Dietary Index for Gut Microbiota and Risk of All-Cause and Cardiovascular Mortality Across Cardiovascular-Kidney-Metabolic Syndrome Stages 0–3: A Nationwide Prospective Cohort Study

Abstract

Background:

Cardiovascular-kidney-metabolic (CKM) syndrome represents a progressive disorder characterized by the interplay of cardiovascular pathologies, chronic renal impairment, and metabolic dysregulation. Therefore, this study aimed to examine the relationship between the dietary index for gut microbiota (DI-GM) and mortality outcomes, including both all-cause and cardiovascular-specific mortality, in individuals classified with CKM syndrome stages 0–3.

Methods:

Our study cohort consisted of 7884 adult participants aged 30–79 years from the National Health and Nutrition Examination Survey (NHANES) conducted between 2007 and 2018. Dietary intake data obtained through 24-hour dietary recalls and food frequency questionnaires were used to calculate the DI-GM scores, incorporating both components beneficial to the microbiota and those with potentially detrimental nutritional effects. The primary and secondary endpoints were all-cause mortality and cardiovascular-related mortality, respectively. The Kaplan–Meier survival analysis, Cox proportional hazards regression models, and restricted cubic spline (RCS) techniques were employed in the statistical analyses.

Results:

The participants had a median age of 50 years, with females comprising 52.97% of the cohort. Over a median follow-up period of 77 months, we documented 469 all-cause deaths (4.56%) and 105 cardiovascular fatalities (1.02%). Elevated beneficial scores for the DI-GM demonstrated significant inverse associations with both all-cause (p < 0.001) and cardiovascular mortality (p = 0.017). However, while the total DI-GM scores showed correlation with decreased all-cause mortality (p < 0.001), no significant association emerged for cardiovascular mortality. Following the employment of a comprehensive adjustment, the hazard ratio (HR) for the total DI-GM score and all-cause mortality was 0.90 (95% confidence interval (CI): 0.82–0.98). For the beneficial components, the HR was 0.88 (95% CI: 0.79–0.98) for all-cause mortality and 0.87 (95% CI: 0.77–0.99) for cardiovascular mortality. RCS modeling revealed a U-shaped correlation between the total DI-GM scores and all-cause mortality, which was in contrast to a linear association for the beneficial scores. The systemic inflammation index (SII) accounted for 5.29% and 8.45% of the observed associations between the total and beneficial DI-GM scores and all-cause mortality, respectively.

Conclusions:

Elevated DI-GM dietary scores, particularly those emphasizing food components beneficial to the gut microbiota, demonstrate protective associations against both all-cause and cardiovascular mortality in individuals with CKM syndrome in stages 0–3. These protective effects appear partially influenced by systemic inflammatory pathways.

Graphical abstract

Keywords

dietary index for gut microbiota / cardiovascular-kidney-metabolic syndrome / mortality / inflammation / NHANES

1. Introduction

In 2023, the American Heart Association (AHA) proposed a novel classification system termed cardiovascular-kidney-metabolic (CKM) syndrome [1]. This framework emphasizes the interrelated nature of cardiovascular disorders, chronic kidney impairment, and metabolic dysfunctions. The CKM model stratifies patients into five progressive phases (stage 0 through 4), with the latter two stages denoting advanced pathological conditions [2]. Epidemiological data from 2011–2020 revealed the following distribution among US adults: 10.6% in stage 0, 25.9% in stage 1, 49.0% in stage 2, 5.4% in stage 3, and 9.2% in stage 4 [3]. While stages 0–3 encompass subjects without clinically manifest cardiovascular disease, these categories demonstrate escalating hazards for future cardiovascular events and all-cause mortality [4, 5]. Early identification and targeted prevention are therefore critical for long-term health burden reduction.

Several factors influence CKM syndrome progression and mortality. Research indicates that certain demographic and socioeconomic factors, including being male, aged 65 or older, and of African American descent, correlate with an elevated likelihood of developing severe CKM syndrome [3]. In addition to these variables, health-related social determinants—specifically limited educational attainment and significant social risk exposure—have been shown to correlate with worse clinical outcomes [6, 7, 8]. Nevertheless, current scientific investigations have not sufficiently examined how different nutritional habits influence the advancement and clinical course of CKM syndrome.

Nutrition plays a crucial role in influencing metabolic and cardiovascular wellbeing. Different eating habits have been demonstrated to affect survival rates among patients diagnosed with disorders including diabetes, high blood pressure, and cardiovascular disease [9, 10, 11]. These effects are mediated through mechanisms like gut microbiota regulation, immune modulation, and antioxidant activity [12, 13, 14]. The dietary index for gut microbiota (DI-GM) was specifically designed to evaluate the impact of 14 distinct dietary elements on intestinal microbial health, classifying these components as either advantageous or detrimental [12]. Furthermore, this index has demonstrated significant associations with the development and progression of various chronic conditions, including cerebrovascular accidents, metabolic disorders, hepatic steatosis, and age-related health decline [15, 16, 17, 18]. Notably, while prior investigations have examined DI-GM in relation to diabetes and stroke, the current study focuses on individuals in CKM stages 0–3, a population characterized by the absence of overt cardiovascular disease but heightened susceptibility to long-term complications, thereby representing a crucial period for implementing preventive measures and assessing DI-GM’s predictive capacity.

Utilizing data from the National Health and Nutrition Examination Survey (NHANES) spanning 2007 to 2018, this investigation aims to examine the relationship between DI-GM (comprising aggregate, favorable, and adverse component scores) and mortality risk among subjects with CKM syndrome across stages 0 through 3. By identifying dietary patterns linked to CKM syndrome prognosis, the study seeks to inform potential nutritional strategies for preventing disease progression and reducing mortality in early-stage CKM syndrome populations.

2. Materials and Methods

2.1 Data Source and Study Population

The research employed data obtained from the NHANES spanning 2007 to 2018, which constitutes a nationally representative study administered collaboratively by the Centers for Disease Control and Prevention (CDC) and National Center for Health Statistics (NCHS). NHANES implements a complex sampling methodology involving multiple stages and stratification, gathering comprehensive health information from both adult and pediatric populations across the United States biennially. All investigative procedures received approval from the NCHS Research Ethics Review Board and strictly complied with Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting standards.

Participants aged 30 to 79 years were considered eligible for inclusion, consistent with the applicability of the PREVENT equations for cardiovascular risk estimation. Participants were excluded from the study if they did not have adequate data to determine their CKM syndrome stage, possessed incomplete dietary records necessary for computing DI-GM scores, or were diagnosed with CKM syndrome stage 4. Following these exclusion criteria, the final study cohort comprised 7884 individuals (Fig. 1). The NCHS Research Ethics Review Board granted approval for all NHANES study protocols, with each participant providing written consent prior to enrollment. This investigation adhered to the ethical principles outlined in the Declaration of Helsinki. Given that NHANES datasets are anonymized and accessible to the public, no further institutional review board authorization was deemed necessary.

2.2 CKM Syndrome Stage Evaluation

CKM syndrome stages (0–4) were defined based on criteria from Aggarwal et al. [3] and adapted for NHANES data using the classification system from Tang et al. [19] (detailed information listed in Supplementary Table 1) [20]. In brief, CKM syndrome stages were categorized as follows:

Stage 0: No health risk factors related to CKM syndrome.

Stage 1: Dysfunctional adiposity.

Stage 2: With metabolic risk factors (hypertension, diabetes, and dyslipidemia), or chronic kidney disease (CKD).

Stage 3: Subclinical CVD on top of stage 2 criteria (10-year CVD risk \ge20%).

Stage 4: Established CVD.

The assessment of CKM syndrome progression stages employed Predicting Risk of Events via Estimated Cardiac Trajectories (PREVENT) equations [21], a validated tool designed for estimating 10-year cardiovascular disease risk in the U.S. adult population aged 30–79 years. Chronic kidney disease staging was determined using the race-neutral creatinine-based equation developed by the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI, 2021).

2.3 DI-GM Calculation

The calculation of DI-GM scores utilized 24-hour dietary recall information obtained from NHANES, incorporating 14 distinct dietary elements according to the scoring methodology established by Kase et al. [12] (refer to Supplementary Table 2). Each study subject provided dietary data through two separate 24-hour recall sessions conducted on non-consecutive days: the initial assessment occurred during face-to-face interviews at the Mobile Examination Center, followed by a subsequent telephone-based recall conducted 3–10 days afterward. To minimize individual variability and obtain more representative dietary patterns, the present analysis employed averaged consumption values derived from both recall sessions for DI-GM computation. This dietary index comprises 14 components classified as either advantageous or detrimental to gut microbiota health. Positive dietary elements encompassed avocado, broccoli, chickpeas, coffee, cranberries, fermented dairy products, dietary fiber, soy products, and whole grains (though green tea consumption data were unavailable in NHANES). Negative components consisted of processed meats, red meats, refined grain products, and high-fat dietary patterns (defined as \ge40% of total energy intake).

Scoring methodology assigned 1 point when beneficial food consumption exceeded gender-specific median values or when unfavorable food intake fell below median levels. The comprehensive DI-GM scale spanned from 0 to 13 points, with beneficial components contributing 0–9 points and unfavorable components accounting for 0–4 points. Study participants were subsequently stratified into quartile groups according to their total scores and beneficial component scores.

2.4 Covariates and Exposure Variables

The selection of covariates was guided by existing research evidence, clinical importance, and statistical associations. These variables encompassed demographic characteristics (age, gender, racial background), socioeconomic indicators (educational attainment, marital situation, poverty-to-income ratio), lifestyle factors (tobacco use, alcohol intake), anthropometric measurements (body mass index), CKM syndrome severity, and biochemical parameters (serum uric acid levels and leukocyte counts). The poverty income ratio was calculated as household earnings relative to federal poverty guidelines and stratified into three categories: below poverty level (1), moderate income (1–3), and higher income (\ge5). Body mass index was derived from the formula of body weight in kilograms divided by the square of height in meters. The systemic inflammation index was determined through the equation: (platelet concentration \times neutrophil count)/lymphocyte concentration [22]. Ethnic classification followed NHANES protocols, including Mexican American, other Hispanic populations, non-Hispanic Caucasian, non-Hispanic African American, and other racial groups. Educational background was dichotomized into incomplete secondary education versus secondary education completion or higher. Marital status was classified as either partnered (married/cohabiting) or unpartnered. Comorbid conditions were identified using established diagnostic criteria. Hypertension was diagnosed based on elevated blood pressure readings (systolic \ge140 mmHg or diastolic \ge90 mmHg), clinician-confirmed diagnosis, or antihypertensive medication use. Diabetes mellitus was defined by fasting glucose levels \ge126 mg/dL, glycated hemoglobin \ge6.5%, physician-documented diagnosis, or hypoglycemic drug therapy. Dyslipidemia criteria included total cholesterol \ge240 mg/dL, triglycerides \ge200 mg/dL, physician diagnosis, or lipid-modifying treatment. Chronic kidney disease was identified when the estimated glomerular filtration rate fell below 60 mL/min/1.73 m² or the urinary albumin-to-creatinine ratio exceeded 30 mg/g, calculated using the race-neutral CKD-EPI (2021) formula.

2.5 Outcomes

The study’s principal endpoint encompassed mortality from any cause, while cardiovascular-related deaths constituted the secondary endpoint. Vital status and specific causes of death were determined by cross-referencing NHANES participants with publicly available mortality records from the National Death Index (NDI), with follow-up data extending until December 31, 2019. Deaths attributed to cardiovascular causes were classified according to International Classification of Diseases (ICD)-10 codes I00–I09, I11, I13, and I20–I51. Follow-up duration for each participant was calculated from their initial examination date until either their death or the study’s termination date, whichever occurred earlier.

2.6 Statistical Analysis

The statistical analyses incorporated NHANES’s sophisticated multistage probability sampling framework through the application of proper sampling weights, stratification variables, and primary sampling units, executed in R software (V4.4.2, R Foundation for Statistical Computing, Vienna, Austria) utilizing the survey package. In compliance with NHANES analytical protocols, descriptive statistics were presented as mean values with SE for normally distributed parameters, median values with interquartile range (IQR) for non-normally distributed variables, and frequency counts with weighted proportions for categorical measures. Comparative analyses between groups employed one-way ANOVA for normally distributed continuous data, Kruskal-Wallis tests for skewed continuous variables, and chi-square tests for categorical data comparisons.

The analysis of survival outcomes utilized Kaplan-Meier plots to evaluate variations in mortality across different DI-GM quartiles. Survey-adjusted Cox proportional hazards regression models were employed to accommodate NHANES’ intricate sampling methodology, generating hazard ratios (HRs) and corresponding 95% confidence intervals (CIs) for both all-cause and cardiovascular-related deaths. Covariates were chosen for model adjustment based on their statistical importance, clinical significance, and existing research evidence. To prevent excessive model adjustment, clinical parameters closely associated with CKM staging (including lipid profiles, blood pressure measurements, and glucose levels) were excluded, consistent with previous investigations [19, 20]. Due to significant data gaps in NHANES, factors such as exercise levels, caloric consumption, and pharmaceutical treatments (including antihypertensive, antidiabetic, and cholesterol-lowering medications) were omitted from the multivariate analyses. Three distinct analytical models were implemented:

Model 1: Unadjusted (crude).

Model 2: Adjusted for age, sex, race, education level, marital status, and poverty income ratio (PIR).

Model 3: Further adjusted for smoking status, drinking status, CKM syndrome stage, body mass index (BMI), white blood cell count, and uric acid levels.

To examine potential linear and non-linear relationships between DI-GM scores (including total, beneficial, and unfavorable components) and mortality outcomes, restricted cubic spline (RCS) modeling was implemented with four predetermined knot positions. These knots were strategically positioned at the 5th, 35th, 65th, and 95th percentile values of the DI-GM score distribution [23]. The mediating effect of SII on the association between DI-GM scores and all-cause mortality was investigated through mediation analysis performed with the R software’s “mediation” package. Additional stratified multivariate regression models were employed to conduct subgroup analyses. To ensure robustness of findings, sensitivity analyses were carried out by excluding individuals who experienced mortality events during the initial 24-month follow-up period.

Statistical processing was performed utilizing R software (version 4.4.2), with a predefined threshold of p 0.05 for determining statistical significance.

3. Results

3.1 Baseline Characteristics

The research involved 7884 American participants aged between 30 and 79 years, showing a median age of 50 (interquartile range: 40–61), with females constituting 52.97% (n = 4182) of the sample (Table 1). The ethnic distribution comprised 7.67% Mexican Americans, 5.49% other Hispanic individuals, 68.87% non-Hispanic Caucasians, 10.61% non-Hispanic African Americans, and 7.36% representing other ethnicities. When analyzing participants based on quartiles of the DI-GM composite score (Table 1), individuals with elevated scores tended to be male, of non-Hispanic White descent, more educated, and with greater household earnings. No notable age variations were found among the groups (p = 0.282). Elevated DI-GM scores also correlated with increased alcohol intake, reduced incidence of diabetes, and chronic kidney disease. Nevertheless, no meaningful relationship emerged between DI-GM scores and CKM syndrome progression (p = 0.181). Conversely, when examining DI-GM beneficial score quartiles (Supplementary Table 3), higher values were connected with decreased occurrence of advanced CKM syndrome stages (Stage 2 and 3, p = 0.004). Furthermore, greater DI-GM total scores corresponded with higher body mass index, whereas increased beneficial scores were associated with reduced leukocyte counts and serum uric acid concentrations.

3.2 Association Between DI-GM and Mortality Outcomes

During a median observation period of 77 months, 469 deaths from any cause (4.56%) and 105 cardiovascular-related fatalities (1.02%) were documented. Survival analysis using Kaplan-Meier curves revealed that individuals in the bottom quartiles for both total DI-GM (Q1: scores 0–2) and beneficial component scores (Q1: scores 0–1) exhibited the greatest likelihood of all-cause mortality (log-rank p 0.001, Fig. 2). The association between adverse scores and overall mortality risk was not statistically significant (log-rank p = 0.062). Regarding cardiovascular mortality, only the beneficial component demonstrated a meaningful correlation (log-rank p = 0.017), with participants in the top quartile (Q4: scores \ge4) displaying the most favorable outcomes.

The results from Cox regression analysis (Fig. 3) demonstrated that every unit increment in the DI-GM total score correlated with a 13% decrease in overall mortality risk (HR = 0.87, 95% CI: 0.80–0.93), while no significant relationship was observed with cardiovascular mortality. Similarly, a one-point rise in the beneficial score corresponded to a 17% reduction in all-cause mortality (HR = 0.83, 95% CI: 0.75–0.92) and a 20% decrease in cardiovascular mortality risk (HR = 0.80, 95% CI: 0.72–0.89). When analyzed by quartiles, participants in the highest quartile of both DI-GM total and beneficial scores exhibited substantially lower all-cause mortality rates compared to the lowest quartile (Q4 vs Q1 HR = 0.48 [95% CI: 0.35–0.67] and 0.45 [95% CI: 0.31–0.65], respectively). Conversely, elevated unfavorable scores were linked to greater all-cause mortality risk (Q4 vs Q1 HR = 1.58, 95% CI: 1.12–2.24), but showed no significant association with cardiovascular mortality.

The findings from the multivariable Cox regression analysis (Model 3) demonstrated that both the overall DI-GM score and its beneficial component exhibited independent correlations with decreased mortality from all causes. Notably, every unit increment in the total score corresponded to a 10% decline in all-cause mortality risk (HR = 0.90, 95% CI: 0.82–0.98), whereas the beneficial component showed an even stronger association with a 12% risk reduction (HR = 0.88, 95% CI: 0.79–0.98; Table 2). In quartile-based comparisons, individuals positioned in the top quartile (Q4) for both the total and beneficial DI-GM scores displayed substantially lower all-cause mortality rates relative to those in the bottom quartile (Q1), with HRs of 0.57 (95% CI: 0.40–0.83) and 0.60 (95% CI: 0.41–0.87), respectively. Regarding cardiovascular mortality outcomes, only the beneficial score maintained statistical significance following comprehensive adjustment (HR = 0.87, 95% CI: 0.77–0.99; Table 3), while the relationship with the total score weakened and lost significance. Importantly, the unfavorable DI-GM score component failed to show any meaningful association with either all-cause or cardiovascular mortality across all adjusted models. When examining quartile distributions, no statistically significant variations in cardiovascular mortality risk were detected between the highest (Q4) and lowest (Q1) quartiles for any DI-GM score components after complete covariate adjustment.

RCS (Fig. 4) revealed a U-shaped relationship between DI-GM total score and all-cause mortality (p for non-linearity = 0.008), a linear association for beneficial scores (p for non-linearity = 0.076), and no significant pattern for unfavorable scores. For cardiovascular mortality (Supplementary Fig. 1), the DI-GM beneficial score showed an inverted U-shaped association (p for non-linearity = 0.031), while total and unfavorable scores showed no significant trends.

3.3 Mediation Analysis

The overall and favorable DI-GM scores showed inverse correlations with SII, demonstrating \beta values of –7.35 (95% CI: –12.46 to –2.24) and –12.86 (95% CI: –19.64 to –6.07), respectively (Table 4). Mediation assessments indicated that SII accounted for 5.29% (95% CI: 1.44%–10%, p = 0.024) of the total DI-GM score’s impact and 8.45% (95% CI: 2.12%–18%, p = 0.016) of the beneficial DI-GM score’s influence on mortality from all causes (Fig. 5). These findings imply that diets promoting gut microbiota health might lower mortality risk in part by decreasing systemic inflammatory responses.

3.4 Subgroup and Sensitivity Analysis

Analyses conducted across various demographic subgroups, including age, gender, ethnicity, body mass index, and stages of CKM syndrome, demonstrated stable relationships between overall DI-GM scores or favorable dietary indices and mortality risks (all interaction p-values exceeded 0.05; refer to Table 5 and Supplementary Table 4). When participants who died within the initial 24 months of follow-up were excluded from consideration, the beneficial DI-GM score maintained its significant correlation with cardiovascular-related deaths (multivariable-adjusted HR = 0.87, 95% confidence interval: 0.77–0.99; see Supplementary Table 5), while its association with overall mortality showed reduced significance (fully adjusted HR = 0.92, 95% CI: 0.81–1.03; Supplementary Table 5).

4. Discussion

This research investigated the association between DI-GM scores (including overall, favorable, and adverse components) and mortality rates, utilizing NHANES data collected from 2007 to 2018. The results demonstrate that dietary patterns promoting beneficial gut microbiota are correlated with decreased risks of both all-cause and cardiovascular-related deaths in patients with CKM syndrome across stages 0 to 3. Notably, every unit increment in the favorable DI-GM component corresponded to a 12% decrease in overall mortality and a 13% decline in cardiovascular-related deaths. The analysis revealed a U-shaped curve for the relationship between total DI-GM scores and all-cause mortality, whereas the beneficial component showed a direct linear correlation. Furthermore, part of this effect may be mediated through reduced systemic inflammation. Notably, sensitivity analyses confirmed that beneficial dietary components had a more consistent association with cardiovascular mortality than with all-cause mortality. The findings indicate that nutritional interventions could contribute to enhancing the prognosis of patients diagnosed with initial-phase CKM syndrome over extended periods.

Extensive research has confirmed the strong association between metabolic disorders and elevated risks of cardiovascular and renal disease occurrence and fatality [24, 25, 26]. The Global Burden of Disease analysis identifies poorly managed blood sugar levels and excessive body weight as key factors in the development of coronary artery disease, particularly in areas with limited healthcare resources [24, 27]. The concept of CKM syndrome emphasizes the complex interplay among metabolic, cardiac, and kidney functions, demonstrating the necessity for comprehensive management strategies. The rising incidence of this condition across the United States in recent years represents a substantial healthcare challenge [3, 5]. Multiple pathophysiological processes, such as persistent inflammatory responses, oxidative damage, diminished insulin sensitivity, lipid-induced cellular injury, and disrupted metabolic pathways, exacerbate clinical outcomes, especially in patients with severe CKM syndrome manifestations [28]. This complexity presents challenges in identifying effective interventions, especially during the earlier stages of the syndrome.

Dietary intake plays a critical role in regulating these pathways. Evidence increasingly supports the influence of diet on health outcomes through mechanisms mediated by the gut microbiota [12, 13, 14]. While the association between dietary habits and various illnesses, including stroke, diabetes, malignancies, and cardiovascular disorders, has been extensively documented, its impact on the clinical outcomes of CKM syndrome has not been sufficiently investigated.

Emerging research has highlighted the crucial involvement of intestinal microbial communities in the pathogenesis and advancement of various diseases. These microbial populations generate bioactive compounds that modulate inflammatory pathways, lipid homeostasis, and insulin sensitivity [29, 30, 31, 32]. Although scientific attention has focused on the microbiota’s impact on disease initiation and progression, its potential link with mortality outcomes, especially in cases of CKM syndrome, remains relatively understudied.

The intestinal microbiome exerts direct effects on cardiovascular health via bioactive metabolites, including trimethylamine-N-oxide, short-chain fatty acids, and bile acids, compounds that contribute to atherosclerotic development and elevated cardiovascular risk [29, 33]. Considering these extensive physiological effects, microbial communities in the gut have emerged as promising intervention targets for cardiovascular disease management, with potential benefits extending to mortality reduction [34]. Our research aligns with this perspective, revealing that elevated DI-GM scores—especially those indicative of favorable dietary constituents—correlate with reduced cardiovascular-related deaths. These observations highlight the significance of nutritional approaches that support intestinal microbial balance and their capacity to enhance cardiovascular prognosis and lifespan among patients in the initial phases of CKM syndrome [35].

Notably, the DI-GM demonstrated a curvilinear relationship with overall mortality rates, indicating that consuming extremely high quantities of foods beneficial to gut microbiota might not yield further advantages and could potentially be detrimental. This pattern of non-linear correlations has been previously documented in research investigating the consumption of proteins, carbohydrates, and fruits concerning mortality from all causes and cardiovascular diseases, highlighting the critical need for maintaining dietary equilibrium [36, 37]. One possible explanation is a secondary imbalance in gut microbiota composition, which warrants further investigation.

Moreover, evidence suggests that chronic systemic inflammation serves as a key pathway linking DI-GM with increased mortality risks. The Dietary Inflammatory Index (DII), a validated tool for assessing the inflammatory properties of food components, quantifies their influence on biomarkers including C-reactive protein, tumor necrosis factor, and various interleukins [13]. Analysis of NHANES 1999–2018 datasets revealed significant correlations between elevated DII scores and heightened risks of all-cause mortality (HR = 1.06, 95% CI: 1.02–1.11) as well as cardiovascular-related deaths (HR = 1.08, 95% CI: 1.01–1.15) in patients diagnosed with cardiovascular disease [38]. These detrimental outcomes are primarily attributed to inflammatory cytokine-induced processes such as impaired endothelial function, enhanced monocyte migration, foam cell development, and excessive lipid deposition [39, 40].

Although DI-GM and DII exhibit distinct compositional elements and scoring approaches, our research demonstrated that dietary patterns promoting gut microbiota health correlated with reduced systemic inflammatory markers. Mediation analysis outcomes additionally indicate that the mortality-protective mechanism of DI-GM could be partly attributed to its inflammation-reducing characteristics. The potential pathways through which gut microbiota affects systemic inflammation may involve bacterial translocation processes and immunoregulatory metabolites [41, 42].

In sensitivity assessments that eliminated subjects deceased during the initial 24-month follow-up period, the relationship between DI-GM beneficial scores and all-cause mortality lost statistical significance. However, the inverse relationship with cardiovascular mortality remained consistent. This finding indicates that the initial association with all-cause mortality may have been partly influenced by underlying health conditions that affected both dietary behaviors and short-term mortality risk. In contrast, the persistent association with cardiovascular mortality suggests a more independent and long-term protective effect of gut microbiota-beneficial dietary patterns, particularly given the chronic nature and gradual progression of cardiovascular disease.

Strengths and Limitations

The research underscores the significant influence of dietary habits on mortality outcomes, particularly for patients in CKM syndrome stages 0–3, a population group where dietary intervention studies remain scarce. Utilizing comprehensive NHANES data enhances the validity and applicability of these findings across diverse demographic groups in the United States. Furthermore, through distinct evaluation of both positive and negative elements within the DI-GM scoring system, our analysis indicates that consuming gut microbiota-friendly foods may serve as a key factor in lowering mortality rates, potentially mediated by their impact on inflammatory responses throughout the body.

Several important limitations warrant consideration in this study. Firstly, the cross-sectional nature of the research design restricts our capacity to establish causal relationships between DI-GM scores and mortality results. Future investigations employing longitudinal approaches would be essential to verify these observed connections and assess the sustained impacts of dietary habits. Secondly, the nutritional data collection relied on participant-reported 24-hour dietary recalls, a methodology potentially vulnerable to memory biases and incomplete reporting, which might compromise the precision of DI-GM computations. Thirdly, despite comprehensive adjustments for numerous sociodemographic characteristics, lifestyle variables, and clinical parameters, the possibility of unmeasured confounding factors cannot be entirely ruled out. In particular, physical activity, total energy intake, and medication use (antihypertensive, antidiabetic, and lipid-lowering drugs) were not incorporated into the models due to a large proportion of missing data, which may have limited our ability to fully account for these influences. Fourth, although NHANES data are nationally representative, findings may not be directly applicable to populations outside the US due to differences in dietary behaviors, cultural practices, and genetic backgrounds. Finally, as with all survival analyses, censoring could have affected the precision of the survival estimates. However, since censoring patterns were comparable across quartiles, the likelihood of systematic bias is minimized.

5. Conclusions

Elevated DI-GM scores, which indicate increased consumption of foods beneficial to gut microbiota, correlate with reduced risks of mortality from all causes and cardiovascular diseases among individuals in CKM syndrome stages 0–3. The association is particularly strong for cardiovascular-related deaths. Mediation analyses additionally indicate that systemic inflammation may serve as a mediating pathway. These results imply that dietary interventions aimed at enhancing gut microbiota health could represent a viable approach for improving long-term prognosis in this patient group. Further investigation through longitudinal cohort studies and clinical trials is necessary to confirm these findings and inform dietary guidelines based on robust evidence.

Availability of Data and Materials

Data of NHANES is available from the NHANES website (https://www.cdc.gov/nchs/nhanes/). And the data used for analysis in this study is available from the corresponding author on reasonable request.

References

[1] Ndumele CE, Rangaswami J, Chow SL, Neeland IJ, Tuttle KR, Khan SS, et al. Cardiovascular-Kidney-Metabolic Health: A Presidential Advisory From the American Heart Association. Circulation. 2023; 148: 1606–1635. https://doi.org/10.1161/CIR.0000000000001184.

[2] Ostrominski JW, Arnold SV, Butler J, Fonarow GC, Hirsch JS, Palli SR, et al. Prevalence and Overlap of Cardiac, Renal, and Metabolic Conditions in US Adults, 1999-2020. JAMA Cardiology. 2023; 8: 1050–1060. https://doi.org/10.1001/jamacardio.2023.3241.

[3] Aggarwal R, Ostrominski JW, Vaduganathan M. Prevalence of Cardiovascular-Kidney-Metabolic Syndrome Stages in US Adults, 2011-2020. JAMA. 2024; 331: 1858–1860. https://doi.org/10.1001/jama.2024.6892.

[4] Li N, Li Y, Cui L, Shu R, Song H, Wang J, et al. Association between different stages of cardiovascular-kidney-metabolic syndrome and the risk of all-cause mortality. Atherosclerosis. 2024; 397: 118585. https://doi.org/10.1016/j.atherosclerosis.2024.118585.

[5] Ji H, Sabanayagam C, Matsushita K, Cheng CY, Rim TH, Sheng B, et al. Sex Differences in Cardiovascular-Kidney-Metabolic Syndrome: 30-Year US Trends and Mortality Risks-Brief Report. Arteriosclerosis, Thrombosis, and Vascular Biology. 2025; 45: 157–161. https://doi.org/10.1161/ATVBAHA.124.321629.

[6] Zhu R, Wang R, He J, Wang L, Chen H, Niu X, et al. Prevalence of Cardiovascular-Kidney-Metabolic Syndrome Stages by Social Determinants of Health. JAMA Network Open. 2024; 7: e2445309. https://doi.org/10.1001/jamanetworkopen.2024.45309.

[7] Ding Y, Wu X, Cao Q, Huang J, Xu X, Jiang Y, et al. Gender Disparities in the Association Between Educational Attainment and Cardiovascular-Kidney-Metabolic Syndrome: Cross-Sectional Study. JMIR Public Health and Surveillance. 2024; 10: e57920. https://doi.org/10.2196/57920.

[8] Li J, Lei L, Wang W, Ding W, Yu Y, Pu B, et al. Social Risk Profile and Cardiovascular-Kidney-Metabolic Syndrome in US Adults. Journal of the American Heart Association. 2024; 13: e034996. https://doi.org/10.1161/JAHA.124.034996.

[9] Yang R, Lei Q, Liu Z, Shan X, Han S, Tang Y, et al. Relationship between timing of coffee and tea consumption with mortality (total, cardiovascular disease and diabetes) in people with diabetes: the U.S. National Health and Nutrition Examination Survey, 2003-2014. BMC Medicine. 2024; 22: 526. https://doi.org/10.1186/s12916-024-03736-x.

[10] Zhao S, Cao Y, Liu H, Liu A. Joint and independent associations of dietary antioxidant intakes with all-cause and cardiovascular mortality among patients with hypertension: a population-based cohort study. Nutrition Journal. 2025; 24: 14. https://doi.org/10.1186/s12937-024-01062-9.

[11] Dehghan M, Mente A, Zhang X, Swaminathan S, Li W, Mohan V, et al. Associations of fats and carbohydrate intake with cardiovascular disease and mortality in 18 countries from five continents (PURE): a prospective cohort study. Lancet (London, England). 2017; 390: 2050–2062. https://doi.org/10.1016/S0140-6736(17)32252-3.

[12] Kase BE, Liese AD, Zhang J, Murphy EA, Zhao L, Steck SE. The Development and Evaluation of a Literature-Based Dietary Index for Gut Microbiota. Nutrients. 2024; 16: 1045. https://doi.org/10.3390/nu16071045.

[13] Shivappa N, Steck SE, Hurley TG, Hussey JR, Hébert JR. Designing and developing a literature-derived, population-based dietary inflammatory index. Public Health Nutrition. 2014; 17: 1689–1696. https://doi.org/10.1017/S1368980013002115.

[14] Wright ME, Mayne ST, Stolzenberg-Solomon RZ, Li Z, Pietinen P, Taylor PR, et al. Development of a comprehensive dietary antioxidant index and application to lung cancer risk in a cohort of male smokers. American Journal of Epidemiology. 2004; 160: 68–76. https://doi.org/10.1093/aje/kwh173.

[15] Zheng Y, Hou J, Guo S, Song J. The association between the dietary index for gut microbiota and metabolic dysfunction-associated fatty liver disease: a cross-sectional study. Diabetology & Metabolic Syndrome. 2025; 17: 17. https://doi.org/10.1186/s13098-025-01589-9.

[16] Liu J, Huang S. Dietary index for gut microbiota is associated with stroke among US adults. Food & Function. 2025; 16: 1458–1468. https://doi.org/10.1039/d4fo04649h.

[17] Wu Z, Gong C, Wang B. The relationship between dietary index for gut microbiota and diabetes. Scientific Reports. 2025; 15: 6234. https://doi.org/10.1038/s41598-025-90854-y.

[18] An S, Qin J, Gong X, Li S, Ding H, Zhao X, et al. The Mediating Role of Body Mass Index in the Association Between Dietary Index for Gut Microbiota and Biological Age: A Study Based on NHANES 2007-2018. Nutrients. 2024; 16: 4164. https://doi.org/10.3390/nu16234164.

[19] Tang J, Xu Z, Ren L, Xu J, Chen X, Jin Y, et al. Association of serum Klotho with the severity and mortality among adults with cardiovascular-kidney-metabolic syndrome. Lipids in Health and Disease. 2024; 23: 408. https://doi.org/10.1186/s12944-024-02400-w.

[20] Wang Y, Dong T, Wang B, Zhang P. The association between life’s essential 8 health behavior component score and all-cause and cardiovascular mortality among U.S. adults with cardiovascular-kidney-metabolic syndrome stages 0-3. Preventive Medicine. 2025; 108360. https://doi.org/10.1016/j.ypmed.2025.108360.

[21] Khan SS, Matsushita K, Sang Y, Ballew SH, Grams ME, Surapaneni A, et al. Development and Validation of the American Heart Association’s PREVENT Equations. Circulation. 2024; 149: 430–449. https://doi.org/10.1161/CIRCULATIONAHA.123.067626.

[22] Dziedzic EA, Gąsior JS, Tuzimek A, Paleczny J, Junka A, Dąbrowski M, et al. Investigation of the Associations of Novel Inflammatory Biomarkers-Systemic Inflammatory Index (SII) and Systemic Inflammatory Response Index (SIRI)-With the Severity of Coronary Artery Disease and Acute Coronary Syndrome Occurrence. International Journal of Molecular Sciences. 2022; 23: 9553. https://doi.org/10.3390/ijms23179553.

[23] Desquilbet L, Mariotti F. Dose-response analyses using restricted cubic spline functions in public health research. Statistics in Medicine. 2010; 29: 1037–1057. https://doi.org/10.1002/sim.3841.

[24] Wang Y, Li Q, Bi L, Wang B, Lv T, Zhang P. Global trends in the burden of ischemic heart disease based on the global burden of disease study 2021: the role of metabolic risk factors. BMC Public Health. 2025; 25: 310. https://doi.org/10.1186/s12889-025-21588-9.

[25] Mitrofanova A, Merscher S, Fornoni A. Kidney lipid dysmetabolism and lipid droplet accumulation in chronic kidney disease. Nature Reviews. Nephrology. 2023; 19: 629–645. https://doi.org/10.1038/s41581-023-00741-w.

[26] Li W, Shen C, Kong W, Zhou X, Fan H, Zhang Y, et al. Association between the triglyceride glucose-body mass index and future cardiovascular disease risk in a population with Cardiovascular-Kidney-Metabolic syndrome stage 0-3: a nationwide prospective cohort study. Cardiovascular Diabetology. 2024; 23: 292. https://doi.org/10.1186/s12933-024-02352-6.

[27] Wang W, Hu M, Liu H, Zhang X, Li H, Zhou F, et al. Global Burden of Disease Study 2019 suggests that metabolic risk factors are the leading drivers of the burden of ischemic heart disease. Cell Metabolism. 2021; 33: 1943–1956.e2. https://doi.org/10.1016/j.cmet.2021.08.005.

[28] Sebastian SA, Padda I, Johal G. Cardiovascular-Kidney-Metabolic (CKM) syndrome: A state-of-the-art review. Current Problems in Cardiology. 2024; 49: 102344. https://doi.org/10.1016/j.cpcardiol.2023.102344.

[29] Wang Z, Zhao Y. Gut microbiota derived metabolites in cardiovascular health and disease. Protein & Cell. 2018; 9: 416–431. https://doi.org/10.1007/s13238-018-0549-0.

[30] Chen Y, Zhou J, Wang L. Role and Mechanism of Gut Microbiota in Human Disease. Frontiers in Cellular and Infection Microbiology. 2021; 11: 625913. https://doi.org/10.3389/fcimb.2021.625913.

[31] Nesci A, Carnuccio C, Ruggieri V, D’Alessandro A, Di Giorgio A, Santoro L, et al. Gut Microbiota and Cardiovascular Disease: Evidence on the Metabolic and Inflammatory Background of a Complex Relationship. International Journal of Molecular Sciences. 2023; 24: 9087. https://doi.org/10.3390/ijms24109087.

[32] Wang Y, Bi L, Li Q, Wang Q, Lv T, Zhang P. Remnant cholesterol inflammatory index and its association with all-cause and cause-specific mortality in middle-aged and elderly populations: evidence from US and Chinese national population surveys. Lipids in Health and Disease. 2025; 24: 155. https://doi.org/10.1186/s12944-025-02580-z.

[33] Canyelles M, Borràs C, Rotllan N, Tondo M, Escolà-Gil JC, Blanco-Vaca F. Gut Microbiota-Derived TMAO: A Causal Factor Promoting Atherosclerotic Cardiovascular Disease? International Journal of Molecular Sciences. 2023; 24: 1940. https://doi.org/10.3390/ijms24031940.

[34] Rahman MM, Islam F, -Or-Rashid MH, Mamun AA, Rahaman MS, Islam MM, et al. The Gut Microbiota (Microbiome) in Cardiovascular Disease and Its Therapeutic Regulation. Frontiers in Cellular and Infection Microbiology. 2022; 12: 903570. https://doi.org/10.3389/fcimb.2022.903570.

[35] Nemet I, Li XS, Haghikia A, Li L, Wilcox J, Romano KA, et al. Atlas of gut microbe-derived products from aromatic amino acids and risk of cardiovascular morbidity and mortality. European Heart Journal. 2023; 44: 3085–3096. https://doi.org/10.1093/eurheartj/ehad333.

[36] Naghshi S, Sadeghi O, Willett WC, Esmaillzadeh A. Dietary intake of total, animal, and plant proteins and risk of all cause, cardiovascular, and cancer mortality: systematic review and dose-response meta-analysis of prospective cohort studies. BMJ (Clinical Research Ed.). 2020; 370: m2412. https://doi.org/10.1136/bmj.m2412.

[37] Wang DD, Li Y, Bhupathiraju SN, Rosner BA, Sun Q, Giovannucci EL, et al. Fruit and Vegetable Intake and Mortality: Results From 2 Prospective Cohort Studies of US Men and Women and a Meta-Analysis of 26 Cohort Studies. Circulation. 2021; 143: 1642–1654. https://doi.org/10.1161/CIRCULATIONAHA.120.048996.

[38] Yang M, Miao S, Hu W, Yan J. Association between the dietary inflammatory index and all-cause and cardiovascular mortality in patients with atherosclerotic cardiovascular disease. Nutrition, Metabolism, and Cardiovascular Diseases: NMCD. 2024; 34: 1046–1053. https://doi.org/10.1016/j.numecd.2023.11.015.

[39] Williams JW, Huang LH, Randolph GJ. Cytokine Circuits in Cardiovascular Disease. Immunity. 2019; 50: 941–954. https://doi.org/10.1016/j.immuni.2019.03.007.

[40] Henein MY, Vancheri S, Longo G, Vancheri F. The Role of Inflammation in Cardiovascular Disease. International Journal of Molecular Sciences. 2022; 23: 12906. https://doi.org/10.3390/ijms232112906.

[41] Chen X, Li P, Liu M, Zheng H, He Y, Chen MX, et al. Gut dysbiosis induces the development of pre-eclampsia through bacterial translocation. Gut. 2020; 69: 513–522. https://doi.org/10.1136/gutjnl-2019-319101.

[42] Malesza IJ, Malesza M, Walkowiak J, Mussin N, Walkowiak D, Aringazina R, et al. High-Fat, Western-Style Diet, Systemic Inflammation, and Gut Microbiota: A Narrative Review. Cells. 2021; 10: 3164. https://doi.org/10.3390/cells10113164.

Funding

Beijing Municipal Natural Science Foundation(7244450)