- Research
- Open access
- Published:
Associations of healthy eating patterns with biological aging: national health and nutrition examination survey (NHANES) 1999–2018
Nutrition Journal volume 23, Article number: 112 (2024)
Abstract
Background
Healthy dietary patterns have been negatively associated with methylation-based measures of biological age, yet previous investigations have been unable to establish the relationship between them and biological aging assessed through blood chemistry-based clinical biomarkers. We sought to assess the associations of 4 dietary metrics with 4 measures of biological age.
Methods
Among 16,666 participants in NHANES 1999–2018, 4 dietary metrics [Dietary inflammatory index (DII), Dietary approaches to stop hypertension index (DASH), Alternate mediterranean diet score (aMED), and Healthy eating index-2015 (HEI-2015)] were calculated through the ‘dietaryindex’ R package. Twelve blood chemistry parameters were utilized to compute 4 indicators of biological age [homeostatic dysregulation (HD), allostatic load (AL), Klemera–Doubal method (KDM), and phenotypic age (PA)]. Binomial logistic regression models and restricted cubic spline (RCS) regression were employed to evaluate the associations.
Results
All 4 dietary metrics were significantly associated with biological age acceleration or deceleration. In comparison to the lowest DII, the odds ratios (ORs) for accelerated HD, AL, KDM, and PA were 1.25 (1.08,1.45), 1.29 (1.11,1.50), 1.34 (1.08,1.65), and 1.61 (1.39,1.87) for the highest. The multivariable-adjusted ORs of the highest quartile of DASH, aMED, and HEI-2015 were 0.85 (0.73,0.97), 0.88 (0.74,1.04), and 0.84 (0.74,0.96) for HD, 0.64 (0.54,0.75), 0.61 (0.52,0.72), and 0.70 (0.59,0.82) for AL, 0.68 (0.54,0.85), 0.62 (0.50,0.76), and 0.71 (0.58,0.87) for KDM, and 0.50 (0.42,0.59), 0.64 (0.54,0.76), and 0.51 (0.44,0.58) for PA when compared with the lowest level. The findings were validated by the best-fitting dose-response curves for the associations. Among participants consuming dietary supplements (Pinteraction < 0.05), the positive effects of a healthy dietary pattern on biological aging were more pronounced. Systemic immune inflammation index (SII) and atherogenic index of plasma (AIP) were identified as being involved in and mediating the associations.
Conclusions
Biological aging assessed through blood chemistry-based clinical biomarkers is negatively associated with diet quality. The anti-aging benefits of improving the diet may be due to its ability to reduce inflammation and lower blood lipids.
Background
Nutrition is a significant ecological stimulus associated with every aspect of health and lifespan [1]. Healthier food choices are associated with lowered risks of cancer [2], cardiovascular disease (CVD) [3], and death [4]. The intricate mechanisms that connect diet and disease are multifaceted and may be related to the process of aging, such as epigenetic modifications [5,6,7]. Additional investigation on the relationship between dietary intake and aging is imperative, which could potentially present novel outlooks on the association of nutrition with disease.
Various molecular and physiological markers of aging have been devised and explored in numerous species [8, 9]. The utilization of biological age as an indicator of disease risk and mortality is acknowledged to be more informative than chronological age [10, 11]. Biological age metrics developed based on age-related clinical indicators have proven to be reliable predictors of longevity, mortality, age-related diseases, comorbidities, and physical function impairment [12, 13], such as HD [14], AL [15], KDM [16], and PA [17]. Individuals with a higher biological age would be more susceptible to disease and experience accelerated aging [18].
One issue with past studies that predominantly investigated the associations of age acceleration metrics with specific nutrients or food items is that focusing exclusively on individual food components may disregard the broader beneficial effects of improving the overall diet [19, 20], since these constituents are consumed in combination and have interconnected associations [21, 22]. Although there are studies on dietary patterns and methylation-based measures of biological age [6], research centered on the biological age measures derived from age-related clinical parameters outcomes is scarce, potentially constraining the ability to compare and comprehensively analyze the findings regarding diet and aging, undermining the scientific validity of studies, and impeding a thorough comprehension of the impact of diet on aging.
We proposed that improved dietary quality, as determined by various recommended guidelines, would be associated with decreased age acceleration, which is intended as a prognosticator of mortality. Here, we evaluated how different dietary indices (DII, DASH, aMED, and HEI-2015) were related to biological aging indicators (HD, KDM, PA, and AL) using data from the 1999–2018 NHANES.
Methods
Study population
NHANES is an extensive, nationwide survey performed by the National Center for Health Statistics (NCHS) to capture an exact analysis of the health and nutritional state of Americans [23]. We utilized cross-sectional data from NHANES 1999–2018 that encompassed 16,666 individuals who satisfied the following criteria: non-pregnant adults (n = 57,540), standard daily energy intake (800–4200 kcal/d for male and 500–3500 kcal/d for female) (n = 48,741) [24], possess all the components of biological ages (n = 35,367), and dietary score components are complete (n = 16,666) (Fig. 1).
Assessment of dietary metrics
Dietary information was obtained through a 24-hour dietary recall for two days that were not consecutive and computed following the guidelines provided by the U.S. Department of Agriculture’s Food and Nutrient Database for Dietary Studies [25]. Using the standardized algorithms for dietary index calculations, 4 dietary metrics (DII, DASH, aMED, and HEI-2015) were computed based on the dietary intake data. Access to the corresponding algorithms and R code for the package ‘dietaryindex’ was made available at https://github.com/jamesjiadazhan/dietaryindex. Dietaryindex is a flexible and validated tool that enables standardized calculations of dietary indices in epidemiological and clinical studies [26]. The calculation process consists of two steps: initially determining the serving size for each food and nutrient category, and then computing individual dietary indexes. In developing the DII, forty-five components identified as having inflammatory properties were included (Supplementary Table 1) [27, 28]. After adding up the scores for each component, the DII produces a single score, where a higher value suggests that the diet has a greater potential for inflammation. Comprising numerous foods and nutrients known to be connected to hypertension [29], the DASH eating plan recommends the consumption of fruits, vegetables, nuts/legumes/vegan protein, whole grains, low-fat dairy, and limiting the intake of sodium, red or processed meats, and sugar-sweetened drinks (Supplementary Table 2). The aMED encompasses 9 components associated with lower risks of chronic diseases [30, 31], including fruits, vegetables, nuts, legumes, whole grains, fish, red and processed meat, the ratio of monounsaturated fat to saturated fat, and alcohol intake (Supplementary Table 3). The HEI-2015 serves as a summary measure of compliance with the USDA 2015–2020 Dietary Guidelines for Americans [32], assessing the consumption of total fruits, whole fruits, total vegetables, greens and beans, total protein food, seafood and plant protein, whole grain, dairy, fatty acids, refined grain, sodium, added sugar, and saturated fat (Supplementary Table 4).
Assessment of biological ages
Using twelve clinical markers (Supplementary Tables 5–8), HD, KDM, and PA were calculated. The algorithms were first calibrated utilizing NHANES 1988–1994 (NHANES III) and originally described by Nakazato et al. [33], Klemera et al. [12], and Levine et al. [13], respectively. The corresponding code was available through the R package “BioAge” at https://github.com/dayoonkwon/BioAge. The Mahalanobis distance metric was used by HD to quantify the difference between an individual’s clinical measurements and the reference established from a young and healthy population [34]. Conducting regression analyses between particular biomarkers and chronological age in the reference population enabled the measurement of KDM [35]. Through the application of elastic-net Gompertz regression, PA was computed by analyzing a range of factors related to mortality risks [36]. AL captured the cumulative effects of chronic stress and life events, and its measurement involved examining the proportion of biomarker levels indicating an increased risk for disease [37]. In our research, we assigned the risk level by identifying individuals in the top quartile of the distribution for eleven of the twelve biomarkers. For albumin, those who fall into the lowest quartile were deemed at risk, as supported by prior research findings [15]. The AL, with values between 0 and 1, denoted the ratio of biomarkers identified as “at risk”.
Assessment of biological aging
To gauge disparities in biological aging, those whose KDM or PA exceeded their chronological age were regarded as experiencing accelerated aging [38]. Participants with higher HD or AL, categorized into groups based on their medians, were deemed to undergo accelerated aging [18].
Assessment of mediation variables
Combining the levels of triglyceride (TG) and high-density lipoprotein cholesterol (HDL-C) [39], AIP was calculated by mathematically deriving it from lg[TG(mmol/L)/HDL-C(mmol/L)] [40]. The SII, a novel inflammation index, was determined by dividing the multiplication of the platelet count and neutrophil count by the lymphocyte count [41].
Assessment of covariates
Information on age, sex, and race was obtained through a standardized questionnaire. Weight in kilograms divided by the square of height in meters yielded the calculation of body mass index (BMI). Data on smoking, drinking, physical activity, education, annual household income, and dietary supplement usage were acquired through questionnaires specifically designed for each. Physical activity was assessed by determining the metabolic equivalent scores for weekly recreational activities, and regular exercise was defined as engaging in a minimum of 150 min of moderate to high-intensity physical activity per week [42]. Information on cancer, CVD, hypertension, and diabetes was collected through individual interviews using a standardized medical condition questionnaire. The definition of chronic kidney disease involves estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73m2 and albumin to creatinine ratio (ACR) ≥ 30 mg/g [43]. A missing indicator category has been used to code covariates with missing data in categorical variables (missing values: smoking n = 387, drinking n = 993, exercise n = 23, income n = 820, nutrient supplement use n = 6, self-reported cardiovascular diseases n = 904, self-reported cancer n = 747, self-reported hypertension n = 21, self-reported diabetes n = 381, and chronic kidney disease n = 2,358).
Statistical analyses
Consultation of the NHANES analytic guidelines ensured proper consideration of sample weights, stratification, and clustering. For continuous variables, baseline characteristics were displayed as means (95% CI), while for categorical variables, they were listed as percentages (n). Multivariate linear regression models were utilized to analyze the associations of different dietary indexes with SII or AIP and the relationships between SII or AIP and biological age indicators. Associations of DII, DASH, aMED, and HEI-2015 with biological age indicators were examined through binomial logistic regression models. The associations between different dietary indexes and biological age indicators were then modeled using RCS regression. The effects of mediation were evaluated using mediation analysis. Several analyses were conducted according to the stratified variable: age (> 60/≤ 60), sex (male/female), race (non-hispanic white/others), BMI (< 30/≥ 30), smoking (yes/no), drinking (yes/no), regular exercise (yes/no), education (above college/others)), income (< $55,000/≥ $55,000), dietary supplements use (yes/no), and self-reported hypertension (yes/no). R 4.1.1 was utilized to perform all the analyses. A significance level of less than 0.05 was used to determine the statistical significance, employing a two-tailed P-value threshold.
Results
Baseline characteristics
The detailed characteristics of participants according to different dietary indexes were presented in Table 1 and Supplementary Tables 9–11. Participants with lower DII or higher DASH, aMED, and HEI-2015 were more likely to be older, non–drinkers, and regular exercisers; have higher education, annual household income, dietary supplement usage, KDM, and PA; as well as lower BMI, SII, AIP, HD, and AL.
Associations of different dietary indexes with SII and AIP
Positive correlations were observed between DII and SII (β 48.28, 95% CI 30.09–66.47) and AIP (β 0.04, 95% CI 0.02–0.06), whereas negative associations were found between DASH, aMED, HEI-2015 and SII and AIP (Supplementary Table 12). In comparison to the lowest quartiles, the beta estimates for SII concerning DASH, aMED, and HEI-2015 were − 52.84 (-69.95, -35.73), -48.86 (-68.15, -29.57), and − 46.49 (-62.97,1–30.00), respectively. There was a positive correlation between AIP and DASH − 0.04 (-0.06, -0.02), aMED − 0.05 (-0.07, -0.02), and HEI-2015 -0.05 (-0.08, -0.03) (Supplementary Tables 13–15).
Associations of SII and AIP with biological age indicators
In terms of SII, participants within the top quartile exhibited a higher HD (β 0.09, 95% CI 0.05–0.13), AL (β 0.01, 95% CI 0.00–0.02), KDM (β 2.36, 95% CI 1.92–2.81), and PA (β 3.95, 95% CI 3.69–4.21) (Supplementary Table 16). AIP also showed positive associations with HD (β 0.17, 95% CI 0.12–0.21), AL (β 0.06, 95% CI 0.05–0.07), KDM (β 2.40, 95% CI 1.93–2.87), and PA (β 1.56, 95% CI 1.25–1.86) (Supplementary Table 17).
Associations of different dietary indexes with biological age indicators
Consistent with expectations, different dietary indexes were meaningfully correlated with measures of biological age (Fig. 2, Supplementary Tables 18–21). In the case of DII, individuals in the top 25% displayed a greater propensity to possess higher HD (OR 1.25, 95% CI 1.08–1.45), AL (OR 1.29, 95% CI 1.11–1.50), KDM (OR 1.34, 95% CI 1.08–1.65), and PA (OR 1.61, 95% CI 1.39–1.87) (Supplementary Table 18). DASH demonstrated significant associations with HD (OR 0.85, 95% CI 0.73–0.97), AL (OR 0.64, 95% CI 0.54–0.75), KDM (OR 0.68, 95% CI 0.54–0.85), and PA (OR 0.50, 95% CI 0.42–0.59) (Supplementary Table 19). HEI-2015 also indicated connections to HD (OR 0.84, 95% CI 0.74–0.96), AL (OR 0.70, 95% CI 0.59–0.82), KDM (OR 0.71, 95% CI 0.58–0.87), and PA (OR 0.51, 95% CI 0.44–0.58) (Supplementary Table 20), whereas aMED displayed statistically significant patterns solely with AL (OR 0.61, 95% CI 0.52–0.72), KDM (OR 0.62, 95% CI 0.50–0.76), and PA (OR 0.64, 95% CI 0.54–0.76) (Supplementary Table 21).
Best-fitting dose-response curves of the associations between different dietary indexes and biological age indicators
As depicted in Fig. 3, DII exhibited both roughly linear and non-linear relationships with accelerated HD, AL, KDM, and PA (all Poverall < 0.001). With the exclusion of PA (Poverall < 0.001, Pnonlinearity < 0.031), DASH revealed linear connections with HD, AL, and KDM (all Poverall < 0.001, all Pnonlinearity > 0.050). Similarly, aMED and HEI-2015 showed monotonic linear associations with lower risks of higher HD, AL, KDM, and PA (all Poverall < 0.001, Pnonlinearity > 0.050).
Mediation effects of SII and AIP on the associations between different dietary indexes and biological age indicators
Our results revealed significant associations between diverse dietary indexes and SII and AIP, as well as between SII and AIP with biological age indicators, prompting us to explore the mediation effects in-depth to uncover the underlying relationship (Fig. 4). The total effects of DII on HD (βTot = 0.08, P < 0.001), AL (βTot = 0.06, P < 0.001), KDM (βTot = 0.07, P < 0.001), and PA (βTot = 0.06, P < 0.001) were demonstrated. Quantitative measures based on standardized regression coefficients revealed the total effects of DASH on HD (βTot = -0.02, P < 0.01), AL (βTot = -0.05, P < 0.001), KDM (βTot = -0.13, P < 0.001), and PA (βTot = -0.16, P < 0.001). The total effects of aMED and HEI-2015 on HD (βTot = -0.03, P < 0.001 and βTot = < -0.01, P > 0.05), AL (βTot = -0.04, P < 0.001 and βTot = -0.03, P < 0.01), KDM (βTot = -0.08, P < 0.001 and βTot = -0.12, P < 0.001), and AL (βTot = -0.10, P < 0.001 and βTot = -0.14, P < 0.001) were assessed after controlling for the covariates in the comprehensive RCS regression model. The percentage contributions of the indirect effects of DII on HD, KDM, PA, and AL mediated by SII and AIP were evaluated to be 5.28, 4.45, 4.59, 6.33, 6.11, 3.58, 16.57, and 7.62%, respectively. The proportions of the total effects of DASH and aMED on HD (29.02, 23.49, 17.17, 35.37%), AL (9.87, 13.12, 8.9, 29.20%), KDM (5.16, 2.85, 6.44, 9.04%), and PA (9.31, 4.07, 10.33, 11.62%) were attributed to the indirect effects mediated by SII and AIP. SII and AIP-mediated indirect effects also accounted for the specific proportions of the total effects of HEI-2015 on AL (19.00, 36.31%), KDM (6.24, 4.98%), and PA (11.51, 7.34%).
Associations of different dietary indexes with biological age indicators stratified by the factors under consideration
When performing comprehensive stratified analyses based on the factors under consideration (Supplementary Tables 22–37), comparable significant associations were observed among participants consuming dietary supplements (Pinteraction < 0.05). The relationships were also found to be strengthened among drinkers, highly educated individuals, and participants with hypertension, although the lack of statistical significance (Pinteraction > 0.05).
Discussion
During our relatively large, national cross-sectional research of the general Americans, we observed that dietary patterns showed significant associations with 4 biological age metrics utilized as predictors of chronological age, and displayed specific dose-response patterns. Exhibiting a higher dietary inflammatory potential was related to a minimum of a 25% elevated risk of larger HD, AL, KDM, and PA. Meanwhile, individuals exhibiting higher DASH, aMED, and HEI-2015 experienced at least a 12% reduced risk of accelerated HD, AL, and KDM, PA. The associations of diet quality with biological aging were partly mediated by SII and AIP, underscoring the vital importance of a moderate diet in anti-inflammatory and lipid-lowering. Stratified analyses indicated that the relationships differed according to dietary supplement use, primarily due to the lack of robustness in those who do not consume.
This research was centered around the associations of well-established dietary indices with biological age metrics developed based on age-related clinical indicators, which may deliver more comprehensive assessments and insights into how food intake and nutrient consumption are associated with biological aging [44]. After all, focusing exclusively on individual food components may disregard the broader beneficial effects of improving the overall diet since these constituents are consumed in combination and have interconnected associations [21, 22]. Epigenetic clocks, relying on DNA methylation at specific locations, are developed to predict disease risk and death, and are utilized to determine the impact of various factors on biological aging [45, 46]. The extent of attrition on telomeres, DNA sequences situated at the termini of chromosomes that aid in preserving genome stability [47], can be employed to investigate the processes that detrimentally affect health and lifespan, such as inflammation, oxidative stress, and dietary intake [48,49,50]. Biological age metrics developed based on age-related clinical indicators, which are reliable predictors of longevity, mortality, and age-related diseases, have also been used to assess potential factors associated with biological aging [12, 13].
Previous research examining the association between diet and biological aging has primarily centered on specific diets and nutrients, biological age indices derived from methylation, or telomere length [6, 51,52,53,54,55,56,57]. The consistent results of our study coincided with prior investigations employing one or multiple of the biological age markers examined in this research, which indicated that the intake of α-tocopherol, vitamin K, and caloric restriction were related to decreased instances of accelerated aging [51,52,53]. The strongest relationships have been observed between higher quality diets (measured using DASH, HEI-2015, and aMED) and methylation-derived assessments of biological age (including Hannum AgeAccel, Horvath AgeAccel, PhenoAgeAccel, and GrimAgeAccel), which reinforces the significant associations between diet quality and longevity [6, 54, 55]. In addition, multiple nutritional patterns and habits, like the Mediterranean diet, exhibit a significant correlation with extended leukocyte telomere length (LTL) [56, 57]. The consistent observations of our research were in line with prior investigations carried out among NHANES 2003–2014, which indicated anti-inflammatory and antioxidant dietary intake was inversely associated with accelerated HD, PA, KDM, and AL [58].
Furthermore, another revelation from our study was the advantageous effects of healthier dietary patterns on SII and AIP, both of which were interconnected with the intricate aging mechanism. As a significant risk factor with the potential to accelerate and exacerbate aging besides chronological age, inflammation could induce cellular impairment and organ injury [59, 60]. Lipids in the bloodstream are indispensable for cellular metabolism [61], and dyslipidemia is a recognized hazard for age-related diseases [61]. There are reports of research suggesting an association between dyslipidemia and its lipid markers, particularly HDL-C, and alterations in telomere length [61]. As an uncomplicated and workable approach, dietary adjustment could help to regulate inflammation and dyslipidemia [62, 63]. The relationship of a rational diet with the body’s reactivity to both internal and external changes implied possible health perks [62]. Based on our findings, it can be inferred that there exist mediation effects of SII and AIP on the associations of healthier dietary patterns with biological aging, highlighting the potential of a balanced diet as a preventive strategy against aging via the modulation of inflammation and blood lipids. Nevertheless, more research is needed to assess alternative mechanisms connecting a balanced diet to the deceleration of aging.
The stratified analyses that explored the potential modifying impacts of the factors generally considered to be crucial in examining health-related issues especially emphasized the decelerated PA, KDM and decreased HD, AL among participants using dietary supplements. In addition to dietary intervention, an expanding body of research has demonstrated the potential for nutritional supplements to be a viable intervention in a comprehensive health-promoting strategy [64], which may be composed of diverse components with immunomodulatory, antioxidant, and specific nutritional properties [65]. Dietary supplement utilization tends to be elevated in pregnant or lactating women, athletes, individuals with dietary limitations, individuals leading busy or highly stressed lifestyles, and individuals with specific medical conditions [66,67,68]. Our study findings suggest that individuals consuming dietary supplements were more prone to being older, having chronic kidney disease (CKD), cancer, CVD, diabetes, and exhibiting higher levels of HD, AL, KDM, PA, as well as certain age-related clinical markers (Supplementary Tables 38–39). Therefore, it is plausible to assume that those with suboptimal health conditions can attain enhanced anti-aging benefits by incorporating dietary supplements into their regimen and complementing them with a higher-quality dietary intervention.
Moreover, besides inflammation and lipid regulation, there is still a great deal unelucidated about the biological pathways through which various dietary patterns might be associated with the aging process. Even though aging may be affected by diabetes and CVD [69, 70], the fundamental connection between quality meals and decelerated aging remained unchanged even when considering these chronic conditions. Given that it is a common epigenetic modification, the pattern and degree of DNA methylation have been validated to be associated with cellular aging as well as age-related diseases [55]. The consumption of certain foods and specific nutrients can bring about changes in DNA methylation through modulation of enzyme activity or modification of substrates and coenzymes [71], such as omega-3 polyunsaturated fatty acids (PUFAs) [46], and vegetables [19]. The microbiome functions as a modulator of healthy aging by transducing environmental signals, conditioning host immunity and metabolism, and modifying the vulnerability to age-related diseases [72]. Gut microbial communities exhibit specificity to their hosts, co-evolve alongside their hosts, and are shaped by dietary habits [73]. To be specific, a diverse range of dietary choices is associated with the stability of the microbiome [74], and meals that are low in fat and high in fiber can have a positive impact on the health of the gut microbiome by increasing the number of probiotic bacteria [75].
The primary features of our study were the substantial sample size that accurately represented the population across the United States, the application of multiple accelerated aging measures, and comprehensive data on various covariates and clinical outcomes. Nevertheless, several notable constraints should be taken into account when interpreting our findings. Initially, this research was conducted in a cross-sectional manner, meaning that both diet and age acceleration were evaluated at the beginning of the study. Consequently, some misclassification was inevitable, and any modifications or persistent dietary routines may not have been fully captured. Although the health benefits of higher-quality diets were widely recognized, there was evidence indicating that unhealthy conditions were associated with reduced compliance with healthy eating [76]. Constrained by the nature of cross-sectional studies, we could not ascertain causation from the observed findings. Considering the lack of comprehensive and detailed food component data from dietary supplements, we excluded this portion of the data from our calculations of various dietary indexes, potentially resulting in a certain difference between our estimates and the actual exposure of individuals. Moreover, when it comes to HD and AL, we stratified individuals with an increased vulnerability to homeostatic disorder or health strain using the median as a cutoff, potentially influencing the precision of age-related risk assessment through the absence of information. Lastly, despite our exploration of various eating patterns, we encountered consistent associations of different dietary indexes with biological age indicators, complicating the determination of the superiority of particular dietary patterns.
Conclusions
Findings derived from our relatively sizable, countrywide, cross-sectional study demonstrated the associations of healthier eating patterns with the deceleration of biological aging, with SII and AIP serving as mediating factors. Notably, this relationship appeared to be more pronounced among individuals who incorporated dietary supplements into their routines. Future research is imperative to confirm our findings, including research with larger cohorts, research encompassing individuals from diverse racial and ethnic backgrounds, investigations into the biological mechanisms involved, and examinations of the causal relationship between dietary factors and biological aging.
Data availability
No datasets were generated or analysed during the current study.
Abbreviations
- NHANES:
-
National Health and Nutrition Examination Survey
- DII:
-
Dietary inflammatory index
- DASH:
-
Dietary approaches to stop hypertension index
- aMED:
-
Alternate mediterranean diet score
- HEI-2015:
-
Healthy eating index-2015
- HD:
-
Homeostatic dysregulation
- AL:
-
Allostatic load
- KDM:
-
Klemera–Doubal method
- PA:
-
Phenotypic age
- RCS:
-
Restricted cubic spline
- ORs:
-
Odds ratios
- SII:
-
Systemic immune inflammation index
- AIP:
-
Atherogenic index of plasma
- CVD:
-
Cardiovascular disease
- NCHS:
-
National Center for Health Statistics
- TG:
-
Triglyceride
- HDL-C:
-
High-density lipoprotein cholesterol
- BMI:
-
Body mass index
- eGFR:
-
Estimated glomerular filtration rate
- ACR:
-
Albumin to creatinine ratio
- LTL:
-
Leukocyte telomere length
- CKD:
-
Chronic kidney disease
- PUFAs:
-
Polyunsaturated fatty acids
References
Rakyan VK, Down TA, Balding DJ, Beck S. Epigenome-wide association studies for common human diseases. Nat Rev Genet. 2011;12(8):529–41.
Schwingshackl L, Hoffmann G. Adherence to Mediterranean diet and risk of cancer: a systematic review and meta-analysis of observational studies. Int J Cancer. 2014;135(8):1884–97.
Park YM, Steck SE, Fung TT, Zhang J, Hazlett LJ, Han K, et al. Mediterranean diet, Dietary approaches to stop hypertension (DASH) style diet, and metabolic health in U.S. adults. Clinical nutrition (Edinburgh. Scotland). 2017;36(5):1301–9.
Park YM, Choi MK, Lee SS, Shivappa N, Han K, Steck SE, et al. Dietary inflammatory potential and risk of mortality in metabolically healthy and unhealthy phenotypes among overweight and obese adults. Clin Nutr. 2019;38(2):682–8.
Park LK, Friso S, Choi SW. Nutritional influences on epigenetics and age-related disease. The Proceedings of the Nutrition Society. 2012;71(1):75–83.
Kresovich JK, Park YM, Keller JA, Sandler DP, Taylor JA. Healthy eating patterns and epigenetic measures of biological age. Am J Clin Nutr. 2022;115(1):171–9.
Zeisel SH. Epigenetic mechanisms for nutrition determinants of later health outcomes. Am J Clin Nutr. 2009;89(5):s1488–93.
Xia X, Wang Y, Yu Z, Chen J, Han JJ. Assessing the rate of aging to monitor aging itself. Ageing Res Rev. 2021;69:101350.
López-Otín C, Blasco MA, Partridge L, Serrano M, Kroemer G. The hallmarks of aging. Cell. 2013;153(6):1194–217.
Khan SS, Singer BD, Vaughan DE. Molecular and physiological manifestations and measurement of aging in humans. Aging Cell. 2017;16(4):624–33.
Ferrucci L, Gonzalez-Freire M, Fabbri E, Simonsick E, Tanaka T, Moore Z, et al. Measuring biological aging in humans: a quest. Aging Cell. 2020;19(2):e13080.
Klemera P, Doubal S. A new approach to the concept and computation of biological age. Mech Ageing Dev. 2006;127(3):240–8.
Levine ME, Lu AT, Quach A, Chen BH, Assimes TL, Bandinelli S, et al. An epigenetic biomarker of aging for lifespan and healthspan. Aging. 2018;10(4):573–91.
Cohen AA, Milot E, Yong J, Seplaki CL, Fülöp T, Bandeen-Roche K, et al. A novel statistical approach shows evidence for multi-system physiological dysregulation during aging. Mech Ageing Dev. 2013;134(3–4):110–7.
Shirazi TN, Hastings WJ, Rosinger AY, Ryan CP. Parity predicts biological age acceleration in post-menopausal, but not pre-menopausal, women. Sci Rep. 2020;10(1):20522.
Earls JC, Rappaport N, Heath L, Wilmanski T, Magis AT, Schork NJ et al. Multi-omic Biological Age Estimation and its correlation with Wellness and Disease phenotypes: a longitudinal study of 3,558 individuals. The journals of gerontology Series A, Biological sciences and medical sciences. 2019;74(Suppl_1):S52–60.
Kuo CL, Pilling LC, Liu Z, Atkins JL, Levine ME. Genetic associations for two biological age measures point to distinct aging phenotypes. Aging Cell. 2021;20(6):e13376.
Chaney C, Wiley KS. The variable associations between PFASs and biological aging by sex and reproductive stage in NHANES 1999–2018. Environ Res. 2023;227:115714.
Quach A, Levine ME, Tanaka T, Lu AT, Chen BH, Ferrucci L, et al. Epigenetic clock analysis of diet, exercise, education, and lifestyle factors. Aging. 2017;9(2):419–46.
Dugué PA, Bassett JK, Joo JE, Baglietto L, Jung CH, Wong EM, et al. Association of DNA methylation-based Biological Age with Health Risk factors and overall and cause-specific mortality. Am J Epidemiol. 2018;187(3):529–38.
Milte CM, McNaughton SA. Dietary patterns and successful ageing: a systematic review. Eur J Nutr. 2016;55(2):423–50.
Schwenke DC. Dietary patterns to promote healthy aging. Curr Opin Lipidol. 2020;31(4):260–1.
Johnson CL, Paulose-Ram R, Ogden CL, Carroll MD, Kruszon-Moran D, Dohrmann SM et al. National health and nutrition examination survey: analytic guidelines, 1999–2010. Vital and health statistics Series 2, Data evaluation and methods research. 2013(161):1–24.
Hou W, Han T, Sun X, Chen Y, Xu J, Wang Y, et al. Relationship between Carbohydrate Intake (Quantity, Quality, and Time Eaten) and mortality (Total, Cardiovascular, and diabetes): Assessment of 2003–2014 National Health and Nutrition Examination Survey participants. Diabetes Care. 2022;45(12):3024–31.
Bodner-Montville J, Ahuja JKC, Ingwersen LA, Haggerty ES, Enns CW, Perloff BP. USDA Food and Nutrient Database for Dietary studies: released on the web. J Food Compos Anal. 2006;19.
Zhan JJ, Hodge RA, Dunlop AL, Lee MM, Bui L, Liang D et al. Dietaryindex: A User-Friendly and Versatile R Package for Standardizing Dietary Pattern Analysis in Epidemiological and Clinical Studies. bioRxiv: the preprint server for biology. 2023.
Shivappa N, Steck SE, Hurley TG, Hussey JR, Hébert JR. Designing and developing a literature-derived, population-based dietary inflammatory index. Public Health Nutr. 2014;17(8):1689–96.
Tabung FK, Steck SE, Zhang J, Ma Y, Liese AD, Agalliu I, et al. Construct validation of the dietary inflammatory index among postmenopausal women. Ann Epidemiol. 2015;25(6):398–405.
Sacks FM, Obarzanek E, Windhauser MM, Svetkey LP, Vollmer WM, McCullough M, et al. Rationale and design of the Dietary approaches to stop hypertension trial (DASH). A multicenter controlled-feeding study of dietary patterns to lower blood pressure. Ann Epidemiol. 1995;5(2):108–18.
Estruch R, Ros E, Salas-Salvadó J, Covas MI, Corella D, Arós F et al. Retraction and Republication: Primary Prevention of Cardiovascular Disease with a Mediterranean Diet. N Engl J Med. 2013;368:1279-90. The New England journal of medicine. 2018;378(25):2441-2.
Sotos-Prieto M, Bhupathiraju SN, Mattei J, Fung TT, Li Y, Pan A, et al. Changes in Diet Quality scores and Risk of Cardiovascular Disease among US men and women. Circulation. 2015;132(23):2212–9.
Krebs-Smith SM, Pannucci TE, Subar AF, Kirkpatrick SI, Lerman JL, Tooze JA, et al. Update of the healthy eating index: HEI-2015. J Acad Nutr Dietetics. 2018;118(9):1591–602.
Nakazato Y, Sugiyama T, Ohno R, Shimoyama H, Leung DL, Cohen AA, et al. Estimation of homeostatic dysregulation and frailty using biomarker variability: a principal component analysis of hemodialysis patients. Sci Rep. 2020;10(1):10314.
Mahalanobis Distance. The Concise Encyclopedia of statistics. New York, NY: Springer New York; 2008. pp. 325–6.
Kwon D, Belsky DW. A toolkit for quantification of biological age from blood chemistry and organ function test data: BioAge. GeroScience. 2021;43(6):2795–808.
Levine ME. Modeling the rate of senescence: can estimated biological age predict mortality more accurately than chronological age? The journals of gerontology Series A, Biological sciences and medical sciences. 2013;68(6):667–74.
McEwen BS, Stellar E. Stress and the individual. Mechanisms leading to disease. Arch Intern Med. 1993;153(18):2093–101.
Gao X, Huang N, Guo X, Huang T. Role of sleep quality in the acceleration of biological aging and its potential for preventive interaction on air pollution insults: findings from the UK Biobank cohort. Aging Cell. 2022;21(5):e13610.
Yin B, Wu Z, Xia Y, Xiao S, Chen L, Li Y. Non-linear association of atherogenic index of plasma with insulin resistance and type 2 diabetes: a cross-sectional study. Cardiovasc Diabetol. 2023;22(1):157.
Dobiásová M, Frohlich J. The plasma parameter log (TG/HDL-C) as an atherogenic index: correlation with lipoprotein particle size and esterification rate in apob-lipoprotein-depleted plasma (FER(HDL)). Clin Biochem. 2001;34(7):583–8.
Hu B, Yang XR, Xu Y, Sun YF, Sun C, Guo W, et al. Systemic immune-inflammation index predicts prognosis of patients after curative resection for hepatocellular carcinoma. Clin cancer Research: Official J Am Association Cancer Res. 2014;20(23):6212–22.
Wang X, Ma H, Gupta S, Heianza Y, Fonseca V, Qi L. The joint secular trends of Sleep Quality and Diabetes among US adults, 2005–2018. J Clin Endocrinol Metab. 2022;107(11):3152–61.
Luo J, Hendryx M. Metal mixtures and kidney function: an application of machine learning to NHANES data. Environ Res. 2020;191:110126.
Hu FB. Dietary pattern analysis: a new direction in nutritional epidemiology. Curr Opin Lipidol. 2002;13(1):3–9.
Ma J, Rebholz CM, Braun KVE, Reynolds LM, Aslibekyan S, Xia R, et al. Whole blood DNA methylation signatures of Diet are Associated with Cardiovascular Disease Risk factors and all-cause mortality. Circulation Genomic Precision Med. 2020;13(4):e002766.
Lu AT, Quach A, Wilson JG, Reiner AP, Aviv A, Raj K, et al. DNA methylation GrimAge strongly predicts lifespan and healthspan. Aging. 2019;11(2):303–27.
Armanios M, Blackburn EH. The telomere syndromes. Nat Rev Genet. 2012;13(10):693–704.
Bountziouka V, Musicha C, Allara E, Kaptoge S, Wang Q, Angelantonio ED, et al. Modifiable traits, healthy behaviours, and leukocyte telomere length: a population-based study in UK Biobank. Lancet Healthy Longev. 2022;3(5):e321–31.
Paul L. Diet, nutrition and telomere length. J Nutr Biochem. 2011;22(10):895–901.
Codd V, Denniff M, Swinfield C, Warner SC, Papakonstantinou M, Sheth S, et al. Measurement and initial characterization of leukocyte telomere length in 474,074 participants in UK Biobank. Nat Aging. 2022;2(2):170–9.
Senior AM, Legault V, Lavoie FB, Presse N, Gaudreau P, Turcot V, et al. Multidimensional associations between nutrient intake and healthy ageing in humans. BMC Biol. 2022;20(1):196.
Dai L, Mafra D, Shiels PG, Hackeng TM, Stenvinkel P, Schurgers LJ. Vitamin K and Hallmarks of Ageing: Focus on Diet and Gut Microbiome. Nutrients. 2023;15(12).
Belsky DW, Huffman KM, Pieper CF, Shalev I, Kraus WE. Change in the rate of Biological Aging in response to caloric restriction: CALERIE Biobank Analysis. Journals Gerontol Ser Biol Sci Med Sci. 2017;73(1):4–10.
Kim Y, Huan T, Joehanes R, McKeown NM, Horvath S, Levy D, et al. Higher diet quality relates to decelerated epigenetic aging. Am J Clin Nutr. 2022;115(1):163–70.
Wright ML, Widen EM. Is high dietary quality the real fountain of youth? Am J Clin Nutr. 2022;115(1):6–7.
Bountziouka V, Nelson CP, Wang Q, Musicha C, Codd V, Samani NJ. Dietary patterns and practices and leucocyte telomere length: findings from the UK Biobank. J Acad Nutr Dietetics. 2023;123(6):912–e2226.
Canudas S, Becerra-Tomás N, Hernández-Alonso P, Galié S, Leung C, Crous-Bou M, et al. Mediterranean Diet and Telomere length: a systematic review and Meta-analysis. Advances in nutrition (Bethesda. Md). 2020;11(6):1544–54.
Wang X, Sarker SK, Cheng L, Dang K, Hu J, Pan S, et al. Association of dietary inflammatory potential, dietary oxidative balance score and biological aging. Clinical nutrition (Edinburgh. Scotland). 2024;43(1):1–10.
Fang J, Seki T, Maeda H. Therapeutic strategies by modulating oxygen stress in cancer and inflammation. Adv Drug Deliv Rev. 2009;61(4):290–302.
Grivennikov SI, Greten FR, Karin M. Immunity, inflammation, and cancer. Cell. 2010;140(6):883–99.
Liu X, Ma T, Yang C, Li J, Zhang Y, Zhao Y. Persistent dyslipidemia increases the longitudinal changes in telomere length. Lipids Health Dis. 2023;22(1):173.
Longo VD, Anderson RM. Nutrition, longevity and disease: from molecular mechanisms to interventions. Cell. 2022;185(9):1455–70.
Iddir M, Brito A, Dingeo G, Fernandez Del Campo SS, Samouda H, La Frano MR et al. Strengthening the Immune System and reducing inflammation and oxidative stress through Diet and Nutrition: considerations during the COVID-19 Crisis. Nutrients. 2020;12(6).
Choi H. Healthy lifestyles and more life years without dementia. BMJ (Clinical Res ed). 2022;377:o885.
Zaman R, Ravichandran V, Tan CK. Role of dietary supplements in the continuous battle against COVID-19. Phytotherapy research: PTR. 2024.
Kim CS, Cha L, Sim M, Jung S, Chun WY, Baik HW et al. Probiotic supplementation improves cognitive function and Mood with changes in Gut Microbiota in Community-Dwelling older adults: a Randomized, Double-Blind, Placebo-Controlled, Multicenter Trial. The journals of gerontology Series A, Biological sciences and medical sciences. 2021;76(1):32–40.
Dalle S, Van Roie E, Hiroux C, Vanmunster M, Coudyzer W, Suhr F, et al. Omega-3 supplementation improves isometric strength but not muscle anabolic and Catabolic Signaling in response to Resistance Exercise in healthy older adults. Journals Gerontol Ser Biol Sci Med Sci. 2021;76(3):406–14.
Azhar G, Wei JY, Schutzler SE, Coker K, Gibson RV, Kirby MF et al. Daily consumption of a specially formulated essential amino acid-based Dietary supplement improves physical performance in older adults with low physical functioning. The journals of gerontology Series A, Biological sciences and medical sciences. 2021;76(7):1184–91.
Lu FP, Lin KP, Kuo HK. Diabetes and the risk of multi-system aging phenotypes: a systematic review and meta-analysis. PLoS ONE. 2009;4(1):e4144.
Peng S, Shen T, Liu J, Tomlinson B, Sun H, Chen X, et al. Uncontrolled hypertension increases with age in an Older Community-Dwelling Chinese Population in Shanghai. Aging Disease. 2017;8(5):558–69.
Kadayifci FZ, Zheng S, Pan YX. Molecular mechanisms underlying the link between Diet and DNA methylation. Int J Mol Sci. 2018;19(12).
Ghosh TS, Shanahan F, O’Toole PW. The gut microbiome as a modulator of healthy ageing. Nat Reviews Gastroenterol Hepatol. 2022;19(9):565–84.
DeJong EN, Surette MG, Bowdish DME. The gut microbiota and unhealthy aging: disentangling cause from Consequence. Cell Host Microbe. 2020;28(2):180–9.
Johnson AJ, Vangay P, Al-Ghalith GA, Hillmann BM, Ward TL, Shields-Cutler RR, et al. Daily Sampling reveals personalized Diet-Microbiome associations in humans. Cell Host Microbe. 2019;25(6):789–e8025.
Zmora N, Suez J, Elinav E. You are what you eat: diet, health and the gut microbiota. Nat Reviews Gastroenterol Hepatol. 2019;16(1):35–56.
Patino-Alonso MC, Recio-Rodríguez JI, Belio JF, Colominas-Garrido R, Lema-Bartolomé J, Arranz AG, et al. Factors associated with adherence to the Mediterranean diet in the adult population. J Acad Nutr Dietetics. 2014;114(4):583–9.
Acknowledgements
The authors would like to acknowledge and thank all the individuals participating in NHANES 1999-2018 for their invaluable contributions.
Funding
The National Natural Science Foundation (82030100) and the National Key Research and Development Program of China (2022YFC2010100) provided funding for all authors involved in the study.
Author information
Authors and Affiliations
Contributions
X.W., X.Y., and Y.L. were led the design and the main contributors to the final content. X.W., X.Y., J.Z., and K.D. offered statistical assistance and conducted the data analysis. X.W., L.C., X.Q., P.S., and X.X. drafted the paper. All authors contributed to revising the manuscript and examined and endorsed the final manuscript.
Corresponding author
Ethics declarations
Ethics approval and consent to participate
The study received ethical clearance from the Institutional Review Board of NCHS, and all subjects agreed to participate after signing the required documentation.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
About this article
Cite this article
Wang, X., Yan, X., Zhang, J. et al. Associations of healthy eating patterns with biological aging: national health and nutrition examination survey (NHANES) 1999–2018. Nutr J 23, 112 (2024). https://doi.org/10.1186/s12937-024-01017-0
Received:
Accepted:
Published:
DOI: https://doi.org/10.1186/s12937-024-01017-0