Wang Z, Shi FC, Hou SB, Sun QQ, Fang CY. Association of dietary index for gut microbiota and all-cause and cardiovascular mortality in patients with diabetes or prediabetes. World J Diabetes 2025; 16(7): 107111 [DOI: 10.4239/wjd.v16.i7.107111]
Corresponding Author of This Article
Zheng Wang, Associate Chief Physician, Department of Cardiology, The Second People's Hospital of Hefei, Hefei Hospital Affiliated to Anhui Medical University, Intersection of Guangde Road and Leshui Road, Yaohai District, Hefei 230000, Anhui Province, China. wang20241007@163.com
Research Domain of This Article
Nutrition & Dietetics
Article-Type of This Article
Retrospective Cohort Study
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Zheng Wang, Department of Cardiology, The Second People's Hospital of Hefei, Hefei Hospital Affiliated to Anhui Medical University, Hefei 230000, Anhui Province, China
Fa-Chao Shi, Department of Cardiology, Maanshan People's Hospital, Maanshan 243000, Anhui Province, China
Shan-Bing Hou, Quan-Quan Sun, Cao-Yang Fang, Department of Emergency, First Affiliated Hospital of University of Science and Technology of China, Anhui Provincial Hospital, Hefei 230000, Anhui Province, China
Co-corresponding authors: Zheng Wang and Cao-Yang Fang.
Author contributions: Fang CY made important contributions to study conception, method design, and writing and editing of the manuscript; Shi FC and Hou SB were primarily responsible for data analysis; Sun QQ was primarily responsible for data collection; Wang Z reviewed and edited the manuscript to ensure its scientific rigor and clarity, contributing critical input in refining the final draft.
Institutional review board statement: The study was exempt from ethical review and approval, as no additional institutional review board approval was necessary for the secondary analysis.
Informed consent statement: The National Health and Nutrition Examination Survey is a public database. All patients included in the database have received ethical approval. Users can download relevant data for free to conduct research and publish relevant articles.
Conflict-of-interest statement: The authors declared no conflict of interest.
STROBE statement: The authors have read the STROBE Statement-checklist of items, and the manuscript was prepared and revised according to the STROBE Statement- checklist of items.
Data sharing statement: The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Zheng Wang, Associate Chief Physician, Department of Cardiology, The Second People's Hospital of Hefei, Hefei Hospital Affiliated to Anhui Medical University, Intersection of Guangde Road and Leshui Road, Yaohai District, Hefei 230000, Anhui Province, China. wang20241007@163.com
Received: March 17, 2025 Revised: April 7, 2025 Accepted: May 26, 2025 Published online: July 15, 2025 Processing time: 122 Days and 4 Hours
Abstract
BACKGROUND
The dietary index for gut microbiota (DI-GM) demonstrates associations with diabetes prevalence and related mortality outcomes, serving as a nutritional assessment tool for microbial community evaluation.
AIM
To investigate connections between DI-GM values and survival endpoints in populations with impaired glucose metabolism, incorporating both total mortality and cardiovascular-related fatal events.
METHODS
Cox proportional hazards modeling through survival analysis evaluated the relationship between DI-GM quartile classifications and fatal event probabilities. Restricted cubic spline modeling evaluated non-linear associations between continuous DI-GM values and mortality endpoints. Stratified analyses and robustness checks ensured the validity of the results.
RESULTS
Higher DI-GM values showed a statistically significant negative correlation with total mortality risk [hazard ratio (HR) = 0.96, 95%CI: 0.93-1.00] and cardiovascular-related fatal outcomes (HR = 0.93, 95%CI: 0.87-0.99). When comparing quartiles, analysis indicated that participants in the upper quartile (Q4) had 17% decreased likelihood of all-cause death (HR = 0.83, 95%CI: 0.69-0.99) and 25% lower probability of cardiovascular mortality (HR = 0.75, 95%CI: 0.54-1.00) relative to those in the lowest quartile (Q1).
CONCLUSION
These findings position DI-GM as a protective determinant against mortality in glucose metabolism disorders. Dietary pattern optimization targeting DI-GM enhancement could constitute a strategic intervention in diabetes care protocols.
Core Tip: There exists a clear relationship between dietary index for gut microbiota and the risk of all-cause mortality and cardiovascular mortality in patients with diabetes or prediabetes. Because of its high clinical significance, it is necessary to have a deep understanding of this relationship. In this study, we provide a new perspective for the management of diabetes and provide a new direction for future research and clinical practice. Through further studies, new biomarkers and interventions may be discovered, thereby improving the quality of life and survival of diabetic patients.
Citation: Wang Z, Shi FC, Hou SB, Sun QQ, Fang CY. Association of dietary index for gut microbiota and all-cause and cardiovascular mortality in patients with diabetes or prediabetes. World J Diabetes 2025; 16(7): 107111
The dietary index for gut microbiota (DI-GM) has become a crucial metric for evaluating dietary effects on intestinal microbial communities, receiving substantial research interest because of its implications in glucose metabolism disorders and associated pathologies. Type 2 diabetes mellitus (T2DM) currently represents a critical global health challenge, exhibiting persistent prevalence escalation and strong associations with comorbidities, including cardiovascular disease (CVD)[1]. The composition of the gut microbiome is intricately related to the host's metabolic health, and changes in specific microbial communities may influence glucose metabolism and inflammatory responses, thereby affecting diabetes onset and progression[2,3]. Consequently, examining the association between DI-GM and mortality risks (both all-cause and cardiovascular) in individuals with diabetes contributes to elucidating gut microbial involvement in diabetes pathophysiology while potentially identifying novel therapeutic avenues for clinical management.
In clinical settings, dietary modification is a crucial therapeutic modality in diabetes care. Nutritional regimens emphasizing fiber intake, particularly whole grains and insoluble dietary fiber, demonstrate significant inverse correlations with T2DM development[4]. Nevertheless, the differential impacts of various fiber subtypes on fatal outcomes in diabetic populations require further investigation, particularly the biological functions of soluble and viscous fibers that continue to generate academic controversy[5]. By clarifying the association between the DI-GM and mortality risk in diabetic patients, we can provide a scientific basis for personalized dietary interventions. These evidence-based recommendations could enable practitioners to optimize therapeutic protocols, thereby enhancing clinical prognosis[6].
With advancing investigations into the links between gut microbiota and metabolic disorders, accumulating evidence suggests that microbial diversity and structural composition exert significant impacts on cardiovascular outcomes[7,8]. Emerging research demonstrates that gut microbial imbalance contributes to chronic inflammatory responses and metabolic dysregulation[9,10], factors recognized as critical contributors to cardiovascular pathogenesis[11-13]. Previous studies have established that diet-driven changes in gut microbiota play a pivotal role in the development and progression of metabolic diseases, including T2DM and CVD[14-16]. Both basic and clinical research have demonstrated that higher DI-GM scores often reflective of greater intake of dietary fiber, whole grains, legumes, and fermented foods promote the proliferation of beneficial taxa such as Faecalibacterium prausnitzii and Bifidobacteria, enhance short-chain fatty acid (SCFA) production, and reduce systemic inflammation[17-19]. This in turn modulates glucose homeostasis, improves insulin sensitivity, and reduces metabolic and cardiovascular risk. Investigating the association of DI-GM with cardiovascular mortality risk could enhance our understanding of dietary influences on cardiovascular pathophysiology while potentially identifying novel preventive strategies. Such exploration may illuminate the mechanisms underlying diet-mediated cardiovascular health modulation. Furthermore, DI-GM optimization might contribute to improved systemic health outcomes in diabetic populations and reduced cardiovascular complication rates.
Investigating the associations of DI-GM with mortality outcomes (both all-cause and cardiovascular) among subjects with impaired glucose metabolism has substantial implications for clinical practice, fulfilling critical needs in preventive medicine and therapeutic strategy development. This research offers a new perspective on diabetes management and paves the way for future research and clinical practice. Further studies may uncover new biomarkers and intervention strategies, ultimately improving the quality of life and survival rates for patients with diabetes.
MATERIALS AND METHODS
Study population
This study leveraged population-level data obtained from the National Health and Nutrition Examination Survey (NHANES) from 2007-2018. A stratified multistage sampling methodology was implemented to collect biomarker and health metrics from a nationally representative population sample, enabling systematic evaluation of dietary and clinical parameters across age-stratified groups[20]. Written informed consent was obtained from all study subjects, with research protocols receiving formal authorization from the National Center for Health Statistics Review Board. Institutional ethics clearance was acquired through standardized exemption protocols supervised by the hospital's ethics oversight body.
The study involved 16468 patients aged ≥ 18 years with diabetes or prediabetes between 2007 and 2018. The exclusion criteria included missing demographic information (n = 1751), such as marital status, poverty income ratio (PIR), and education level; incomplete survey data (n = 1480) related to smoking and drinking habits, body mass index (BMI), cancer status, and CVD; missing glycated hemoglobin (HbA1c) data (n = 152); missing serum creatinine data (n = 210); and missing uric acid data (n = 5). Ultimately, the analysis included 12870 eligible participants, as shown in Figure 1.
Diabetes diagnostic classifications adhered to American Diabetes Association (ADA) criteria[21] and utilized NHANES-derived data. Diagnostic criteria included the following: Fasting plasma glucose levels meeting or exceeding 7.0 mmol/L; 2-hour postprandial glucose concentrations ≥ 11.1 mmol/L following oral glucose tolerance tests; random blood glucose measurements ≥ 11.1 mmol/L; HbA1c values ≥ 6.5%; active pharmacological management with hypoglycemic agents or insulin; or clinician-verified diagnoses[22]. Prediabetes classification required: Fasting glucose measurements ranging from 5.6 to 7.0 mmol/L; 2-hour glucose readings between 7.8 and 11.0 mmol/L during oral tolerance tests; HbA1c levels between 5.7% and 6.5%; or medical documentation of prediabetic status[23].
DI-GM
Based on the evaluation framework developed by Kase et al[2], beneficial classes of foods such as avocado, chickpea, whole grains, legumes, nuts, fresh fruits and vegetables, and foods rich in dietary fiber were generally considered to have elevated intakes associated with increased abundance of beneficial bacteria such as Bifidobacteria, Lactobacilli, and Faecalibacterium prausnitzii, to promote SCFA production, improve the intestinal barrier, and reduce inflammatory responses[24]. In particular, soluble dietary fiber and polyphenol-rich foods such as chickpeas and avocados have been shown to positively modulate gut microbiota in clinical/interventional and systematic reviews. Harmful classes such as red meat, processed meat, sugar-sweetened beverages, and some high-fat and high-sugar superprocessed products, whose high intake is associated with increased abundance and reduced microbial diversity of endotoxin-producing/inflammatory bacteria such as Bilophila and Desulfovibrio, are included in the "harmful" food group with broad consensus[25]. DI-GM computation employed NHANES 2007-2020 dietary recall data. Participants scoring 1 point demonstrated intake levels reaching sex-stratified median thresholds for microbiota-beneficial dietary items, with non-compliant cases assigned 0 points. For foods detrimental to gut microbiota, participants received 0 points if their intake equaled or exceeded the gender median or constituted 40% of a high-fat diet, while they received 1 point otherwise. The total DI-GM score, ranging from 0 to 13 (with a beneficial range of 0-9 and a detrimental range of 0-4), was generated by summing all individual scores. Participants were categorized into quartiles based on DI-GM scores: Q1 (0-4), Q2 (5), Q3 (6), and Q4 (7-14)[26].
Death-related information
Two principal outcomes were assessed: Mortality from all causes and cardiovascular-related deaths. For participants enrolled in NHANES 2007-2018, mortality data were determined by cross-referencing National Death Index records. Participants' survival status was confirmed using probabilistic matching techniques applied to national mortality records. Cardiovascular mortality classification adhered to International Classification of Diseases-10 codes (I00-I09, I11, I13, I20-I51, I60-I69). Follow-up duration extended until participant demise or the end of the study (December 31, 2019)[27].
Covariates
The study incorporated covariates such as age, ethnicity, sex, educational attainment, marital status, PIR, hypertension status, cancer history, CVD, BMI, tobacco and alcohol use patterns, diabetes medication, along with biochemical markers including serum creatinine, uric acid, blood urea nitrogen, and HbA1c. Household earnings were stratified into three tiers according to PIR thresholds: < 1.3, 1.3–3.5, and > 3.5. Tobacco use was segmented into three groups: (1) Non-smokers (lifetime cigarette consumption < 100); (2) Former smokers (> 100 cigarettes consumed historically with current abstinence); and (3) Active smokers (> 100 cigarettes consumed with intermittent or daily usage). Alcohol intake was defined by five classifications: Abstainers (< 12 Lifetime drinking episodes), past-year abstainers (≥ 12 episodes historically but none in preceding year), heavy consumers (women ≥ 3 drinks/day, men ≥ 4 drinks/day, or ≥ 5 binge episodes monthly), moderate consumers (women ≥ 2 drinks/day, men ≥ 3 drinks/day, or ≥ 2 binge episodes monthly), and light consumers (remaining cases). Hypertension diagnosis required systolic pressure ≥ 130 mmHg, diastolic pressure ≥ 80 mmHg, or self-reported antihypertensive drug usage.
Mediation analysis
To investigate the potential mediating roles of HbA1c and BMI in the relationship between DI-GM and mortality risk, mediation analyses were performed using the bootstrapping method. This approach allows for estimation of the direct, indirect, and total effects of DI-GM on all-cause and cardiovascular mortality, with HbA1c and BMI as mediators.
Statistical analysis
This study utilized R software (version 4.3.2, https://www.r-project.org) for data analysis. MEC sample weights (WTMEC2YR/7) were applied to data for weighting. Continuous data were expressed as mean values with SE, and categorical data were reported using frequency distributions (%). To examine relationships between DI-GM and mortality outcomes in populations with diabetes spectrum disorders, multivariable-adjusted Cox regression models with sampling weights were employed. The first model remained unadjusted; the second incorporated adjustments for basic demographic variables (age, racial background, sex); while the third model expanded upon this framework by integrating supplementary variables including behavioral patterns (smoking, alcohol consumption), PIR, BMI, clinical comorbidities (malignancy, hypertensive disorders, CVD), and sociodemographic indicators (marital standing, educational background). Restricted cubic spline methodology was employed to investigate nonlinear associations between DI-GM concentrations and mortality endpoints within this cohort.
Subgroup analyses were stratified across demographic and clinical variables including age categories (< 65 vs ≥ 65 years), sex (male/female), racial groups (Mexican American, non-Hispanic White, non-Hispanic Black, other), marital status, educational attainment, BMI classifications (< 25, 25-30, > 30), hypertension status, income tiers defined by PIR (< 1.3, 1.3-3.5, > 3.5), cancer history, and CVD. To verify model stability, three sensitivity analyses were performed. Initially, participants with diabetes or prediabetes actively receiving glucose-lowering therapies were excluded, followed by re-examination of DI-GM's association with all-cause and cardiovascular mortality. To reduce confounding, baseline cancer-diagnosed individuals were subsequently removed. Lastly, unweighted multivariable logistic regression was applied to evaluate hemoglobin glycation index's relationship with mortality outcomes in this cohort. A two-tailed P-value threshold of < 0.05 defined statistical significance.
RESULTS
General demographic characteristics of study subjects
Demographic and clinical characteristics of the study population stratified by DI-GM quartiles (Q1-Q4) are summarized in Table 1. A progressive increase in participant age was observed with ascending DI-GM quartiles (P < 0.0001). Notably, the Q4 cohort had the lowest mean HbA1c levels (P < 0.001), implying enhanced glycemic regulation among individuals with elevated DI-GM scores. BMI distribution patterns revealed divergent trends: The Q4 group contained the highest proportion of participants with BMI < 25 (22.00%), whereas the Q1 group exhibited the greatest prevalence of BMI > 30 (52.43%), signifying an inverse association between DI-GM scores and obesity prevalence. We identified substantial variations in sociodemographic parameters (gender distribution, racial composition, marital status, and educational attainment) across DI-GM quartiles (P < 0.001), indicating potential linkages between DI-GM stratification and lifestyle/health determinants. These observations establish critical epidemiological evidence for investigating DI-GM prognostic value in diabetes-related mortality outcomes.
Table 1 Basic participant characteristics by dietary index for gut microbiota scores, mean (SE)/ n (%).
Variables
Total
Q1 (0-4)
Q2 (5)
Q3 (6)
Q4 (7-14)
P value
Age
53.84 (0.25)
52.07 (0.34)
53.19 (0.46)
54.63 (0.45)
57.14 (0.39)
< 0.0001
Creatinine
80.80 (0.44)
82.90 (0.92)
79.90 (0.60)
79.78 (0.96)
78.85 (0.50)
0.004
UA
338.42 (1.20)
341.76 (1.67)
337.00 (2.27)
338.33 (2.80)
333.91 (2.30)
0.05
BUN
5.21 (0.03)
5.27 (0.04)
5.16 (0.05)
5.18 (0.06)
5.19 (0.06)
0.15
HbA1c
6.06 (0.02)
6.13 (0.02)
6.06 (0.03)
5.99 (0.03)
6.02 (0.03)
< 0.001
BMI, % (SE)
< 0.0001
< 25
18.42 (0.01)
16.75 (0.80)
17.98 (1.10)
18.47 (1.11)
22.00 (1.15)
25-30
32.52 (0.01)
30.83 (0.98)
32.02 (1.12)
34.07 (1.63)
34.81 (1.21)
> 30
49.06 (0.01)
52.43 (1.13)
50.00 (1.33)
47.46 (1.37)
43.19 (1.32)
Sex, % (SE)
< 0.001
Male
52.17 (0.01)
55.30 (0.87)
51.35 (1.17)
50.70 (1.66)
48.64 (1.11)
Female
47.83 (0.01)
44.70 (0.87)
48.65 (1.17)
49.30 (1.66)
51.36 (1.11)
Race, % (SE)
< 0.0001
Mexican American
8.62 (0.01)
10.27 (1.01)
9.49 (1.13)
7.41 (0.80)
5.67 (0.68)
Non-Hispanic Black
11.72 (0.01)
15.16 (1.21)
11.29 (0.87)
9.50 (0.78)
7.87 (0.73)
Non-Hispanic White
66.87 (0.03)
62.20 (1.84)
65.80 (1.91)
70.65 (1.43)
73.24 (1.58)
Other
12.80 (0.00)
12.37 (0.80)
13.43 (0.86)
12.44 (0.83)
13.22 (0.90)
Marital status, % (SE)
< 0.0001
Married
59.18 (0.02)
55.78 (1.22)
60.15 (1.35)
59.17 (1.24)
64.45 (1.71)
Never married
11.77 (0.01)
13.65 (0.81)
11.49 (0.81)
12.21 (1.02)
8.19 (0.83)
Divorced
11.93 (0.01)
12.50 (0.79)
10.77 (0.88)
11.89 (0.98)
12.24 (1.09)
Unmarried but have/had partner
17.11 (0.01)
18.07 (0.71)
17.59 (0.97)
16.73 (0.94)
15.12 (0.96)
Education, % (SE)
< 0.0001
Less than high school
17.68 (0.01)
22.00 (0.89)
17.80 (0.87)
15.34 (0.96)
11.69 (0.94)
High school or equivalent
24.64 (0.01)
27.55 (0.88)
25.78 (1.43)
21.90 (1.46)
20.48 (1.24)
College or above
57.67 (0.02)
50.45 (1.11)
56.42 (1.64)
62.77 (1.66)
67.83 (1.61)
Smoke, % (SE)
< 0.0001
Never
51.48 (0.01)
49.70 (1.18)
51.84 (1.24)
53.65 (1.41)
52.36 (1.51)
Former
30.07 (0.01)
29.22 (1.07)
28.85 (1.09)
27.92 (1.13)
35.03 (1.63)
Now
18.46 (0.01)
21.09 (0.77)
19.30 (1.03)
18.43 (1.18)
12.61 (0.90)
Alcohol use, % (SE)
< 0.0001
Never
11.57 (0.01)
11.55 (0.61)
13.11 (1.10)
10.83 (0.80)
10.56 (0.82)
Former
16.56 (0.01)
17.18 (0.85)
17.34 (1.06)
16.69 (1.14)
14.39 (0.92)
Mild
39.62 (0.01)
36.77 (1.16)
37.13 (1.46)
39.37 (1.61)
48.01 (1.36)
Moderate
14.99 (0.01)
14.27 (0.71)
15.17 (0.90)
16.25 (1.26)
14.97 (0.89)
Heavy
17.25 (0.01)
20.23 (0.87)
17.26 (1.03)
16.86 (1.24)
12.07 (1.09)
Cancer, % (SE)
< 0.001
Yes
13.14 (0.01)
11.59 (0.60)
12.17 (0.86)
14.15 (0.92)
16.22 (0.87)
No
86.86 (0.02)
88.41 (0.60)
87.83 (0.86)
85.85 (0.92)
83.78 (0.87)
CVD, % (SE)
0.59
Yes
13.84 (0.01)
14.46 (0.60)
13.71 (0.78)
13.15 (0.97)
13.47 (0.88)
No
86.16 (0.02)
85.54 (0.60)
86.29 (0.78)
86.85 (0.97)
86.53 (0.88)
Hypertension, % (SE)
< 0.0001
Yes
52.43 (0.02)
52.89 (1.07)
52.32 (1.36)
51.16 (1.37)
52.86 (1.31)
No
47.57 (0.01)
47.11 (1.07)
47.68 (1.36)
48.84 (1.37)
47.14 (1.31)
PIR, % (SE)
< 0.0001
< 1.3
21.12 (0.01)
26.21 (0.88)
21.95 (0.96)
18.03 (1.02)
13.55 (0.96)
1.3-3.5
37.62 (0.01)
39.26 (1.18)
37.52 (1.42)
37.65 (1.58)
34.65 (1.77)
> 3.5
41.26 (0.02)
34.53 (1.37)
40.53 (1.71)
44.32 (1.58)
51.80 (2.08)
Antidiabetic treatment, % (SE)
0.02
Yes
18.42 (0.01)
19.78 (0.79)
19.05 (1.14)
16.14 (0.82)
17.30 (0.95)
No
81.58 (0.02)
80.22 (0.79)
80.95 (1.14)
83.86 (0.82)
82.70 (0.95)
Association between DI-GM and all-cause mortality in patients with diabetes or prediabetes
As detailed in Table 2, weighted multivariable Cox regression analyses revealed a negative association between DI-GM and all-cause mortality among individuals with diabetes or prediabetes in the fully adjusted model (Model 3). The hazard ratio (HR) per unit increment in DI-GM was 0.96 (95%CI: 0.93–1.00), implying a 4% reduction in mortality risk for each DI-GM unit elevation. We compared extreme quartiles and found that the Q4 cohort had a 17% lower risk relative to the Q1 cohort (HR: 0.83; 95%CI: 0.69–0.99; P = 0.03). Bivariate correlations evaluating constituent elements of DI-GM against mortality endpoints are shown in Supplementary Table 1. Furthermore, restricted cubic spline modeling confirmed a monotonic inverse dose-response pattern, linking DI-GM levels with all-cause mortality risk in this population (Figure 2A).
Figure 2 Restricted cubic spline analysis demonstrates an association between gut microbiota dietary index and risk of all-cause and cardiovascular mortality in patients with diabetes or prediabetes.
A: All-cause mortality; B: Cardiovascular mortality; y-axis indicates log hazard ratio, solid line indicates estimated hazard ratio, and shaded area indicates 95%CI; DI-GM: Dietary index for gut microbiota.
Table 2 Association of diabetes and prediabetes all-cause mortality and cardiovascular risk with dietary index for gut microbiota.
Association between DI-GM and cardiovascular mortality in patients with diabetes or prediabetes
As detailed in Table 2, weighted multivariable Cox regression analyses demonstrated a negative relationship connecting DI-GM with cardiovascular mortality among diabetes/prediabetes patients in the fully adjusted model (Model 3), with an HR of 0.93 (95%CI: 0.87–0.99). This represents a 7% decreased cardiovascular mortality risk per DI-GM unit increment. Interquartile comparisons indicated a 25% reduced all-cause mortality risk for the highest vs lowest DI-GM quartiles (HR: 0.75; 95%CI: 0.54–1.00; P = 0.05). Component-wise correlations of DI-GM factors with cardiovascular mortality outcomes are detailed in Supplementary Table 1. Restricted cubic spline evaluations confirmed a nonlinear negative correlation between DI-GM concentrations and cardiovascular mortality risk within this population (Figure 2B).
Mediation analysis results
We performed mediation analyses and found that HbA1c partially mediates the association between DI-GM and all-cause and cardiovascular mortality. Through HbA1c-mediated pathways, DI-GM had an indirect effect on all-cause mortality (mediation proportion: 3.87%; total effect magnitude: 7.43; P < 0.0001). Similarly, HbA1c had an indirect effect on cardiovascular mortality, with a 3.42% mediation proportion and total effect size of 50.35 (P < 0.0001). We did not identify any significant mediation for BMI in these relationships (Figure 3).
Figure 3 Mediation analysis of HbA1c and body mass index in the association of dietary index for gut microbiota with all-cause and cardiovascular mortality in patients with diabetes or prediabetes.
A: Mediation analysis of HbA1c in all-cause mortality; B: Mediation analysis of body mass index (BMI) in all-cause mortality; C: Mediation analysis of HbA1c in cardiovascular mortality; D: Mediation analysis of BMI in cardiovascular mortality. BMI: Body mass index; DI-GM: Dietary index for gut microbiota.
Subgroup analysis and sensitivity analysis
We performed subgroup evaluations to investigate potential modifying influences of demographic and clinical characteristics (age, sex, race/ethnicity, marital status, educational attainment, BMI classifications, hypertensive status, poverty-income ratio tiers, oncological history, cardiovascular conditions) on DI-GM-mortality correlations in glucose dysregulation cohorts. Statistical testing identified significant effect modifications between DI-GM and all-cause mortality across racial/ethnic categories (P = 0.02). However, these associations demonstrated stability within other predefined population strata (interaction P values > 0.05 for both mortality endpoints). Detailed stratification outcomes are systematically documented in Tables 3 and 4.
Table 3 Diabetes and prediabetes all-cause mortality risk subgroup analyses.
Variables
Q1
Q2 HR (95%CI)
Q3 HR (95%CI)
Q4 HR (95%CI)
P for trend
P for interaction
Age (years)
0.07
< 65
Reference
1.27 (0.94-1.71)
0.93 (0.65-1.34)
1.11 (0.73-1.69)
0.88
≥ 65
Reference
0.82 (0.68-0.99)
1.01 (0.84-1.22)
0.74 (0.62-0.89)
0.21
Sex
0.50
Male
Reference
0.94 (0.76-1.17)
0.89 (0.69-1.15)
0.91 (0.70-1.19)
0.87
Female
Reference
0.94 (0.73-1.21)
0.98 (0.77-1.23)
0.71 (0.56-0.90)
0.09
Race
0.03
Mexican American
Reference
0.77 (0.48-1.25)
1.05 (0.63-1.75)
0.61 (0.34-1.08)
0.34
Non-Hispanic Black
Reference
1.14 (0.82-1.58)
0.77 (0.54-1.11)
0.62 (0.38-1.01)
0.31
Non-Hispanic White
Reference
0.93 (0.76-1.14)
0.94 (0.78-1.14)
0.88 (00.72-1.07)
0.73
Other
Reference
0.81 (0.47-1.41)
0.96 (0.64-1.44)
0.33 (0.19-0.55)
0.001
Marital
0.42
Married
Reference
0.94 (0.76-1.17)
0.93 (0.76-1.14)
0.79 (0.63-0.99)
0.28
Never married
Reference
1.05 (0.50-2.19)
0.77 (0.27-2.19)
1.46 (0.53-4.02)
0.30
Divorced
Reference
0.78 (0.53-1.15)
0.85 (0.53-1.34)
1.20 (0.75-1.92)
0.61
Unmarried but have/had partner
Reference
1.01 (0.76-1.36)
1.00 (0.75-1.35)
0.67 (0.50-0.91)
0.04
Education
0.13
Less than high school
Reference
1.01 (0.78-1.31)
1.06 (0.82-1.36)
0.81 (0.58-1.13)
0.24
High school or equivalent
Reference
0.84 (0.57-1.23)
1.22 (0.91-1.64)
0.86 (0.62-1.18)
0.90
College or above
Reference
0.97 (0.75-1.26)
0.74 (0.59-0.92)
0.79 (0.61-1.03)
0.51
BMI
0.29
< 25
Reference
0.89 (0.64-1.25)
0.82 (0.58-1.15)
0.56 (0.39-0.80)
0.08
25-30
Reference
1.07 (0.77-1.49)
1.03 (0.77-1.37)
0.86 (0.62-1.19)
0.23
> 30
Reference
0.89 (0.70-1.14)
0.94 (0.73-1.21)
0.96 (0.73-1.25)
0.73
Hypertension
0.42
Yes
Reference
0.87 (0.72-1.05)
0.92 (0.78-1.09)
0.80 (0.65-0.98)
0.05
No
Reference
1.22 (0.87-1.71)
0.97 (0.70-1.34)
0.85 (0.60-1.20)
0.35
PIR
0.99
< 1.3
Reference
0.99 (0.79-1.24)
0.90 (0.70-1.17)
0.82 (0.56-1.19)
0.74
1.3-3.5
Reference
0.92 (0.71-1.19)
1.00 (0.79-1.28)
0.83 (0.65-1.08)
0.29
> 3.5
Reference
0.98 (0.67-1.42)
0.84 (0.59-1.18)
0.84 (0.58-1.22)
0.22
Cancer
0.85
Yes
Reference
0.97 (0.72-1.32)
0.92 (0.67-1.26)
0.92 (0.69-1.23)
0.88
No
Reference
0.94 (0.78-1.14)
0.94 (0.77-1.14)
0.78 (0.64-0.95)
0.22
CVD
0.27
Yes
Reference
0.76 (0.60-0.97)
0.83 (0.63-1.09)
0.71 (0.55-0.92)
0.22
No
Reference
1.07 (0.86-1.32)
1.00 (0.81-1.22)
0.90 (0.71-1.13)
0.32
Table 4 Diabetes and prediabetes cardiovascular mortality risk subgroup analyses.
Variables
Q1
Q2 HR (95%CI)
Q3 HR (95%CI)
Q4 HR (95%CI)
P for trend
P for interaction
Age
0.05
< 65
Reference
1.13 (0.59-2.18)
0.32 (0.13-0.79)
1.02 (0.39-2.65)
0.10
≥ 65
Reference
0.78 (0.56-1.09)
1.05 (0.76-1.46)
0.70 (0.50-0.97)
0.50
Sex
0.62
Male
Reference
0.87 (0.58-1.30)
0.70 (0.45-1.10)
0.81 (0.52-1.26)
0.78
Female
Reference
0.84 (0.52-1.36)
0.99 (0.66-1.49)
0.69 (0.42-1.13)
0.53
Race
0.33
Mexican American
Reference
0.78 (0.25-2.39)
1.31 (0.54-3.17)
0.51 (0.15-1.68)
0.23
Non-Hispanic Black
Reference
0.95 (0.60-1.52)
0.53 (0.27-1.07)
0.34 (0.13-0.87)
0.04
Non-Hispanic White
Reference
0.85 (0.56-1.29)
0.82 (0.59-1.13)
0.81 (0.56-1.19)
0.56
Other
Reference
0.95 (0.32-2.86)
1.25 (0.55-2.80)
0.50 (0.16-1.60)
0.11
Marital status
0.46
Married
Reference
1.08 (0.72-1.63)
0.90 (0.61-1.34)
0.94 (0.59-1.49)
0.66
Never married
Reference
0.56 (0.14-2.26)
0.76 (0.13-4.28)
1.01 (0.07-13.51)
0.55
Divorced
Reference
0.76 (0.39-1.48)
0.26 (0.09-0.80)
0.75 (0.33-1.70)
0.20
Unmarried but have/had partner
Reference
0.72 (0.45-1.14)
0.90 (0.57-1.43)
0.50 (0.32-0.77)
0.11
Education
0.20
Less than high school
Reference
0.90 (0.56-1.46)
1.19 (0.80-1.78)
0.94 (0.50-1.74)
0.73
High school or equivalent
Reference
0.89 (0.42-1.90)
1.39 (0.86-2.24)
0.71 (0.35-1.42)
0.54
College or above
Reference
0.78 (0.49-1.25)
0.48 (0.31-0.74)
0.62 (0.40-0.95)
0.94
BMI
0.48
< 25
Reference
1.25 (0.60-2.61)
0.90 (0.53-1.53)
0.69 (0.34-1.38)
0.18
25-30
Reference
1.02 (0.64-1.61)
0.96 (0.54-1.70)
0.58 (0.31-1.07)
0.12
> 30
Reference
0.70 (0.40-1.20)
0.70 (0.47-1.03)
0.89 (0.55-1.46)
0.48
Hypertension
0.14
Yes
Reference
0.75 (0.53-1.07)
0.87 (0.67-1.13)
0.76 (0.52-1.10)
0.17
No
Reference
1.35 (0.71-2.55)
0.68 (0.34-1.34)
0.68 (0.35-1.32)
0.58
PIR
0.93
< 1.3
Reference
1.06 (0.68-1.67)
0.83 (0.51-1.33)
1.01 (0.49-2.07)
0.69
1.3-3.5
Reference
0.73 (0.44-1.20)
0.87 (0.57-1.33)
0.66 (0.42-1.04)
0.12
> 3.5
Reference
1.06 (0.52-2.16)
0.82 (0.43-1.55)
0.84 (0.46-1.54)
0.43
Cancer
0.26
Yes
Reference
0.94 (0.48-1.84)
1.00 (0.49-2.02)
1.21 (0.61-2.37)
0.63
No
Reference
0.85 (0.59-1.23)
0.80 (0.60-1.06)
0.62 (0.43-0.88)
0.39
CVD
0.68
Yes
Reference
0.73 (0.46-1.15)
0.82 (0.51-1.31)
0.61 (0.40-0.94)
0.21
No
Reference
0.98 (0.63-1.53)
0.80 (0.54-1.18)
0.85 (0.53-1.36)
0.76
To mitigate potential bias from incomplete data, we initially excluded patients with diabetes or prediabetes undergoing glucose-lowering therapy. Subsequent analyses demonstrated preserved stability in DI-GM's association with both all-cause and cardiovascular mortality risks within this population (Supplementary Table 2). Through additional exclusion criteria, we removed baseline participants with self-reported malignancy diagnoses. Following comprehensive covariate adjustment, DI-GM-mortality correlations maintained congruence with our primary findings (Supplementary Table 3). Comparative evaluation of weighted vs unweighted models revealed marginally attenuated risk estimates (HR) in weighted analyses, though statistical significance thresholds remained comparable (Supplementary Table 4). Furthermore, due to the unique sampling characteristics of the NHANES data, weight-adjusted results are more accurate.
DISCUSSION
In this study, we utilized nationally representative NHANES datasets to examine mortality correlations with DI-GM among individuals with impaired glucose regulation. Primary results demonstrate that elevated DI-GM values inversely correlated with mortality risks, showing 17% and 25% reductions in all-cause and cardiovascular-specific death probabilities, respectively, within this patient cohort. These results suggest that improving dietary patterns to enhance DI-GM could serve as a protective measure against mortality in diabetic patients.
DI-GM has emerged as a prominent metric for evaluating dietary influences on gut microbial ecosystems. Previous studies have identified strong correlations between DI-GM values and gut microbiota compositional diversity, which subsequently modulates host metabolic homeostasis. Elevated DI-GM scores frequently correspond to greater consumption of dietary fibers; such nutritional components enhance proliferation of advantageous microbial communities, strengthen intestinal barrier integrity, and attenuate systemic inflammatory responses[28,29]. A study of middle-aged and older adults found that individuals with higher DI-GM scores exhibited significantly increased gut microbial diversity and had a negative correlation with reduced T2DM risk[30]. DI-GM alterations are associated with favorable modifications in multiple metabolic indicators, including insulin sensitivity and glycemic control[4]. Consequently, DI-GM optimization not only enhances gastrointestinal health but also emerges as a significant factor in mitigating and controlling metabolic disorders.
Diabetes mellitus and its precursor conditions remain a major global health burden. Emerging evidence highlights dietary factors as pivotal determinants in diabetes pathogenesis and disease trajectory, with substantial research focusing on fiber consumption patterns and diabetes risk modulation[31]. High-fiber nutritional regimens enhance glycemic regulation while mitigating systemic inflammation through gut microbial modulation, consequently reducing diabetes susceptibility[5,32]. Fiber-rich nutritional intake stimulates colonic generation of SCFAs, bioactive metabolites that enhance insulin sensitivity and metabolic homeostasis[33]. Gut microbiota compositional profiles demonstrate significant associations with diabetes pathophysiology, where specific taxa exhibit protective effects against diabetes development, whereas others potentially exacerbate insulin resistance[34,35]. These insights underscore the necessity for investigating dietary modification strategies targeting gut microbiome remodeling, potentially yielding novel approaches for diabetes prevention and therapeutic intervention strategies.
All-cause and cardiovascular mortality rates serve as critical metrics for evaluating population health status. Emerging evidence demonstrates strong associations between dietary habits and risks of mortality from all causes and cardiovascular conditions. Substantial inverse correlations have been identified between elevated dietary fiber consumption and both all-cause and cardiovascular mortality risks[36,37]. For example, consumption of abundant fruits and vegetables enhances gut microbiota diversity while mitigating cardiovascular risks through anti-inflammatory effects and metabolic improvements[38]. Notably, population-based investigations involving middle-aged and elderly participants revealed markedly lower all-cause and cardiovascular mortality risks among those with elevated DI-GM scores, underscoring dietary optimization as a key intervention for health promotion[39]. By employing multivariable-adjusted Cox proportional hazards models that incorporated sampling weights, we identified inverse associations between DI-GM scores and mortality risks in populations with impaired glucose metabolism. Quantitatively, each DI-GM unit elevation corresponded to a 4% reduction in all-cause mortality risk (HR = 0.96, 95%CI: 0.93-1.00) and 7% decreased cardiovascular mortality probability (HR = 0.93, 95%CI: 0.87-0.99)[28,40]. These findings align with existing epidemiological evidence that dietary pattern influences survival outcomes in diabetes cohorts, underscoring the critical role of nutritional optimization in therapeutic interventions[4,30].
The distinctive aspect of this study lies in its more detailed stratified analysis, which explores the interaction between DI-GM and the risks of all-cause and cardiovascular mortality. Notably, we identified a significant interaction within the racial subgroup (P = 0.03), suggesting that different racial backgrounds may influence the relationship between DI-GM and mortality risk[40]. In contrast, many previous studies often failed to adequately consider the potential impact of race and other socioeconomic factors on the outcomes, which may limit the generalizability of their findings[41,42]. While we were unable to explore the underlying genetic mechanisms due to data limitations, we acknowledge that genetic predisposition, socioeconomic factors, and cultural dietary habits may all play a role. For instance, cultural dietary habits such as traditional consumption patterns, food preparation methods, and the affordability of fiber-rich or fermented foods, can markedly shape gut microbiota composition among different populations[43]. Moreover, food insecurity and limited access to fresh, healthy foods are more prevalent in socioeconomically disadvantaged communities, further contributing to disparities in dietary quality and microbiome-related health outcomes[44]. While our current analysis adjusted for major demographic and socioeconomic covariates, additional stratified or culturally tailored analyses could deepen our understanding of how these contextual factors interact with DI-GM and influence mortality. Future studies with more detailed data on cultural dietary practices, food environment, and microbiome composition across diverse populations are warranted to explore these relationships.
At the same time, our findings suggest that HbA1c may be an important pathway through which DI-GM impacts the risk of all-cause and cardiovascular mortality in diabetic patients. This is consistent with previous studies that have shown the importance of glycemic control in reducing mortality risk of in this population. We selected HbA1c and BMI as potential mediators in this study based on their well-documented roles in metabolic regulation and chronic disease risk, as well as their recognized associations with both dietary patterns and gut microbiota composition. Dietary factors can modulate the gut microbiome, in turn influencing glucose homeostasis and adiposity. Elevated HbA1c reflects long-term glycemic status and has been linked to dietary intake and microbial metabolites, notably SCFA, influencing metabolic health and mortality risk[45]. As an indicator of adiposity, BMI is closely associated with dietary quality and gut microbiota diversity. Furthermore, it independently predicts morbidity and all-cause mortality[46]. Several studies have identified both HbA1c and BMI as intermediate variables through which dietary interventions and gut microbiota composition impact metabolic outcomes and long-term health[47]. Therefore, the inclusion of these markers as mediators is supported by substantial biological and epidemiological evidence.
To assess potential bias from incomplete data, sensitivity analyses were performed through multiple analytical scenarios. These analyses confirmed persistent stability in DI-GM's association with both all-cause and cardiovascular mortality risks across variable conditions, thereby strengthening the robustness of study conclusions[34]. While previous studies have also performed sensitivity analyses, they failed to adequately explore the effects of different medications or comorbidities on outcomes[35]. By excluding patients using anti-diabetes medications and self-reported cancer patients, we ensured the accuracy and stability of the analysis and results. The methodological robustness significantly enhances confidence in study outcomes. Analyses employing restricted cubic spline models demonstrate a nonlinear inverse association between DI-GM values and mortality probabilities (both all-cause and cardiovascular) in populations with impaired glucose regulation. These observations provide novel epidemiological evidence regarding DI-GM's differential mortality impacts across its quantile distribution. Such nonlinear associations were insufficiently characterized in prior investigations, highlighting the intricate dynamics and clinical relevance of nutritional modulation strategies for diabetes care[48].
Our investigation revealed a robust inverse correlation between DI-GM and mortality risk among individuals with diabetes; however, the biological pathways mediating this association remain poorly understood, in part due to inherent constraints within the NHANES dataset. However, we can hypothesize several potential mechanisms. First, DI-GM can influence gut microbiota composition and diversity. Elevated DI-GM dietary patterns characterized by abundant fiber and botanical components stimulate microbial diversity, supporting beneficial taxa including Faecalibacterium prausnitzii and Bifidobacterial species that synthesize SCFA such as butyric acid, acetic acid, and propionic acid[49-51]. These microbial metabolites strengthen intestinal epithelial integrity and demonstrate anti-inflammatory properties through multiple biological pathways[52,53]. Conversely, DI-GM-deficient diets containing excessive red/processed meats and refined carbohydrates can facilitate the proliferation of pathogenic microorganisms, exacerbating enteric inflammation and metabolic dysfunction[54]. Additionally, DI-GM potentially regulates systemic inflammatory responses. Nutritionally optimized DI-GM regimens could potentially downregulate pro-inflammatory mediators such as interleukin-6 and tumor necrosis factor-alpha, consequently mitigating cardiovascular pathologies and premature mortality risks. Future studies should utilize cohort studies with gut microbiota and inflammatory biomarker data, interventional studies, and multi-omics approaches to more comprehensively investigate these complex relationships.
We utilized the NHANES database in this study, which contains a wealth of health and nutrition data and is characterized by high representativeness and reliability, thus ensuring the validity of the research findings. However, this study has several limitations. Firstly, as an observational study, it cannot establish causal relationships. The conclusions of the study are mainly aimed at the general adult population in the United States, and the generalizability of other populations requires external validation. In addition, although we have performed multiple sensitivity analyses to increase the robustness of the results, the conclusions of this study need to be further validated and expanded in combination with longitudinal cohorts and multicenter studies. Secondly, DI-GM calculation relies on participants' dietary intake data, which may be subject to self-reporting bias. Participants may underestimate or overestimate their intake of certain foods, leading to inaccurate DI-GM scores. These biases are difficult to completely avoid despite a standardized assessment process. Future studies may combine objective dietary biomarkers with multi-omics techniques to improve the accuracy of exposure assessment. Thirdly, NHANES data is cross-sectional, which limits our ability to assess changes in dietary habits over time. To minimize memory-related biases while enhancing measurement precision of nutritional intake datasets, subsequent investigations should prioritize implementing objective dietary assessment tools, including 24-hour dietary recalls, validated food frequency instruments, and biological markers. Longitudinal studies that track dietary habits over time would also be valuable. Combining self-reported dietary data with objective measures could further enhance the reliability of dietary assessments. Fourthly, although the NHANES database is representative, the study only included eligible patients with diabetes and prediabetes, which may lead to selection bias and limit the generalizability of the findings. Finally, despite the adjustment for various confounding factors, other lifestyle factors, such as physical activity levels and mental health, were not adequately considered in relation to their impact on mortality risk, which could affect the comprehensiveness of the results. Although NHANES includes data on these factors, the quality and completeness of these data may be limited. Physical activity data is based on self-report questionnaires, which are subject to recall bias and reporting errors. Mental health data may not fully capture the complexity of individual mental health status. These variables might modify the link between DI-GM and mortality outcomes. Subsequent research should investigate the effects of physical activity and psychological well-being on the association between DI-GM and mortality by incorporating enhanced measurement methodologies.
CONCLUSION
In summary, this study aligns with previous literature in confirming the negative correlation between the DI-GM and mortality risk among patients with diabetes. However, by employing more comprehensive analytical methods and considering various potential confounding factors, it provides deeper insights. These results highlight the critical role of diet-targeted interventions in diabetes care and establish novel research priorities for subsequent investigations. Subsequent investigations should further investigate the generalizability of DI-GM across diverse demographic groups while developing potential therapeutic approaches, with the ultimate goal of formulating enhanced nutritional frameworks for diabetes prevention and clinical management.
Footnotes
Provenance and peer review: Unsolicited article; Externally peer reviewed.
Peer-review model: Single blind
Specialty type: Endocrinology and metabolism
Country of origin: China
Peer-review report’s classification
Scientific Quality: Grade A, Grade B, Grade B, Grade B, Grade B, Grade C
Novelty: Grade A, Grade B, Grade B, Grade B, Grade B, Grade C
Creativity or Innovation: Grade A, Grade B, Grade B, Grade B, Grade B, Grade C
Scientific Significance: Grade A, Grade A, Grade B, Grade B, Grade B, Grade C
P-Reviewer: Cheng TH; Wang W; Yang XL S-Editor: Liu H L-Editor: Filipodia P-Editor: Xu ZH
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