Abstract
Background
Chronic low-grade inflammation and insulin resistance are hallmark features of metabolic syndrome (MetS). The pathways through which healthy diets influence metabolic health outcomes remain unclear.
Methods
This study utilized six cycles (2005–2016) of cross-sectional data from the National Health and Nutrition Examination Survey (NHANES). A total of 10,518 adults (≥ 20 years) with complete data on diet, metabolic risk factors, and relevant covariates were included. Dietary live microbe intake was quantified and categorized into three groups—low, medium, and high— on the basis of estimated colony-forming units in foods, as well as by categorizing the consumption of foods with medium/high microbial content (MedHi). MetS was defined according to the National Cholesterol Education Program Adult Treatment Panel III criteria. Multivariate logistic regression, restricted cubic spline analyses, and mediation analyses were performed to examine the associations and potential mechanistic pathways via inflammation [white blood cell (WBC) counts, neutrophil counts, serum albumin, and systemic immune-inflammation (SII) index] and insulin resistance [triglyceride-glucose (TyG) index and homeostatic model assessment of insulin resistance (HOMA-IR)].
Results
Compared with the non-MedHi group participants, the risk of MetS was 17% lower (odds ratio [OR]: 0.83, 95% confidence interval [CI]: 0.71, 0.98) in the high-MedHi food intake group. Compared with that of the low microbe food group, the risk of MetS was 16% lower (OR: 0.84, 95% CI: 0.72, 0.98) in the medium microbe food group. Mediation analysis indicated that the association between dietary live microbe intake and a reduced risk of MetS was mediated by improved systemic inflammation, but no significant mediating effect of insulin resistance was found.
Conclusions
Moderately increasing the intake of foods rich in high level microbes in the daily diet is associated with a reduced risk of MetS and may have potential benefits for maintaining stable blood pressure and blood lipid levels. Systemic inflammation markers, including the serum neutrophil counts and SII, partially mediate the association between dietary live microbe intake and MetS.
Supplementary Information
The online version contains supplementary material available at 10.1186/s41043-025-01182-w.
Keywords: Dietary live microbe intake, Metabolic syndrome, Inflammation, Insulin resistance, Mediation analysis, National health and nutrition examination survey
Introduction
Metabolic syndrome (MetS) is a complex cluster of metabolic abnormalities, including insulin resistance, atherogenic dyslipidemia, central obesity, and hypertension [1]. According to the Centers for Disease Control and Prevention (CDC), the prevalence of MetS in the United States increased by 35% from the 1980 s to 2012 [2]. In addition to genetic and epigenetic factors, several lifestyle and environmental influences, such as diet, gut microbiota dysbiosis, and insufficient physical activity, have been identified as major risk factors for MetS [3, 4].
Chronic low-grade inflammation and insulin resistance are hallmark features of MetS [5]. Diet, as a key regulator of the body’s metabolic landscape, has a significant regulatory effect on inflammation and metabolism [6]. Live microbes, which are safe for consumption and are typically obtained through dietary intake, support gut health by interacting with the mucosal surfaces of the gastrointestinal tract and influencing host metabolic processes through multiple pathways [7, 8].
Marco et al. proposed a classification system to define and estimate the dietary intake of live microbes using data from the National Health and Nutrition Examination Survey (NHANES) [9]. The relationship between the dietary intake of live microbes and conditions such as cardiovascular disease, non-alcoholic fatty liver disease, and cognitive function has been extensively investigated [10–12]. Current research predominantly focuses on the impact of artificial probiotic supplementation on MetS. Previously, Wang et al. reported that dietary live microbe intake and nondietary prebiotics/probiotics were negatively associated with the prevalence of metabolic syndrome [13]. However, there is currently a lack of information regarding the potential mechanisms and pathways underlying the association between dietary live microbe intake and MetS, warranting further exploration to elucidate the implications of MetS prevention.
The aim of this study was to investigate the relationship between dietary live microbe intake and MetS in American adults, with the hypothesis that chronic inflammation and insulin resistance mediate this relationship. By analyzing the interplay between dietary live microbe intake, inflammation, metabolism, and MetS, our objective was to elucidate the roles of inflammation and metabolism in this relationship. Understanding these connections not only aids in the prevention and management of MetS but also informs comprehensive treatment strategies for MetS, thereby improving the overall health of affected patients.
Methods
Study design and participants
The data utilized in this study were obtained from NHANES. The NHANES program is designed to assess the health and nutritional status of individuals in the United States. It is a multistage, stratified, and nationally representative cross-sectional study of the U.S. population. The survey collects a broad range of data, including demographic information, dietary intake, physical examination results, laboratory findings, and health-related interview responses. Further details are available online (https://www.cdc.gov/nchs/nhanes/index.htm).
Participants were included if they were aged ≥ 20 years and had complete data on dietary intake, metabolic syndrome components, and covariates. Participants were excluded if they (1) were younger than 20 years; (2) had missing dietary or metabolic data; or (3) lacked information on covariates or mediators. Because NHANES represents the U.S. population, our findings may not be directly generalizable to other populations.
Six cycles of NHANES data (2005–2016) were utilized in this study to ensure an adequate sample size. A total of 26,756 participants were excluded because they were under 20 years of age, 3629 participants were excluded because of incomplete dietary, 17,597 participants were excluded because of incomplete metabolic syndrome data, and 2317 participants were excluded because of missing covariates and mediations. A total of 10,637 participants were included in this study, comprising 4205 MetS patients and 6432 non-MetS patients. Extended screening details are elaborated in Fig. 1.
Fig. 1.
Flowchart of the study and excluded participants, from NHANES 2005–2016
The NHANES studies were approved by the National Center for Health Statistics and Research’s Ethics Review Committee, and all participants provided written informed consent. Access to the NHANES database does not require ethical or administrative approval.
Dietary live microbe data
The intake of energy and nutrients was estimated using 24-hour dietary recall data, which were linked to the Food and Nutrient Database for Dietary Studies corresponding to the NHANES cycle, as provided by the U.S. Department of Agriculture (USDA). To estimate the number of live microbes per gram for foods corresponding to the 9,388 food codes across 48 subgroups in the NHANES database, four experts (Maria L. Marco, Mary E. Sanders, Robert Hutkins, and Colin Hill) evaluated each food based on prior literature, authoritative reviews, and known effects of food processing, such as pasteurization, on microbial viability. Each food was subsequently classified into one of three categories: low (< 10⁴ CFU/g), medium (10⁴–10⁷ CFU/g), or high (>10⁷ CFU/g) expected numbers of live microbes. These categories corresponded to pasteurized foods (< 10⁴ CFU/g), unpeeled fresh fruits and vegetables (10⁴–10⁷ CFU/g), unpasteurized fermented foods and probiotic supplements (>10⁷ CFU/g), respectively. Additionally, a fourth category, termed MedHi, was introduced to encompass foods from the Med, Hi, or both the Medium and High categories. When Med and Hi foods were analyzed separately as distinct variables in the same model, their coefficients were found to be similar. This finding supports a more parsimonious model, combining Med and Hi foods into a single variable termed MedHi [9]. The classification of each food was determined through intra- and interterm consultation, with external consultation from Fred Breidt, a USDA Agricultural Research Service microbiologist. The classification of each food has been described previously and is shown in Table S1.
Following the methodology of Sanders et al. [9], participants were classified into three groups based on their overall intake of foods with varying microbial contents: low (consumption of foods with low microbial content only), medium (intake of foods with moderate, but not high, microbial content), and high (intake of foods with high microbial content). Additionally, live microbe intake was quantified by categorizing participants into three groups based on their consumption of MedHi foods: Non-MedHi (participants who did not consume any MedHi foods); Low MedHi (participants whose MedHi intake was above zero but below the median level); and High MedHi (participants who consumed MedHi foods above the median level). This previously validated method allows for the classification of participants’ diets based on estimated levels of live microbes [10, 14].
Definition of metabolic syndrome
MetS was defined according to the NCEP-ATP III guidelines [15]. Participants were diagnosed with MetS if they met at least three of the following five criteria: (1) central obesity: waist circumference ≥ 102 cm in men or ≥ 88 cm in women; (2) hypertriglyceridemia: serum triglycerides (TGs) ≥ 150 mg/dL; (3) low high-density lipoprotein cholesterol (HDL-C): serum HDL-c < 40 mg/dL in men and < 50 mg/dL in women; (4) hypertension: systolic blood pressure (SBP) ≥ 130 mm Hg, diastolic blood pressure (DBP) ≥ 85 mm Hg, or current antihypertensive treatment; (5) hyperglycemia: fasting plasma glucose (FPG) ≥ 100 mg/dL or current antihyperglycemic treatment. Data on waist circumference, body weight, and height were collected according to standardized procedures during physical examinations. SBP and DBP were calculated as the arithmetic averages of repeated measurements (up to four times) for each participant. Serum TGs and HDL-C levels were measured, and fasting glucose was assessed in plasma.
Covariates
Age, gender, race/ethnicity, education, marital status, poverty-income ratio (PIR), smoking status, alcohol consumption, physical activity (PA), body mass index (BMI), estimated glomerular filtration rate (eGFR), serum uric acid levels, and daily energy intake were considered potential confounders. Race/ethnicity was classified into the following categories: Mexican American, non-Hispanic White, non-Hispanic Black, other Hispanic, and other (including multiracial participants). Educational attainment was categorized as less than high school, high school or equivalent, and more than high school. PIR was stratified into three categories: ‘Low income’ (PIR ≤ 1.3), ‘Moderate income’ (1.3 < PIR < 3.5), and ‘High income’ (PIR ≥ 3.5). Information on PA, smoking status, and alcohol consumption was obtained through self-reported survey questionnaires. Participants who reported never smoking or smoking fewer than 100 cigarettes in their lifetime were classified as never smokers, while those who reported having smoked 100 cigarettes but were not current smokers were classified as former smokers. Current smokers were defined as individuals who reported smoking at least 100 cigarettes in their lifetime or on some days. Alcohol consumption was assessed based on the question: “Had at least 12 alcoholic drinks in the past year?” Physical activity was categorized into vigorous physical activity (e.g., high-intensity activities, fitness, and sports such as running or basketball) and moderate physical activity (e.g., brisk walking, swimming, and cycling at a regular pace), as reported by participants. BMI was calculated as weight (kg) divided by height squared (m²). Serum creatinine and uric acid data were extracted from the NHANES survey. eGFR was estimated using the formula developed by the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) in 2012, which incorporates variables such as age, gender, race/ethnicity, and serum creatinine (SCr) for different populations [16]. Daily energy intake was calculated as the average value obtained from 24-hour dietary recall interviews.
Inflammation indicators as mediating factors
We describe the WBC counts, neutrophil counts, serum albumin, and SII index as inflammatory indicators. In the NHANES database, professional researchers use automatic hematology analysis equipment to measure and record the complete blood count on blood specimens. The calculation formula for SII is (platelet count × neutrophil count)/lymphocytes count [17]. According to the NHANES quality control and quality assurance protocol, which meets the requirements of the 1988 Clinical Laboratory Improvement Act, professional researchers use the bromocresol purple method to measure serum albumin concentration.
Insulin resistance indicators as mediating factors
We describe TyG index and HOMA-IR as inflammatory indicators. The TyG index was calculated as Ln [fasting TG (mg/dL) × fasting glucose (mg/dL)/2] [18]. HOMA-IR was calculated as fasting glucose (mmol/L) × fasting insulin (µU/mL)/22.5 [19]. Fasting insulin was measured using the AIA-PACK IRI. The AIA-PACK IRI is a two-site immunoenzymometric assay, which is performed on Tosoh AIA system analyzer.
Statistical analysis
Given the complex stratified probability sampling characteristics of the NHANES database, we conducted a weighted analysis based on the weights recommended by the database. The weights included in this study comprise 1-day dietary interview weights (WTDR1D), strata (SDMVSTRA), and primary sampling units (SDMVPSU). Categorical variables were reported as unweighted frequencies and weighted percentages (%), while continuous variables were expressed as weighted means with interquartile ranges (IQRs). Variations in participant characteristics across different intake patterns were assessed using Chi-square tests for categorical variables and analysis of variance (ANOVA) for continuous variables. This study constructed weighted univariable and multivariable logistic regression models and linear regression models to separately evaluate the relationships among live microbe intake, inflammation and insulin resistance indicators, and MetS. No covariates were adjusted in the univariable model, whereas in the multivariable model adjustments were made for sex, age, race/ethnicity, education attainment, PIR, drinking status, physical activity, BMI, smoking status, alcohol consumption, serum uric acid, and daily energy intake.
To assess potential nonlinear relationships between live microbial intake and MetS and its components, total live microbe intake was modeled with restricted cubic splines (RCSs) (4 knots at the 5th/35th/65th/95th percentiles), where the knot number was optimized through Akaike information criterion (AIC) minimization.
This study applied mediation analysis aimed at determining whether WBC, leukocytes, serum albumin, SII, TyG index and HOMA-IR mediate the relationship between live microbe intake and MetS. The total effect represents the direct relationship between live microbe intake and MetS, unaffected by mediator variables. The indirect effect refers to the impact of mediator variables on MetS through live microbe intake. The direct effect signifies the impact of live microbe intake on MetS after controlling for mediator variables.
All statistical tests were two-tailed, and a p-value of less than 0.05 was considered statistically significant. Statistical analyses were performed using R version 4.2.2.
Results
Baseline characteristics according to MedHi category
A total of 10,637 participants were included in the study, with males accounting for 49% of the sample. The median age of the participants was 48 years, and 4205 participants were diagnosed with MetS. Among participants with non-MetS and MetS, significant differences were observed in age, race/ethnicity, education level, marital status, PIR, physical activity, smoking status, alcohol consumption, BMI, eGFR, serum uric acid, energy intake, WBC count, neutrophils count, SII, serum albumin, TyG index and HOMA-IR, while gender was not significantly different. Notably, participants in the non-MetS group had higher total MedHi intake and higher MedHi food intake. The baseline characteristics of the participants are presented in Table 1.
Table 1.
The clinical characteristics of the study population with and without metabolic syndrome
| Characteristic | Non-metabolic syndrome N = 6361 (62%) |
Metabolic syndrome N = 4205 (38%) |
P Value |
|---|---|---|---|
| Age (years) | 42.61 (30.00, 56.00) | 55.00 (44.00, 66.00) | < 0.001 |
| Gender (%) | 0.4 | ||
| Male | 3164 (51%) | 2080 (50%) | |
| Female | 3197 (49%) | 2077 (50%) | |
| Race/ethnicity (%) | < 0.001 | ||
| Mexican American | 911 (7.3%) | 674 (8.0%) | |
| Non-Hispanic White | 2930 (70%) | 2058 (73%) | |
| Non-Hispanic Black | 1235 (9.9%) | 778 (9.1%) | |
| Other | 1285 (13%) | 661 (9.5%) | |
| Education level (%) | < 0.001 | ||
| Below high school | 1280 (14%) | 1161 (18%) | |
| High School or above | 5081 (86%) | 2996 (82%) | |
| Marital status (%) | < 0.001 | ||
| Married/cohabiting | 3794 (62%) | 2600 (66%) | |
| Widowed/divorced/separated | 1141 (16%) | 1121 (23%) | |
| Never married | 1426 (22%) | 436 (11%) | |
| PIR (%) | 0.011 | ||
| Low income | 1872 (21%) | 1348 (22%) | |
| Moderate income | 2324 (34%) | 1618 (37%) | |
| High income | 2165 (46%) | 1191 (41%) | |
| Physical activity (%) | < 0.001 | ||
| Inactive | 3166 (47%) | 2288 (49%) | |
| Moderate | 1609 (26%) | 1042 (29%) | |
| Vigorous | 339 (5.3%) | 184 (4.1%) | |
| Both moderate and vigorous | 1247 (22%) | 643 (17%) | |
| Smoking status (%) | < 0.001 | ||
| Non-smoker | 3623 (56%) | 2049 (49%) | |
| Former smoker | 1404 (23%) | 1291 (31%) | |
| Current smoker | 1334 (21%) | 817 (20%) | |
| Alcohol consumption (%) | < 0.001 | ||
| Current drinker | 4273 (74%) | 2302 (62%) | |
| Former drinker | 531 (6.8%) | 590 (12%) | |
| Non-drinker | 1557 (19%) | 1265 (26%) | |
| BMI (Kg/m 2 ) | 25.60 (22.77, 28.93) | 31.54 (28.30, 35.82) | < 0.001 |
| eGFR (mL/min/1.73 m 2 ) | 98 (84, 112) | 89 (74, 103) | < 0.001 |
| Serum uric acid (mg/dL) | 5.10 (4.30, 6.00) | 6.00 (5.00, 6.90) | < 0.001 |
| Energy intake (Kcal/d) | 2057 (1529, 2709) | 1953 (1459, 2565) | < 0.001 |
| Total MedHi Intake (100 g/d) | 0.56 (0.00, 1.82) | 0.33 (0.00, 1.62) | 0.001 |
| MedHi Group (%) | 0.001 | ||
| Non-MedHi | 2475 (35%) | 1758 (39%) | |
| Low MedHi | 594 (9%) | 432 (10%) | |
| High MedHi | 3292 (56%) | 1967 (50%) | |
| White blood cell count (1000 cell/uL) | 6.10 (5.20, 7.40) | 7.10 (5.90, 8.40) | < 0.001 |
| Neutrophils count (1000 cell/uL) | 3.50 (2.80, 4.40) | 4.10 (3.30, 5.20) | < 0.001 |
| SII index | 441 (323, 614) | 490 (356, 700) | < 0.001 |
| Serum albumin (g/dL) | 4.30 (4.10, 4.50) | 4.20 (4.00, 4.40) | < 0.001 |
| TyG index | 8.34 (8.01, 8.66) | 9.07 (8.71, 9.42) | < 0.001 |
| HOMA-IR | 1.71 (1.15, 2.69) | 4.06 (2.61, 6.35) | < 0.001 |
Median (IQR) for continuous; N (%) for categorical. Wilcoxon rank-sum test for complex survey samples; Chi-squared test with Rao & Scott’s second-order correction. Characteristics with bold headings indicate statistically significant differences between the non-metabolic syndrome group and metabolic syndrome group (P < 0.05). PIR, poverty income ratio; BMI, body mass index; eGFR, estimated Glomerular Filtration Rate; SII, systemic immune-inflammation index; TyG index, Triglyceride-Glucose index; homeostatic model assessment of IR, HOMA-IR.
Association between dietary live microbe intake and MetS
In the weighted univariate logistic regression model, a 100-unit increase in MedHi food intake was significantly associated with a reduced risk of MetS [odds ratio (OR): 0.93, 95% confidence interval (CI): 0.89, 0.97] when live microbe intake was treated as a continuous variable. However, when more potential confounding factors were considered, this effect did not appear to be significant (OR: 0.95, 95% CI: 0.90, 1.00).
The results of the univariate weighted logistic regression model revealed that, compared with the non-MedHi group, the risk of MetS was 20% lower (OR: 0.80, 95% CI: 0.69, 0.92) in the high-MedHi food intake group, while there was no significant difference in the risk of MetS between the non-MedHi food intake group and the low-MedHi food intake group (OR: 1.01, 95% CI: 0.85, 1.20). The results of the multivariate weighted logistic regression model showed that, compared with that in the non-MedHi group, the risk of MetS was 17% lower (OR: 0.83, 95% CI: 0.71, 0.98) in the high-MedHi food intake group. Similarly, there was no significant difference in MetS risk between the non-MedHi food intake group and the low-MedHi food intake group (OR: 1.04, 95% CI: 0.84, 1.28).
When the participants were stratified into three groups based on intake of foods with varying microbial contents, the results of the univariate weighted logistic regression model showed that, compared with the low microbe food group, the risk of MetS was 23% lower (OR: 0.77, 95% CI: 0.66, 0.90) in the high microbe food group, while no significant difference in the medium microbe food group (OR: 0.87, 95% CI: 0.76, 1.00) was found. The results of the multivariate weighted logistic regression model showed that, compared with that in the low microbe food group, the risk of MetS was 16% lower (OR: 0.84, 95% CI: 0.72, 0.98) in the medium microbe food group, while no significant difference in the high microbe food group (OR: 0.90, 95% CI: 0.74, 1.10) was found. More details are presented in Table 2.
Table 2.
Associations between dietary live microbe intake and metabolic syndrome
| Metabolic syndrome | Univariable model1 | Multivariable model2 | ||
|---|---|---|---|---|
| OR (95%CI) | P-value | OR (95%CI) | P-value | |
| Per 100-unit increase | 0.93(0.89, 0.97) | < 0.001 | 0.95(0.90, 1.00) | 0.063 |
| Category of MedHi food intake | ||||
| Non-MedHi | Reference | Reference | ||
| Low-MedHi | 1.01(0.85, 1.20) | 0.899 | 1.04(0.84, 1.28) | 0.753 |
| High-MedHi | 0.80(0.69, 0.92) | 0.002 | 0.83(0.71, 0.98) | 0.031 |
| Category of microbe contents food intake | ||||
| Low | Reference | Reference | ||
| Medium | 0.87(0.76,1.00) | 0.060 | 0.84(0.72,0.98) | 0.026 |
| High | 0.77(0.66,0.90) | 0.001 | 0.90(0.74,1.10) | 0.303 |
1 No covariates were adjusted
2 Adjusted for sex, age, race/ethnicity, education attainment, poverty-income ratio, drinking status, physical activity, body mass index, smoking status, alcohol consumption, serum uric acid, and daily energy intake
CI, confidence interval; OR, odds ratio
In the regression analysis of live microbe intake and MetS components, we found that the risk ratio of low HDL-C in the high-MedHi group was lower than that in the non-MedHi group (OR: 0.86, 95% CI: 0.75, 0.99). For a 100-unit increase in MedHi food intake, the risk of elevated BP decreased (OR: 0.90, 95% CI: 0.85, 0.94). The risk of elevated BP was lower in the high-MedHi group than in the non-MedHi group (OR: 0.80, 95% CI: 0.67, 0.95); the risk of elevated BP was lower in the high live microbe food group than in the low live microbe food group (OR: 0.76, 95% CI: 0.61, 0.95). More details are presented in TableS2.
Association between dietary live microbe intake and inflammation indicators
According to the univariate model, compared with the Non-MedHi group, the low-MedHi group had lower white blood cell counts (β: −0.09, 95% CI: −0.28, 0.11), neutrophil counts (β: −0.05, 95% CI: −0.22, 0.12), SII index (β: −1.58, 95% CI: −32.37, 29.21), and higher serum albumin levels (β: 0.003, 95% CI: −0.03, 0.04); the white blood cell counts (β: −0.42, 95% CI: −0.54, −0.30), neutrophil counts (β: −0.29, 95% CI: −0.38, −0.20), and SII index (β: −24.56, 95% CI: −43.04, −6.08) were lower in the high-MedHi group, while serum albumin (β: 0.04, 95% CI: 0.03, 0.06) was greater. In the multivariate model, compared with the Non-MedHi group, the low-MedHi group had lower SII index (β: −0.48, 95% CI: −29.93, 28.97) and higher white blood cell counts (β: 0.04, 95% CI: −0.15, 0.22), neutrophil counts (β: 0.02, 95% CI: −0.13, 0.18), and serum albumin level (β: 0.003, 95% CI: −0.02, 0.03); the white blood cell counts (β: −0.14, 95% CI: −0.26, −0.03), neutrophil counts (β: −0.12, 95% CI: −0.21, −0.04), and SII index (β: −20.70, 95% CI: −40.10, −1.31) were lower in the high-MedHi group, while serum albumin (β: 0.04, 95% CI: 0.02, 0.05) was higher.
In the univariate model, compared with the low microbe food group, the medium microbe food group had lower white blood cell counts (β: −0.33, 95% CI: −0.46, −0.21), neutrophil counts (β: −0.24, 95% CI: −0.33, −0.15), SII index (β: −17.70, 95% CI: −42.47, 7.07), and higher serum albumin levels (β: 0.04, 95% CI: 0.02, 0.06); the white blood cell counts (β: −0.42, 95% CI: −0.57, −0.28), neutrophil counts (β: −0.27, 95% CI: −0.39, −0.15), and SII index (β: −23.69, 95% CI: −41.34, −6.04) were lower in the high microbe food group, while serum albumin levels (β: 0.04, 95% CI: 0.02, 0.06) was greater. In the multivariate model, compared with the low microbe food group, the medium microbe food group had lower white blood cell counts (β: −0.10, 95% CI: −0.23, 0.02), neutrophil counts (β: −0.10, 95% CI: −0.19, −0.01), SII index (β: −20.70, 95% CI: −39.22, −2.17) and higher serum albumin levels (β: 0.003, 95% CI: −0.02, 0.03); the white blood cell counts (β: −0.13, 95% CI: −0.27, 0.02), neutrophil counts (β: −0.10, 95% CI: −0.22, 0.02), and SII index (β: −12.69, 95% CI: −37.87, 12.50) were lower in the high microbe food group, while serum albumin (β: 0.03, 95% CI: 0.01, 0.05) was greater. For more specific details, refer to Table 3.
Table 3.
Association between dietary live microbe intake and inflammation indicators
| Univariable model1 | Multivariable model2 | |||
|---|---|---|---|---|
| β(95%CI) | P-value | β(95%CI) | P-value | |
| WBC counts | ||||
| Category of MedHi food intake | ||||
| Non-MedHi | Reference | Reference | ||
| Low-MedHi | −0.09(−0.28, 0.11) | 0.378 | 0.04(−0.15, 0.22) | 0.694 |
| High-MedHi | −0.42(−0.54, −0.30) | < 0.001 | −0.14(−0.26, −0.03) | 0.018 |
| Category of microbe contents food intake | ||||
| Low | Reference | Reference | ||
| Medium | −0.33(−0.46, −0.21) | < 0.001 | −0.10(−0.23, 0.02) | 0.099 |
| High | −0.42(−0.57, −0.28) | < 0.001 | −0.13(−0.27, 0.02) | 0.085 |
| Neutrophil counts | ||||
| Category of MedHi food intake | ||||
| Non-MedHi | Reference | Reference | ||
| Low-MedHi | −0.05(−0.22, 0.12) | 0.551 | 0.02(−0.13, 0.18) | 0.782 |
| High-MedHi | −0.29(−0.38, −0.20) | < 0.001 | −0.12(−0.21, −0.04) | 0.006 |
| Category of microbe contents food intake | ||||
| Low | Reference | Reference | ||
| Medium | −0.24(−0.33, −0.15) | < 0.001 | −0.10(−0.19, −0.01) | 0.028 |
| High | −0.27(−0.39, −0.15) | < 0.001 | −0.10(−0.22, 0.02) | 0.098 |
| Serum albumin | ||||
| Category of MedHi food intake | ||||
| Non-MedHi | Reference | Reference | ||
| Low-MedHi | 0.003(−0.03, 0.04) | 0.825 | 0.003(−0.02, 0.03) | 0.812 |
| High-MedHi | 0.04(0.03, 0.06) | < 0.001 | 0.04(0.02, 0.05) | < 0.001 |
| Category of microbe contents food intake | ||||
| Low | Reference | Reference | ||
| Medium | 0.04(0.02, 0.06) | < 0.001 | 0.03(0.02, 0.05) | < 0.001 |
| High | 0.04(0.02, 0.06) | < 0.001 | 0.03(0.01, 0.05) | 0.014 |
| SII index | ||||
| Category of MedHi food intake | ||||
| Non-MedHi | Reference | Reference | ||
| Low-MedHi | −1.58(−32.37, 29.21) | 0.919 | −0.48(−29.93, 28.97) | 0.974 |
| High-MedHi | −24.56(−43.04, −6.08) | 0.010 | −20.70(−40.10, −1.31) | 0.037 |
| Category of microbe contents food intake | ||||
| Low | Reference | Reference | ||
| Medium | −17.70(−42.47, 7.07) | 0.009 | −20.70(−39.22, −2.17) | 0.029 |
| High | −23.69(−41.34, −6.04) | 0.159 | −12.69(−37.87, 12.50) | 0.319 |
1 No covariates were adjusted
2 Adjusted for sex, age, race/ethnicity, education attainment, poverty-income ratio, drinking status, physical activity, body mass index, smoking status, alcohol consumption, serum uric acid, and daily energy intake
WBC, white blood cell; SII, systemic immune-inflammation; CI, confidence interval
Association between insulin resistance indicators and MetS
In the univariate model, compared with the Non-MedHi group, the low-MedHi group had greater TyG index (β: 0.02, 95% CI: −0.02, 0.07) and HOMA-IR (β: 0.01, 95% CI: −0.37, 0.38); TyG index (β: −0.05, 95% CI: −0.09, −0.02) and HOMA-IR (β: −0.66, 95% CI: −0.94, −0.38) were lower in the high-MedHi group. In the multivariate model, compared with the Non-MedHi group, the low-MedHi group had greater TyG index (β: 0.03, 95% CI: −0.02, 0.07) and HOMA-IR (β: 0.08, 95% CI: −0.24, 0.40); TyG index (β: −0.02, 95% CI: −0.05, 0.01) and HOMA-IR (β: −0.25, 95% CI: −0.52, 0.03) were lower in the high-MedHi group.
In the univariate model, compared with the low microbe food group, the medium microbe food group had a lower TyG index (β: −0.02, 95% CI: −0.05, 0.02) and HOMA-IR (β: −0.45, 95% CI: −0.74, −0.16); the TyG index (β: −0.08, 95% CI: −0.12, −0.04) and HOMA-IR (β: −0.70, 95% CI: −1.03, −0.37) were lower in the high microbe food group. In the multivariate model, compared with the low microbe food group, the medium microbe food group had a lower TyG index (β: −0.01, 95% CI: −0.04, 0.02), and HOMA-IR (β: −0.20, 95% CI: −0.46, 0.07); the TyG index (β: −0.02, 95% CI: −0.06, 0.01) and HOMA-IR (β: −0.20, 95% CI: −0.53, 0.13) were lower in the high microbe food group. For more specific details, refer to Table 4.
Table 4.
Associations between dietary live microbe intake and resistance indicators
| Univariable model1 | Multivariable model2 | |||
|---|---|---|---|---|
| OR (95%CI) | P-value | OR (95%CI) | P-value | |
| TyG index | ||||
| Category of MedHi food intake | ||||
| Non-MedHi | Reference | Reference | ||
| Low-MedHi | 0.02(−0.02, 0.07) | 0.334 | 0.03(−0.02, 0.07) | 0.223 |
| High-MedHi | −0.05(−0.09, −0.02) | 0.002 | −0.02(−0.05, 0.01) | 0.130 |
| Category of microbe contents food intake | ||||
| Low | Reference | Reference | ||
| Medium | −0.02(−0.05, 0.02) | 0.425 | −0.01(−0.04, 0.02) | 0.608 |
| High | −0.08(−0.12, −0.04) | < 0.001 | −0.02(−0.06, 0.01) | 0.187 |
| HOMA-IR | ||||
| Category of MedHi food intake | ||||
| Non-MedHi | Reference | Reference | ||
| Low-MedHi | 0.01(−0.37, 0.38) | 0.972 | 0.08(−0.24, 0.40) | 0.632 |
| High-MedHi | −0.66(−0.94, −0.38) | < 0.001 | −0.25(−0.52, 0.03) | 0.074 |
| Category of microbe contents food intake | ||||
| Low | Reference | Reference | ||
| Medium | −0.45(−0.74, −0.16) | 0.002 | −0.20(−0.46, 0.07) | 0.146 |
| High | −0.70(−1.03, −0.37) | < 0.001 | −0.20(−0.53, 0.13) | 0.238 |
1No covariates were adjusted
2Adjusted for sex, age, race/ethnicity, education attainment, poverty-income ratio, drinking status, physical activity, body mass index, smoking status, alcohol consumption, serum uric acid, and daily energy intake
TyG, triglyceride-glucose index; HOMA-IR, homeostatic model assessment of insulin resistance; CI, confidence interval; OR, odds ratio
Nonlinearity analysis
We used the RCS model to assess the dose–response relationship between live microbe intake and MetS as well as its components. The model established nodes at the 5th, 35th, 65th, and 95th percentiles of total MedHi food intake, with the 5th percentile as the reference value. RCS analysis indicated that non-linear associations were statistically non-significant (P-nonlinear > 0.05) between MedHi food intake and MetS. Among the components of MetS, there is a significant nonlinear relationship between increased MedHi food intake and increased WC (p nonlinear = 0.040), presenting a U-shaped curve with an inflection point at 0.674. (Fig. 2).
Fig. 2.
Fig. 2. Association between per 100-unit total MedHi intake increase and metabolic syndrome with its components via a restricted cubic spline regression model. Panels (A-F) represent the associations for metabolic syndrome (A), elevated waist circumference (B), elevated blood pressure (C), elevated triglycerides (D), low high-density lipoprotein cholesterol (E), and elevated fasting plasma glucose (F), respectively. OR, odds ratio; CI, confidence interval.
Mediation analysis
In the mediation analysis, dietary live microbe intake was used as the independent variable; MetS as the dependent variable; and WBC count, neutrophil count, serum albumin, SII index, TyG index, and HOMA-IR were considered mediator variables. The neutrophil counts mediated the association between high MedHi food intake and MetS, explaining a total of 16.9% of the correlation with a significant mediating effect of − 0.006 (95% CI: − 0.01, 0.00, p: 0.01) (Fig. 3). SII index mediated the association between high MedHi food intake and MetS, explaining a total of 6.3% of the correlation with a significant mediating effect of − 0.002 (95% CI: − 0.00, 0.00, p: 0.04). More details are presented in Table S3.
Fig. 3.
Path diagram of mediation analysis models. (A) High live microbe food intake - WBC -MetS. (B) Medium live microbe food intake - WBC - MetS. (C) Medium live microbe food intake - Neutrophils - MetS. (D) High live microbe food intake - Neutrophils - MetS. (E) Medium live microbe food intake - Serum albumin - MetS. (F) High live microbe food intake - Serum albumin - MetS. (G) Medium live microbe food intake - SII index - MetS. (H) High live microbe food intake - SII index - MetS. (I) Medium live microbe food intake - TyG index - MetS. (J) High live microbe food intake - TyG index - MetS. (K) Medium live microbe food intake - HOMA-IR - MetS. (L) High live microbe food intake - HOMA-IR - MetS. (M) Low MedHi food - WBC - MetS. (N) High MedHi food - WBC - MetS. (O) Low MedHi food - Neutrophils - MetS. (P) High MedHi food - Neutrophils - MetS. (Q) Low MedHi food - Serum albumin - MetS. (R) High MedHi food - Serum albumin - MetS. (S) Low MedHi food - SII index - MetS. (T) High MedHi food - SII index - MetS. (U) Low MedHi food - TyG index - MetS. (V) High MedHi food - TyG index - MetS. (W) Low MedHi food - HOMA-IR - MetS. (X) High MedHi food - HOMA-IR - MetS
Discussion
In this cross-sectional study, which utilized a large nationally representative sample of the general population, we identified an inverse association between the intake of live microbe and MetS. Comprehensive analyses of both the quantity and quality of live microbes consumption revealed that increased intake was associated with a reduced risk of developing MetS. Compared with that in the non-MedHi group, the risk of MetS was 17% lower in the high-MedHi food intake group; compared with that in the low microbe food group, the risk of MetS was 16% lower in the medium microbe food group. These findings suggest that the appropriate consumption of foods containing live microbes is crucial for maintaining normal metabolic function.
The pathogenesis of MetS involves multiple genetic and acquired factors. Its core features include central obesity, hypertriglyceridemia, low HDL-C, hypertension, and hyperglycemia [20, 21]. These features interact synergistically to induce a state of insulin resistance and chronic low-grade inflammation, ultimately contributing to the development of obesity as well as various cardiovascular and cerebrovascular diseases [5, 22]. The gut microbiota plays a crucial role in the potential pathogenic mechanisms shared by type 2 diabetes mellitus, obesity, and cardiovascular disease, including chronic low-grade inflammation, alterations in intestinal permeability (potentially leading to endotoxemia), and reduced production and absorption of short-chain fatty acids (SCFAs), which align with the pathophysiology of MetS [23–25].
In addition to assessing the impact of live microbe intake on MetS itself, our analysis of MetS components revealed that, compared with those in the non-MedHi group, the risks of low HDL-C and elevated blood pressure were 14% and 20% lower in the high-MedHi food intake group, respectively. A meta-analysis involving seven studies demonstrated a significant beneficial effect of Lactobacillus plantarum supplementation on lowering both systolic and diastolic blood pressure [26], which is consistent with our findings. This effect may, in part, be attributed to the modulatory influence of SCFAs (specifically acetate and butyrate) produced by probiotics on the immune system, the renin-angiotensin system, and the sympathetic nervous system [27, 28].
We also identified the beneficial effects of dietary live microbe intake on reducing serum inflammatory indicators such as WBC counts, neutrophil counts, and SII index, although these effects have not been clearly defined with respect to insulin resistance related indicators. When live dietary microbes are consumed as a part of the diet, they can interact with other resident microbiota and potentially exert positive effects [29]. The benefits of beneficial microbiota to the body include enhancing intestinal function, reducing inflammation and oxidative stress, increasing immunoregulatory activity, and increasing insulin sensitivity [30, 31].
Our research demonstrated that the positive effects of dietary live microbe on MetS may be mediated through the alleviation of systemic low-grade inflammation, which is consistent with the principles of the Mediterranean diet (MedDiet). The MedDiet is a plant-based diet characterized by high consumption of vegetables (including leafy greens), fruits, whole grains, pulses, legumes, nuts, and extra virgin olive oil, which is rich in monounsaturated fatty acids (MUFAs) and serves as the primary source of fat [32, 33]. Mediterranean diets confer a protective effect against metabolic syndrome and exert beneficial influences on blood pressure, blood lipid profiles, lipoprotein particle distributions, inflammation, oxidative stress, carotid atherosclerosis, and the expression of proatherogenic genes involved in vascular events and thrombus formation [34, 35]. Francesca et al. suggested that vegetable-based dietary intake is associated with an abundance of Prevotella and certain fiber-degrading Firmicutes, which may exert beneficial effects on host metabolic health [36].
The gastrointestinal tract, along with its associated intestinal microbiota, can be considered analogous to an endocrine organ owing to its effects on circulating metabolites and immune function. Disruptions in the intestinal microbiota are believed to contribute to increased insulin resistance [37]. Lipopolysaccharide (LPS) exposure, which is induced by intestinal bacteria, triggers the production of proinflammatory cytokines, resulting in metabolic endotoxemia. Ecological dysregulation of the gut microbiota further enhances LPS secretion, thereby exacerbating metabolic disturbances in a vicious cycle [38]. Systemic chronic low-grade inflammation alters intestinal permeability through endotoxemia. A study by Fabian Frost et al. demonstrated that significant changes in the intestinal microbiota precede the onset of metabolic diseases [39]. Therefore, maintaining stable intestinal microbiota is critical for the prevention of metabolic disorders, and dietary interventions may serve as effective strategies to achieve this goal.
Our findings are consistent with those of previous studies and further support the potential benefits of dietary interventions. Wang et al. investigated the relationship between dietary live microbe and MetS and found dietary live microbe intake and nondietary probiotic intake were negatively associated with the prevalence of MetS and its components [40]. However, our study emphasized the direct association between live microbe intake and MetS and identified a potential mediating role for inflammation between these two factors [13].
Our study has several strengths. First, in terms of study design, the data used were derived from NHANES, a nationally representative database. The rigorous quality control procedures employed by NHANES in data collection, combined with its sophisticated sampling design, enabled us to assess the associations between live microbe intake and MetS and its components in a representative sample of U.S. adults. The robust sample size and comprehensive adjustment for covariates further enhanced the validity and generalizability of our findings.
However, our study has several limitations. First, given its cross-sectional design, we were unable to determine the temporal relationship between the dietary intake of live microbe and the onset of MetS, nor could we establish a causal relationship. Larger prospective studies are therefore needed to clarify the causality of this association. Second, while the dietary intake of live microbe was categorized through an expert review of the literature and discussion, the relatively simple grouping methodology may limit the precision of quantitative measurements, particularly in accounting for the intake of different microbial types, which could impact the results. Third, although we considered demographic characteristics, lifestyle factors, and certain medical conditions, the influence of potential confounders could not be fully ruled out. For example, the intake of live microbe through the diet may be influenced by certain medications (e.g., diabetes treatments, proton pump inhibitors), and our study did not examine the potential effects of these factors. Fourthly, genetic predisposition to metabolic syndrome could not be assessed because NHANES does not include genotyping data linked with dietary data. Future studies integrating genetic information are warranted.
Further research is warranted to confirm and extend our findings. Longitudinal studies should examine temporal sequences among dietary live microbe intake, related biomarkers, and the onset of MetS. Intervention studies are needed to determine whether improving the quality and quantity of live microbe food and related biomarkers could effectively prevent or delay MetS.
Conclusion
This study identified that moderately increasing the intake of foods rich in high level microbe in the daily diet is associated with a reduced risk of metabolic syndrome and may have potential benefits for maintaining stable blood pressure and blood lipid levels. Systemic inflammation markers, including serum neutrophil counts and SII, partially mediate the association between dietary live microbe intake and MetS. Our findings suggest that dietary live microbes may serve as potential targets for preventing metabolic syndrome, which has significant implications for public health and dietary guidelines. Further studies are needed to elucidate the complex interplay between live microbe intake and metabolic processes in the body.
Supplementary Information
Acknowledgements
The data used in this study were from the NHANES. We thank all the staff of and participants in the NHANES for their contribution.
Abbreviations
- MetS
Metabolic syndrome
- NHANES
National health and nutrition examination survey
- WBC
White blood cell
- SII
Serum albumin, and systemic immune-inflammation
- TyG
Triglyceride-glucose
- HOMA-IR
Homeostatic model assessment of insulin resistance
- OR
odds ratio
- CI
Confidence interval
- CDC
Centers for disease control and prevention
- USDA
U.S. Department of agriculture
- TG
Triglyceride
- HDL-C
High-density lipoprotein cholesterol
- SBP
Systolic blood pressure
- DBP
Diastolic blood pressure
- FPG
Fasting plasma glucose
- PIR
Poverty-income ratio
- PA
Physical activity
- BMI
Body mass index
- eGFR
estimated glomerular filtration rate
- CKD-EPI
Chronic kidney disease epidemiology collaboration
- SCr
Serum creatinine
- IQR
Interquartile ranges
- ANOVA
Analysis of variance
- RCS
Restricted cubic splines
- AIC
Akaike Information Criterion
- SCFAs
Short-chain fatty acids
- MedDiets
Mediterranean diet
- MUFAs
Monounsaturated fatty acids
- LPS
Lipopolysaccharide
Author contributions
Zhi Wang and Zhaobin Sun designed the study and wrote the main manuscript text. Data preprocessing and analysis were carried out by Zhi Wang; Zhaobin Sun prepared all the tables; Tongyu Tang reviewed, edited and supervise the manuscript. All authors have read and approved the final version of this manuscript.
Funding
No funding.
Data availability
No datasets were generated or analysed during the current study.
Declarations
Ethics approval and consent to participate
This study was conducted according to the guidelines laid down in the Declaration of Helsinki, and all procedures involving research study participants were approved by the institutional review board of the National Center for Health Statistics (NCHS). Written informed consent was obtained from all participants/patients.
Human ethics and consent to participate declarations
Not applicable.
Consent for publication
Not applicable.
Clinical trial number
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Zhi Wang and Zhaobin Sun contributed equally to this work.
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Supplementary Materials
Data Availability Statement
No datasets were generated or analysed during the current study.