What this is
- This analysis explores the link between dietary intake of live microbes and mortality in adults with ().
- Using data from the National Health and Nutrition Examination Survey (NHANES) spanning 1999-2018, it examines 8725 participants.
- The study finds that higher consumption of live microbes correlates with lower mortality rates from all causes and cardiovascular disease.
Essence
- Increased dietary intake of live microbes is associated with a lower risk of all-cause and cardiovascular mortality in adults with .
Key takeaways
- Higher intake of dietary live microbes correlates with reduced mortality. Participants in the highest intake group showed a 20% lower risk of all-cause mortality and a 26% lower risk of cardiovascular mortality compared to the lowest intake group.
Caveats
- The study relies on self-reported dietary data, which may introduce recall bias. It also assesses dietary live microbe intake only once, potentially missing changes over time.
Definitions
- Chronic Kidney Disease (CKD): A long-term condition characterized by the gradual loss of kidney function, often leading to kidney failure.
- MedHi: Refers to foods with moderate to high live microbial content, specifically those with more than 10 CFU/g.
Simplified
Introduction
Chronic kidney disease (CKD) is a significant global health challenge, with an estimated global prevalence of about 10% and over 800 million affected individuals [1]. Notably, the prevalence of CKD is higher in the United States (US), reaching approximately 15%, primarily attributed to the increased prevalence of hypertension, diabetes, and cardiovascular disease (CVD) [2]. In 2017 alone, CKD accounted for 1.2 million deaths, and between 1990 and 2017, the global mortality rate associated with CKD increased by 41.5% [3]. Therefore, identifying modifiable factors is crucial for preventing CKD or delaying premature death.
CKD is characterized by dysbiosis and accumulation of toxic metabolites [4, 5]. Specifically, the increase in intestinal urea concentration can result in alterations in gut microbiota, and in turn, an increase in pathogenic gut microbiota can lead to an increase in uremic toxins [6]. There is growing recognition of the potential of dietary interventions targeting the gut microbiota-gut-kidney axis to delay the progression of CKD [7]. A recent cross-sectional survey has indicated a potential association between the consumption of probiotics, prebiotic supplements, or yogurt and a reduced risk of CKD and its progression [8]. A recent meta-analysis has suggested that non-dietary probiotics, prebiotics, and synbiotics have the potential to reduce inflammation, oxidative stress, and blood lipids in CKD patients [9]. Live microbes exist in various foods, including unpeeled fruits and vegetables, as well as fermented dairy products [10]. These foods are easy to obtain, inexpensive, and can be consumed regularly. Previous studies have shown that dietary live microbes can survive in the digestive tract contributing to the improvement of gut microbiota and modulation of the immune system [11, 12].
However, the existing literature does not provide sufficient evidence regarding whether dietary live microbes can improve the prognosis of CKD patients. Thus, the primary aim of this study was to elucidate the relationship between dietary live microbe consumption and the occurrence of all-cause mortality and CVD mortality in adults diagnosed with CKD in the US. This will contribute to the development of novel interventions aimed at improving the prognosis of CKD patients.
Methods
Study population
The NHANES study is carried out every two years by the National Center for Health Statistics (NCHS) to estimate the nutritional and health conditions of the entire population of the US employing intricate and multi-level probability sampling design methods.
CKD is characterized by an estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73 m2 and/or urinary albumin to creatinine ratio (UACR) ≥ 30 mg/g, as per the KDIGO 2021 guideline [13]. The eGFR was computed through the CKD-Epidemiology Collaboration equation, which includes serum creatinine, age, sex, and race/ethnicity [14]. The calculation for eGFR was as follows: 141 × min (SCr/κ, 1)α × max (SCr/κ, 1)−1.209 × 0.993Age × 1.018 if female × 1.159 if Black, where SCr represents serum creatinine, κ is 0.7 for females and 0.9 for males, α is − 0.329 for females and − 0.411 for males, min denotes the lesser value between SCr/κ or 1, and max signifies the greater value between SCr/κ or 1. In the present analysis, we initially identified CKD participants aged 20 and above from a series of 10 consecutive cycles spanning from 1999 to 2018. Afterward, we removed pregnant females and individuals who did not have 24-h dietary recalls and follow-up information, which led to a total of 8725 participants in the final sample size (Supplementary Fig. 1).
Covariates
Given possible variables that could affect the results, this study incorporated several covariates to clarify any uncertainty in the correlation between the consumption of dietary live microbes and mortality. Demographic parameters, such as age, gender, race/ethnicity (including Black, White, Mexican, and others), body mass index (BMI), educational attainment (ranging from below high school to above high school), and poverty income ratio (classified as < 1.3, 1.3–3.5, and ≥ 3.5), were collected through questionnaires. BMI was determined through anthropometric measurements. The participants self-reported their habits and health conditions, encompassing smoking habits (never, former, current), alcohol intake (none, moderate, heavy, and binge), levels of physical activity (none, moderate, and vigorous), hypertension, diabetes, hyperlipidemia, and CVD. The diagnosis of diabetes met one of four different criteria: self-reported diagnosis, the use of diabetes medications, a hemoglobin A1c ≥ 6.5%, or a fasting plasma glucose ≥ 126 mg/dL. The definition of hyperlipidemia was based on three distinct criteria: (1) the utilization of lipid-lowering drugs; (2) triglyceride levels equal to or greater than 150 mg/dL; (3) total cholesterol levels equal to or greater than 200 mg/dL, with high-density lipoprotein cholesterol below 40 mg/dL for males or below 50 mg/dl for females, or low-density lipoprotein cholesterol levels equal to or greater than 130 mg/dL. Hypertension was determined through the use of antihypertensive drugs or systolic/diastolic blood pressure ≥ 140/90 mmHg. CVD was ascertained by self-reported presence of one of five subtypes from among congestive heart failure, coronary heart disease, angina, heart attack, or stroke. Additional covariate indicators included Healthy Eating Index-2015 (HEI-2015) and laboratory data, including UACR and serum creatinine. The HEI-2015 score ranges between 0 and 100 points. A higher score reflects healthier eating.
Mortality
The Public Use Linked Mortality File was used to determine the outcomes of the participants including data on their survival status from the National Death Index until December 31st, 2019. The primary cause of death was identified following ICD-10 codes, encompassing deaths caused by CVD (I00-I09, I11, I13, I20-I51, and I60-I69) and other causes.
Estimating dietary live microbe intake
Dietary data are collected using an in-person 24-h dietary recall. NHANES dietary data were obtained from the What We Eat in America (WWEIA) program conducted by the United States Department of Agriculture (USDA), which aims to provide the nutrient values for foods and beverages reported in WWEIA (https://www.ars.usda.gov/northeast-area/beltsville-md-bhnrc/beltsville-human-nutrition-research-center/food-surveys-research-group/↗). The method for determining dietary live microbes was performed as before [10], and has been employed in multiple studies [15, 16]. In short, to estimate the number of live microbes per gram of food corresponding to the 9388 food codes from 48 subgroups in the NHANES database, four experts (Maria L. Marco, Mary E. Sanders, Robert Hutkins, and Colin Hill) conducted evaluations of each food item. Their assessments were based on a comprehensive review of existing literature, authoritative reviews, and the known impacts of food processing techniques, such as pasteurization, on microbial viability. The differences in each food classification were established through internal team consultations and validated by external consultation with Fred Breidt, a microbiologist at the USDA Agricultural Research Service. Based on the expected number of live microbes, the food was classified as low (< 104 CFU/g), medium (104–107 CFU/g), or high (> 107 CFU/g). Foods with low microbial content primarily consisted of pasteurized products, foods with moderate microbial content mainly included unpeeled fresh fruits and vegetables, and foods with high microbial content included unpasteurized fermented foods. MedHi refers to food classified as having moderate or high microbial content, and we calculated the grams of food classified as MedHi for each individual. The classification of each food item was documented in the prior study [10].
Statistical analysis
Following the NHANES analysis guidelines, we employed sampling weights (WTDRD1) and masked variance in R 4.2.2 to account for the intricate study design of NHANES. We examined the characteristics of individuals with and without all-cause mortality using Student’s t-tests for continuous factors and chi-square tests for categorical factors. In regression analysis, our primary focus was to investigate the correlation between exposure to MedHi dietary intake and the rate of mortality. Thus, we conducted an examination of MedHi as both a continuous and categorical variable. The categorization involved dividing the participants into three groups based on tertile distribution: Tertile 1 referred to individuals who did not consume any live microbe foods classified as Med and Hi, the Tertile 2 group included individuals whose consumption of MedHi foods was in the range of 0–110 g/d, and the Tertile 3 group consisted of individuals with consumption exceeding 110 g/d. Weighted multivariate Cox regression analysis was adopted to examine the relationship between MedHi dietary live microbe consumption and mortality across three different models. In Model I, adjustments were made for age, gender, and race/ethnicity. Model II incorporated age, gender, race/ethnicity, educational attainment, poverty income ratio, BMI, smoking habits, alcohol consumption, and physical activity levels, while Model III further incorporated health conditions (hypertension, diabetes, hyperlipidemia, and CVD), serum creatinine, UACR and HEI-2015. Restricted cubic splines (RCS) with four knots based on multivariate regression analysis were implemented to visually represent the linear or nonlinear relationship between MedHi dietary active microbe intake and mortality rate. Stratified analyses were implemented to verify the robustness of our findings based on gender, age, hyperlipidemia, hypertension, diabetes, and CVD. Additionally, we implemented several sensitivity analyses. Firstly, since prebiotic/probiotic supplements were not included in the dietary live microbes, we further adjusted for Prebiotic/Probiotic supplements (only information concerning 4659 participants was available). Secondly, given that unpasteurized foods contain a large amount of vitamins, we further adjusted for dietary factors including vitamin A, vitamin C, vitamin E, and carotenoids. Thirdly, to reduce the risk of reverse causality bias, the analysis excluded individuals who died within a 2-year time frame. Fourthly, survival times were censored at the latest at 15 years to ensure consistency. Finally, individuals with implausible energy intake levels falling below 500 kcal or exceeding 4000 kcal per day were excluded from the analysis. A p < 0.05 showed statistical significance.
Results
In the conducted sensitivity analyses in Supplementary Table 1, the inclusion of prebiotic/probiotic supplements, or dietary factors (vitamin A, vitamin C, vitamin E, and carotenoids) in the adjusted full model did not alter the negative correlation between MedHi foods and both all-cause and CVD mortality. Consistent results were obtained when we implemented a censoring mechanism at the 15-year follow-up. When individuals with extreme energy intake were excluded, the results remained consistent. Upon excluding adult CKD individuals who died within the initial two years, the correlation between MedHi food and all-cause mortality did not undergo any material alteration. However, the correlation between MedHi food and CVD mortality did not reach statistical significance.
Restricted cubic spline presented the linear association between the consumption of MedHi foods and mortality. A clear dose–response relationship between the consumption of MedHi foods and all causes () and CVD () mortality were obtained. HR, hazard ratios; MedHi: > 10CFU/g foods; CVD, cardiovascular diseases A B 4
| Variable | Total | Survivors | Non-survivors | P |
|---|---|---|---|---|
| = 8725N | = 5224N | = 3501N | ||
| Age (years) | 60.59 (0.47) | 53.32 (0.59) | 71.84 (0.46) | < 0.001 |
| Sex (%) | < 0.001 | |||
| Male | 43.38 (0.01) | 41.39 (1.04) | 47.30 (0.99) | |
| Female | 56.62 (0.02) | 58.61 (1.04) | 52.70 (0.99) | |
| Race/ethnicity (%) | < 0.001 | |||
| Black | 12.46 (0.01) | 13.10 (0.86) | 11.19 (0.86) | |
| White | 70.11 (0.03) | 66.03 (1.39) | 78.17 (1.35) | |
| Mexican | 6.61 (0.01) | 8.08 (0.66) | 3.71 (0.57) | |
| Other | 10.83 (0.01) | 12.80 (0.80) | 6.93 (0.86) | |
| BMI (kg/m)2 | 29.29 (0.18) | 29.50 (0.22) | 28.96 (0.26) | 0.082 |
| Education level (%) | < 0.001 | |||
| Less than high school | 24.96 (0.01) | 20.18 (0.78) | 34.50 (1.29) | |
| High school graduates | 26.17 (0.01) | 26.00 (0.93) | 26.61 (0.96) | |
| Above high school | 48.75 (0.02) | 53.82 (1.13) | 38.89 (1.21) | |
| Marital status (%) | < 0.001 | |||
| Separated | 42.92 (0.01) | 39.46 (1.06) | 51.06 (1.18) | |
| Married | 56.16 (0.02) | 60.54 (1.06) | 48.94 (1.18) | |
| PIR (%) | < 0.001 | |||
| < 1.3 | 24.45 (0.01) | 25.07 (0.91) | 29.39 (1.34) | |
| 1.3–3.5 | 38.13 (0.01) | 38.60 (0.99) | 46.83 (1.22) | |
| ≥ 3.5 | 29.61 (0.01) | 36.33 (1.23) | 23.79 (1.24) | |
| Smoking status (%) | < 0.001 | |||
| Never | 49.73 (0.01) | 53.39 (1.01) | 42.61 (1.16) | |
| Former | 32.69 (0.01) | 28.53 (0.83) | 40.99 (1.20) | |
| Current | 17.50 (0.01) | 18.08 (0.84) | 16.40 (0.88) | |
| Alcohol consumption (%) | < 0.001 | |||
| None | 21.52 (0.01) | 20.34 (0.89) | 26.79 (1.24) | |
| Moderate | 46.54 (0.01) | 46.47 (1.23) | 52.99 (1.21) | |
| Heavy | 15.20 (0.01) | 19.32 (0.79) | 9.09 (0.75) | |
| Binge | 12.39 (0.01) | 13.87 (0.83) | 11.14 (0.62) | |
| Physical activity (%) | < 0.001 | |||
| Never | 58.42 (0.02) | 54.07 (0.99) | 67.05 (1.19) | |
| Moderate | 25.11 (0.01) | 25.34 (0.83) | 24.66 (1.02) | |
| Vigorous | 16.45 (0.01) | 20.58 (0.81) | 8.29 (0.67) | |
| Hypertension (%) | 68.92 (0.02) | 61.11 (1.06) | 84.35 (0.78) | < 0.001 |
| Diabetes (%) | 33.15 (0.01) | 29.72 (0.93) | 39.92 (1.02) | < 0.001 |
| Hyperlipidemia (%) | 80.91 (0.02) | 79.49 (0.86) | 83.73 (0.73) | < 0.001 |
| CVD (%) | 24.97 (0.01) | 16.36 (0.65) | 42.02 (1.19) | < 0.001 |
| UACR (mg/g) | 43.16 (1.03) | 41.31 (1.03) | 47.23 (1.04) | < 0.001 |
| Serum creatinine (mg/dl) | 1.02 (1.01) | 0.96 (1.01) | 1.16 (1.01) | < 0.001 |
| HEI-2015 | 51.57 (13.58) | 52.24 (13.05) | 51.22 (13.82) | 0.016 |
| Low (grams/d) | 2667.28 (49.78) | 2906.71 (71.79) | 2296.70 (52.97) | < 0.001 |
| Med (grams/d) | 97.50 (3.72) | 104.40 (5.26) | 86.82 (4.30) | 0.009 |
| Hi (grams/d) | 15.87 (1.34) | 17.36 (1.66) | 13.57 (2.13) | 0.046 |
| MedHi (grams/d) | 113.37 (4.44) | 121.76 (6.04) | 100.39 (5.34) | 0.005 |
| MedHi | P for trend | Per one-unit increment in MedHi | |||
|---|---|---|---|---|---|
| Tertile 1 | Tertile 2 | Tertile 3 | |||
| HR (95% CI) | HR (95% CI) | HR (95% CI) | |||
| All-cause mortality | |||||
| Model I | Reference | 0.80 (0.70, 0.91)# | 0.72 (0.65, 0.81)# | < 0.001 | 0.91 (0.88, 0.94)# |
| Model II | Reference | 0.90 (0.78, 1.02) | 0.83 (0.74, 0.94)† | 0.002 | 0.93 (0.89, 0.97)# |
| Model III | Reference | 0.90 (0.78, 1.03) | 0.80 (0.70, 0.91)† | 0.002 | 0.92 (0.88, 0.96)† |
| CVD mortality | |||||
| Model I | Reference | 0.80 (0.65, 0.98)* | 0.68 (0.57, 0.81)# | < 0.001 | 0.88 (0.83, 0.93)# |
| Model II | Reference | 0.91 (0.72, 1.14) | 0.79 (0.65, 0.96)* | 0.02 | 0.91 (0.85, 0.97)† |
| Model III | Reference | 0.90 (0.70, 1.16) | 0.74 (0.59, 0.93)* | 0.02 | 0.89 (0.83, 0.96)† |
| MedHi | for trendP | for interactionP | |||
|---|---|---|---|---|---|
| Tertile 1 | Tertile 2 | Tertile 3 | |||
| HR (95% CI) | HR (95% CI) | HR (95% CI) | |||
| Age | 0.306 | ||||
| < 65 years | Reference | 0.81 (0.54, 1.22) | 0.95 (0.60, 1.59) | 0.754 | |
| ≥ 65 years | Reference | 0.92 (0.79, 1.06) | 0.77 (0.67, 0.89)# | < 0.001 | |
| Sex | 0.364 | ||||
| Male | Reference | 0.88 (0.73, 1.06) | 0.73 (0.61, 0.88)† | 0.002 | |
| Female | Reference | 0.94 (0.79, 1.13) | 0.89 (0.73, 1.09) | 0.259 | |
| Hypertension | 0.496 | ||||
| Yes | Reference | 0.87 (0.74, 1.01) | 0.78 (0.68, 0.89)† | < 0.001 | |
| No | Reference | 1.03 (0.77, 1.38) | 0.91 (0.66, 1.26) | 0.582 | |
| Diabetes | 0.109 | ||||
| Yes | Reference | 0.94 (0.77, 1.16) | 0.93 (0.77, 1.10) | 0.386 | |
| No | Reference | 0.87 (0.74, 1.02) | 0.72 (0.60, 0.86)# | < 0.001 | |
| Hyperlipidemia | 0.223 | ||||
| Yes | Reference | 0.88 (0.76, 1.03) | 0.84 (0.73, 0.96)† | 0.008 | |
| No | Reference | 0.98 (0.72, 1.35) | 0.65 (0.46, 0.96)* | 0.02 | |
| CVD | 0.023 | ||||
| Yes | Reference | 0.72 (0.59, 0.87)# | 0.79 (0.66, 0.94)† | 0.005 | |
| No | Reference | 1.02 (0.87, 1.20) | 0.86 (0.73, 1.02) | 0.08 | |
Discussion
Our findings show an inverse linear relationship between high intake of dietary live microbes and risk of mortality from any cause and for CVD. Furthermore, stratified analyses and multiple sensitivity analyses confirmed this relationship.
The primary focus of previous studies has mainly been on probiotics and dairy products that have undergone fermentation as sources of live microorganisms, suggesting a positive impact on kidney disease. A cross-sectional study revealed a significant relationship between regular consumption of yogurt and/or probiotics and reduced odds of proteinuria [17]. Furthermore, a meta-analysis indicated that probiotics have the potential to reduce various indicators of kidney disease in individuals with diabetic nephropathy, including blood urea nitrogen, serum creatinine, cystatin C, and UACR [18]. According to a recent large survey, there was an inverse correlation between the intake of probiotics, prebiotics, or yogurt and the risk of CKD [8]. Previous studies have found that synbiotics can reduce uremic toxins such as cresyl sulfate, and improve fecal microbiota by reducing Ruminococcaceae and increasing Bifidobacteria in predialysis CKD patients [19]. Additionally, fresh fruits and vegetables are crucial sources of probiotics [10]. Of note, participants with CKD or those undergoing dialysis who consume fewer fruits and vegetables face an elevated risk of all-cause mortality [20, 21]. These studies provide support for exploring the prognostic impact of dietary live microbes on CKD patients. Our study is the first to show that individuals with CKD who consume high amounts of dietary live microbes have a reduced risk of all-cause and CVD mortality. While research on the advantageous effects of probiotics often focuses on examining specific strains, substantial evidence indicates that consuming adequate amounts of probiotics may yield health benefits irrespective of the strain [22]. Our results suggest that consuming moderate or high levels of dietary live microbes above approximately 110 g per day may have a positive impact on the prognosis of individuals with CKD. In light of these results, we propose the implementation of a long-term dietary plan for CKD patients, which poses no harm and aims to enhance their prognosis.
The gut-kidney axis [7, 23] offers explanatory mechanisms for the observed correlation. Microbes present in food enter the digestive system and establish themselves without being digested, thus modifying the composition of the gut microbiome [11, 12, 24]. A randomized clinical trial revealed that a high-fiber diet increases Lactobacillus and Bifidobacterium while reducing the number of opportunistic pathogens such as Desulfovibrio and Klebsiella in patients with type 2 diabetes [25]. Additionally, an interventional study indicated that a diet rich in fruits and vegetables can increase Prevotella, Lactobacillus, and Bifidobacterium [26]. Furthermore, a randomized cross-over study involving 14 healthy participants demonstrated that the abundance of Bifidobacterium species increased after consuming fermented dairy products [27]. CKD patients often have an impaired gut mucosal barrier function and disrupted gut microbiota, leading to elevated levels of aerobic bacteria and decreased levels of anaerobic bacteria [28]. Anaerobic bacteria, including Lactobacillus and Bifidobacterium, lead to carbohydrate fermentation to produce short-chain fatty acids, which effectively reduce pH and impede the growth of pathogenic bacteria [29, 30]. As a result, the levels of uremic toxins are diminished, alongside with the inhibition of inflammatory responses and oxidative stress, ultimately leading to a slower progression of renal function impairment [9, 30].
The present study has a substantial number of participants and strong statistical methods, nevertheless, it is crucial to acknowledge limitations in our study. Firstly, the classification of dietary live microbes was obtained by experts and through literature analysis. Secondly, a 24-h dietary recall might introduce recall biases, which would limit the accuracy of the findings. Thirdly, this analysis only evaluates the consumption of dietary live microbes once, without considering potential changes in diet during follow-up. Regrettably, the limitations of NHANES precluded dynamic adjustments. Fourthly, due to the limitations inherent in the NHANES database, our investigation examined only the association between dietary live microbe intake and mortality. It is important to further explore the relationship between dietary live microbe intake and other outcomes in patients with CKD, including renal failure. Finally, our main focus is on intake of food rich in live microbes rather than on the exact microbial counts. Despite accounting for confounding variables like HEI-2015, vitamin A, vitamin C, vitamin E, and carotenoids, additional elements, apart from live microbes, might also contribute to reduce mortality.
Conclusion
Individuals who consume high levels of live microbes have a reduced risk of all-cause and CVD mortality.
Supplementary Information
Below is the link to the electronic supplementary material. Supplementary file1 (DOC 96 KB) Supplementary file2 (DOC 56 KB)