Abstract
Objective
The associations between a diet that associated with a healthy gut microbiota and the risk of frailty, as well as mortality in older adults with frailty, remains unclear. Therefore, this study aimed to examine the associations of dietary index for gut microbiota (DI-GM) with frailty risk, as well as the all-cause mortality and cause-specific mortality in frail participants.
Methods
A total of 6,104 participants aged 65 and older from six cycles of the National Health and Nutrition Examination Survey (2007–2018) were included in this study. DI-GM was calculated based on the intake of 13 dietary components, with a higher score reflecting a diet that associated with a more favorable gut microbiota profile. Frailty was assessed using the 48-item frailty index, and participants with a frailty index exceeding 0.21 were classified as having frailty. Mortality status and cause of death were determined through linkage to the National Death Index records until December 31, 2019. Weighted multivariable logistic regression was conducted to investigate the associations between DI-GM and the risk of frailty. Weighted multivariable Cox proportional hazards regression models were used to examine the associations of DI-GM with all-cause mortality, cardiovascular disease (CVD) mortality, and cancer mortality in frail participants.
Results
Compared with participants in the lowest DI-GM quintile, those in the highest quintile were associated with a 32% (95% confidence interval [CI]: 14% to 46%) reduced risk of frailty. Over a median 5.3 years follow-up, 778 deaths occurred among participants with frailty, including 258 deaths due to cardiovascular disease and 139 deaths due to cancer. The hazard ratios and 95% CI for all-cause mortality, CVD mortality, and cancer mortality were 0.73 (0.57, 0.93), 0.59 (0.36, 0.97), and 1.41 (0.76, 2.62), respectively, when comparing the extreme DI-GM quintiles.
Conclusion
DI-GM was inversely associated with the risk of frailty in older adults. Additionally, a higher DI-GM was associated with a reduced risk of all-cause mortality and CVD mortality in individuals with frailty. Diet linked to a healthy gut microbiota might be associated with a reduced risk of frailty in older adults and a decreased risk of premature mortality in individuals with frailty.
Supplementary Information
The online version contains supplementary material available at10.1186/s12967-025-07472-5.
Keywords: Dietary index for gut microbiota, Frailty, All-cause mortality, Cardiovascular disease mortality
Introduction
Frailty is a state characterized by cumulative declines in multiple physiological systems during the aging process [1–4]. It was accompanied by dysregulated stress responses and increased vulnerability to adverse health-related outcomes [1–4]. Frailty was associated with an increased risk of various adverse health-related outcomes, including falls, mobility impairment, hospitalizations, and mortality [1–4]. Due to the rapid aging of the population, frailty has emerged as a major public health concern [2, 5, 6]. The pooled prevalence of frailty among individuals aged 50 and older across 62 countries/territories ranged from 12% to 24% [7]. Given that frailty is dynamic and preventable, it is essential to develop strategies to prevent its onset and reduce the risk of frailty-related adverse outcomes [8].
Several interventions are available for the management of frailty, such as physical activity, protein-calorie supplementation, and the de-prescription of inappropriate medications [4]. While these interventions address frailty from different perspectives, emerging evidence suggests that the gut microbiota may serve as a potential target for intervention to prevent frailty. Dysbiosis of the gut microbiota, characterized by reduced diversity, depletion of beneficial microbiota, and a decline in health-promoting metabolites, was associated with an increased risk of frailty [9, 10]. Therefore, interventions targeting gut microbiota may help prevent frailty. Given that diet is a key determinant of gut microbiota composition and function, dietary interventions associated with a more favorable gut microbiota profile may be beneficial in the prevention of frailty [11, 12].
The dietary index for gut microbiota (DI-GM), developed by Kase et al. using evidence from individuals across diverse geographic and ethnic backgrounds, serves as a comprehensive tool for exploring associations between diet linked to a healthy gut microbiota and risk of frailty [13]. The DI-GM was constructed based on evidences examining the relationships between dietary components and features of gut microbiota, with higher scores indicating a diet that associated with a healthy gut microbiota [13]. A higher DI-GM was validated to be associated with an improved gut microbiota profile, as measured by 16S rRNA sequencing, and an increased level of fecal butyrate [14]. However, few studies have investigated the associations between DI-GM and risk of frailty in older adults.
Furthermore, although epidemiological studies have indicated that gut microbiota features predict survival in older adults [15], few studies have investigated the associations between gut microbiota and mortality in individuals with frailty. Thus, it remains unclear whether a diet linked to a healthy gut microbiota is associated with all-cause and cause-specific mortality in older adults with frailty.
Therefore, the primary objective of this study was to examine the associations between DI-GM and risk of frailty in older adults. Additionally, the study aimed to prospectively explore the associations of DI-GM with all-cause and cause-specific mortality in older individuals with frailty.
Methods
Study participants
Participants in this study were selected from the National Health and Nutrition Examination Survey (NHANES), which employed a complex, multistage probability sampling design to select participants representative of the civilian, non-institutionalized U.S. population [16]. A total of 8,513 individuals aged 65 years and older from six 2-year NHANES cycles (2007–2018) were included in this study. After excluding participants with missing information on dietary intake (n = 1,949), incomplete or low-quality data for diagnosing frailty (n = 214), and missing information on covariates (smoking, education level, marital status, physical activity, and body mass index [BMI]; n = 135), as well as those with extreme dietary energy intake ( < 800 kcal/day or > 4,200 kcal/day for men and < 500 kcal/day or > 3,500 kcal/day for women; n = 111), a total of 6,104 older participants were included in assessing the associations between DI-GM and frailty risk. In examining the relationships between DI-GM and mortality in frail participants, a total of 3,003 individuals with frailty were included in the analysis. During the follow-up period, participants without available data on mortality status (n = 2) were excluded. In addition, participants who died within the first two years of follow-up were excluded from the analyses to minimize the potential bias due to reverse causation (n = 242), resulting in an analytic sample of 2,759 frail participants. Details of the participant selection process were provided in Fig. 1.
Fig. 1.
The flowchart of participants selection
Assessment of DI-GM
To collect information on dietary intake, each participant completed two 24-hour dietary recall surveys. Food and beverage consumptions from the day prior to the interview was recorded using the United States Department of Agriculture’s 5-step Automated Multiple-Pass Method (AMPM) by trained staff [17]. The validity of dietary recall methods and the accuracy of the AMPM for obtaining dietary data have been established in previous studies [18–21]. The DI-GM was comprised of 14 dietary components, including 10 beneficial components (avocados, broccoli, chickpeas, coffee, cranberries, fermented dairy, fiber, green tea, soybeans, and whole grains) and 4 unfavorable components (processed meat, red meat, refined grains, and a high-fat diet) [13]. The components of the DI-GM were selected based on their beneficial or unfavorable effects on gut microbiota, as indicated by changes in microbiota diversity indices, levels of short-chain fatty acids (SCFAs) production, or the abundance of specific bacteria [13]. A score of 1 was assigned to each beneficial component if its consumption exceeded the sex-specific, energy-adjusted median, while a score of 0 was given otherwise. For unfavorable components, the scoring algorithm was reversed: a score of 1 was assigned if the intake of the dietary component was below the sex-specific, energy-adjusted median, or if the proportion of energy derived from fat was less than 40% [13]. Since data on green tea intake data were unavailable for participants from NHANES 2007–2012, it was not included in the calculation of DI-GM, resulting in DI-GM scores ranging from 0 to 13. Participants were subsequently categorized into quintiles based on their DI-GM scores.
Definition of frailty
Frailty was evaluated using the frailty index developed by Hakeem et al following the standard procedure for constructing a frailty index [22, 23]. The frailty index comprised 49 health deficits, covering cognitive function, depressive symptoms, dependency, comorbidities, general health, healthcare utilization, laboratory examinations, anthropometric measurements, and physical performance. In this study, because handgrip strength measurement was only available for participants from NHANES 2011–2014, it was not included in the calculation of frailty index, resulting in a 48-item frailty index. For each health deficit, a score ranging from 0 (no deficit) to 1 (most severe deficit) was assigned based on the severity of deficit [23]. The sum of all health deficit scores was divided by the total number of potential health deficits to calculate the frailty index, resulting in an index that ranges from 0 to 1. To ensure high-quality diagnoses of frailty, only participants completing at least 80% of the health deficits were included in the calculation of frailty index [23]. Participants were classified as frail if their frailty index exceeds 0.21 [23, 24]. The details of the frailty index calculation were provided in Supplementary Table 1.
Ascertainment of mortality
Mortality data for each participant were obtained through linkage to death certificate records from the National Death Index, with data available through 31 December 2019 [25]. Causes of death were classified according to the International Classification of Diseases, Tenth Revision (ICD-10). Cardiovascular disease (CVD) mortality included deaths attributed to heart diseases (I00–I09, I11, I13, I20–I51) and cerebrovascular diseases (I60–I69). Cancer mortality refers to deaths resulting from malignant neoplasms (C00–C97). The follow-up duration was calculated from the survey date to either death or the end of follow-up, whichever occurred first.
Evaluation of mediators
To explore the mechanisms underlying the associations between DI-GM and frailty risk, we examined potential mediators that might mediate these associations. BMI was calculated by dividing weight (in kilograms) by the square of height (in meters), with a value of 30 kg/m2 or higher classified as obesity. The white blood cell (WBC) count is a well-established indicator of inflammation [26]. WBC count was measured using the Beckman Coulter method of counting and sizing on the Beckman Coulter® HMX Hematology Analyzer (Beckman Coulter, Brea, CA, USA), combined with an automatic diluting and mixing device for sample processing. The WBC count was log-transformed to address skewness in the distribution. Serum albumin was widely regarded as a marker of oxidative stress. Its concentration was measured using a bichromatic digital endpoint method on the Beckman Coulter UniCel® DcX800 (Beckman Coulter, Brea, CA, USA) [27].
Assessment of covariates
The covariates included in the regression models were selected based on evidence from existing literature and clinical expertise [28–31]. Previous studies examining the associations between DI-GM and adverse outcomes commonly adjusted for factors such as age, gender, race, marital status, education, income, smoking, alcohol consumption, physical activity, and dietary energy intake, all of which influence both dietary patterns and the risk of adverse outcomes like frailty and mortality [3, 32, 33]. Data on most covariates were collected through a questionnaire survey, with the following groupings: age (continuous), gender (men or women), race (Mexican American, Non-Hispanic White, Non-Hispanics Black, or Other race), marital status (not married, married/cohabiting, or widowed/separated/divorced), education level (below high school, or high school or above), income level, smoking status (non-smoker, or smoker), alcohol consumption (no, yes, or unknown), and physical activity. The poverty income ratio (PIR), calculated as the total household income divided by the poverty guidelines threshold set by the Department of Health and Human Services, was used to assess income level [34]. PIR categories were classified as ≤ 1.3, 1.3–3.5, > 3.5, or as unknown. Metabolic equivalents of task (MET) minutes per week (MET-min/week) were calculated using data from the Global Physical Activity Questionnaire, with participants exceeding 600 MET-min/week classified as physically active [35, 36].
Statistical analyses
To account for the complex sampling design of NHANES, participants’ characteristics were presented as weighted means with standard errors for continuous variables, and as frequencies with weighted percentages for categorical variables. One-way analysis of variance and Chi-square test were used to compare participants’ characteristics across DI-GM quintiles.
Weighted multivariable logistic regression models were applied to estimate the odds ratios (OR) and 95% confidence intervals (CI) for the associations between DI-GM and frailty risk. Trend analyses were performed by assigning the median value of each DI-GM quintile as a continuous variable. Restricted cubic spline analyses were conducted to explore the non-linear, dose-response relationship between DI-GM and the risk of frailty.
Stratified analyses were conducted to determine whether the associations between DI-GM and frailty risk varied across participants with different demographic characteristics, socio-economic status, lifestyle factors, and dietary energy intake.
Causal mediation analyses were conducted within the counterfactual causal framework to assess whether obesity, WBC count, and serum albumin mediated the associations between DI-GM and risk of frailty. The causal mediation analyses were conducted using the R package “Mediation” (Version 4.5.0), with direct, indirect, and total effects estimated through a nonparametric bootstrapping method with 10,000 resamples [37].
For the secondary analyses, weighted multivariable Cox proportional hazard regression models was applied to estimate the hazard ratios (HR) and 95% CI for the associations of DI-GM with all-cause mortality, CVD mortality, and cancer mortality in frail participants. Given that obesity, hypertension, CVD, diabetes, and cancer are known contributors to mortality risk, these variables were additionally adjusted to assess the robustness of our findings [31, 38].
Several sensitivity analyses were conducted to assess the robustness of our findings. First, the analyses were restricted to participants from NHANES 2013–2018 to determine whether including green tea consumption in the DI-GM calculation influenced its association with frailty risk. Second, missing values in the PIR categories and alcohol consumption were imputed five times using the R package “MICE”. The estimates from the five imputed datasets were combined into a single estimate using the “pool” function to investigate the associations between DI-GM and frailty risk. Third, because frail participants may consume less food and have low dietary energy intake due to their condition, which might influence the associations between DI-GM and frailty risk; therefore, frail participants with low energy intake ( < 1,200 kal/day) were excluded from the analyses [39]. Fourth, handgrip strength was included as one of the components in the calculation of frailty index. Fifth, considering the role of inflammation in frailty, we investigated the associations between DI-GM and high-sensitivity C-reactive protein. Sixth, the history of hypertension, diabetes, CVD, and cancer were further adjusted in investigating the associations of DI-GM with all-cause, CVD, and cancer mortality in frail participants.
All statistical analyses were performed using the Stata/SE 15.0 (StataCorp, College Station, TX, USA) and R (version 4.4.1). Statistical significance was defined as a two-tailed p-value below 0.05.
Results
A total of 6,104 participants were included in the cross-sectional analyses, with 3,003 of them identified as having frailty. The weighted characteristics of participants across the quintiles of DI-GM were presented in Table 1. Compared to participants in the lowest quintile of the DI-GM, those in the highest quintile were more likely to be non-Hispanic White (84.2% vs. 78.5%), high school graduates or higher (88.5% vs. 75.4%), non-smokers (49.5% vs. 45.7%), drinkers (66.0% vs. 56.2%), and physically active (65.3% vs. 42.7%). Additionally, compared to participants in the lowest quintile, those in the highest quintile of DI-GM had a higher percentage of PIR > 3.5 (34.8% vs. 23.7%), and a lower prevalence of obesity (29.0% vs.45.5%) and frailty (36.2% vs.51.7%).
Table 1.
The characteristics of participants across quintiles of dietary index for gut microbiota
| Characteristics | Quintiles of dietary index for gut microbiota | P | ||||
|---|---|---|---|---|---|---|
| Q1 (0–3) | Q2 (4) | Q3 (5) | Q4 (6) | Q5 (7–11) | ||
| No. of participants | 1534 | 1273 | 1211 | 1037 | 1049 | |
| Age, years | 72.6 (0.2) | 72.9 (0.3) | 72.9 (0.2) | 72.9 (0.3) | 72.6 (0.3) | 0.708 |
| Men, n (%) | 779 (46.4) | 627 (44.1) | 581 (45.1) | 511 (42.4) | 477 (44.2) | 0.763 |
| Race, n (%) | < 0.001 | |||||
| Mexican American | 128 (3.2) | 131 (4.4) | 117 (3.6) | 87 (3.1) | 90 (3.0) | |
| Non-Hispanics White | 833 (78.5) | 706 (79.5) | 711 (81.1) | 632 (84.2) | 666 (84.2) | |
| Non-Hispanics Black | 368 (10.9) | 268 (9.1) | 197 (6.3) | 155 (5.2) | 111 (4.0) | |
| Other Race | 205 (7.5) | 168 (7.1) | 186 (9.0) | 163 (7.4) | 182 (8.8) | |
| Marital status, n (%) | 0.474 | |||||
| Not married | 69 (3.9) | 50 (3.1) | 44 (2.6) | 38 (2.6) | 35 (2.7) | |
| Married/cohabiting | 864 (62.2) | 742 (62.5) | 661 (60.0) | 605 (62.1) | 624 (66.1) | |
| Widowed, separated, or divorced | 601 (33.9) | 481 (34.4) | 506 (37.4) | 394 (35.3) | 390 (31.2) | |
| Education level, n (%) | < 0.001 | |||||
| Below high school | 508 (24.6) | 385 (20.4) | 324 (16.4) | 262 (15.1) | 208 (11.5) | |
| High school or above | 1,026 (75.4) | 888 (79.6) | 887 (83.6) | 775 (84.9) | 841 (88.5) | |
| Poverty income ratio categories, n (%) | < 0.001 | |||||
| ≤1.3 | 416 (18.2) | 351 (20.2) | 299 (13.9) | 234 (14.4) | 229 (12.5) | |
| 1.3–3.5 | 674 (44.9) | 515 (38.8) | 486 (42.8) | 411 (41.0) | 386 (36.7) | |
| > 3.5 | 240 (23.7) | 253 (29.6) | 255 (30.7) | 246 (32.8) | 284 (34.8) | |
| Unknown | 204 (13.2) | 154 (11.4) | 171 (12.6) | 146 (11.8) | 150 (16.0) | |
| Smoker, n (%) | 845 (54.3) | 679 (49.5) | 597 (52.2) | 490 (46.3) | 504 (50.5) | 0.010 |
| Alcohol consumption, n (%) | 0.041 | |||||
| No | 636 (40.3) | 480 (36.8) | 470 (38.3) | 394 (31.7) | 341 (30.9) | |
| Yes | 846 (56.2) | 751 (61.1) | 697 (59.1) | 609 (65.1) | 672 (66.0) | |
| Unknown | 52 (3.4) | 42 (2.2) | 44 (2.6) | 34 (3.2) | 36 (3.1) | |
| Physically active, n (%) | 606 (42.7) | 539 (45.3) | 547 (50.9) | 505 (51.6) | 598 (65.3) | < 0.001 |
| Dietary energy intake, kcal | 1879.9 (24.5) | 1850.1 (26.3) | 1820.7 (29.2) | 1807.0 (32.7) | 1774.5 (30.9) | 0.124 |
| White blood cell count, 1000 cells/µL | 7.39 (0.19) | 7.43 (0.20) | 7.11 (0.09) | 6.93 (0.10) | 6.84 (0.08) | 0.022 |
| Albumin, g/dL | 4.12 (0.01) | 4.13 (0.02) | 4.18 (0.02) | 4.21 (0.02) | 4.25 (0.02) | < 0.001 |
| Obesity, n (%) | 643 (45.5) | 513 (40.3) | 450 (39.8) | 354 (32.3) | 331 (29.0) | < 0.001 |
| Frailty, n (%) | 829 (51.7) | 657 (50.4) | 598 (47.0) | 470 (42.7) | 449 (36.2) | < 0.001 |
Note: Weighted means (standard errors) and frequency (weighted percentages) were presented for continuous variables and categorical variables, respectively
In the age- and gender-adjusted model, participants in the highest quintile of the DI-GM were associated with a reduced risk of frailty compared to those in the lowest quintile (Table 2). After further adjustment for multiple covariates, including race, marital status, education level, poverty income ratio categories, smoking status, alcohol consumption, physical activity, and dietary energy intake, participants in the highest quintile of DI-GM had a 32% lower risk of frailty (95% CI: 14% to 46%) compared to those in the lowest quintile. The restricted cubic spline analyses indicated a linear relationship between DI-GM and risk of frailty (Pnonlinearity = 0.247) (Fig. 2).
Table 2.
Associations between dietary index for gut microbiota and risk of frailty
| Age and gender-adjusted model | Multivariable-adjusted model * | |||
|---|---|---|---|---|
| OR (95% CI) | P | OR (95% CI) | P | |
| DI-GM quintiles | ||||
| Q1 (0–3) | 1 (Reference) | 1 (Reference) | ||
| Q2 (4) | 0.91 (0.72, 1.16) | 0.458 | 0.98 (0.78, 1.23) | 0.870 |
| Q3 (5) | 0.79 (0.64, 0.99) | 0.038 | 0.91 (0.74 1.13) | 0.406 |
| Q4 (6) | 0.67 (0.53, 0.81) | < 0.001 | 0.77 (0.61, 0.98) | 0.030 |
| Q5 (7–11) | 0.51 (0.40, 0.63) | < 0.001 | 0.68 (0.54, 0.86) | 0.001 |
| P for trend | < 0.001 | < 0.001 | ||
* The multivariable model was adjusted for age, gender, race, marital status, education level, poverty income ratio categories, smoking status, alcohol consumption, physical activity, and dietary energy intake
Fig. 2.
Restricted cubic spline analyses of associations between dietary index for gut microbiota and risk of frailty, the model was adjusted for age, gender, race, marital status, education level, poverty income ratio categories, smoking status, alcohol consumption, physical activity, and dietary energy intake
DI-GM, dietary index for gut microbiota; OR, odds ratio; CI, confidence interval.
The stratified analyses revealed that age, gender, race, marital status, education level, PIR categories, smoking status, alcohol consumption, and dietary energy intake did not modify the associations between DI-GM and frailty risk (P for interaction > 0.05) (Fig. 3).
Fig. 3.
Stratified analyses of the associations between dietary index for gut microbiota and risk of frailty, the model was adjusted for age, gender, race, marital status, education level, poverty income ratio categories, smoking status, alcohol consumption, physical activity, and dietary energy intake. OR odds ratio; CI, confidence interval
The mediation analyses indicated that obesity, WBC count, and serum albumin partially mediated the associations between DI-GM and the risk of frailty, with mediation proportions of 54.8%, 18.3%, and 28.2%, respectively (Fig. 4).
Fig. 4.
Mediation effect of obesity, white blood cell count, and serum albumin on the associations between dietary index for gut microbiota and risk of frailty (comparing the highest quintile of dietary index for gut microbiota with the lowest quintile), the model was adjusted for age, gender, race, marital status, education level, poverty income ratio categories, smoking status, alcohol consumption, physical activity, and dietary energy intake. DI-GM, dietary index for gut microbiota; WBC, white blood cell. ACME, average causal mediation effects; ADE, average direct effects
During a median follow-up of 5.3 years (interquartile range: 3.2- 8.3), a total of 778 deaths occurred among frail participants, including 258 CVD-related deaths and 139 cancer-related deaths. The Supplementary Table 2 exhibited the weighted characteristics of frail participants according to the quintiles of DI-GM. Compared to frail participants in the lowest quintile of the DI-GM, those in the highest quintile were more likely to be non-Hispanic White (82.1% vs. 77.5%), more highly educated (81.8% vs. 72.4%), and had a lower prevalence of obesity (40.6% vs. 53.6%).
The results of the association of DI-GM with the risk of all-cause mortality, CVD mortality, and cancer mortality in frail participants were presented in Table 3. After adjusting for age, gender, race, marital status, education level, poverty income ratio categories, smoking status, alcohol consumption, physical activity, dietary energy intake, and BMI categories in the fully-adjusted model, the HRs and 95% CI for all-cause mortality, CVD mortality, and cancer mortality in comparing extreme DI-GM quintiles were 0.73 (0.57, 0.93), 0.59 (0.36, 0.97), and 1.41 (0.76, 2.62), respectively.
Table 3.
Associations of dietary index for gut microbiota with all-cause mortality, cardiovascular mortality, and cancer mortality in participants with frailty
| DI-GM | Case/participants | Age and gender-adjusted model | Multivariable-adjusted model * | Fully-adjusted model † | |||
|---|---|---|---|---|---|---|---|
| HR (95% CI) | P | HR (95% CI) | P | HR (95% CI) | P | ||
| All-cause mortality | |||||||
| Q1 (0–3) | 209/700 | 1 (Reference) | 1 (Reference) | 1 (Reference) | |||
| Q2 (4) | 158/581 | 0.98 (0.69, 1.40) | 0.911 | 1.00 (0.70, 1.43) | 0.985 | 1.00 (0.70, 1.43) | 0.988 |
| Q3 (5) | 160/594 | 0.91 (0.66, 1.24) | 0.545 | 0.93 (0.67, 1.28) | 0.636 | 0.91 (0.65, 1.27) | 0.569 |
| Q4 (6) | 129/442 | 0.95 (0.71, 1.28) | 0.753 | 0.97 (0.72, 1.30) | 0.846 | 0.95 (0.70, 1.29) | 0.743 |
| Q5 (7–10) | 129/442 | 0.72 (0.57, 0.90) | 0.004 | 0.76 (0.60, 0.97) | 0.030 | 0.73 (0.57, 0.93) | 0.013 |
| P for trend | 0.020 | 0.088 | 0.041 | ||||
| Cardiovascular mortality | |||||||
| Q1 (0–3) | 72/700 | 1 (Reference) | 1 (Reference) | 1 (Reference) | |||
| Q2 (4) | 55/581 | 0.79 (0.50, 1.25) | 0.306 | 0.79 (0.50, 1.26) | 0.323 | 0.80 (0.50, 1.27) | 0.338 |
| Q3 (5) | 60/594 | 1.23 (0.74, 2.04) | 0.419 | 1.26 (0.76, 2.11) | 0.367 | 1.26 (0.75, 2.12) | 0.379 |
| Q4 (6) | 35/442 | 0.77 (0.44, 1.35) | 0.357 | 0.74 (0.41, 1.34) | 0.322 | 0.75 (0.41, 1.35) | 0.331 |
| Q5 (7–10) | 36/442 | 0.59 (0.36, 0.96) | 0.034 | 0.59 (0.36, 0.99) | 0.045 | 0.59 (0.36, 0.97) | 0.037 |
| P for trend | 0.078 | 0.097 | 0.086 | ||||
| Cancer mortality | |||||||
| Q1 (0–3) | 37/700 | 1 (Reference) | 1 (Reference) | 1 (Reference) | |||
| Q2 (4) | 23/581 | 1.56 (0.50, 4.87) | 0.440 | 1.53 (0.53, 4.44) | 0.427 | 1.54 (0.53, 4.45) | 0.418 |
| Q3 (5) | 28/594 | 0.93 (0.53, 1.64) | 0.799 | 0.93 (0.52, 1.66) | 0.794 | 0.92 (0.51, 1.65) | 0.769 |
| Q4 (6) | 21/442 | 1.19 (0.62, 2.27) | 0.602 | 1.15 (0.59, 2.25) | 0.673 | 1.17 (0.59, 2.31) | 0.647 |
| Q5 (7–10) | 30/442 | 1.44 (0.80, 2.60) | 0.226 | 1.39 (0.76, 2.54) | 0.279 | 1.41 (0.76, 2.62) | 0.274 |
| P for trend | 0.588 | 0.694 | 0.662 | ||||
The multivariable model was adjusted for age, gender, race, marital status, education level, poverty income ratio categories, smoking status, alcohol consumption, physical activity, and dietary energy intake
The fully-adjusted model was further adjusted for body mass index categories
The sensitivity analyses showed that including green tea consumption in the DI-GM calculation did not significantly alter the estimates of the associations between DI-GM and the risk of frailty (Supplemental Table 3). Similar results were observed in examining the associations between DI-GM and frailty risk after imputing missing values in the PIR categories and alcohol consumption (Supplemental Table 3). Excluding frail participants with low dietary energy intake yielded similar results (Supplemental Table 3). Including handgrip strength as one of the components in the calculation of the frailty index did not materially change the results (Supplemental Table 3). An inverse association was observed between DI-GM and high-sensitivity C-reactive protein levels (Supplemental Table 4). The associations between DI-GM and both all-cause and cause-specific mortality remained largely unchanged after additional adjustment for history of hypertension, diabetes, CVD and cancer (Supplemental Table 5).
DI-GM, dietary index for gut microbiota; HR, hazard ratio; CI, confidence interval.
Discussion
In this study, higher DI-GM was inversely associated with the risk of frailty in older adults, and these associations were not modified by age, gender, race, marital status, education level, PIR categories, smoking status, alcohol consumption, and dietary energy intake. In addition, higher DI-GM was associated with a reduced risk of all-cause mortality and CVD mortality in frail individuals.
The inverse associations observed between DI-GM and risk of frailty in older adults might attributed to the close relationship between gut microbiota and frailty [9, 10, 40]. A systematic review comprising 11 studies revealed that the gut microbiota of older adults with frailty was characterized by lower alpha-diversity, significant difference in beta-diversity, an increased abundance of the genera Dialister, Lactobacillus, and Ruminococcus, and a decreased abundance of the phylum Firmicutes and genera Eubacterium, compared to healthy older adults [40]. In addition, higher intake of probiotics and live microbes was also associated with the reduced risk of frailty [41, 42]. A randomized, controlled trial found that a 12-week prebiotic intervention (a mixture of inulin and oligofructose) improved frailty status among frail and prefrail older Chinese adults, as evidenced by increased walking speed in frail participants and reduced exhaustion in prefrail participants [41]. An epidemiological study involving 11,529 adults observed that participants with a high dietary intake of live microbes had a lower risk of frailty compared to those with a low dietary intake [42]. Our findings suggested that beyond direct supplementation with prebiotic or live microbes, increasing the dietary intake of foods or nutrients linked to a healthy gut microbiota was also associated with a reduced risk of frailty in older adults. Our studies have added new evidence to the health benefit of DI-GM, which have been demonstrated to be associated with a reduced risk of stroke, depression, and metabolic dysfunction-associated steatotic liver disease in previous research [29, 30, 43].
In addition, the associations between DI-GM and frailty risk were not modified by demographic variables, socioeconomic status, lifestyle behaviors, and dietary energy intake, indicating that these findings are applicable to a broad range of individuals.
The present study found that obesity, WBC count, and serum albumin partially mediated the associations between DI-GM and frailty risk, indicating the possible roles of obesity, inflammation, and oxidative stress in these associations. Obesity contributes to the development of multiple adverse outcomes, including cardiovascular disease, inflammatory bowel disease, and cognitive impairment [44–46]. The accumulation of these obesity-related diseases could increase the risk of frailty. The gut microbiota and its metabolites, such as SCFAs, regulate body weight by promoting satiety and reducing food intake [47]. Additionally, inflammation might establish a connection between the gut microbiota and frailty. Sarcopenia and neurodegenerative disorders are key manifestations of frailty. Chronic inflammation could accelerate muscle mass and strength loss by disrupting muscle protein metabolism, regeneration, and mitochondrial function [48]. It also contributes to neurodegenerative disorders by promoting nerve inflammation [49]. The gut microbiota and its metabolites SCFA could exert anti-inflammatory effects by reducing gut permeability, stimulating the production of anti-inflammatory cytokines, and inhibiting the infiltration of lipopolysaccharides (LPS) and the production of pro-inflammatory cytokines [50–53]. Furthermore, oxidative stress might bridge the gut microbiota and the prevalence of frailty. Serum levels of oxidative stress biomarkers, such as 8-hydroxy-2’-deoxyguanosine, oxidized glutathione, and malondialdehyde were higher in individuals with frailty than in those without [54, 55]. Animal studies have shown that oxidative stress enhances muscle proteasomal activity, promotes muscle breakdown, and triggers apoptosis in muscle cells [56, 57]. Oxidative stress also induced neuronal death by promoting the oxidation of proteins, lipids, and deoxyribonucleic acid [58]. The gut microbiota could enhance antioxidant activities in epithelial cells, either directly or indirectly through SCFAs production and the regulation of immune homeostasis [59].
Beyond the potential benefit in preventing frailty, our study also found that DI-GM was associated with a decreased risk of all-cause mortality in older adults with frailty. The ability of the gut microbiota to predict survival has been demonstrated in older adults. A study involving individuals aged 85 years and older reported that both a high relative abundance of Bacteroides and a low gut microbiota uniqueness measure were associated with decreased survival over a 4-year follow-up [15]. This ability was also observed in patients with poor health, where lower microbial diversity was associated with higher mortality in both hemodialysis and critically ill patients [60, 61]. In individuals with frailty, our study is the first to demonstrate that diet linked to a healthy gut microbiota was associated with a reduced risk of mortality. Furthermore, the protective effect of DI-GM on mortality is attributed to its ability to prevent cardiovascular-related deaths rather than those associated with cancer. The inverse associations between DI-GM and CVD mortality may be attributed to the gut microbiota’s potential to reduce inflammation [50–53], which was associated with an increased risk of CVD-related mortality [62]. In addition, SCFAs could alleviate dyslipidemia by inhibiting lipogenesis and promoting β-oxidation [63], thereby reducing the risk of hyperlipidemia- related CVD mortality [64]. The absence of significant associations between DI-GM and cancer mortality may be attributed to the insufficient statistical power, resulting from the small sample size and the relatively short follow-up period. With the global population aged 65 and older projected to increase to 2.2 billion by the late 2070s and frailty becoming more common [7, 65], diets linked to a more favorable gut microbiota may be associated with a decreased risk of mortality among the growing number of frail older adults.
To the best of our knowledge, this study is the first to show that DI-GM is associated with reduced all-cause and CVD mortality among old frail individuals. In addition, our study evaluated the role of several potential mediators—including obesity, WBC count, and albumin—in the associations between DI-GM and frailty risk among older adults. However, several limitations of the present study should be acknowledged. First, as an observational study, the bias introduced by unmeasured or residual confounders could not be completely ruled out, and causal inference cannot be established. Reverse causality might exist in examining the associations between DI-GM and the risk of frailty, as frail individuals may consume less food and have lower dietary energy intake due to their condition. To reduce this bias, DI-GM was calculated based on energy-adjusted intake of foods or nutrients. Additionally, stratified analyses indicated that the associations between DI-GM and frailty risk was not modified by dietary energy intake, and sensitivity analyses excluding frail participants with low energy intake yielded similar results. Future studies with a prospective design are needed to validate the inverse associations between DI-GM and frailty risk. Second, since all participants were from the U.S. and dietary habits and gut microbiota composition varied across regions and cultures, generalizing our findings to populations with different cultural, dietary, and geographic backgrounds should be done with caution [66]. However, as the U.S. is a multiethnic country, we were able to assess whether race influenced the associations between DI-GM and frailty risk. Adjusting for race in the regression models did not materially change the results, and stratified analyses indicated that race did not modify these associations, suggesting these findings may be broadly applicable across racial groups. Further studies performed in other regions are needed to confirm the robustness of our findings. Third, information on dietary intake was collected through dietary recall, with NHANES employing strategies such as the AMPM and repeated recalls to reduce bias [17, 18, 21]. Similarly, covariates like smoking, alcohol consumption, and physical activity were assessed via questionnaires. Both methods are subject to recall and social desirability biases. However, these methods have been validated and remain the most practical and widely used approaches for large population-based surveys [18–20, 67]. Additionally, while errors associated with self-reported dietary data can attenuate correlations between diet and health outcomes [18], our study still observed significant positive associations between DI-GM and both frailty and mortality risk in frail individuals. Nevertheless, further research utilizing objective measures is necessary to confirm these findings. Fourth, the DI-GM was developed based on existing evidence investigating the associations between dietary components and the characteristics of the gut microbiota [13]. Dietary components associated with the gut microbiota that have not been investigated were not included as components of DI-GM. The DI-GM should be updated as new evidence emerges regarding the associations between other dietary components and the gut microbiota. Fifth, data for most items of the frailty index were collected via a questionnaire survey, indicating that the definition of frailty might not be fully accurate. Further studies incorporating more clinical, functional, and biological markers for frailty assessment are needed to validate the robustness of our findings. Sixth, although DI-GM was validated to be associated with higher Shannon index values and fecal butyrate level [14], it represents a dietary pattern associated with a healthy gut microbiota, rather than a biomarker of microbial composition or metabolites. As high-throughput sequencing was not performed, both the direct validation of the associations between DI-GM and gut microbiota in the U.S. population and the identification of specific gut microbiota taxa or metabolites contributing to DI-GMs associations with frailty risk and mortality in frail individuals remain unavailable. Further studies are required to address these uncertainties. Seventh, although mediation analyses provided insights into potential pathways linking DI-GM to frailty risk, they cannot establish causal relationships in the context of an observational study. Furthermore, while the frailty index, white blood cell count, and albumin level are well-established indicators for assessing frailty, inflammation, and oxidative stress in population-based study [2, 26, 27], these constructs are inherently complex and multidimensional and cannot be fully captured by a limited set of proxy measures. Accordingly, our mechanistic interpretations should be regarded as hypothesis-generating rather than confirmatory. Future studies integrating multi-omic data, functional assessments, experimental approaches, and intervention trials will be essential to clarify the causal relationships and elucidate the detailed mechanisms linking DI-GM to frailty risk. Eighth, the relatively small sample size of frail older adults and the short follow-up period may limit the statistical power to detect associations between DI-GM and mortality risk. Future studies with larger cohorts and extended follow-up periods are warranted to further evaluate the associations between DI-GM and both all-cause and cause-specific mortality.
Conclusion
In conclusion, DI-GM was inversely associated with the risk of frailty in older adults. In addition, DI-GM was associated with a reduced risk of all-cause mortality and CVD mortality in individuals with frailty. Our findings suggested that a diet linked to a healthy gut microbiota might be associated with a reduced risk of frailty in older adults and a lower risk of premature mortality in individuals with frailty. Further studies are needed to confirm the robustness of these findings, assess their applicability to other populations, and identify the specific gut microbiota, metabolites, or mechanism underlying these associations.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Acknowledgements
We thank the National Center for Health Statistics (NCHS) of the Center for Disease Control (CDC) and Prevention, and all staffs and participants of the National Health and Nutrition Examination Survey.
Author contributions
DG, XBW, and XH designed the study, DG, YYS, and QHF analyzed the data. DG and YYS wrote the manuscript. DG, YYS, YYZ, XYW, QHF, WJL, HHY, LWL, BL, ZY, XBW, and XH provided comments and revised the manuscript. XBW and XH supervised the study. All authors read and approved the final version of the manuscript.
Funding
The present study was supported by grants from the Jiangxi Provincial Natural Science Foundation (20232BAB216102 and 20232BAB206093), the National Natural Science Foundation of China (82404356 and 32060163), and the Horizontal research funds (HX202309 and HX202411).
Data availability
This study was carried out using publicly available data from [National Health and Nutrition Examination Survey] at [https://www.cdc.gov/nchs/nhanes/].
Conflict of interest
The authors declared no conflict of interest.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Contributor Information
Xuebiao Wu, Email: China.xuebiaowu2022@163.com.
Xia Huang, Email: ndyfy00382@ncu.edu.cn.
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Supplementary Materials
Data Availability Statement
This study was carried out using publicly available data from [National Health and Nutrition Examination Survey] at [https://www.cdc.gov/nchs/nhanes/].