The association of dietary polyphenols with obesity: a systematic review and meta-analysis

Abstract

Background and aim

Accumulating evidence has proposed that dietary polyphenols may be linked to lower odds of obesity; nevertheless, the results are inconclusive. This meta-analysis was performed to examine the relationship between dietary intake of polyphenols and the odds of obesity.

Methods

A systematic literature search in PubMed, Web of Science, and Scopus was conducted up to April 2025 to obtain all eligible studies. Odds ratios (OR) and 95% confidence intervals (95%CI) were pooled using the random effects model to test the odds of obesity (assessed by body mass index) for the highest versus the lowest intakes of total, subclasses and individual polyphenols.

Results

A total of 3,113 studies were retrieved, of which 3,103 unrelated studies were excluded. Finally, 10 studies with 106,302 participants were included in the meta-analysis. When different polyphenolic compounds were pooled, they were linked to lower odds of obesity (OR: 0.91; 95%CI: 0.85–0.97), with significant evidence of heterogeneity (I2 = 84.0%, P < 0.001). The association was not modified by the age of participants. Higher intakes of total flavonoids (OR: 0.77; 95%CI: 0.64–0.92) and isoflavones (OR: 0.93; 95%CI: 0.88–0.98) were also significantly linked to the reduced odds of obesity. Moreover, a higher intake of flavonols was protective against obesity in some subgroups. No significant association was found for flavones, flavanones, flavanols, and anthocyanins. In the dose-response meta-analysis, the association of total flavonoids with obesity did not follow a linear (P = 0.18) or non-linear (P nonlinearity = 0.18) pattern.

Conclusion

The findings of this meta-analysis revealed that higher polyphenol intake is associated with decreased odds of obesity.

Clinical trial registration

Not applicable.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12902-026-02249-3.

Introduction

Obesity, featured by excess adipose mass, has become a global epidemic, with an increasing prevalence in both developed and developing countries, affecting over 27.5% of the global population [1, 2]. Obesity is closely linked to adverse health consequences, including increased odds of type 2 diabetes, cardiovascular disease, metabolic diseases, some cancers, and mortality [3, 4]. It also has unfavorable effects on the self-esteem and life quality of the affected people, leading to social withdrawal and depression [5, 6]. It is a multifactorial complex condition resulting from the interactions among genetic variants and environmental factors, such as energy-rich diets, sedentary lifestyles, and several toxins [7–9]. Extensive efforts for maintainable weight loss have had limited long-term attainments [10]. Thus, identifying its risk factors is of high importance to develop preventive measures against obesity development.

Accumulating data have recognized that high-quality diets, such as vegetarian and Mediterranean diets have protective impacts against obesity progression or even could be followed for weight loss [11, 12]. The beneficial impacts of plant-rich dietary patterns are attributed for the most part to the remarkable contents of dietary bioactive components with established anti-oxidant and anti-inflammatory properties [13]. Indeed, chronic low-grade inflammation is a significant contributing factor to the etiology of obesity [14]. In this regard, there is strong evidence indicating that phytochemicals elevate metabolic rate by increasing thermogenesis and improving β-oxidation, while reducing oxidative stress and inflammation; thus, may help prevent adiposity accumulation and its related metabolic complications [15]. Polyphenols are the main dietary source of phytochemicals, which are a diverse family of bioactive antioxidants comprising four main classes, such as stilbenes, lignans, flavonoids, and phenolic acids [16]. Polyphenols are available in plant-based foods such as fruits, nuts, cocoa products, whole grains, vegetables, and beverages like tea and coffee [17, 18], and their potential preventive and therapeutic effects against obesity have been investigated broadly in pre-clinical and epidemiological studies. Despite promising effects in pre-clinical experiments [19], epidemiological studies in humans have yielded contradictory findings regarding the association between the dietary intakes of polyphenols and their subclasses with the odds of obesity. Some studies have reported a protective effect of polyphenols against obesity [16, 20], while others have found no association [21, 22]. A cross-sectional study by Grosso et al. found that both total and specific classes/subclasses of dietary polyphenols were inversely linked to MetS and its components [20]. Similarly, research in an Iranian population demonstrated that higher intake of flavonoids and lignans correlated with reduced odds of MetS and its components [16]. However, findings varied by demographic factors as studies have noted an inverse association between flavonoid intake and MetS in African American and White males but not in females [21]. In contrast, a prospective cohort study with six years of follow-up found that higher flavonoid intakes were associated with lower odds of developing MetS, although total polyphenol intakes and other subclasses showed no significant association with MetS or its components [22]. These inconsistencies could be attributed to the heterogeneity in the population characteristics, specific polyphenol compounds, or study design. This systematic review and meta-analysis was conducted to comprehensively evaluate the association between dietary polyphenols and various polyphenol subclasses and odds of obesity. The novelty of this study stems from providing comprehensive pooled estimates for both total and specific polyphenol subclasses, systematically identifying sources of heterogeneity, and examining dose-response relationships. This comprehensive approach resolves inconsistencies found in earlier research and offers deeper insights into the complex role of dietary polyphenols in obesity prevention.

Materials and methods

We used the guidelines presented by The Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) to conduct this meta-analysis [23]. The protocol of the study was registered in PROSPERO (CRD42023451118).

Search strategy

To find all relevant publications, a systematic literature search without any language restriction was performed in Scopus, Web of Science, and PubMed from the beginning to April 2025 using the text words related to polyphenols and obesity as described in Table S1. Moreover, we manually screened the references of all related reviews and included studies to obtain unrecognized publications.

Eligibility criteria and study selection

Two independent reviewers (ZL and NG) screened titles and abstracts using predefined inclusion and exclusion criteria, followed by full-text assessment of potentially eligible studies to confirm inclusion. The process of study selection was conducted independently by two authors and disagreements were settled by a group discussion. The consistency between the two reviewers regarding the data screening and selection process was evaluated using Cohen’s Kappa to measure inter-rater reliability. A Kappa coefficient of 0.81 was achieved for the selection of titles and abstracts, 0.87 for full-text screening, and 0.89 for data extraction, demonstrating a remarkable level of inter-rater reliability. Studies should meet the following criteria to be included in the analysis: (1) publications that investigated the relationship between dietary intake of polyphenols or their subclasses as exposure and general obesity as outcome, (2) the study design was observational (cross-sectional, cohort, case-control), (3) the studied population was adults (age ≥ 18 years), (4) risk estimates, such as hazard ratio (HR), odds ratio (OR), or relative risk (RR) with their 95% confidence intervals (CI) (or appropriate data to calculate them) had to be reported in the studies. We excluded animal studies, publications on gene expression, letters, book chapters, cell culture studies, reviews, publications with irrelevant exposure/outcome, and studies that were conducted on children, adolescents, or pregnant women.

Data extraction and quality assessment

The following data were extracted with the use of a predefined Excel form: first author’s name, year of publication, study design, country, age and sex of subjects, total number of participants and subjects with obesity, risk estimates (OR, RR, or HR) with their 95%CI for the relationships, method of exposure evaluation, type of polyphenols or their subclasses, and confounding factors adjusted for in the analyses. Data extraction was independently performed by two reviewers and inconsistencies were solved by discussion. To assess the quality of the studies, we applied Newcastle-Ottawa Scale (NOS), with a total score of 9 with scores 7–9, 4–6, and 0–3 indicating a high, moderate, and low quality, respectively [24]. The quality of evidence was evaluated using the criteria of Grading of Recommendations Assessment, Development, and Evaluation (GRADE) [25].

Statistical analysis

The relation of polyphenols and their subclasses to obesity was evaluated by pooling ORs and 95%CIs as effect size for the highest categories of intakes, compared with the lowest consumptions. Data were pooled using the random-effects model suggested by DerSimonian and Laird [26]. Q and I2 statistics were used to test the degree of heterogeneity across the studies, with p < 0.10 showing a significant heterogeneity [27, 28]. Subgroup analysis according to polyphenols subclasses, study design, sex of participants, and exposure assessment method as well as meta-regression analysis by the age of participants were conducted to assess potential sources of heterogeneity. Because of the availability of data to perform dose-response analysis only for total flavonoids, we perform a linear and non-linear dose-response analysis for the association of total flavonoids with obesity. The linear dose-response association for each 100 mg/day increments in dietary total flavonoid intake and odds of obesity was implemented using the method of Greenland and Longnecker [29]. To conduct nonlinear analysis, restricted cubic splines for each publication with ≥ 3 categories of exposure were calculated using three fixed knots at 10, 50, and 90% through the total distribution of reported exposure then combined using multivariate meta-analysis [30–32]. The median intake of total flavonoids, the number of cases and non-cases, and the ORs with the 95%CIs for at least three categories of exposure were required to perform the dose-response analysis. To examine the stability of results, a sensitivity analysis was undertaken by excluding one study at a time. Egger’s test was used to test the publication bias [33]. All statistical analyses were performed by Stata MP 14.0 (Stata Corp., College Station, TX, USA).

Results

Characteristics of studies

A total of 3,113 articles were retrieved by the systematic literature search. After full-text evaluation of potentially pertinent studies, Studies were excluded because they were clinical trials [34–37], preclinical investigations [38–52], reviews [15, 44, 53–56], commentaries and editorials [57–59], did not provide risk estimates [1, 60–65], evaluated serum or urinary polyphenols [66–70], involved study populations of children [71], or had irrelevant exposures or outcomes [72–74]. Eventually, this meta-analysis included a total of 10 publications with 45 effect sizes [16, 75–83], comprising a total of 106,302 participants. Figure 1 presents the process of study selection. The sample size of the studies varied between 227 and 53,711 individuals. The ages of the subjects ranged between 33.8 ± 12.4 and 61.31 ± 11.27 years. The study design was cross-sectional in 9 studies [16, 76–83] and prospective cohort in one study [75]. Exposure was evaluated using a food-frequency questionnaire (FFQ) in 7 studies [16, 75, 77, 79–81, 83], a 24-hour dietary recall in 2 studies [76, 78], and an internet-based dietary and lifestyle questionnaire for Chinese (IDQC) in one study [82]. Data obtained for meta-analysis were 10 studies (45 effect sizes) on various polyphenolic compounds [16, 75–83], 3 studies (3 effect sizes) on total polyphenols [16, 80, 82], 5 studies (7 effect sizes) on total flavonoids [75–79], 3 studies (5 effect sizes) on flavonols [75, 78, 79], 4 studies (6 effect sizes) on isoflavones [75, 78, 81, 83], 2 studies (3 effect sizes) on flavones [78, 79], 3 studies (4 effect sizes) on flavanones [75, 78, 79], and 2 studies (3 effect sizes) on flavanols [78, 79]. Based on the NOS scores, the quality of the included studies was moderate to high, with scores ranging from 4 to 9 (Table S2). The characteristics of the studies are presented in Table 1.

Fig. 1.

Flow diagram of the study

Table 1.

Characteristics of studies

Author	year	Country	Study design	Mean age, y	Gender (% women)	Total sample size	Cases with obesity	Obesity definition	Dietary assessment	Exposure	Sex	Odds ratio (95% CI)	Adjustment
Kim	2020	Korea	Cross-sectional	45.0 ± 0.2	59.9%	23,118	7,265	BMI ≥ 25 kg/m2	24-h dietary recall	Flavonols	Men	1.14 (0.99–1.31)	Adjusted for age, household income, alcohol consumption, smoking status, and physical activity level
										Flavones	Men	1.16 (1.02–1.33)
										Flavanones	Men	1.07 (0.94–1.23)
										Flavanols	Men	1.18 (1.04–1.35)
										Isoflavones	Men	1.09 (0.95–1.25)
										Anthocyanidins	Men	1.04 (0.92–1.18)
										Proanthocyanidins	Men	1.27 (1.11–1.46)
										Total flavonoids	Men	1.06 (0.92–1.23)
										Flavonols	Women	0.9 (0.81–1.01)
										Flavones	Women	1.06 (0.95–1.19)
										Flavanones	Women	1.08 (0.95–1.23)
										Flavanols	Women	0.89 (0.79–1.01)
										Isoflavones	Women	0.98 (0.87–1.11)
										Anthocyanidins	Women	0.85 (0.74–0.96)
										Proanthocyanidins	Women	0.96 (0.85–1.08)
										Total flavonoids	Women	0.96 (0.85–1.08)
Esposito	2021	Italy	Cross-sectional	55.6 ± 11.7	51.8%	4,592	1,443	BMI ≥ 30 kg/m2	FFQ	Total flavonoids	Both	1.4 (1.13–1.73)	Crude
Ahn	2020	Korea	Cross-sectional	36.1 ± 0.4	60.6%	10,326	2,917	BMI ≥ 25 kg/m2	24-h dietary recall	Total flavonoids	Men	0.82 (0.7–0.96)	Crude
										Total flavonoids	Women	0.83 (0.71–0.96)
Marranzano	2018	Italy	Cross-sectional	46.36 ± 18.79	58.5%	1,936	317	BMI ≥ 30 kg/m2	FFQ	Anthocyanins	Both	0.38 (0.21–0.66)	Adjusted for age, energy intake, smoking status, alcohol consumption, physical activity level, educational level, occupational level, and Mediterranean Diet score
										Flavonols	Both	1.16 (0.68–1.98)
										Flavanols	Both	0.63 (0.39–0.99)
										Flavanones	Both	0.58 (0.19–1.73)
										Flavones	Both	0.85 (0.55–1.31)
										Catechins	Both	1.18 (0.74–1.88)
										Apigenin	Both	0.33 (0.25–0.55)
										Hesperetin	Both	1.11 (0.68–1.83)
										Naringenin	Both	0.82 (0.53–1.27)
										Quercetin	Both	1.31 (0.8–2.15)
										Kaempferol	Both	0.72 (0.43–1.18)
										Total flavonoids	Both	0.88 (0.48–1.62)
Moslehi	2020	Iran	Prospective cohort (5.4 years follow-up)	33.8 ± 12.4	67.4%	1,114	151	BMI ≥ 30 kg/m2	FFQ	Flavan-3-ols	Both	0.46 (0.35–0.6)	Adjusted for age, gender, baseline BMI, BMI-change, and energy intakes
										Flavonols	Both	0.47 (0.36–0.62)
										Flavonols	Both	0.59 (0.48–0.74)
										Isoflavones	Both	0.7 (0.56–0.86)
										Flavanones	Both	0.89 (0.74–1.07)
										Anthocyanins	Both	0.81 (0.63–1.04)
										Total flavonoids	Both	0.76 (0.58–0.98)
Mompeo	2020	UK	Cross-sectional	61.31 ± 11.27	96.5%	1,874	320	BMI ≥ 30 kg/m2	FFQ	Stilbenes	Both	0.77 (0.65–0.91)	Adjusted for age, fiber intake, total daily energy intake and family relatedness
										Total polyphenols	Both	0.8 (0.73–0.87)
Rosli	2019	Malaysia	Cross-sectional	50–59	61.2%	227	63	BMI ≥ 30 kg/m2	FFQ	Isoflavones	Both	0.45 (0.16–1.22)	Crude
Zhu	2021	china	Cross-sectional	57.3 ± 11.3	61.1%	53,711	6,748	BMI ≥ 28 kg/m2	FFQ	Isoflavones	Men	0.89 (0.79–0.99)	Adjustment for sex, age, education level, marital status, smoking, alcohol drinking, tea consumption, sedentary time, physical activity level, sleep duration, energy intake, and prevalent chronic diseases
											Women	0.91 (0.82–1)
Wang	2021	china	Cross-sectional	42.45 ± 16.61	51.72%	6,786	2,667	BMI ≥ 24 kg/m2	An internet-based dietary and lifestyle questionnaire for Chinese (IDQC)	Total polyphenols	Both	0.96 (0.91–1.02)	Adjusted by age, gender, drinking, smoking, physical activities level, education status, income, total energy intake
Sohrab	2013	Iran	Cross-sectional	41.5 + 14.8	56%	2,618	1,670	BMI ≥ 25 kg/m2	FFQ	Total polyphenols	Both	1.85 (1.47–2.33)	Adjusted for age, gender, physical activity, smoking status, educational levels, study center, total energy intake, and BMI

FFQ: food frequency questionnaire, BMI: body mass index

Meta-analysis

Higher consumption of dietary polyphenolic compounds was significantly associated with reduced odds of obesity (OR: 0.91; 95% CI: 0.85–0.97), with significant evidence of between-study heterogeneity (Fig. 2). In the subgroup analysis, this protective association was also supported by studies on women, studies with a prospective design, and studies that used FFQ for dietary assessment (Table 2).

Fig. 2.

Meta-analysis of the association between various polyphenolic compounds and risk of obesity

Table 2.

Meta-analysis of main results and subgroup analysis for the association of various polyphenolic compounds and odds of general obesity

subgroup				Test of association		Heterogeneity			Publication bias
	Subgroups		Effect sizes (N)	OR	95%CI	I2 (%)		P	P
All polyphenolic compounds	Overall	45 (10)		0.91	0.85–0.97		84.0	< 0.001	0.12
	Prospective cohort	7 (1)		0.66	0.54–0.79		78.3	< 0.001
	Cross-sectional	38 (9)		0.97	0.91–1.03		80.1	< 0.001
	FFQ	26 (7)		0.79	0.70–0.90		84.8	< 0.001
	24-hour recalls	18 (2)		1.01	0.95–1.07		70.4	< 0.001
	IDQC	1 (1)		0.96	0.90–1.01		-	-
	Men	10 (3)		1.06	0.98–1.15		71.6	< 0.001
	Women	10 (3)		0.94	0.89–0.99		44.3	0.06
	Both genders	25 (7)		0.79	0.69–0.90		86.6	< 0.001
Total polyphenols	Overall	3 (3)		1.38	0.93–2.06		77.4	0.01	0.44
	Cross-sectional	3 (3)		1.38	0.93–2.06		77.4	0.01
	FFQ	3 (3)		1.38	0.93–2.06		77.4	0.01
	Both genders	3 (3)		1.38	0.93–2.06		77.4	0.01
Total flavonoids	Overall	7 (5)		0.77	0.64–0.92		87.2	< 0.001	0.06
	Prospective cohort	1 (1)		0.46	0.35–0.60		-	-
	Cross-sectional	6 (4)		0.85	0.73–0.99		80.2	< 0.001
	FFQ	3 (3)		0.54	0.34–0.84		85.3	0.001
	24-hour recalls	4 (2)		0.92	0.79–1.06		76.9	0.005
	Men	2 (2)		0.97	0.70–1.34		89.3	0.002
	Women	2 (2)		0.87	0.80–0.96		0.0	0.39
	Both genders	3 (3)		0.54	0.34–0.84		85.3	0.001
Flavonols	Overall	5 (3)		0.82	0.63–1.07		90.4	0.001	0.12
	Prospective cohort	2 (1)		0.64	0.54–0.76		17.3	0.27
	Cross-sectional	3 (2)		1.04	0.86–1.24		68.6	0.04
	FFQ	3 (2)		0.64	0.56–0.74		0.0	0.54
	24-hour recalls	2 (1)		1.10	1.00-1.20		3.0	0.31
	Men	1 (1)		1.16	1.02–1.32		-	-
	Women	1 (1)		1.06	0.95–1.19		-	-
	Both genders	3 (2)		0.64	0.56–0.74		0.0	0.54
Isoflavones	Overall	6 (4)		0.93	0.88-98		29.3	0.22	0.41
	Prospective cohort	1 (1)		0.89	0.74–1.07		-	-
	Cross-sectional	5 (3)		0.93	0.88-99		40.0	0.15
	FFQ	3 (2)		0.90	0.84–0.96		0.0	0.95
	24-hour recalls	2 (1)		0.94	0.77–1.15		79.0	0.001
	IDQC	1 (1)		0.96	0.91–1.02		-	-
	Men	2 (2)		0.96	0.82–1.12		69.7	0.06
	Women	2 (2)		0.89	0.82–0.96		0.0	0.41
	Both genders	2 (2)		0.95	0.90–1.01		0.0	0.44
Flavones	Overall	3 (2)		1.08	0.98–1.18		0.0	0.92	0.18
	FFQ	1(1)		1.18	0.74–1.88		-	-
	24-hour recalls	2 (1)		1.08	0.98–1.18		0.0	0.92
Flavanones	Overall	4 (3)		0.95	0.78–1.15		76.5	0.005	0.62
	Prospective cohort	1 (1)		0.81	0.63–1.04		-	-
	Cross-sectional	3 (2)		0.99	0.78–1.25		80.3	0.006
	FFQ	2 (2)		0.82	0.66–1.02		0.0	0.85
	24-hour recalls	2 (1)		1.02	0.78–1.35		89.5	0.02
	Men	1 (1)		1.18	1.04–1.34		-	-
	Women	1 (1)		0.89	0.79–1.01		-	-
	Both genders	2 (2)		0.82	0.66–1.02		0.0	0.85
Flavanols	Overall	3 (2)		1.02	0.92–1.14		13.5	0.31	0.59
	FFQ	1 (1)		0.58	0.19–1.75		-	-
	24-hour recalls	2 (1)		1.03	0.93–1.14		22.6	0.25
Anthocyanins	Overall	2 (2)		0.88	0.59–1.30		48.4	0.16	-

IDQC; An internet-based dietary and lifestyle questionnaire for Chinese, OR, odd ratio; CI, confidence interval

N: number of studies

Furthermore, higher intakes of total flavonoids and isoflavones were related to the decreased odds of obesity.Higher intake of flavonols was linked to lower odds of obesity in studies with a prospective cohort design, studies on both genders, and studies that applied FFQ for exposure assessment (Table 2).

In the dose-response meta-analysis, the relation of total flavonoids to obesity did not show a linear (P = 0.18) (Fig. S1) or non-linear (P nonlinearity = 0.18) (Fig. S2) pattern.

Moreover, catechins, flavan-3-ols, and stilbenes showed an inverse association with the odds of obesity, but, no significant association was found for the other polyphenolic compounds (Table 2).

Sensitivity analysis

The sensitivity analysis indicated that the pooled effect sizes were not significantly altered by any single study, suggesting the reliability of the results (Fig. S3).

Meta-regression and publication bias

Meta-regression analysis based on the age of participants identified that the association of polyphenolic compounds with obesity was not modified by age (p = 0.15) (Fig. 3). In addition, there was no evidence for publication bias according to the Egger’s test (p = 0.12) (Fig. 4).

Fig. 3.

Meta-regression analysis according to the age of participants

Fig. 4.

Funnel plot for publication bias

GRADE assessment

Based on the GRADE criteria, the quality of evidence was moderate for isoflavones, low for all polyphenolic compounds, total flavonoids, flavones, and anthocyanins, and very low for other compounds (Table 3).

Table 3.

Quality of evidence based on the GRADE criteria

Discussion

The current meta-analysis demonstrated that higher intake of dietary polyphenols, including total flavonoids, isoflavones, and flavonols is associated with lower odds of obesity. No significant relationship was found for other subclasses of flavonoids (flavones, flavanones, flavanols, or anthocyanins) possibly due to the low number of analyzed publications for these phytochemicals. The relation of flavonoids to obesity did not follow a dose-response manner.

Human diets rich in edible plants supply significant amounts of polyphenols [84]. A growing body of evidence from animal and human studies suggests that polyphenols may have a preventive or delaying effect on obesity [15, 53]. However, epidemiological research on the relationship between polyphenols and obesity has yielded inconsistent results. A cross-sectional study on 3438 children, adolescents, and young adults (2–24 years old) in Spain revealed a strong negative relationship between dietary intake of lignans and obesity [85]. In another study, pregnant women who had a higher intake of total flavonoids and anthocyanidins were found to have a lower likelihood of being obese, in comparison to women with lower intakes [86]. The results of three prospective cohorts including 124,086 Americans confirmed that higher intakes of foods rich in flavonols, flavan-3-ols, anthocyanins, and flavonoids were inversely correlated to self-reported body weight change during 24 years of follow-up [87]. Moreover, the prospective cohort study by Castañeda et al. [88] on 349,165 participants (aged 25 to 70 years), in line with our study, showed that total flavonoids intake was negatively related to 5-year body weight changes, although, the intake of total polyphenols was directly linked to body weight gain. A cohort study (5 years follow-up) involving 349,165 adults from nine European countries identified that polyphenols from whole grains, cocoa, fruits, and tea may play a role in body weight maintenance in adults [62]. In agreement with the present meta-analysis, a longitudinal cohort study by Hughes et al. [89] found that higher intake of dietary flavonols is related to a lower elevation in BMI over time (after 14 years). Isoflavones intakes have been also shown to be inversely correlated to BMI and total body fat mass [90]. In the study by Kim et al., conducted on Korean adults, the association of flavonoids with obesity depended on the type of flavonoid subclasses so that flavanones, flavanols, proanthocyanidins, and isoflavones, were negatively related to obesity, while, the intakes of anthocyanidins, flavanones, and flavonols were positively linked to obesity [78]. In contrast, the studies by Sohrab et al. [16], Rosli et al. [81], and Esposito et al. [77] failed to identify any association between the intake of total polyphenols and the odds of obesity. The inconsistencies among the previous studies might result from the considerable variety in polyphenol intakes in different geographic regions, differences in main food sources of polyphenols, type of investigated polyphenols, study design, method of dietary intake, and genetic background of studied populations. This meta-analysis decreased these contradictions by elevating the pooled sample size and identified significant protective effects of total flavonoids, isoflavones, and flavonols against obesity.

Several factors may explain why the protective association between dietary polyphenol intake and reduced odds of obesity was observed in women but not men, in prospective but not cross-sectional studies, and studies using FFQ rather than other dietary assessment tools. The significant association in women but not men may reflect biological, hormonal, or behavioral differences. Women may have different patterns of polyphenol metabolism [91], hormonal regulation (such as estrogen effects) [92], or dietary habits [93] that influence the impact of polyphenols on adiposity. Estrogens may enhance the metabolic and anti-inflammatory effects of polyphenols in women, leading to stronger associations with reduced odds of obesity [92]. Additionally, women may be more likely to accurately report dietary intake, reducing measurement errors in observational studies [94]. The association was evident in prospective studies but not in cross-sectional ones. Prospective designs are less prone to certain biases (e.g., recall or reverse causation) and better capture long-term dietary effects, while cross-sectional studies may not reflect temporal relationships and are more susceptible to confounding [95]. In addition, the long-term metabolic benefits of polyphenols, such as gradual improvements in lipid metabolism and gut microbiota [96], are more likely to be captured in prospective studies, while cross-sectional studies may miss these cumulative effects. Studies using FFQs showed significant associations, possibly because FFQs are designed to capture habitual intake over longer periods, providing a more accurate estimate of usual polyphenol consumption than short-term recall or dietary records. Other tools may not adequately capture episodic intake or the diversity of polyphenol-rich foods [97].

Mechanistically, polyphenols may have protective effects against obesity by modulating adipocyte differentiation, improving glucose and lipid metabolism, reducing inflammation, regulating energy metabolism, regulating appetite and satiety, and modulating the gut microbiota [79–81]. Flavonoids and isoflavones, a subclass of flavonoids that are primarily found in soy-based products, may inhibit the differentiation of pre-adipocytes into mature adipocytes and suppress the viability of adipocytes, as well as reduce lipogenesis and increase fatty acids β-oxidation, which can reduce the amount of fat stored in adipose tissue [81, 83, 98]. It has been recognized that polyphenolic compounds inhibit adipogenic mechanisms by downregulating the peroxisome proliferator-activated receptor c (PPARc), sterol regulatory element binding protein 1c (SREBP1c), and lipoprotein lipase (LPL) [99]. They can also promote pancreatic cell proliferation, insulin secretion, glucose transporters, and reduce insulin resistance, an important mechanism involved in adipogenesis [78]. These compounds have also been found to be effective in improving lipid and glucose metabolism by regulating hepatic enzymes involved in glycolysis and gluconeogenesis, increasing the excretion of triglycerides in feces, and inhibiting lipid-metabolizing enzymes [100]. Polyphenols have been shown to inhibit the production of inflammatory chemokines and cytokines such as tumor necrosis factor-alpha (TNF-a) and interleukin 1 beta (IL-1b) [79]. By reducing inflammation, flavonoids may help to prevent the development of insulin resistance, improve antioxidant defense, and result in a substantial reduction in body weight [101]. Flavonoids may regulate energy metabolism by activating AMP-activated protein kinase (AMPK), a key enzyme that plays a central role in energy homeostasis. AMPK is activated in response to changes in cellular energy status, and it promotes energy expenditure and fat oxidation while inhibiting lipogenesis and gluconeogenesis. By activating AMPK, flavonoids may increase energy expenditure and reduce fat accumulation in adipose tissue [102, 103]. Isoflavones may regulate appetite and satiety by modulating the secretion of hormones that regulate hunger and fullness, such as leptin and ghrelin [83]. Isoflavones may increase leptin secretion, which promotes satiety and reduces food intake, while decreasing the secretion of ghrelin, which stimulates hunger and increases food intake [104, 105]. Finally, it is well-identified that the gut microbiota plays a key role in the regulation of energy metabolism and dysbiosis has been linked to obesity [106]. Polyphenol intake, particularly isoflavones, may modulate the gut microbiota independent of fiber intake by increasing the abundance of beneficial bacteria, such as Faecalibacterium and Veillonella, an energy-enhancing bacteria [80, 83], which can improve gut barrier function, reduce inflammation, and promote metabolic health. These mechanisms may act synergistically to reduce the odds of obesity and improve metabolic health. The results of this study suggest that adherence to a diet with high contents of polyphenol-rich foods such as fruits, vegetables, virgin olive oil, nuts, and spices and seasonings with aromatic plants could have the potential to prevent obesity.

To the best of our knowledge, our study is the first meta-analysis exploring the association of dietary polyphenols and their main subclasses to obesity. There are some strengths in the present meta-analysis worth noting. This review comprehensively included various polyphenolic compounds/subclasses with a relatively high sample size from different geographic regions, which may be representative of different populations. We did not consider a time or language restriction in the search to obtain all related studies; this was confirmed by the lack of publication bias in the analyses. In addition, the results were stable in the sensitivity analysis, indicating the reliability of the results. The findings of this meta-analysis should be interpreted in light of several limitations. First, the heterogeneity was significant in the analyses. Stratified analysis disclosed that the detected heterogeneity was explainable by differences in dietary assessment tools, type of polyphenols, and study design. The difference in the age of participants was not a source for the heterogeneity. Second, because of the observational nature of the included publications, causality cannot be inferred for associations. Thus, clinical trials are required to establish the findings. Third, although the results of the majority of the analyzed studies were adjusted for the potential confounders, in some studies the effect sizes were obtained from the crude analysis, making the results susceptible to unmeasured/residual confounding. Fourth, dietary intake in studies was measured using retrospective questionnaires, which are prone to recall biases. Fifth, because of the small number of the analyzed studies in some subgroups, the results of the stratified analyses should be interpreted with caution. Lastly, the majority of the included studies were cross-sectional, which is more prone to bias compared to cohort studies. While the results of cross-sectional studies may not be as reliable as those of cohort studies, their inclusion highlights gaps in existing research and underscores the need for higher-quality studies in this area. It is important to note that, according to the NOS scale, 6 out of the 10 included studies achieved a perfect score of 9 out of 9, indicating their high quality despite being cross-sectional in design. Additionally, the remaining studies demonstrated moderate to high methodological quality, indicating the reliability of the findings. Nevertheless, prospective cohort studies are required to confirm our results.

Conclusion

In conclusion, the findings of this meta-analysis suggested that a higher intake of dietary polyphenols is linked to lower odds of obesity. Further studies, in particular prospective cohorts and clinical trials, should be conducted to approve these findings.

Supplementary Information

Below is the link to the electronic supplementary material.

Author contributions

Study design: Zhao Lihong, Mandeep Singh, Mohammed Yousif Merza, Malathi H., and Hashem O. Alsaab. Search strategy, study selection and data extraction: Zahraa F. Hassan, Syeda Wajida Kazmi, and Mamata Chahar. Literature assessment: Mamata Chahar and [Nazila Garousi](https://pubmed.ncbi.nlm.nih.gov/?size=200%26term=GarousiN%26cauthor_id=34247342). Manuscript preparation: Zhao Lihong, Mandeep Singh, Mohammed Yousif Merza, Syeda Wajida Kazmi, and Mamata Chahar. Data curation and writing-original draft preparation: Mohammed Yousif Merza, Malathi H., Hashem O. Alsaab and Mamata Chahar. Supervision: [Nazila Garousi] (https://pubmed.ncbi.nlm.nih.gov/?size=200%26term=GarousiN%26cauthor_id=34247342). Edition and rewriting the manuscript: [Nazila Garousi](https://pubmed.ncbi.nlm.nih.gov/?size=200%26term=GarousiN%26cauthor_id=34247342) and Zhao Lihong.

Funding

None.

Data availability

The data presented in this study are included in the article materials. Further inquiries can be directed to the corresponding author.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.