Abstract
Objective
Diets rich in live microbes can bring various health benefits. However, the association between dietary live microbe intake and frailty has not been studied.
Methods
The study utilized data from the National Health and Nutrition Examination Survey (NHANES) 2007–2018. A total of 11,529 participants were included. Sanders et al. classified the level of live microbes in foods into low (<104 CFU/g), medium (104–107 CFU/g), or high (>107 CFU/g). With the methodology of Sanders et al. and dietary questionnaire data, participants were divided into three groups: (1) low dietary live microbe intake group (only low-level foods), (2) medium dietary live microbe intake group (medium but not high-level foods), and (3) high dietary live microbe intake group (any high-level foods). Additionally, foods with medium and high live microbe content were aggravated as MedHi. Frailty index ≥0.25 is defined as frailty. The weighted logistic regression analysis was conducted to examine the relationship between the intake of dietary live microbe and frailty. The restricted cubic splines (RCS) were employed to detect the nonlinear relationships.
Results
In the fully adjusted model, participants with high dietary intake of live microbe had a significantly lower risk of frailty than those with low dietary intake of live microbe (OR = 0.67, 95% CI: 0.56, 0.79). For every 100 grams of MedHi food consumed, the risk of frailty decreased by 11% (OR = 0.89, 95% CI: 0.85, 0.92) after adjusting all covariates. The RCS indicated the existence of non-linear relationships. For those who consumed less than 100 grams of MedHi, increasing MedHi intake may significantly reduce the risk of frailty, but after exceeding 100 grams, the curve gradually levels off.
Conclusions
Our results suggested that increasing dietary live microbe intake was associated with a lower risk of frailty. However, more research is needed to verify this.
Keywords: Dietary live microbes, Frailty, Probiotics, NHANES, Cross-sectional study
1. Introduction
The global elderly demographic is experiencing significant expansion, drawing increased focus on the issue of frailty [1]. Frailty manifests as an impaired capacity to respond effectively to external stressors, impeding the individual’s ability to recuperate [2]. It ultimately results in the loss of personal autonomy, increased healthcare expenditures, and a higher risk of premature mortality [3]. A comprehensive meta-analysis involving 62 countries and territories used the frailty index to estimate that approximately 24% of individuals aged ≥50 were affected by frailty [4]. Consequently, frailty has been a prominent global public health concern [5].
Gut flora, a critical component of the human life cycle, has gained extensive recognition for its multifaceted roles in health, as revealed by research from the American Human Microbiome Project (HMP) and the European Metagenomics of the Human Intestinal Tract (MetaHIT) [[6], [7], [8], [9]]. Beyond its fundamental functions in digestion and nutrient absorption, it is also instrumental in drug metabolism, antimicrobial protection, immunomodulation, and maintaining intestinal tract integrity [10]. Contemporary research underscores the intricate relationship between changes in gut flora’s stability, diversity, and composition and the onset of various diseases [11].
Specifically, the gut microbiota plays an important role in the pathophysiology of frailty [12]. A study from Ireland demonstrated that long-term residence in residential facilities was associated with gut flora dysbiosis and metabolic abnormalities in older adults [13]. Notably, although clinical trials focusing on probiotics as a treatment for human frailty are currently limited, studies have demonstrated that oral administration of Akkermansia can improve frailty status in aged mice [14]. Consequently, interventions for gut flora may represent a promising approach for ameliorating frailty [12].
Dietary intervention may be a simple method [15]. However, current research primarily focused on dietary patterns like Mediterranean, anti-inflammatory, and DASH (Dietary Approaches to Stop Hypertension) diets, often overlooking the specific impact of dietary live microbes on health [[16], [17], [18]]. The Framingham Heart Study found that increased yogurt intake was associated with a reduced risk of frailty [19]. Nevertheless, dietary live microbes extend beyond fermented foods, also encompassing unpeeled fruits and vegetables [20]. Therefore, assessing the overall intake levels of dietary live microbes is crucial.
To address this research gap, Sanders et al. categorized dietary microbes of various foods from the National Health and Nutrition Examination Survey (NHANES) into different levels [21]. They also found that increasing the intake of dietary live microbes was associated with health improvements involving blood pressure, metabolism, BMI, and waist circumference [22]. Despite these benefits, studies directly linking dietary live microbe intake to frailty remain scarce. Our study employed NHANES data and Sander’s assessment method to explore the association between dietary live microbe intake and frailty.
2. Methods
2.1. Data source and participants
NHANES, a recurring national survey of the American population conducted biennially since 1999, employed the complex multi-stage probability sampling method. The National Center for Health Statistics (NCHS) Institutional Review Board has approved NHANES, and the associated ethical documentation and materials are subject to regular updates. To increase the sample size, data from six cycles spanning from 2007 to 2018 were combined, initially encompassing 59,842 participants. Exclusions were made for participants under 18 years (N = 23,262), pregnant individuals (N = 374), those with incomplete dietary data (N = 4,132), those lacking reliable frailty index assessments (N = 18,043), and those missing key covariates (N = 2,502), leaving 11,529 participants for final analysis, as shown in Fig. 1. More information can be found at www.cdc.gov/NHANES.
Fig. 1.
Participant flowchart.
2.2. Explanatory variable
Dietary live microbe intake was estimated using 24-h dietary recall data from NHANES. The classification of live microbe content in foods was derived from the research conducted by Sanders et al. [21]. A team of four experts (MLM, MES, RH, and CH) approximated live microbe levels, expressed in colony-forming units per gram (CFU/g), for 9,388 NHANES food codes across 48 subgroups according to primary literature values. These experts subsequently categorized foods into three classes according to their live microbe levels, namely low (<104 CFU/g), medium (104–107 CFU/g), or high (>107 CFU/g). They engaged in external consultation for uncertain or conflicting data. Generally, foods pasteurized or processed at high temperatures are considered low levels. Unpeeled fresh vegetables and fruits fell into the medium levels, whereas unpasteurized fermented foods and probiotic supplements are classified as high levels.
Since the method defines live microbes in foods, we need to classify live microbe levels across the entire diet. With the methodology of Sanders et al., participants were divided into three groups: (1) low dietary live microbe intake group (only low-level foods), (2) medium dietary live microbe intake group (medium but not high-level foods), and (3) high dietary live microbe intake group (any high-level foods). In addition, we aggregated the amount of medium-level foods and high-level foods consumed per person (in 100 grams) and defined it as MedHi. MedHi was also converted into the categorical variable for analysis: G1, people who did not consume any MedHi foods; G2, people who consumed MedHi foods but less than the median MedHi intake; G3, people who consumed more MedHi foods than the median MedHi intake. These analysis methods have been used in several studies [[22], [23], [24]]. Statistical analysis using two methods representing dietary live microbe levels can make our results more robust.
2.3. Outcome variable
Frailty was the primary outcome, assessed using the frailty index established by Wael Sabbah et al. [25], which incorporates 49 diagnostic criteria following Searle and colleagues’ standard procedures [26]. These diagnostic items cover cognition function, dependency, depression, comorbidities, medical status, hospital usage, general health, anthropometrics, and laboratory tests. Each entry has adjudication criteria, assigning a score between 0 (no defect present) and 1 (most severe defect). The frailty index is calculated by dividing the total score by the number of items answered. To ensure the quality of frailty diagnoses, we included participants who responded to at least 80% of the items. The frailty index greater than or equal to 0.25 is defined as frailty [27]. Detailed the scoring criteria are provided in the Supplementary Table 1.
2.4. Covariates
To avoid the influence of confounding factors, we adjusted for known covariates. These confounding factors encompassed age, gender, race, education, marital status, poverty income ratio (PIR), body mass index (BMI), smoking, alcohol use, and energy intake. Races were classified into four categories: Mexican, non-Hispanic white, non-Hispanic black, and other races. Educational level was divided into three categories: less than high school, high school or general educational development (GED), and above high school. Marital status was divided into four categories: married, never married, living with partner, and others (widowed, divorced, or separated). BMI was calculated as weight (kg) divided by the square of height (m). PIR was categorized as follows: low income (PIR ≤ 1.3), middle income (1.3 < PIR ≤3.5), and high income (PIR > 3.5) [28]. Individuals were classified as smokers if they had a history of smoking at least 100 cigarettes throughout their lifetime [29,30]. Alcohol use was classified based on current drinking status and extent. Current non-drinkers were divided two categories: never (less than 12 drinks in their lifetime) and former (stop drinking last year but had ≥ 12 drinks in their lifetime). Current drinkers were divided into three categories: heavy (≥ 3 drinks/day for females, ≥ 4 drinks/day for males, or binge drinking at least 5 dyas per month); moderate (≥ 2 drinks/day for females, ≥ 3 drinks/day for males, or binge drinking at least 2 days per month); mild (not meet above criteria) [31]. Binge drinking was defined as having at least 4 drinks on same occasion for females or at least 5 drinks on same occasion for males.
2.5. Statistical analyses
All analyses took into account the NHANES design. The R software (Core Team, Vienna, Austria, version 4.1.2) and the survey package were applied. Continuous variables were reported as weighted means with standard error (SE), while categorical variables were reported as weighted proportions with SE. The baseline clinical characteristics were compared using weighted t-tests and Rao-Scott chi-square tests. Weighted logistic regression analyzed the association between dietary intake of live microbes and frailty. Three models were constructed: Crude model, without adjustment for covariates; Model 1, adjusted for age, gender, race, and education; Model 2, further adjusted for marital status, PIR, smoking, alcohol use, BMI, and energy intake. We performed subgroup analyses to identify potential effect modification through stratified weighted regression for age, gender, BMI, smoking, and alcohol use. Trend tests were used to estimate linear trends between categorical variables representing dietary live microbe intake levels and frailty.
RCS was used to explore the nonlinear dose-response relationship between MedHi consumption and frailty. It is a commonly used nonlinear analysis method in medical research that allows flexible modeling of variables using piecewise cubic polynomials that are smoothly connected at knots. Following Harrell’s recommendation, RCS uses four knots [32]. Piecewise regression and likelihood ratio tests revealed slope differences on both sides of the turning point.
To ascertain the robustness of the study’s conclusions, we conducted four sensitivity analyses: 1) additional adjustments accounting for hypertension, diabetes, chronic kidney disease (CKD), cardiovascular disease (CVD), chronic obstructive pulmonary disease (COPD), medication usage, dietary factors including the healthy eating index-2015 (HEI-2015) and the dietary inflammation index (DII); 2) modification of the frailty diagnostic criteria to the threshold of 0.21; 3) exclusion of participants with extreme energy intake, defined as more than two standard deviations above or below the mean energy intake; 4) imputation by the MissForest method [33]. Two-sided P values less than 0.05 were considered statistically significant.
3. Results
3.1. The characteristics of study participants
Table 1 showed the clinical characteristics of participants stratified by dietary live microbe intake groups. The study enrolled 11,529 individuals, with a mean age of 60.17 ± 0.30 years. The gender distribution was 53.48% female and 46.52% male. Significantly, the overall prevalence of frailty was observed at 34.17%, with notable differences among dietary live microbe intake groups. The group with high dietary live microbe intake exhibited the lowest prevalence of frailty at 26.53%, followed by the medium intake group at 32.31%. In contrast, the group with low intake showed the highest frailty prevalence, at 43.00%. Moreover, frail individuals reported lower MedHi consumption, as indicated in Supplementary Table 2.
Table 1.
The clinical characteristics of participants stratified by dietary live microbe intake groups.
| Characteristics | Total | Low dietary live microbe intake group | Medium dietary live microbe intake group | High dietary live microbe intake group | P value |
|---|---|---|---|---|---|
| Age (years) | 60.17 (0.30) | 57.75 (0.46) | 61.79 (0.36) | 60.62 (0.43) | <0.001 |
| Gender (%) | <0.001 | ||||
| Female | 53.48 (0.02) | 48.35 (1.10) | 53.17 (0.88) | 59.96 (1.39) | |
| Male | 46.52 (0.02) | 51.65 (1.10) | 46.83 (0.88) | 40.04 (1.39) | |
| Race (%) | <0.001 | ||||
| Black | 9.65 (0.01) | 15.01 (1.29) | 8.68 (0.71) | 4.77 (0.44) | |
| Mexican | 5.01 (0.01) | 5.83 (0.88) | 5.70 (0.72) | 3.03 (0.46) | |
| White | 75.22 (0.03) | 66.99 (2.01) | 76.49 (1.35) | 83.03 (1.17) | |
| Other | 10.12 (0.01) | 12.17 (0.95) | 9.13 (0.67) | 9.18 (0.76) | |
| Marital status (%) | <0.001 | ||||
| Married | 55.76 (0.02) | 47.97 (1.49) | 57.47 (1.28) | 62.42 (1.67) | |
| Live with others | 4.86 (0.00) | 5.81 (0.55) | 4.02 (0.49) | 4.97 (0.59) | |
| Never married | 11.20 (0.01) | 14.00 (0.98) | 10.28 (0.69) | 9.25 (0.90) | |
| Others | 28.19 (0.01) | 32.22 (1.24) | 28.23 (0.93) | 23.36 (1.15) | |
| Education level (%) | <0.001 | ||||
| Less than high school | 17.68 (0.01) | 25.35 (1.27) | 16.26 (0.85) | 10.75 (0.92) | |
| High school or GED | 25.47 (0.01) | 29.86 (1.09) | 25.22 (1.04) | 20.68 (1.22) | |
| Above high school | 56.85 (0.02) | 44.79 (1.27) | 58.52 (1.36) | 68.58 (1.57) | |
| PIR (%) | <0.001 | ||||
| Low income | 25.01 (0.01) | 36.08 (1.50) | 21.95 (1.00) | 16.49 (1.28) | |
| Med income | 36.98 (0.01) | 38.93 (1.28) | 38.06 (1.27) | 33.09 (1.18) | |
| High income | 38.02 (0.02) | 24.99 (1.28) | 40.00 (1.49) | 50.43 (1.80) | |
| Alcohol use (%) | <0.001 | ||||
| Never | 12.29 (0.01) | 13.71 (0.79) | 13.18 (0.73) | 9.32 (1.01) | |
| Former | 19.16 (0.01) | 22.61 (1.03) | 19.23 (0.88) | 15.00 (0.95) | |
| Mild | 40.47 (0.02) | 31.39 (1.32) | 42.86 (1.31) | 47.61 (1.75) | |
| Moderate | 14.17 (0.01) | 14.07 (1.06) | 13.32 (0.76) | 15.54 (1.27) | |
| Heavy | 13.90 (0.01) | 18.21 (1.09) | 11.40 (0.82) | 12.53 (0.96) | |
| Smoking (%) | 52.78 (0.02) | 58.40 (1.33) | 52.15 (0.88) | 47.10 (1.45) | <0.001 |
| BMI (Kg/m²) | 29.84 (0.12) | 30.36 (0.18) | 29.80 (0.16) | 29.29 (0.22) | <0.001 |
| Energy (Kcal) | 2003.99 (14.02) | 1950.46 (24.42) | 1984.57 (18.95) | 2095.69 (23.74) | <0.001 |
| Frailty (%) | <0.001 | ||||
| No | 65.83 (0.02) | 57.00 (1.18) | 67.69 (1.12) | 73.47 (1.46) | |
| Yes | 34.17 (0.01) | 43.00 (1.18) | 32.31 (1.12) | 26.53 (1.46) |
GED, general educational development; PIR, poverty income ratio; BMI, body mass index.
3.2. The association between dietary live microbe intake and frailty
The analysis of the relationship between dietary live microbe intake and frailty is presented in Table 2. In the model adjusting for all covariates, compared to individuals in the low intake group, those in the high intake group exhibited a significantly lower risk of frailty by 33% (OR = 0.67, 95% CI: 0.56, 0.79), and the medium intake group also demonstrated lower frailty risks by 24% (OR = 0.76, 95% CI: 0.66, 0.88).
Table 2.
Association between dietary live microbe intake and frailty.
| ORa (95% CIb) P-value |
|||
|---|---|---|---|
| Crudec | Model 1d | Model 2e | |
| Dietary Live Microbe Intake group | |||
| Low | Reference | Reference | Reference |
| Medium | 0.63 (0.55,0.73) <0.001 | 0.67 (0.58,0.78) <0.001 | 0.76 (0.66,0.88) <0.001 |
| High | 0.48 (0.41,0.56) <0.001 | 0.53 (0.45,0.62) <0.001 | 0.67 (0.56,0.79) <0.001 |
| P for trend | <0.001 | <0.001 | <0.001 |
| MedHi | |||
| Continuous | 0.82 (0.79,0.86) <0.001 | 0.85 (0.81,0.88) <0.001 | 0.89 (0.85,0.92) <0.001 |
| Categories | |||
| G1 | Reference | Reference | Reference |
| G2 | 0.67 (0.58,0.78) <0.001 | 0.71 (0.61,0.83) <0.001 | 0.82 (0.70,0.96) 0.015 |
| G3 | 0.48 (0.41,0.56) <0.001 | 0.53 (0.45,0.62) <0.001 | 0.64 (0.55,0.75) <0.001 |
| P for trend | <0.001 | <0.001 | <0.001 |
ORa: odds ratio;
95% CIb: 95% confidence interval;
Crudec: adjusted for non covariates;
Model 1d: adjusted for age, gender, race, and education;
Model 2e: adjusted for age, gender, race, and education, marital status, poverty income ratio, body mass index, smoking, alcohol use, and energy intake.
Furthermore, in the fully adjusted model, each additional 100 g MedHi consumption was associated with an 11% lower risk of frailty (OR = 0.89, 95% CI: 0.85, 0.92), and the G3 had a 36% reduced risk of frailty compared with the G1 (OR = 0.64, 95% CI: 0.55, 0.75).
The RCS results indicated that the risk of frailty decreased significantly as MedHi intake increased (P for nonlinear <0.001). Beyond 100 grams, the curve’s slope gradually gentler (Fig. 2). The piecewise regression further confirmed the statistical difference in the changes in slope (Supplementary Table 3).
Fig. 2.
RCS plot.
3.3. Subgroup analyses
As shown in Table 3, the subgroup analysis suggested that the association appeared more stable and effective across subgroups in the high intake group than in the medium intake group. Interaction analyses showed that alcohol use status changed the association (P for interaction = 0.037). Increased dietary live microbe intake was associated with a lower risk of frailty in those with alcohol use, but it was not observed in those without alcohol use.
Table 3.
Subgroup analysis.
| Subgroup | Low dietary live microbe intake group | Medium dietary live microbe intake group | High dietary live microbe intake group | P for trend | P for interaction |
|---|---|---|---|---|---|
| Age | 0.264 | ||||
| <60 | Reference | 0.72 (0.57,0.92) 0.010 | 0.62 (0.47,0.82) 0.001 | <0.001 | |
| ≥60 | Reference | 0.87 (0.76,1.01) 0.061 | 0.78 (0.65,0.93) 0.007 | 0.006 | |
| Gender | 0.308 | ||||
| Female | Reference | 0.86 (0.70,1.07) 0.172 | 0.74 (0.59,0.93) 0.012 | 0.012 | |
| Male | Reference | 0.67 (0.55,0.81) <0.001 | 0.60 (0.48,0.76) <0.001 | <0.001 | |
| BMI | 0.067 | ||||
| <25 | Reference | 0.62 (0.46,0.84) 0.003 | 0.53 (0.38,0.73) <0.001 | <0.001 | |
| ≥25 | Reference | 0.80 (0.69,0.94) 0.006 | 0.69 (0.57,0.84) <0.001 | <0.001 | |
| Smoking | 0.468 | ||||
| No | Reference | 0.79 (0.63,0.99) 0.042 | 0.75 (0.56,1.01) 0.061 | 0.058 | |
| Yes | Reference | 0.76 (0.62,0.94) 0.010 | 0.63 (0.50,0.79) <0.001 | <0.001 | |
| Alcohol use | 0.037 | ||||
| No | Reference | 1.07 (0.79,1.46) 0.642 | 0.90 (0.60,1.36) 0.622 | 0.740 | |
| Yes | Reference | 0.71 (0.61,0.84) <0.001 | 0.63 (0.52,0.76) <0.001 | <0.001 |
The results of the subgroup analysis were adjusted for all covariates except the effect modifier.
3.4. Sensitivity analyses
Sensitivity analyses were conducted to ensure the robustness of our findings. These included additional adjustments for disease, medication use, and dietary factors, modifications to frailty diagnostic criteria, exclusion of participants with abnormal energy intake, and performing imputations. All results consistently supported the strength and reliability of the observed associations (Supplementary Table 4−7).
4. Discussion
Our research, utilizing a nationally representative sample of U.S. adults, established a significant association between increased intake of dietary live microbes and a reduced risk of frailty. Notably, the relationship remained consistent after adjusting for potential confounders. Additionally, subgroup analysis revealed variations in the association across different demographic groups, and a series of sensitivity analyses confirmed the stability of these results.
The strong association between frailty and gut flora is well-established [34]. A meta-analysis encompassing 11 studies revealed significant alterations in the abundance of more than 50 microbial species in frail individuals [35]. Frail individuals exhibited increased pro-inflammatory microbes and decreased anti-inflammatory microbes [36]. Furthermore, abnormalities of gut flora metabolites were observed. In frail individuals, a decrease in anti-inflammatory substances, such as Short-Chain Fatty Acids (SCFA), coupled with an increase in pro-inflammatory substances like Trimethylamine N-oxide (TMAO), has been observed [37,38]. The imbalance may intensify inflammation and oxidative stress, potentially leading to tissue damage [39]. Moreover, frail individuals frequently experience alterations in intestinal epithelial permeability, suboptimal dietary habits, and aberrations in intestinal absorption function [34].
Increasing the intake of live microbes may be an important strategy to improve frailty. Several studies employing Sanders’ classification method have demonstrated that diets rich in live microbes were associated with various positive health outcomes. These included healthier metabolism, lower BMI, lower CVD risk, lower depression risk, and better cognitive function [[22], [23], [24]]. Although direct intervention studies about frailty are scarce, numerous studies have highlighted the health effects of probiotics in the general population. Probiotic supplementation can improve gastrointestinal symptoms [40] and intestinal barrier function [41]. It is worth noting that the benefits were not limited to the gastrointestinal tract. Research suggested that probiotic can also increase muscle mass and overall muscle strength [42], reduce visceral fat accumulation [43], improve depressive symptoms and cognitive function [44], and reduce systemic inflammation levels [45]. Although these intervention studies are not specifically for frail individuals, these benefits may have implications for diminished frailty risk.
In our study, not only high live microbe diets but also medium live microbe diets, are associated with a reduced risk of frailty. Interestingly, prior studies observed that diets with medium live microbes were more strongly associated with CVD and cognitive function [23,24]. Moreover, a dietary intervention study found that supplementation with fresh fruits and vegetables altered the composition and metabolites of the gut microbiota, which may help reduce pro-inflammatory responses and antioxidant capacity [46]. In addition, multiple studies indicated the Mediterranean diet may modulate the microbiome in a direction positively associated with health by altering bacterial abundance and metabolism [[47], [48], [49]]. Thus, except fermented foods, vegetables, and fruits are the important sources of dietary live microbes.
Subgroup analyses showed that the association was stable across age, gender, BMI, and smoking stratification. However, the interaction for alcohol use was statistically significant. Increasing dietary live microbe intake was associated with reduced risk of frailty among drinkers, whereas this association was not observed among non-drinkers. This may be due to more severe metabolic disorders in drinkers. Therefore, it may be important for drinkers to increase microbial levels in their diets. Nonetheless, the heterogeneity of the association observed in subgroup analyses warrants testing in a larger population.
The diet quality is also an important factor affecting frailty. HEI-2015 is an index that assesses overall diet quality, while the DII represents the anti-inflammatory potential of the overall diet [50,51]. Our sensitivity analyses, which further adjusted for the HEI-2015 and DII, affirmed the significance of these findings, suggesting that the benefits of increased live microbe intake might transcend the quality of food consumed. Therefore, assessment of dietary live microbes may be important.
RCS suggested the existence of nonlinear relationships between MedHi consumption and reduced risk of frailty. Before reaching 100 grams, the risk reduction is notably pronounced with the increase of MedHi intake. However, the curve became slightly flattened as the intake exceeded 100 grams, indicating a slowdown in this potential benefit. Interestingly, another study found a similar trend, with increasing MedHi food intake to about 100 grams associated with a significant reduction in depressive symptoms, followed by a slight decrease [52]. Consequently, for individuals with a lower 100 g MedHi intake, it may be advisable to consider a diet rich in live microbes. However, it is essential to note that the 100-gram estimate in our study serves as a preliminary observation and does not provide direct dietary recommendations. Further research is necessary to establish specific nutritional guidelines.
Despite the potential benefits of increasing dietary microbes, some concerns still require careful consideration. The safety of increasing live microbe intake, particularly for populations with intestinal disorders, severe infections, or those undergoing transplants, warrants careful consideration. Food poisoning is not uncommon, especially with produce [20]. It is also important to note that the impact of dietary interventions on intestinal flora is transient; once the intervention ceases, the effects on the intestinal flora diminish. Determining the optimal intake level remains an area for further exploration. Moreover, frailty is a dynamic condition with the potential for reversal. Whether an increase in dietary microbes can contribute to the reversal is a question that remains open for investigation. More studies are needed to assess their health benefits and long-term effects.
Our comprehensive, multi-ethnic study, utilizing the NHANES study design, provides novel insights into the effects of dietary live microbe intake on frailty. However, several limitations must be acknowledged. Firstly, the cross-sectional nature of our research highlights associations but cannot establish causality. Secondly, the generalizability of our results is limited due to the study population. The NHANES data, based on the U.S. population, may not accurately reflect global trends, and the exclusion of hospitalized individuals with more complex health conditions warrants further investigation. Thirdly, the methods used to estimate microbial content may lack precision. While we employed Sanders' classification for estimating live microbes in foods, this approach is not as accurate as direct measurement methods. Additionally, reliance on 24 -h dietary recall data raises concerns about recall bias, and factors such as transportation, storage, and cooking methods could alter levels of live dietary bacteria. Furthermore, assessing overall dietary live microbe intake levels is complex. To ensure robustness in our conclusions, we adopted dual approaches in measuring dietary live microbe levels. Remarkably, our conclusions remained consistent across these different analytical methods. Despite adjustments for various covariates, we acknowledged the potential for residual confounding factors. Our sensitivity analyses made further adjustments for dietary factors, illness, medication use, and other variables. Recognizing the limitations, we emphasize the need for further research in this area. Future studies should aim to overcome these challenges, perhaps through longitudinal designs, broader population samples, and more precise microbial measurement techniques. Such research is vital to deepen our understanding of dietary live microbes’ role in health and validate our findings. Nonetheless, our study highlights the importance of assessing dietary microbes and may have important implications for developing future dietary guidelines.
5. Conclusions and implications
Our study demonstrated that more intake of dietary live microbes was associated with a reduced risk of frailty among U.S. adults. Consequently, increasing the intake of dietary live microbes may be considered a novel dietary strategy to alleviate frailty. Additionally, it may be important to assess the intake of live microbes. However, given the limitations of our study, these findings should be approached with caution. Large-scale prospective studies are necessary to further validate and substantiate our results, establish causality, and explore the mechanistic underpinnings of these associations.
Author contributions
All authors meet the criteria for authorship stated in the Uniform Requirements for Manuscripts Submitted to Biomedical Journals. Study concept and design: Xingwei Huo. Acquisition of data: Xingwei Huo. Analysis and interpretation of data: Xingwei Huo. Drafting of the manuscript: Xingwei Huo, Shanshan Jia, Lirong Sun, Yuanyuan Yao, Hang Liao, Xiaoping Chen. Critical revision of the manuscript for important intellectual content: Xingwei Huo, Xiaoping Chen.
Funding
This study was supported by the National Natural Science Foundation of China (No. 81900404) and Sichuan Natural Science Foundation Project (NO.2023NSFSC1632).
Declarations
The study complies with the current laws of the country in China.
Ethics approval and consent to participate
The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). All information from the NHANES program is available and free for public, so the agreement of the medical ethics committee board was not necessary. Ethical approval for NHANES has been obtained from the NCHS Institutional Review Board. And it was a continuation of protocols #98-12, #2005-06, #2011-17, and #2018-01. More information can be found on the CDC’s official website at www.cdc.gov/nchs/nhanes/irba98.htm (accessed on 24 January 2024).
Availability of data and materials
All data can be found at http://www.cdc.gov/nchs/nhanes (accessed on 24 January 2024).
Consent for publication
All authors have read and approved the article.
Competing interests
The authors declare no conflicts of interest.
Acknowledgments
Thanks to all NHANES staff and CDC for their support. Thanks to Chunrong Cao’s support and encouragement.
During the preparation of this work, the authors did not use artificial intelligence tools.
Footnotes
Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j.jnha.2024.100171.
Appendix A. Supplementary data
The following is Supplementary data to this article:
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
All data can be found at http://www.cdc.gov/nchs/nhanes (accessed on 24 January 2024).