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The effect of healthy eating index-2015 in the associations of biological aging and non-alcoholic fatty liver disease: an interaction and mediation analysis

Journal of Health, Population and Nutrition volume 44, Article number: 18 (2025) Cite this article

Abstract

Background

The present study explored the association between biological aging (BA), healthy eating index-2015 (HEI-2015) and non-alcoholic fatty liver disease (NAFLD) in the general population of the United States.

Methods

We used data from the NHANES database between 2017–2018 years to conduct the study. Weighted multivariable logistic regression analysis, restricted cubic spline (RCS), and subgroup analysis were performed to analyze the association of BA and HEI-2015 with prevalence of NAFLD and the mediation effect of HEI-2015 was also discussed. Additionally, generalized additive model was conducted to investigate the association of BA and HEI-2015 with ZJU index, BARD score, and NAFLD fibrosis score.

Results

There was a total of 2,421 individuals. RCS shown that BA was positively correlated with prevalence of NAFLD, while HEI-2015 was negative correlated with NAFLD risk. After adjusting for interfering factors, compared with the lowest quartiles of BA and HEI-2015, the odds ratios with 95% confidence intervals for NAFLD across the quartiles were (1.24 (0.84, 1.84), 2.07 (1.15, 3.73) and 2.49 (1.16, 5.38)) and (0.89 (0.66, 1.18), 0.87 (0.65, 1.16) and 0.64 (0.46, 0.87)), respectively. The BA was linear positive with ZJU index, BARD score and NAFLD fibrosis score. However, the linear negative correlation existed between HEI-2015 and ZJU index, BARD score and NAFLD fibrosis score. Mediation analysis showed that the positive correlation between BA and NAFLD could be mediated and weakened by HEI-2015.

Conclusions

The prevalence of NAFLD gradually increases with BA, but this positive association can be weakened by the healthy diet.

Introduction

As one of the most prevalent liver diseases worldwide, non-alcoholic fatty liver disease (NAFLD) can be classified histologically into non-alcoholic fatty liver or non-alcoholic steatohepatitis [1]. Due to changes in lifestyle, the number of patients with NAFLD has increased in recent years, which has also become a public health concern that cannot be ignored [2]. Worldwide, it is estimated that 1.7 billion people suffer from non-alcoholic fatty liver disease [3]. As well as developing into liver-specific complications such as cirrhosis and liver cancer, NAFLD is closely related to a wide variety of extrahepatic complications, including chronic kidney disease, cardiovascular disease, and malignant tumours, which will lead to increased medical costs and decreased patient quality of life [4]. At present, the pathogenesis of NAFLD is not clear, and there is a lack of treatment drugs [5].

Aging is a complex biological process that involves multiple timescales of cells, tissues, and organs [6]. However, due to individual differences, aging progresses differently for each person. The process of aging is typically marked by biological aging (BA), which is affected by mental state, environmental factors, genetic, and lifestyle habits [7, 8]. Aging and cellular senescence are a vital risk factor for the occurrence of NAFLD and promote the progression of NAFLD through multifactorial mechanisms [9]. It is well known that unhealthy eating habits can lead to chronic diseases [10]. In accordance with federal dietary guidance, the Healthy Eating Index (HEI) can be used to measure the quality of an American’s diet and applies to any group of foods [11]. The HEI-2010 is based on the dietary guidelines for Americans 2010 and consists of 12 components that add up to a total score of 100 points [12]. However, the latest HEI-2015 consists of 13 dietary components. With HEI-2015, nutrition-related interventions in different populations can be assessed [13]. The primary purpose of HEI is to encourage healthy eating among the general population [14]. A healthy lifestyle, including dietary recommendations, plays an essential role in the treatment of NAFLD [15]. According to previous studies, E. prolifera polysaccharides diminish non-alcoholic fatty liver disease in rats on a high-fat diet [16]. In addition, a high-calorie, low-fibre diet also is closely linked to obesity to cause NAFLD [17]. It has been also found that diets rich in saturated fatty acids can lead to metabolic disorders, coronary heart disease, and NAFLD [18]. There is increasing evidence that high HEI scores (which correspond to a healthy eating pattern) reduce chronic disease risk [19]. Current research has not been able to determine whether the BA and HEI-2015 are associated with NAFLD risk in the general United States (US) population. Considering the harmful effects of NAFLD, early recognition of risk factors and the development of appropriate preventive measures to reduce the incidence. Thus, we analysed data from the Nutrition and Health Examination Survey (NHANES) 2017–2018 to determine if BA, HEI-2015 were associated with prevalence of NAFLD.

Material and methods

Study population

An annual nationwide health and nutrition assessment survey called the National Health and Nutrition Examination Survey (NAHNES) was implemented for the first time in 1999 to collect data via interviews and physical examinations in order to evaluate the health and nutritional status of adults and children in the US (https://www.cdc.gov/nchs/nhanes/) [20]. In this study, the NHANES data from 2017 to 2018 years was used for the analysis. Firstly, we excluded 3,306 participants without NAFLD data and 499 subjects without HEI-2015 data from the total sample (n = 9,254). Moreover, 3,028 participants without ZJU index, BARD score, or NAFLD fibrosis score also were excluded. In total, 2,421 individuals were included in this study (Fig. 1). Every participant in the NHANES study signed an informed consent form. Additionally, the study was authorized and approved by the Research Ethics Review Committee of the National Center for Health Statistics [21].

Fig. 1
figure 1

Flow chart of this study. NAFLD, non-alcoholic fatty liver disease, HEI−2015, healthy eating index, NHANES, National Health and Nutrition Examination Surveys

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Covariates

In the study, the following covariates were considered: demographic data (age, sex (male/female), race/ethnicity (Mexican American/Non-Hispanic White/Other Hispanic/Non-Hispanic Black/Other Race), family poverty income ratio (PIR), education level (less than high school/high school/more than high school), marital status (having a partner/no partner/unmarried)), complication (hypertension and diabetes mellitus (DM)), body measurement index (body mass index (BMI) and waist circumference), biochemical index (fast blood glucose (FBG), estimated glomerular filtration rate (eGFR), high-sensitivity C-reactive protein (hs CRP), alanine aminotransferase (ALT), albumin (Alb), aspartate amino transferase (AST), glycosylated hemoglobin (HbA1c), alkaline phosphatase (ALP), serum creatinine (Scr), gamma-glutamyl transpeptidase (GGT), triglyceride (TG), uric acid (UA), blood urea nitrogen (BUN), and high-density lipoprotein-cholesterol (HDL-C)), smoker (no/former/now), drinker (never/mild/moderate/heavy), work activity (no/moderate/both/vigorous), recreational activity (no/moderate/both/vigorous), diastolic blood pressure (DBP), systolic blood pressure (SBP), ZJU index, BARD score, and NAFLD fibrosis score [22,23,24]. You can find more information about the variables in this study here www.cdc.gov/nchs/nhanes/.

BA and HEI-2015 calculation

Klemera P et al. presented and calculated BA based on eight biomarkers (Ln-CRP, Scr, HbA1c, Alb, TC, BUN, ALP, and SBP). BA was estimated using the Klemera-Doubal method, which amalgamates information from the regressions of selected biomarkers against chronological age [25,26,27]. Therefore, a package called NhanesR developed by Jing Zhang was used in the study to calculate biological aging (BA), as used by Wenmin Xing et al. [28]. Detailed calculation formulas are provided in Supplementary Fig. 1. In the NHANES dietary data, 24-h dietary recalls are collected by computer-assisted dietary interview software using an automated multiple-pass process. Based on the USDA Food and Nutrient Database for Dietary Studies, a nutrient value has been assigned to each food [29]. Except for fatty acids, food components are scored according to density (per 1,000 kcal or as a percentage of energy). Fatty acids are expressed as unsaturated/saturated fatty acid ratios. HEI-2015 consists of 13 dietary components [30]. A total of nine adequate components are included: all vegetables, all protein foods, seafood and vegetable proteins, whole grains, total fruit, dairy products, all fruit, greens and legumes, and fatty acids. Added sugars, sodium, saturated fats and refined grains are moderate components. Each element can be rated up to 5 points or a maximum of 10 points. The total score is 100, with higher scores indicating better quality for the adequacy components [13]. Supplementary Table 1 shown the nutritional components and scoring standards.

NAFLD measurement

NAFLD was defined using the US fatty liver index (FLI), a well-validated diagnostic index, which was employed utilizing NHANES III data and calculated as an equation according to a previous study that included information on BMI, GGT, TG, and waist circumference [31]. The FLI scores of ≥ 60 was defined as NAFLD. Here is the FLI formula:

FLI = (e0.953∗ln (TG)+0.139∗BMI+0.718∗ln (GGT)+0.053∗waist circumference−15.745) / (1 + e0.953∗ln (TG)+0.139∗BMI+0.718∗ln (GGT)+0.053∗waist circumference−15.745) ∗ 100 (31).

Statistical analysis

In our study, P-value < 0.05 was regarded as statistically significant. All analysis was performed with R version 3.6.4 and SPSS version 24.0. The BA and HEI-2015 were divided into quartiles ((Q1: − 5.700–27.298; Q2:27.299–45.723; Q3: 45.724–61.205; Q4: 61.206–128.515) and (Q1:6.260–38.384; Q2:38.385–47.612; Q3: 47.613–58.488; Q4: 58.489–90.905), and the lowest quartile (Q1) served as the reference group. To calculate differences between groups, we used weighted student’s t test for continuous variables that expressed as the means ± (standard deviations, SDs) and weighted chi-square tests for categorical variables that presented as numbers (%). The weighted multivariate logistic regression analysis was used to investigate the association of BA and HEI-2015 with risk of NAFLD. Firstly, model 1 was adjusted for age and sex. Secondly, model 2 was adjusted for model 1 variables plus race/ethnicity, marital status, family PIR, smoking status, education level, drink status, the complication of hypertension, and DM. Finally, model 3 was adjusted for model 2 variables plus BMI, TG, waist circumference, BUN, FBG, Scr, HbA1C, hs CRP, eGFR, ALT, AST, GGT, UA and HDL-C. In mediation analysis, the association between independent variables (X) and dependent variables (Y) is mediated by mediating variable (M) [32]. In the study, the BA (X) was the independent variable, NAFLD (Y) was the outcome variable, and HEI-2015 was the mediating variable. Total effect (TE), indirect effect (IE), and direct effect (DE) were used to analyse whether HEI-2015 mediated the association BA with NAFLD. Restricted cubic splines (RCS) were used to model nonlinear relationships between independent and dependent variables. The spline curve is essentially a continuous smooth piecewise cubic polynomial, which is limited by some control points, called “nodes”. The “nodes” are placed at multiple locations within the data range, and the type of polynomial and the number and location of nodes determine the type of spline. The number of RCS nodes is more important than the location [33]. To achieve an optimal balance between model fit and overfitting for the primary splines of BA and HEI-2015, the number of knots, ranging from three to seven, was selected based on the minimum Akaike Information Criterion value [34]. However, the more nodes, the more degrees of freedom, and the more complex the model.

Results

Baseline characteristics

Table 1 shown the baseline characteristics of the participants who participated in the research study. According to the study results, the number of participants (2,421) is likely representative of the total population of 70,474,358 in the US as a whole. The prevalence of NAFLD was 34.8%. There is a significant difference in age, marital status, alcohol user, hypertension, DM, BMI, recreational activity, SBP, FBG, BUN, HbA1c, waist circumference, ALT, hs CRP, AST, albumin, HDL-C, TG, BARD score, eGFR, ZJU index, NAFLD fibrosis score, BA and HEI-2015 among non-NAFLD and NAFLD group.

Table 1 Characteristics of the study population
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Association of BA and HEI-2015 with NAFLD

RCS shown that BA was positively correlated with prevalence of NAFLD, while HEI-2015 was negative correlated with NAFLD risk. Therefore, we take three-knots to fit the association BA and HEI-2015 with prevalence of NAFLD in this study. In the RCS plot, we found that BA was positively correlated with prevalence of NAFLD, while HEI-2015 was negative correlated with NAFLD risk (Fig. 2A and B). After adjusting for interfering factors, compared with the lowest quartiles of BA and HEI-2015, the odds ratios with 95% confidence intervals for NAFLD across the quartiles were (1.24 (0.84, 1.84), 2.07 (1.15, 3.73) and 2.49 (1.16, 5.38)) and (0.89 (0.66, 1.18), 0.87 (0.65, 1.16) and 0.64 (0.46, 0.87)), respectively (Table 2). Based on the multivariate regression analysis of the HEI-2015 components, in fully adjusted model III, we found that whole fruits were significantly associated with NAFLD (P < 0.05, Table 3).

Fig. 2
figure 2

RCS for the association of BA and HEI-2015 with prevalence of NAFLD. (A) BA and NAFLD. (B) HEI-2015 and NAFLD. OR odd ratio, BA biological aging, CI confidence interval, HEI−2015 healthy eating index, RCS restricted cubic spline, NAFLD non-alcoholic fatty liver disease

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Table 2 Association of biological aging and HEI-2015 with risk of NAFLD
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Table 3 Association of HEI-2015 components with risk of NAFLD
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Association of BA and HEI-2015 with ZJU index, BARD score and NAFLD fibrosis score

The BA was linear positive with ZJU index, BARD score and NAFLD fibrosis score (Fig. 3A–C). However, the linear negative correlation existed between HEI-2015 and ZJU index, BARD score and NAFLD fibrosis score (Fig. 4A–C). Additionally, we also found the TC was linear positive with TG (Supplementary Fig. 2).

Fig. 3
figure 3

Associations of BA with ZJU index, BARD score, and NAFLD fibrosis score. (A) Association of BA with ZJU index. (B) Association of BA with BARD score. (C) Association of BA with NAFLD fibrosis score. BA biological aging, NAFLD non-alcoholic fatty liver disease

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Fig. 4
figure 4

Associations of HEI-2015 with ZJU index, BARD score, and NAFLD fibrosis score. (A) Association of HEI-2015 with ZJU index. (B) Association of HEI-2015 with BARD score. (C) Association of HEI-2015 with NAFLD fibrosis score. NAFLD non-alcoholic fatty liver disease, HEI−2015 healthy eating index-2015

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Mediation analysis and subgroup analyses

Mediation analysis showed that the positive correlation between BA and NAFLD could be mediated and weakened by HEI-2015. The HEI-2015 was estimated to explain − 5.79% of the association between BA and NAFLD (Total effect (TE): β = 0.00534 and P = 0.013; Indirect effect (IE): β = − 0.00035 and P = 0.0420; Direct effect (DE): β = 0.00569 and P = 0.007) (Fig. 5). Additionally, stratified by sex, hypertension, race/ethnicity, DM, and BMI, subgroup analysis was conducted to determine the association of BA and HEI-2015 with prevalence of NAFLD (Supplementary Fig. 3 and 4; Supplementary Table 2 and 3). The linear positive association of BA with NAFLD were found among participants in all race (Mexican American, Non-Hispanic White, Other Hispanic, Non-Hispanic Black and Other Race), male or female, without hypertension or DM, and with BMI of < 30 or ≥ 30 kg/m2. Additionally, HEI-2015 and NAFLD risk were linear negative correlated among participants in male, Mexican American, Other Hispanic and Non-Hispanic White, with or without hypertension.

Fig. 5
figure 5

Mediation analysis of HEI-2015 on the interaction between BA and NAFLD. Mediation models of HEI-2015, BA and NAFLD: direct effect (TE = 0.00534; P = 0.013) of BA (exposure) toward NAFLD (outcome), and HEI-2015 mediation proportion is −5.79%; indirect effect (IE = 0.00035; P = 0.042) of BA (exposure) toward HEI-2015 (mediator) and effect NAFLD (DE = 0.00569; P = 0.007), from HEI-2015 (mediator) toward NAFLD (outcome). BA biological aging, TE total effect, HEI−2015 healthy eating index, DE direct effect, NAFLD non-alcoholic fatty liver disease, IE indirect effect

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Discussion

As one of the most common liver diseases, NAFLD is poorly understood as to its underlying mechanism of occurrence [35]. There are a number of risk factors (glucose intolerance, age, metabolic syndrome, central obesity and blood pressure) associated with NAFLD [36,37,38]. According to recent studies, the prevention and treatment of NAFLD involves modifying diet and increasing physical activity [39]. As far as we know, no correlation analysis has ever investigated the association between BA, HEI-2015 and NAFLD. Thus, the aim of the study was to explore the association between BA, HEI-2015 and NAFLD and find which component of food intake is critical for the prevention of NAFLD.

Firstly, we found that a linear positive association between BA and prevalence of NAFLD. The stratified analysis revealed same positive associations between BA and NAFLD among participants in all race (Non-Hispanic White/Mexican American/Other Race/Other Hispanic/Non-Hispanic Black), male or female, without hypertension or DM, and with BMI of < 30 or ≥ 30 kg/m2. With the increase of age, the metabolic rate of the human body gradually decreases, which easily leads to the accumulation of fat in the liver. In addition, aging may also be associated with an increased risk of other chronic diseases (high blood pressure, DM and hyperlipidemia), which are also closely associated with the development of NAFLD [40]. Telomeres shorten with age. By activating the p53-p21 and p16-Rb pathways, telomere shortening triggers disease senescence in hepatocytes and leads to steatosis [41]. Tang L et al. found that long telomere length was associated with reduced risk of NAFLD incidence. Addtionally, telomere length played a role in mediating the association between age and NAFLD incidence [42]. This is consistent with our results. Secondly, a linear negative association was observed in this study between HEI-2015 and prevalence of NAFLD. The subgroup analysis also revealed the same negative associations of HEI-2015 with NAFLD among participants in male, Mexican American, Other Hispanic and Non-Hispanic White, with or without hypertension. Dietary patterns and reduced risk of NAFLD may be related to high fiber and antioxidant content, which can reduce oxidative stress and insulin resistance [43, 44]. Western dietary patterns rich in fructose and saturated fats may adversely affect lipid and glucose homeostasis and increase the risk of NAFLD [45,46,47]. In their study, Zelber-Sagi and Di Minno found that high consumption of saturated fatty acids, cholesterol, n6/n3 ratio and carbohydrate may increase the risk of NAFLD, while low consumption of unsaturated fatty acids may reduce the risk [48, 49]. Additionally, findings from studies related to western dietary patterns were also contradictory. A study conducted in Australia demonstrated that eating a western diet high in processed meat, full-fat dairy products, and fried potato increased the risk of developing NAFLD [50]. Additionally, similar results were obtained in another study. Yang found that Chinese adults were more likely to develop NAFLD when they consumed "Animal food" as a dietary pattern [51]. Furthermore, Kalafati and his team found that patients with NAFLD ate primarily fast food-type diets rather than a prudent, high-protein, or unsaturated fatty acids diet. Taking a fast food-type diet significantly increases the chances of developing NAFLD as well as the levels of CRP and UA. Also, eating a prudent diet, characterized by olive oil, vegetables, fish, legumes, fruits and a diet rich in unsaturated fatty acids has been shown to reduce several NAFLD-related biomarkers [52]. In contrast, other studies did not report any significant results. In China population, Jia Q found that males with high-protein/cholesterol pattern scores and females with high-carbohydrate/sweet pattern scores have a higher prevalence of NAFLD [53]. Furthermore, Chung GE showed that in the Korean population, traditional dietary patterns increased the risk of NAFLD, while simple meal patterns decreased it [54]. Additionally, in the multivariate logistic regression analysis, the significant relationship was found between whole fruits and risk of NAFLD. Based on a study on the elderly participants, those who ate high levels of white meat, olive oil, fruits, vegetables and legume were at lower risk of developing NAFLD [55]. Nevertheless, another study found no association between NAFLD and high intakes of legumes, whole grains, vegetables, fibre, fruits and fish as well as low intake of refined sugar, saturated fat and total fat [50]. Considering the entire diet as a dietary pattern may help us gain a better understanding of the connection between food and chronic diseases, since nutrients and foods cannot be consumed alone, and their simultaneous consumption can have synergistic or antagonistic effects [56,57,58]. Finally, we revealed that the prevalence of NAFLD gradually increases with BA, but this positive association can be weakened by a healthy diet for the first time. The possible reasons are as follows. In Table 1, we found that people without NAFLD had higher levels of education level and higher incomes. Through lifestyle-related parameters such as diet, socio-economic status indicators (family income, education level, occupation) have been shown to be inversely related to chronic diseases [59,60,61]. The socio-economically advantaged subjects may contribute to increased food security, dietary knowledge, and the decision to choose a healthier diet [62]. Additionally, people with higher socioeconomic status have more time to exercise, which can effectively delay aging and a range of diseases. Additionally, in the study, we also found the TC was linear positive with TG. In patients with NAFLD, TC and TG levels often rise simultaneously. This phenomenon may be related to the imbalance between production and consumption of lipids in the liver. When the liver is unable to metabolize lipids efficiently, the synthesis of cholesterol and triglycerides may increase, causing their levels to be positively correlated [63]. On the basis of standardized data collection protocols, the NHANES database provides national representative estimates. Therefore, the findings of the current study can be generalized widely. It’s worth noting that there are some limitations to this study. Firstly, as result of the limits of the year, only the general US population from NHANES 2017–2018 years was included in the study. Secondly, the accuracy of dietary recall is influenced by many factors, such as individual memory ability, subjective assessment, and social expectations. Therefore, information of dietary questionnaires was self-reported, which may result in recall bias. Thirdly, deletion of missing data can result in deviation from the sample, especially if the missing data is non-random. This may result in the representation of the sample being affected, thus affecting the universality of the study results. Deleting data may lead to increased uncertainty in the results, as a reduction in sample size may affect the stability and reliability of the study results. Deletion of missing data may reduce the generalization of the findings. If the deleted data represents a particular type of individual or condition, the findings may not generalize to the entire population. Finally, our cross-sectional study cannot determine a causal relationship between BA and NAFLD.

Conclusion

In conclusion, BA was positively correlated with prevalence of NAFLD, while HEI-2015 was negative correlated with NAFLD risk in the American population. The prevalence of NAFLD gradually increases with BA, but this positive association can be weakened by the healthy diet.

Availability of data and materials

This study analyzed publicly available datasets; these can be found here: https://www.cdc.gov/nchs/nhanes/.

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Acknowledgements

Thanks are due to the NHANES study staff and participants for their valuable contributions.

Funding

This work was supported by the Zhenjiang “Jinshan Talents” high-level talent’s introduction project in medical field, The “Light of Taihu Lake” Science and Technology Project of Wuxi Science and Technology Bureau (Y20222002), and The Scientific Health Research Projects of Wuxi Health Committee (M202106).

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Authors and Affiliations

  1. Department of General Surgery, Wuxi No.2 People’s Hospital, No.68 Zhongshan Road, Wuxi, 214001, Jiangsu, China

    Xiang Zhang

  2. Department of Hepatobiliary Surgery, The Affiliated Wuxi No.2 People’s Hospital of Nanjing Medical University, No.68 Zhongshan Road, Wuxi, 214001, Jiangsu, China

    Zhijie Ding, Yong Yan, Weiming Yang & Yongping Zhou

  3. Department of General Surgery, The Affiliated Hospital of Jiangsu University, No.438 Jiefang Road, Jingkou District, Zhenjiang, 212008, Jiangsu, China

    Xiaoming Ai

  4. Department of Hepatobiliary Surgery, Wuxi No.2 People’s Hospital, No.68 Zhongshan Road, Wuxi, 214001, Jiangsu, China

    Zhijie Ding, Yong Yan, Weiming Yang & Yongping Zhou

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  1. Xiang Zhang
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  2. Zhijie Ding
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Contributions

Xiang Zhang, Xiaoming Ai, and Yongping Zhou contributed to hypothesis development and manuscript preparation. Xiang Zhang, Zhijie Ding and Yong Yan, contributed to the study design. Xiang Zhang, Yong Yan, and Weiming Yang undertook data analyses. Xiang Zhang, Xiaoming Ai, and Yongping Zhou drafted and revised the manuscript. All authors approved the final draft of the manuscript for publication.

Corresponding authors

Correspondence to Xiaoming Ai or Yongping Zhou.

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Ethics approval and consent to participate

All NHANES participants provided written informed consent and the National Center for Health Statistics obtained institutional review board approval prior to data collection. Because NHANES data are de-identified and publicly available, the analysis presented here was exempt from IRB review.

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The authors declare no competing interests.

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Zhang, X., Ding, Z., Yan, Y. et al. The effect of healthy eating index-2015 in the associations of biological aging and non-alcoholic fatty liver disease: an interaction and mediation analysis. J Health Popul Nutr 44, 18 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s41043-025-00755-z

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Keywords

Journal of Health, Population and Nutrition

ISSN: 2072-1315

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