Metabolic syndrome (MetS) represents a constellation of metabolic abnormalities including insulin resistance, abdominal obesity, elevated blood glucose, lipid abnormalities, and high blood pressure, posing a substantial worldwide public health concern (1). Research suggests that roughly 25% of the world’s population—exceeding 1 billion individuals—suffers from this condition, while in the United States, approximately one-third of adults meet diagnostic criteria (2). Beyond being a clinical condition resulting from the convergence of multiple metabolic risk factors, MetS significantly increases vulnerability to numerous chronic conditions such as heart disease and type 2 diabetes (3). Consequently, prevention and management of MetS are essential to interrupt the onset of associated diseases, slow disease progression, and reduce the overall risk of morbidity and mortality.

Modifiable lifestyle factors, particularly dietary habits, play a pivotal role in the pathogenesis of MetS. Recent findings underscore their reciprocal relationship with gut microbiota in modulating metabolic well-being. Western dietary patterns, characterized by high fat consumption, disrupt gut microbiota composition by diminishing beneficial bacteria (e.g., Bifidobacterium) and fostering the growth of opportunistic pathogens, thereby reducing short-chain fatty acids (SCFAs) production, compromising gut barrier function, and triggering systemic inflammation that exacerbates insulin resistance and metabolic dysfunction (4). In contrast, diets rich in dietary fiber and polyphenols enhance microbial diversity, promote SCFAs synthesis, and inhibit endotoxin-producing bacteria, thereby collectively improving glucose regulation and lipid metabolism (5, 6). Therefore, gut microbiota is a key mediator linking dietary patterns with MetS development. Traditional dietary indices like the Mediterranean diet (MD) and the Dietary Approaches to Stop Hypertension (DASH) have demonstrated protective relationships with MetS by emphasizing anti-inflammatory and cardiometabolic advantages (7, 8). However, these indices do not explicitly account for diet-microbiome interactions, thereby limiting their capacity to evaluate the microbial mechanisms of dietary impacts. To fill this gap, the dietary index for gut microbiota (DI-GM) was developed by Kase et al. as a novel tool to quantify dietary quality related to gut microbiota health (9). The DI-GM comprises 14 food components: beneficial components (e.g., whole grains, fermented dairy products, soybeans, green tea) that enhance microbial diversity and the production of SCFAs, and harmful components (e.g., red meat, refined grains, high-fat diets) that are linked to microbiota imbalances. Initial research indicates that higher DI-GM scores are linked to a decreased risk of diabetes and depression (10, 11), underscoring their potential as indicators of microbial-mediated metabolic health. Despite advancements, comprehensive epidemiological evidence establishing a connection between DI-GM and MetS is lacking, and it remains uncertain whether body mass index (BMI), a fundamental measure of obesity, mediates the impact of DI-GM on MetS.

Therefore, this study utilized data from the National Health and Nutrition Examination Survey (NHANES) 2007–2020 to investigate the association between DI-GM score and MetS prevalence in American adults, as well as the mediating role of BMI. These analyses aim to provide evidence for developing targeted dietary intervention strategies to address MetS.

This study analyzed the NHANES data from 2007 to 2020 to investigate the association between DI-GM and MetS. Findings revealed that higher DI-GM scores were linked to lower metabolic syndrome prevalence, even after adjusting for multiple covariates. RCS analysis additionally verified the linear association between these factors. Furthermore, this relationship was more evident among individuals maintaining healthy lifestyle behaviors, such as non-smoking, non-drinking, and exercising. It is important to note that BMI plays a key mediating role (mediating effect accounted for 52.71%). The findings indicate that dietary patterns targeting gut microbiota can reduce the risk of MetS by regulating body weight and directly improving metabolic homeostasis, thereby emphasizing the distinct value of DI-GM in comparison to conventional dietary indices.

It is important to emphasize that DI-GM is a proxy indicator constructed based on food components known to influence the composition and function of gut microbiota. Although DI-GM does reflect dietary patterns associated with gut health, it does not directly measure microbial diversity, composition, or metabolite levels. Consequently, the present findings indicate an association between diet and metabolic syndrome, which is mediated through putative microbial pathways rather than directly confirmed microbial causal changes. Notwithstanding this limitation, the dynamic interaction of dietary components with intestinal flora provides clear theoretical support for the underlying mechanism of DI-GM (see Supplementary Table S1 for details). Short-term dietary changes can quickly and reversibly alter microbiota composition, while long-term changes may lead to lasting changes in the microbial genome (13). DI-GM is designed on this principle by integrating 14 key dietary ingredients that affect gut flora, beneficial ingredients (e.g., whole grains, fermented dairy products, coffee) modulate insulin sensitivity by promoting short-chain fatty acid (SCFA) production, increasing the abundance of beneficial bacteria, improving intestinal barrier function, and inhibiting inflammation (14–16); harmful ingredients (e.g., red meat, high-fat diets) induce flora dysregulation, producing pro-inflammatory metabolites (e.g., TMAO), and exacerbating metabolic derangements (17–19). In comparison with conventional indices such as the Mediterranean Diet (MD) and the Dietary Approaches to Stop Hypertension (DASH), which demonstrate protective effects against MetS through anti-inflammation, modulation of lipid metabolism, or control of blood pressure (20, 21), the DI-GM is distinct in its approach. The DI-GM explicitly integrates foods that can be used as substrates for bacterial fermentation or that can inhibit bacterial dysbiosis (e.g., yogurt, kefir), and categorizes high-fat diets (≥40% of energy from fat) as unfavorable components (9). This dietary effect on flora is the result of multicomponent synergism intervening at the root of flora dysbiosis, providing a more mechanistic strategy for the prevention of MetS and a more precise tool for the assessment of diet-flora-metabolism interactions.

Further analysis in this study revealed that higher scores on the DI-GM score for harmful ingredients (i.e., lower intake of harmful ingredients) were associated with significantly lower prevalence of MetS. This finding is consistent with previous research that the DI-GM reduces the risk of diabetes by improving metabolic disorders (22). This suggests that reducing harmful ingredient intake is generalisable to improving the metabolic core mechanisms. However, scores on beneficial dietary components other than avocado, chickpeas, and coffee were not significantly associated with lower MetS prevalence. This phenomenon may be indicative of the heterogeneity of effects of different components in the diet-gut microbiota axis, or may be limited by the accuracy of short-term dietary assessments. Furthermore, it has been determined that specific beneficial components may exert a “double-edged sword” effect. In the male population, abnormal glucose metabolism has been identified as a significant contributor to the development of MetS, while the consumption of certain dairy products may indirectly lead to the onset of obesity and insulin resistance by increasing total calorie intake, a consequence of the presence of added sugars (23). Concurrently, excessive intake of green tea has the potential to attenuate its anticipated metabolic protective effects (24). The findings of this study suggest that a reduction in the consumption of detrimental substances (e.g., red meat, high-fat diets) may exert a more immediate effect on the prevention of MetS than merely increasing the consumption of beneficial substances.

Moreover, in subgroup analyses, the inverse correlation between DI-GM and MetS was more pronounced in exercise, non-smoking, and non-drinking individuals. However, it is important to note that these analyses were exploratory studies without statistical correction for multiple comparisons, which may increase the risk of false positives. Nevertheless, these results suggest a potential synergistic effect between DI-GM and health-promoting behaviors. These findings suggest a synergistic effect between DI-GM and health-promoting behaviors. Exercise improves gut microbiota composition and diversity by increasing beneficial bacteria, reducing gut inflammation, and enhancing intestinal barrier function (25). When combined with a high-quality DI-GM diet, they may synergistically enhance beneficial gut microbial effects, more effectively reducing MetS risk. Smoking and drinking are linked to dysbiosis and increased gut permeability (26, 27). Conversely, refraining from harmful habits maintains gut microbiota integrity, thereby enabling the beneficial components of DI-GM to more effectively regulate gut microbiota and support metabolic health. These interactions highlight the importance of combining dietary interventions with health-behavior promotion in MetS prevention and management, surpassing single-intervention limitations to maximize MetS risk reduction.

Notably, this study first quantified the mediating role of BMI in the association between the DI-GM score and MetS, expanding our understanding of the diet-gut microbiota-metabolism axis. These findings highlight weight management as a critical pathway through which diet improves metabolic health, likely due to the DI-GM diet’s high fiber content, abundant in whole grains and prebiotics such as fermented dairy products, that enhances satiety, reduces energy intake, and promotes BMI reduction (28). Additionally, restricting red meat and high-fat diets impedes fat accumulation, further alleviating visceral obesity (29). These results align with prior research showing that dietary interventions may improve metabolic disorders through the microbiota-metabolism axis and weight control (30). Importantly, the direct effect indicates the existence of BMI-independent pathways (e.g., enhancement of intestinal barrier), corroborating the “gut microbiota-metabolism” direct axis identified in earlier studies (31, 32). By verifying both the BMI-dependent and-independent mechanisms, this study enriches the theoretical framework for diet-metabolic disease relationships. Future research could deeply investigate the interaction between these two pathways using metabolomics, providing a more comprehensive understanding of dietary influences on metabolic health.

Although this study, based on NHANES 2007–2020 data, included 21,100 participants (representing 160 million after weighting), ensuring a large, nationally representative sample that enhances the universality and statistical power of the results. However, several limitations should be acknowledged. First, the cross-sectional design cannot establish a temporal relationship between DI-GM and MetS, raising the possibility of reverse causality. Individuals at high risk of MetS might alter their dietary behaviors due to metabolic abnormalities. Second, despite controlling for major confounders, unmeasured and unknown confounders could still influence the results. Third, dietary assessment relies on 24-h recalls, which may not accurately reflect long-term eating patterns and are susceptible to recall bias. Fourth, DI-GM infers gut microbiota status from dietary components but lacks direct biological data on microbiota composition, abundance, and functional metabolites. Lastly, NHANES samples are predominantly from the US, so extrapolating results to other ethnicities or cultural backgrounds should be done cautiously. In the future, it is recommended that the causality be verified through prospective cohorts and that the mechanism be explored in more depth using macrogenomic sequencing, in order to further verify whether the DI-GM-related dietary pattern plays a protective role through specific changes in the microbiota, and to carry out validation studies in multi-ethnic groups.

This study demonstrated that DI-GM was significantly and negatively associated with the prevalence of metabolic syndrome (MetS), and that body mass index (BMI) played an important partial mediating role in this association. A healthy lifestyle (e.g., regular exercise, not smoking or drinking alcohol) could reinforce the protective effect of DI-GM. This suggests that dietary strategies targeting gut flora could directly regulate metabolic homeostasis through microbial metabolism and indirectly reduce the risk of MetS by managing weight. In the future, it will be necessary to analyze the molecular mechanism of the ‘diet-flora-BMI-metabolism’ axis by combining longitudinal cohort and multi-omics techniques. This will provide a basis for integrating dietary interventions and weight control strategies.