The effect of fermented dairy intake and abdominal obesity in adults: a systematic review and dose–response meta-analysis of cohort studies

Abstract

Objectives

Diverse analysis has analyzed the potential efficacy of consuming foods created through the fermentation of dairy in mitigating abdominal obesity. The current meta-analysis aims to determine the impacts of consuming fermented dairy foods and the occurrence of abdominal obesity.

Methods

Web of Science, PubMed, and Scopus databases were queried for records published before January 13, 2023, to investigate proportionate cohort studies. We employed a random-effects model to appraise the relative risk (RR); effect size was assessed through the 95% confidence interval (CI). Additionally, a one-stage dose–response analysis was executed, quality assessment was conducted through the ROBINS-E tool.

Results

Consequently, five publications, comprising 41,430 cases, were included as selected studies. The pooled effect shows an effect on the abdominal obesity risk; however, the effect was not significant. Subgroup analyses revealed a potential risk reduction effect in high- and low-fat and fermented dairy productions, although the findings were not statistically significant. Furthermore, the dose–response analysis indicated a linear decrease in risk with increasing consumption of high-fat fermented yogurt, with an HR of 0.84 (95% CI 0.71, 0.99) by 8 servings/week and an HR of 0.37 (95% CI 0.19, 0.71) by 21 servings/week.

Conclusion

These findings imply the potential effectiveness of fermented dairy products, particularly high-fat yogurt, in diminishing the obesity risk. However, further research addressing the limitations of previous studies is essential to confirm these results.

Evidence-based medicine level: No level of evidence: Level of evidence III.

PROSPERO registration number: CRD42023387538 (http://www.crd.york.ac.uk/PROSPERO).

Supplementary Information

The online version contains supplementary material available at 10.1007/s40519-025-01733-4.

Introduction

Obesity is a global health issue that continues to escalate. In 2015, the global prevalence of overweight and obesity was documented at 39%, with projections indicating an increase to more than 55% by 2030 [1, 2]. Obesity is defined as a chronic condition resulting from prolonged excessive caloric intake, affecting individuals across different age groups. While it is widespread in high-income countries across both genders, it is more prevalent in middle-aged women in low-income countries [3]. The impact of obesity extends beyond physical appearance, as it is associated with numerous health complications, including diabetes, cardiovascular disease, certain cancers, and dental issues, all of which significantly reduce the quality of life for those affected [4–8].

Several factors contribute to the development of obesity, with lifestyle choices being one of the most influential [9–14].

The relationship between dairy intake and obesity has been explored in various studies, yielding mixed findings. Some studies suggest that high-fat dairy consumption might promote weight loss and does not appear to significantly affect the risk of metabolic syndrome or cardiovascular disease [15–19]. On the other hand, the consumption of full-fat dairy products, such as whole milk, cheese, and butter, has been linked to an increased risk of coronary heart disease due to altered fat metabolism, in contrast to low-fat dairy, which shows no such association [20]. Interestingly, while high-fat dairy may increase the risk of obesity, it has not been strongly associated with changes in blood lipid profiles or an increased risk of death from cardiovascular disease [21]. Moreover, low-fat dairy products have been suggested to reduce levels of inflammatory markers, potentially mitigating chronic inflammation, particularly in obese individuals [22].

Fermented dairy products, including cheese and yogurt, have shown promise in influencing lipid metabolism and supporting immune function [23]. These products are distinguished by lower levels of saturated fatty acids and higher concentrations of short-chain fatty acids (SCFAs), which may lead to more significant effects on weight management and gut health compared to long-chain fatty acids [24]. Moreover, short- and medium-chain triglycerides present in fermented dairy products may contribute to these effects [25, 26]. The high calcium content of fermented dairy products may also play a role in reducing fat absorption, providing an additional preventive effect against obesity [27, 28]. Fermented dairy products have long been valued for their potential health benefits, particularly due to their probiotic content. These probiotics may enhance intestinal flora, which has been suggested to reduce the risk of cardiovascular diseases [29]. In addition, regular consumption of fermented dairy has been associated with favorable changes in lipid profiles, such as reductions in low-density lipoprotein (LDL) cholesterol levels, LDL/HDL ratio, and adipocyte size. These effects collectively contribute to the prevention of obesity [30].

Despite these established benefits, the relationship between fermented dairy, its fat composition, and abdominal obesity risk remains underexplored. Given this context, the present study aims to investigate this relationship by reviewing cohort studies with a dose–response design, using a pairwise meta-analysis approach. This analysis seeks to evaluate the impact of fermented dairy consumption and its fat composition on abdominal obesity risk, shedding light on their potential role in obesity prevention.

Methods

Protocol and registration

This systematic review was conducted following the guidelines outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) [31], which ensures transparency and rigor in the review process. The review protocol was registered with PROSPERO (Registration number: CRD42023387538), an international database for the registration of systematic reviews, to promote methodological transparency and prevent duplication of efforts. The registration included detailed information about the review objectives, inclusion criteria, search strategy, and planned data analysis approach.

Search strategy

International databases, including Web of Science, Scopus, and PubMed, were searched independently by two authors (FHJ and MN) to identify relevant studies that examined the impact of fermented dairy intake on abdominal obesity (Supplementary file). The literature search covered studies published from inception to December 2024. The search strategy was based on a combination of Medical Subject Headings (MeSH) terms and keywords, including "Fermented foods", "Fermented Dairy", "Yogurt", "Obesity", "Dose-response", and "Cohort study." These terms were used to ensure a comprehensive search of the relevant literature. No restrictions were applied to publication dates, and only studies published in English were considered. To ensure thoroughness, the reference lists of included articles were manually reviewed to identify additional studies that may have been missed in the initial search. This process aimed to minimize the risk of overlooking relevant research.

Selection criteria

The eligibility of the studies was assessed according to the PICOS framework (Population, Intervention/Exposure, Comparator, Outcome, and Study Design). The studies identified in the literature search were managed using Endnote reference management software and were systematically evaluated based on predefined inclusion and exclusion criteria to ensure they met the focus of the review—assessing the clinical efficacy of fermented dairy intake on abdominal obesity. Inclusion criteria for studies were as follows: (1) cohort studies that investigated the impact of at least two quantitative categories of dietary fermented dairy intake on abdominal obesity; (2) studies involving adult human subjects; (3) studies in which fermented dairy intake was administered as the primary intervention; (4) studies that reported effect sizes such as rate ratio (RR), hazard ratio (HR), or relative risk (RR) with corresponding 95% confidence intervals (95% CIs). Otherwise, (1) article types such as reviews, case reports, editorials; (2) studies with incomplete data; (3) articles with no accessible full-texts; 4) studies involving animal models; (5) duplicates; (6) studies published in gray literature as well as studies not published in English were omitted. The selection process was conducted independently by two authors (FHJ and MN), who first reviewed the titles and abstracts to identify potentially eligible studies. Full-text articles were then assessed for inclusion based on the criteria mentioned above. Any disagreements regarding study eligibility were resolved through discussion between the two authors to ensure consensus.

Data extraction

Data extraction was carried out independently by two investigators (FHJ and MN) to ensure reliability and minimize bias. The following information was extracted from each eligible study: the first author, publication date, country of origin, participants' age, gender distribution, characteristics of study participants, sample size, duration of follow-up, dietary evaluation methodology, and a comprehensive set of variables incorporated in the statistical models to account for potential confounding factors. These data are systematically summarized in Table 1 to provide an overview of the study characteristics. The inclusion of these variables was intended to capture key factors that might influence the outcomes and to facilitate a detailed comparison across the studies. The use of two independent reviewers for this process, along with the comprehensive set of extracted data, aimed to ensure a thorough, consistent, and standardized evaluation of the relevant study characteristics.

Table 1.

Baseline characteristics of included cohort studies

Author, year	Cohort’s name (region)	Gender	Sample sizes/cases	Dairy type	Median intake1,2 (no. in each group)	Outcome	Follow-up (years)	Adjustment for covariates	Studies quality
Martinez-Gonzalez, 2014	SUN (Spain)	Both	20,490/8516	Total yogurt	1 (2899) 3.5 (2335) 6 (852) 7 (1352)  > 7 (1078)	Obesity	6.6	Age, sex, baseline weight, PA, hours of TV watching, leisure time, smoking status, snacking between meals, following a special diet, TEI, marital status, education	Serious
Martinez-Gonzalez, 2014	SUN (Spain)	Both	20,490/8516	LF yogurt	1 (6236) 3.5 (1063) 6 (249) 7 (694)  > 7 (274)	Obesity	6.6	Age, sex, baseline weight, PA, hours of TV watching, leisure time, smoking status, snacking between meals, following a special diet, TEI, marital status, education	Serious
Martinez-Gonzalez, 2014	SUN (Spain)	Both	20,490/8516	HF yogurt	1 (4614) 3.5 (1980) 6 (412) 7 (1207)  > 7 (303)	Obesity	6.6	Age, sex, baseline weight, PA, hours of TV watching, leisure time, smoking status, snacking between meals, following a special diet, TEI, marital status, education	Serious
Sayón-Orea, 2015	SUN (Spain)	Both	15,909/8063	Total yogurt	1 (2689) 7 (2285)	Central obesity	6	Age, sex, baseline weight, TEI, intake of alcohol, soft drinks, red meat, French fries, fast food, Mediterranean diet, PA, sedentary behavior, leisure time, smoking status, snacking between meals, following a special diet	Serious
Sayón-Orea, 2015	SUN (Spain)	Both	15,909/8063	LF yogurt	1 (5990) 7 (831)	Central obesity	6	Age, sex, baseline weight, TEI, intake of alcohol, soft drinks, red meat, French fries, fast food, Mediterranean diet, PA, sedentary behavior, leisure time, smoking status, snacking between meals, following a special diet	Serious
Sayón-Orea, 2015	SUN (Spain)	Both	15,909/8063	HF yogurt	1 (4160) 7 (1482)	Central obesity	6	Age, sex, baseline weight, TEI, intake of alcohol, soft drinks, red meat, French fries, fast food, Mediterranean diet, PA, sedentary behavior, leisure time, smoking status, snacking between meals, following a special diet	Serious
Babio, 2015	PREDIMED (Spain)	Both	7447/1868	Total yogurt	0.2 (not mentioned) 3.6 (not mentioned)	Abdominal obesity	3.2	Age, sex, leisure time, PA, BMI, smoking status, use of hypoglycemic or, hypolipidemic or, antihypertensive or, and insulin treatment at baseline, mean consumption during follow-up of vegetables, fruit, legumes, cereals, fish, red meat, cookies, olive oil, and nuts, alcohol	Moderate
Babio, 2015	PREDIMED (Spain)	Both	7447/1868	LF yogurt	0.02 (not mentioned) 3.5 (not mentioned)	Abdominal obesity	3.2	Age, sex, leisure time, PA, BMI, smoking status, use of hypoglycemic or, hypolipidemic or, antihypertensive or, and insulin treatment at baseline, mean consumption during follow-up of vegetables, fruit, legumes, cereals, fish, red meat, cookies, olive oil, and nuts, alcohol	Moderate
Babio, 2015	PREDIMED (Spain)	Both	7447/1868	HF yogurt	0 (not mentioned) 1.31 (not mentioned)	Abdominal obesity	3.2	Age, sex, leisure time, PA, BMI, smoking status, use of hypoglycemic or, hypolipidemic or, antihypertensive or, and insulin treatment at baseline, mean consumption during follow-up of vegetables, fruit, legumes, cereals, fish, red meat, cookies, olive oil, and nuts, alcohol	Moderate
Santiago, 2016	PREDIMED (Spain)	Both	7447/4545	Total yogurt	0.09 (909) 0.6 (909) 1.81 (909) 3.39 (909) 6 (909)	Abdominal obesity	4.9	Age, sex, PA, Mediterranean diet adherence, TEI, smoking status, baseline BMI	Moderate
Santiago, 2016	PREDIMED (Spain)	Both	7447/4545	LF yogurt	0.04 (909) 0.38 (909) 1.19 (909) 2.31 (909) 4.6 (909)	Abdominal obesity	4.9	Age, sex, PA, Mediterranean diet adherence, TEI, smoking status, baseline BMI	Moderate
Santiago, 2016	PREDIMED (Spain)	Both	7447/4545	HF yogurt	0.04 (909) 0.28 (909) 0.62 (909) 2.31 (909) 1.5 (909)	Abdominal obesity	4.9	Age, sex, PA, Mediterranean diet adherence, TEI, smoking status, baseline BMI	Moderate
Rautiainen, 2016	Women’s Health Study (USA)	Female	39,876/18438	Total dairy	4.2 (3649) 9.1 (3759) 12.6 (3690) 18.2 (3705) 27.3 (3605)	Obesity	11.2	Age, randomization treatment, smoking status, PA, baseline BMI, postmenopausal status, postmenopausal hormone use, history of hypercholesterolemia, history of hypertension, multivitamin use, alcohol intake, intake of energy, fruit, and vegetable	Moderate
Rautiainen, 2016	Women’s Health Study (USA)	Female	39,876/18438	Total yogurt	0 (7442) 1.75 (9482) 5 (566) 7 (605)	Obesity	11.2	Age, randomization treatment, smoking status, PA, baseline BMI, postmenopausal status, postmenopausal hormone use, history of hypercholesterolemia, history of hypertension, multivitamin use, alcohol intake, intake of energy, fruit, and vegetable	Moderate
Rautiainen, 2016	Women’s Health Study (USA)	Female	39,876/18438	Total cheese	0 (1232) 1.75 (14,702) 5 (1332) 7 (1029)	Obesity	11.2	Age, randomization treatment, smoking status, PA, baseline BMI, postmenopausal status, postmenopausal hormone use, history of hypercholesterolemia, history of hypertension, multivitamin use, alcohol intake, intake of energy, fruit, and vegetable	Moderate
Rautiainen, 2016	Women’s Health Study (USA)	Female	39,876/18438	LF cheese	0 (7598) 1.75 (10,337) 5 (159) 7 (87)	Obesity	11.2	Age, randomization treatment, smoking status, PA, baseline BMI, postmenopausal status, postmenopausal hormone use, history of hypercholesterolemia, history of hypertension, multivitamin use, alcohol intake, intake of energy, fruit, and vegetable	Moderate
Rautiainen, 2016	Women’s Health Study (USA)	Female	39,876/18438	HF cheese	0 (9611) 1.75 (8219) 5 (99) 7 (82)	Obesity	11.2	Age, randomization treatment, smoking status, PA, baseline BMI, postmenopausal status, postmenopausal hormone use, history of hypercholesterolemia, hypertension, multivitamin use, TEI, intake of, fruit, vegetable, and alcohol	Moderate

LF low-fat, HF high-fat, PA physical activity, TEI total energy intake, BMI body mass index

¹Presented as serving/week

Quality assessment

The quality of the included studies was assessed using the ROBINS-I tool (Risk Of Bias In Non-randomized Studies—of Interventions) [32], specifically designed for evaluating non-randomized studies. This tool evaluates several key areas that are critical for assessing the risk of bias in non-randomized research, including participant recruitment, control of confounding factors, exposure assessment, handling of missing data, follow-up misclassification, outcome evaluation, and selective reporting of outcomes. Each of these areas was assessed individually, with studies being assigned a risk-of-bias rating for each criterion, ranging from "low" to "serious" risk based on the evaluation. The cumulative results of these individual assessments were used to provide an overall evaluation of the studies' quality and the potential for bias. The quality assessment process was conducted independently by two authors (FHJ and MN), and any disagreements that arose during the evaluation were resolved through discussion to reach a consensus.

Statistical analysis

A random-effects model was applied to calculate the relative risk (RR) and its corresponding 95% confidence interval (CI) for each study [33]. In cases where the effect size was reported as hazard ratio (HR) or odds ratio (OR), these were treated as equivalent to the HR for consistency in the meta-analysis [34]. Initially, a meta-analysis was performed to estimate the overall RRs across studies that examined the impact of fermented dairy intake, comparing the highest and lowest categories of intake. When studies reported separate effect sizes for males and females, a combined effect size was calculated using a fixed-effect model before proceeding with subsequent analyses. This approach ensured that gender-specific variations were accounted for in the overall estimation. To assess heterogeneity between studies, both the Cochrane Q test and I² statistics were used [33]. These tests evaluate the variation in effect sizes across studies, helping to determine the degree of inconsistency. Publication bias was also examined using Egger's test [33, 34] and funnel plots, which are standard methods for detecting potential bias in the reporting of studies. A sensitivity analysis was conducted by systematically excluding one prospective cohort study at a time to assess its impact on the overall estimate. This approach helped evaluate the robustness of the results and the influence of any individual study on the findings.

If a study provided sufficient data, a random-effects dose–response meta-analysis was performed to estimate the RR for abdominal obesity with a specific increase in yogurt intake. This analysis followed the method outlined by Greenland and colleagues requiring data on the distribution of cases and person-years, as well as the effect size for each category of yogurt intake and the respective median intake values for each category [35, 36]. Finally, a meta-analysis was conducted to assess the dose–response relationship more explicitly [37, 38]. All statistical analyses were performed using STATA version 17.0, with a significance level set at P < 0.05.

Results

Study characteristics

Table 1 summarizes the characteristics of the eligible studies. After an initial search of 5,719 records, 5,628 studies were excluded based on title or abstract, leaving 91 articles for full-text screening. Finally, 5 studies met the inclusion criteria (Fig. 1). These studies were geographically diverse, with one conducted in America and four in Europe. All studies used a food-frequency questionnaire (FFQ) to assess participants' diets, and the follow-up duration ranged from 3.2 years to 11.2 years [39, 40]. All studies included both genders, except for one that focused exclusively on females [40].

Fig. 1.

PRISMA flow diagram illustrating the literature search and study selection process. A total of 5,719 records were identified from database searches. After removing duplicates, 5,628 studies were excluded based on title or abstract. 91 articles were screened for full-text eligibility, and after a detailed evaluation, 5 studies were included in the final analysis. The figure outlines the number of records at each stage of the process, including reasons for exclusions at the full-text screening phase

Meta-analysis

Seventeen effect sizes from five studies were included, with hazard ratios (HRs) ranging from 0.74 to 1.29. The pooled HR for fermented dairy intake and abdominal obesity risk was 0.96 (95% CI 0.83, 1.11; I² = 81.2%; tau2: 0.16), suggesting no significant risk reduction (Fig. 2). In addition, there is no meaningful association regarding the impact of low-fat fermented dairy consumption on the risk of obesity in adults (HR: 0.92; 95% CI 0.83, 1.02; I² = 7.1%; tau2: 0.03). In relation to the efficacy of high-fat fermented dairy (Tables 2 and 3) intake and the susceptibility to abdominal obesity, the pooled HR was 0.86 (95% CI 0.72, 1.01; I² = 58.9%; tau2: 0.22), indicating a trend toward lower risk, but the result was not statistically significant (Supplementary file).

Fig. 2.

Forest plot showing the association between fermented dairy product consumption and the risk of abdominal obesity. The pooled hazard ratio (HR) for the association was 0.96 (95% CI 0.83, 1.11), indicating a non-significant reduction in risk. Individual study HRs ranged from 0.74 to 1.29. Heterogeneity across studies was high (I² = 81.2%). The plot displays the effect size for each study, along with the 95% confidence intervals (CIs), and highlights the overall pooled estimate

Table 2.

Pairwise analysis of the effect of the different types of dairy consumption on abdominal obesity risk

	Fermented dairy	Low-fat fermented	High-fat fermented
Number of papers/studies	5	5	5
Range	0.74, 1.29	0.78, 1.02	0.62, 1.21
Pooled HR (95% CI)	0.96 (0.83, 1.11)	0.92 (0.83, 1.02)	0.86 (0.72, 1.01)
Heterogeneity* (%)	81.2	7.1	58.9
Eager (P-value)	0.74	0.44	0.86

*Significant heterogeneities are bolded

Table 3.

The effects of different doses of yogurt consumption on abdominal obesity risk from the non-linear dose–response meta-analysis (hazard ratio and 95% confidence interval)

Low-fat yogurt (serving/week)	0.09 (Ref)	1	3.5	7	10.5	14	17.5	21
HR (95% CI)	1 (1,1)	1.06 (0.92, 1.23)	1.17 (0.80, 1.71)	1.04 (0.88, 1.23)	0.92 (0.73, 1.16)	0.81 (0.50, 1.33)	0.72 (0.33, 1.55)	0.64 (0.22, 1.81)
High-fat yogurt (serving/week)	0.09 (Ref)	1	3.5	7	10.5*	14	17.5	21
HR (95% CI)	1 (1,1)	1.09 (0.99, 1.21)	1.12 (0.93, 1.35)	0.90 (0.77, 1.05)	0.72 (0.57, 0.91)	0.58 (0.40, 0.83)	0.46 (0.28, 0.77)	0.37 (0.19, 0.71)

HR hazard ratio, CI confidence interval

*HR, turns to significant values after consumption of 8 servings/week (HR: 0.84 CI 0.71, 0.99)

Quality assessment

The quality assessment of the included studies is summarized in Table 4. The ROBINS-E tool evaluated bias across domains such as participant recruitment, confounders, exposure evaluation, and result measurement. Three of the five studies showed low bias, while two had a high risk of bias, particularly due to shortcomings in outcome measurement. However, all studies demonstrated high quality in participant selection, selective reporting, and handling missing data.

Table 4.

Quality assessment of included studies based on the ROBINS-I tool

Studies	Bias due to confounding	Bias due to the selection of participants	Bias due to exposure assessment	Bias due to misclassification during follow-up	Bias due to missing data	Bias due to measurement of the outcome	Bias due to selective reporting of the results	Overall judgment
Martinez-Gonzalez, 2014	Moderate	Low	Moderate	Low	Low	Serious	Low	Serious
Sayón-Orea, 2015	Moderate	Low	Moderate	Moderate	Low	Serious	Low	Serious
Babio, 2015	Moderate	Low	Moderate	Low	Low	Moderate	Low	Moderate
Santiago, 2016	Moderate	Low	Moderate	Moderate	Low	Moderate	Low	Moderate
Rautiainen, 2016	Moderate	Low	Moderate	Moderate	Low	Moderate	Low	Moderate

Dose–response analysis

Dose-dependence of the effects evaluated for yogurt consumption are detailed in Table 3. For low-fat yogurt, the dose–response analysis indicated a slight increase in abdominal obesity risk up to 3.5 servings per week, but the results were not statistically significant (HR: 1.17, 95% CI 0.80, 1.71; I²: 26.5; tau2: 0.02). A decrease in risk was observed at higher intake levels, though this was also not significant (HR: 0.64, 95% CI 0.22, 1.81; I²: 58.9; tau2: 0.06). For high-fat yogurt, a significant risk reduction was observed at 8 servings per week, with a 63% decrease in abdominal obesity risk at 21 servings per week (HR: 0.37, 95% CI 0.19, 0.71; I²: 96.1; tau2: 4.5). No significant results were observed for intakes below 8 servings per week. The spaghetti plot in Fig. 3 underscores the linear dose–response association.

Fig. 3.

Non-linear and linear dose–response relationships between high-fat yogurt consumption and the risk of abdominal obesity (P_{dose-response}: 0.002, P_non-linear: 0.02). The plot illustrates both the non-linear and linear associations, with a significant dose–response effect (P < 0.002 for the overall relationship, P < 0.02 for the non-linear component). The risk of abdominal obesity decreased significantly with increasing consumption, showing a 63% reduction in risk at 21 servings per week (HR: 0.37, 95% CI 0.19, 0.71). The figure distinguishes between the linear and non-linear components of the relationship, highlighting the dose-dependent risk reduction

Publication bias

The evaluation of potential bias in the reporting of results, using funnel plots and Egger's test, indicated no evidence of publication bias across all study categories (Sect. "Publication bias").

Discussion

This meta-analysis represents the first comprehensive investigation into the relationship between fermented dairy product consumption and abdominal obesity risk. A total of five studies, collectively involving a significant number of participants, were reviewed. While the overall findings suggest that consuming dairy products does not significantly influence abdominal obesity risk, the dose–response analysis revealed a noteworthy trend for high-fat fermented yogurt. Specifically, a linear decrease in abdominal obesity risk was observed with the consumption of 8 to 21 servings per week of high-fat fermented yogurt. These results align with previous studies that have reported a negative correlation between dairy consumption and obesity-related outcomes [40, 41]. However, it is essential to note that some studies have found non-significant effects, further emphasizing the need for caution when interpreting these associations [42–44].

The discrepancies in findings across studies can likely be attributed to various factors, including the heterogeneity of included studies, potential unaccounted confounding variables, variations in participant characteristics, and differences in study design. Small sample sizes, limited duration of consumption, and the types of fermented dairy products assessed could all contribute to the observed inconsistencies. These factors underscore the complexity of understanding the relationship between dairy intake and obesity.

Mechanisms underlying the effects of dairy consumption

The differential impact of high-fat versus low-fat dairy products on obesity risk is a key finding of this study. High-fat dairy appears to have a more substantial effect in reducing obesity risk compared to low-fat alternatives. Several mechanisms may explain this phenomenon:

1. Appetite suppression: high-fat dairy products induce greater satiety, potentially leading to a reduction in overall calorie intake. The increased fat content may contribute to longer-lasting feelings of fullness, which can help control hunger and reduce overeating.

2. Sugar content in low-fat dairy products: many low-fat dairy foods compensate for reduced fat content by adding sugar to improve taste. However, increased sugar intake has been associated with weight gain, which may counteract the potential benefits of low-fat dairy consumption.

3. Fatty acids and weight regulation: specific fatty acids in high-fat dairy products, such as pentadecanoic acid, have been negatively correlated with central obesity. These fatty acids may influence weight regulation and metabolic processes, contributing to reduced abdominal fat accumulation.

In addition to these mechanisms, several factors related to the fermentation process may further contribute to the effects of fermented dairy on obesity risk:

1. Slowing gastric emptying and appetite hormones: fermented dairy products may slow gastric emptying, extending the sensation of fullness and reducing overall food intake. Additionally, they can influence appetite-regulating hormones, leading to reduced hunger [45].

2. Short-chain fatty acids (SCFAs): fermentation produces SCFAs, which have been associated with enhanced satiety. SCFAs may also trigger the release of appetite-regulating hormones and influence the gut–brain axis, contributing to weight management [46, 47].

3. Inhibition of inflammatory pathways: fermented dairy products possess anti-inflammatory properties, which could help in weight loss or prevent further weight gain. Chronic inflammation is often associated with obesity, and the reduction of inflammation may play a significant role in managing body weight [45, 47].

The role of dairy in abdominal obesity risk

Dairy intake and abdominal obesity risk have a multifaceted relationship, influenced by various nutritional components such as calcium, probiotics, and specific fatty acids [48]. Dairy is a valuable source of protein, which has been shown to induce greater satiety than carbohydrates or fats. Protein, alongside other nutrients like calcium, may play an essential role in regulating body weight. Calcium, in particular, may alter lipid profiles by reducing fat reabsorption and increasing fecal fat excretion [49, 50]. Animal studies have also suggested that calcium can influence gut microbiota, potentially benefiting body weight regulation [50, 51].

Other components of dairy, such as whey protein, medium-chain fatty acids, and conjugated linoleic acid, are involved in lipogenesis and promote fat oxidation in adipocytes, contributing to weight management [52, 53]. Additionally, the fermentation process enhances the probiotic content of dairy, which may help improve gut health and further influence weight regulation.

This meta-analysis provides the first comprehensive evaluation of the association between fermented dairy product consumption and abdominal obesity risk, with a specific focus on dose–response relationships. While overall findings indicate no significant effect of fermented dairy intake on abdominal obesity, the study reveals a potential protective effect of high-fat fermented yogurt when consumed in moderate to high amounts (8–21 servings per week). By incorporating both linear and non-linear dose–response analyses, this study offers novel insights into the potential differential effects of high-fat versus low-fat fermented dairy products on body fat distribution. These findings contribute to a growing body of evidence suggesting that the type and composition of dairy products, rather than overall dairy intake alone, may play a crucial role in obesity-related outcomes.

What is already known on this subject?

Previous studies have explored the relationship between dairy consumption and obesity, particularly focusing on the impact of fermented dairy products. Some research has indicated that dairy intake, especially high-fat varieties, may be associated with a reduced risk of obesity due to factors such as satiety-inducing properties and specific fatty acids that influence metabolic processes. Fermented dairy, including yogurt, has also been linked to improvements in gut health and weight regulation, partly through the presence of probiotics and short-chain fatty acids (SCFAs). Additionally, certain bioactive compounds, such as calcium and whey protein, in dairy products have been suggested to play a role in weight management by affecting fat metabolism and appetite regulation. However, despite these promising findings, the evidence remains inconsistent, with some studies reporting minimal or no effect of dairy on obesity risk, underscoring the need for further investigation to clarify the role of dairy, particularly fermented dairy, in obesity prevention.

What does this study add?

This study provides the first comprehensive meta-analysis specifically investigating the relationship between fermented dairy product consumption and abdominal obesity risk, with a particular focus on dose–response relationships. Unlike previous studies, it highlights the differential effects of high-fat versus low-fat fermented dairy, revealing a potential protective effect of high-fat fermented yogurt when consumed in moderate to high amounts (8–21 servings per week). The study also offers valuable insights into the underlying mechanisms, such as appetite suppression, the influence of short-chain fatty acids (SCFAs), and the anti-inflammatory properties of fermented dairy, which may contribute to weight regulation. Furthermore, the use of both linear and non-linear dose–response analyses adds depth to the understanding of the impact of fermented dairy consumption on abdominal obesity, providing more nuanced evidence that the type and composition of dairy may be more significant than the overall quantity consumed. These findings contribute to the growing body of evidence on dairy and obesity, suggesting that certain fermented dairy products, especially high-fat yogurt, may play a role in mitigating obesity risk.

Strengths and limitations of the study

This meta-analysis has several strengths, including a rigorous search methodology, a comprehensive evaluation of different types of fermented dairy products, and the incorporation of both linear and non-linear dose–response analyses to enhance the accuracy of findings. The study also employed strict inclusion and exclusion criteria, sensitivity analyses, and risk-of-bias assessments using the ROBINS-E tool to minimize potential biases. However, some limitations should be acknowledged. The inclusion of observational studies introduces the possibility of residual confounding and measurement bias, limiting the ability to infer causality. Additionally, variations in study designs, participant characteristics, and dietary assessment methods may contribute to heterogeneity in findings. The limited availability of studies specifically examining different fermented dairy products further restricts the generalizability of results. Future research should focus on well-designed randomized controlled trials with long-term follow-up to confirm these associations and elucidate underlying mechanisms, such as the role of probiotics, short-chain fatty acids, and specific fatty acid profiles in abdominal obesity risk.

Conclusion

This meta-analysis provides valuable insights into the potential role of fermented dairy products, particularly high-fat varieties, in reducing abdominal obesity risk. While no significant overall association between fermented dairy intake and abdominal obesity was found, the dose–response analysis indicated that high-fat fermented yogurt may reduce the risk of abdominal obesity, with a linear decrease observed when consumed in amounts of 8 to 21 servings per week. The findings underscore the complexity of the relationship between fermented dairy and obesity, highlighting the importance of various dietary components, including protein, fat, calcium, and probiotics. These factors may interact to influence metabolic processes and fat distribution, suggesting the need for a more holistic approach to dietary recommendations for obesity prevention. However, the observational nature of the studies included in this analysis limits the ability to draw definitive causal conclusions. Further research, particularly randomized controlled trials, is needed to confirm these findings and explore the underlying mechanisms, such as the role of probiotics, fatty acids, and short-chain fatty acids, in reducing abdominal obesity risk across diverse populations.

Supplementary Information

Below is the link to the electronic supplementary material.

Author contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by [Fatemeh Sadat Hashemi Javaheri], [Milad Nasiri jounaghani] and [Amirhossein Sahebkar]. The first draft of the manuscript was written by [Mostafa Norouzzadeh] and [Pedram Delgarm] and [Hossein Shahinfar] and [Artemiss Mirdar Harijani] and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Funding

No grants were received for this research from public, commercial, or non-profit funding agencies.

Data availability

No datasets were generated or analyzed during the current study.

Declarations

Conflict of interest

The authors declare no competing interests.