recent meta-analysis conducted by Lee et al.., the focus of the study was on the results of studies conducted in people with MetS [29], however, our study examined a broader population encompassing individuals from all health categories. Another unique aspect of our study is that we also investigated the impact of CM on intakes of energy and macronutrient, however, none of above-mentioned meta-analyses examined the impact of CM consumption on energy and macronutrient intake, which could influence cardiometabolic indices. Given that one of CM’s effects in clinical trials involves weight loss and fat mass reduction, understanding its influence on calorie intake and macronutrient distribution is crucial. Specifically, we explored whether this impact stems from reduced caloric intake, or whether it results from CM’s effects on other metabolic pathways described in previous studies. Notably, our research did not observe any significant effects of CM consumption on calorie intake or macronutrient consumption, suggesting that the bioactive compounds in CM, such as anthocyanins and polyphenols, exert beneficial effects on anthropometric measures through multiple metabolic pathways [49–52], including enhancing insulin sensitivity, suppressing lipogenesis, reducing adipocyte differentiation, decreasing lipid accumulation, and modulating intestinal fat absorption. Furthermore, a key difference lies in the statistical methods used. The study by Lee et al. employed the Standardized Mean Difference (SMD) to express their findings, a method with both advantages and limitations. While SMD facilitates the comparison of results across different outcomes, it (unitless) expresses effects in standard deviations, which obscures clinical relevance unless back-translated. In contrast, we used the Weighted Mean Difference (WMD), which offers a more concrete measure of effect size and reports results in original units, making findings directly actionable for clinicians.

Anthocyanins are a valuable nutrient found in CM fruit [53]. These compounds offer cardiometabolic benefits by regulating lipid and glucose profiles, oxidative stress, and anthropometric measurements. Both animal and cellular-molecular studies have suggested that anthocyanin, which is present in various fruits, particularly red fruits, can positively impact anthropometric measurements through several mechanisms [54–56]. Anthocyanins may hinder the synthesis of lipids, leading to better lipid oxidation and lower fat accumulation in the liver [51]. They can also regulate the expression of peroxisome proliferator-activated receptors (PPARs) and increase the activity of the AMP-activated protein kinase (AMPK) pathway in white adipose tissue [49, 50]. This can result in reduced levels of adiponectin and adipocytokines, decreased activity of pancreatic lipase, and lower lipid absorption, thus reducing the risk of obesity [57]. The beneficial effect of CM consumption on lipid profile by the potential mechanisms, such as suppression of the expression of lipogenic enzymes (fatty acid synthase, acyl-CoA synthase 1, and glycerol-3-phosphate acyltransferase) in the liver and adipose tissue as well as increasing lipoprotein lipase activity in skeletal muscle and diminishing it in visceral adipose tissue [51, 52].

Several animal studies have shown that CM extract and/or dried fruits can reduce FBS and insulin levels, which is consistent with our findings [58–64]. However, some of the other studies did not show a decrease in insulin levels after intervention [65–67]. While most research has shown the benefits of CM extract and/ or dried fruits in reducing FBS and insulin levels, we cannot draw a conclusive result due to insufficient and conflicting results from a few studies. The included studies investigated the reasons for non-significant results in insulin and HOMA-IR parameters. The trials were conducted only in women with a nearly normal glycemic range at baseline, limiting the generalization of results to other ages or genders. However, some compounds in CM may improve glycemic index levels, and dietary anthocyanins can hinder α-glucosidase and α-amylase enzyme activities enzyme activities [63, 68]. Polyphenols in fruits can improve insulin sensitivity and prevent sodium-glucose co-transporter 1 (SGLT-1) activity. Ursolic acid in CM fruits can lead to insulin receptor phosphorylation, and consuming CM can increase the expression of GLUT4 mRNA and prevent glucose absorption from the intestines [69–71].

This meta-analysis examined two interventional studies and found that consuming CM had no significant effect on liver enzymes, specifically ALT and AST. The included studies showed some heterogeneity, and previous research has also reported no significant impact of anthocyanin-rich foods (such as fruits and vegetables) or pure anthocyanins on ALT and AST levels [72–80]. However, some studies have found a positive effect of anthocyanin supplementation on liver enzyme levels, in contrast to our study. These discrepancies suggest that there is still much debate surrounding the effects of fruit consumption on liver enzymes [81, 82].

CM and most fruits contain phytochemicals, anthocyanins, and antioxidants that can improve CMR factors [83]. Certain bodily substances could remove harmful free radicals that can damage cells, leading to cellular inflammation and diseases such as cancer, cardiovascular disease (CVD), and type 2 diabetes (T2D). These substances regulate nuclear factor-kB and mitogen-activated protein kinase signaling pathways, which can help to reduce pro-inflammatory cytokines and decrease levels of proxy nitrate and reactive oxygen species (ROS) [84–87]. Moreover, CM fruit has a lot of antioxidants, phenol, and ascorbic acid, as well as anthocyanin contents include delphinidin 3-galactoside, cyanidin 3-galactoside, cyanidin 3-rhamnosylgalactoside, pelargonidin 3-galactoside, and pelargonidin 3-rhamnosylgalactoside [88]. Guo et al. discovered that these anthocyanins, derived from soybeans, not only have the potential to inhibit lipid accumulation in vitro but also suppress the expression of PPAR-γ [89]. PPAR-γ agonists, such as thiazolidinedione, are drugs used to reduce the adverse effects of chronic disorders like atherosclerosis and diabetes [90]. Moreover, CM fruit (cornelian cherry) is a rich source of highly soluble fiber, containing a special type of fiber called calcium pectate [91]. Past studies have found that soluble fiber can form a gel in the intestine that traps fat, which may reduce the risk of insulin resistance and improve blood lipid profile, including LDL-C and TG [92, 93]. Our study’s results support the beneficial effect of fiber consumption on obesity, which is consistent with the findings of other studies [94].

Our meta-analysis demonstrated that CM consumption exerts clinically meaningful benefits on cardio-metabolic indices, suggesting its potential for managing CVDs, T2D, MetS, and adiposity. Consumption of CM may serve as a complementary therapeutic approach for individuals with T2D or prediabetes by improving glycemic control and enhancing insulin sensitivity, which could potentially reduce medication dependence or potentiate pharmacological effects. Also, integrating CM supplementation into weight management for overweight or obese individuals may enhance intervention effectiveness. Additionally, its demonstrated benefits on lipid modulation and blood pressure reduction suggest cardioprotective potential in CVDs prevention and management. Furthermore, using the CM supplementation as a nutritional intervention supplementation are generally better accepted than pharmacological treatments due to their natural composition and easy incorporation into varied diets. This inherent adaptability enhances long-term adherence and patient preference compared to rigid medication protocols. Thus, wide-ranging benefits of CM supplementation on cardiometabolic indices suggest that it may improve overall metabolic health and reduce the risk of various chronic diseases.

Strengths, limitations, and directions for future research

Our study is the first systematic review and meta-analysis to evaluate the effect of CM consumption on CMR factors, with low heterogeneity among the included studies. However, the small number of available studies limits our ability to perform subgroup analysis. Different types and dosages used in the studies caused suffering and led to inconclusive results. Also, small sample sizes may have overestimated treatment effects. Some studies included were single-center with self-reported dietary intake data, resulting in possible biases. Short follow-up times and gender exclusions may also affect the overall findings. The current systematic review was conducted prior to protocol registration in PROSPERO. Although, all methods were defined a priori and adhered to PRISMA guidelines, the lack of prospective registration may limit transparency and reproducibility. Future reviews should register protocols to minimize bias and align with best practices. As the first meta-analysis, we used GRADE analysis to systematically rate the certainty (or quality) of the evidence for each outcome across studies included in the current review. Using GRADE assessment, we aimed to provide a transparent and structured process for evaluating key factors such as risk of bias, inconsistency, indirectness, imprecision. In our study, the certainty of evidence was mostly rated as low or very low, may be driven by methodological limitations in the included studies (e.g., ineffective supplement dosage used, lower follow-up, small sample sizes) and heterogeneity in interventions. Therefore, any recommendations or conclusions drawn from this evidence should be interpreted with caution and may require further high-quality research to confirm the findings. Additionally, emphasize that future studies, especially well-designed RCTs, are needed to improve the certainty and reliability of the evidence before stronger recommendations can be made.

Conclusions

A systematic review and meta-analysis of RCTs found that consuming CM can have several positive effects on CMR factors. These effects include improvement in various anthropometric variables, reduction in glycemic parameters (FBS and HbA1c), and improvement in lipid profile (TGs, HDL-C). However, the consumption of CM had no significant effect on insulin, HOMA-IR, TC, ALT, and AST levels. Overall, it can be concluded that CM consumption can be considered a suitable dietary option for managing and controlling cardio-metabolic risk factors, thereby reducing the risk of chronic diseases, such as CVDs and T2D.

Supplementary Information

Below is the link to the electronic supplementary material.

Abbreviations

ALT

Alanine transaminase

AMPK

AMP-Activated protein kinase

AST

Aspartate transaminase

BP

Blood pressure

BW

Body weight

CI

Confidence interval

CM

Cornus mas L

CMR

Cardiometabolic risk

GRADE

Grading of Recommendations Assessment, Development, and Evaluation

FBS

Fasting blood sugar

HC

Hip circumference

HDL-C

High-density lipoprotein-cholesterol

HOMA-IR

Homeostatic model assessment of insulin resistance

HbA1C

Hemoglobin A1C

LDL-C

Low-density lipoprotein-cholesterol

MD

Mean difference

MetS

Metabolic syndrome

PPARs

Peroxisome proliferator-activated receptors

PRISMA

Preferred Reporting Items for Systematic Reviews and Meta-analyses

RCTs

Randomized controlled trials

SD

Standard deviation

T2D

Type 2 diabetes

TC

Total cholesterol

TGs

Triglycerides

WC

Waist circumference

Author contributions

M.O. and F.T. contributed to the study concept and design. M.O., F.T., and P.M. developed the overall research plan and study oversight. M.O. and ST.R. conducted the research. M.HR. and M.N. independently screened all records based on their titles and abstracts. M.N. and H.A. performed the data extraction, data analyses, and interpretation of data. M.O., M.A., H.F., and M.KJ. drafted the manuscript. All authors provided intellectual comments and performed the critical revision of the manuscript. M. Kh. contributed to the extensive revision and editing of the revised manuscript. H.F. and ST.R. supervised the study. All authors approved the final version of the manuscript.

Funding

No funding.

Data availability

No datasets were generated or analysed during the current study.

Declarations

Ethics approval and consent to participate

Formal ethical approval is not required as primary data were not collected.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.