Abstract
Purpose
To examine the relationship between biological aging and the prevalence of NAFLD.
Method
We used the recommended sampling weights to account for the complex survey design of NHANES. The analysis, utilizing data from 2005 to 2016, aimed to investigate the impact of biological aging on NAFLD prevalence using various statistical methods. A restricted cubic spline (RCS) model was applied to explore the dose-response relationship, while logistic regression examined linear associations. The robustness of the association across different subgroups was also tested.
Result
The study included 2786 participants. We found significant associations between NAFLD and the following biological aging metrics: AL score (OR (95%CI) = 1.1932 (1.0597 ~ 1.3435), P = 0.0035), HD (OR (95%CI) = 1.2092 (1.0565 ~ 1.3839), P = 0.0058), and PA (OR (95%CI) = 1.7564 (1.1949 ~ 2.5818), P = 0.0042). All biological aging metrics were identified as independent predictors. PA was most associated with the prevalence of NAFLD. The associations persisted across most subgroups.
Conclusion
The prevalence of NAFLD was associated with biological aging, emphasizing the importance of addressing potential health risks related to aging.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12876-025-03955-3.
Keywords: Klemera-doubal method biological age, Phenotypic age, Homeostatic dysregulation, Allostatic load, Nonalcoholic fatty liver disease, National health and nutrition examination survey
Introduction
Over the past two decades, nonalcoholic fatty liver disease (NAFLD) has emerged as a leading cause of chronic liver disease worldwide. The global pooled prevalence of NAFLD is currently estimated at 30% [1–4]. Individuals with NAFLD face an increased risk of premature mortality, not only due to liver-related causes but also from cardiovascular disease and extrahepatic cancers [5, 6]. Aging is considered a key factor contributing to many chronic metabolic diseases. Chronological age (CA), based on birth date, is traditionally used to assess aging. However, individuals with the same CA can experience varying rates of aging, suggesting that CA may not accurately reflect the true extent of aging in different individuals.
Biological aging refers to the progressive decline in system integrity with age [7]. It is thought to result from the accumulation of molecular changes, or “hallmarks,” that impair the function and resilience of tissues and organs, eventually leading to disease and death [8–12], and homeostatic dysregulation (HD) [13] are among the most advanced algorithms for estimating biological aging. KDM-BA (Klemera-Doubal Method Biological Age) is based on the Klemera-Doubal model to estimate the biological age of an individual. The model predicts the biological age of an individual by analyzing data such as blood biochemical indexes to assess the degree of aging.PA (Phenotypic Age) is the biological age estimated based on an individual’s phenotypic data (e.g., body weight, blood pressure, blood glucose, etc.). It reflects the difference between an individual’s physiological state and his or her actual age. HD (Homeostatic Dysregulation) refers to homeostatic dysregulation, which estimates biological age by assessing the degree of homeostatic dysregulation of body systems. A higher degree of homeostatic dysregulation usually means that the individual is biologically older. The Aging Index (AL) is a combination of biomarkers that assesses the degree of aging in an individual, with higher AL levels indicating more severe aging.The discrepancy between these biological aging metrics and chronological age, known as biological age acceleration (BAA) [14], has been shown to predict mortality in various populations [12, 15].
This study aims to investigate the relationship between biological aging and the prevalence of NAFLD using NHANES data.
Method
Study population
This study utilized data from the National Health and Nutrition Examination Survey (NHANES), overseen by the CDC’s Center for Health Statistics. The survey focuses on the U.S. population living outside of institutions and employs a stratified multistage sampling design to represent the national demographic profile (https://www.cdc.gov/nchs/nhanes/index.htm). Health and nutritional data were initially collected through personal interviews, mobile unit examinations, and various laboratory tests.
Ethical approval
Informed consent was obtained from all participants. Ethic approval received from NCHS Ethics Review Board (Protocol #2011-17 and Protocol #2005-06).
Inclusion and exclusion criteria
Initially, 8958 participants were included, where 4953 participants were excluded because of missing data. Further, 1307 were excluded because of the significant alcohol use and the prevalence of liver diseases. Thus, 2786 participants were finally included. (Fig. 1).
Fig. 1.
Study flowchart
Calculation of biological aging
Our study included four biological aging indicators: Klemera-Doubal method biological age (KDM-BA), PA, homeostatic dysregulation (HD), and allostatic load (AL) [16, 17]. Aging acceleration was assessed by calculating both KDM-BA and PA acceleration [16]. Further details can be found in the cited references.
Outcome assessment
Diagnosis of NAFLD was defined as FLI ≥ 60.
The Fatty Liver Index (FLI) serves as a non-invasive diagnostic method employing simple clinical metrics to evaluate metabolic dysfunction-associated steatotic liver disease (MASLD) and related hepatic lipid accumulation, demonstrating broad applicability in both research and clinical settings. FLI scores are stratified into three principal tiers: values below 30 indicate minimal risk of liver fat deposition; 30–60 corresponds to moderate risk, necessitating supplemental diagnostic assessments to confirm intermediate-stage steatosis; and ≥ 60 strongly correlates with advanced steatosis, warranting a definitive fatty liver diagnosis. Validation studies affirm FLI’s reliability as a screening tool for metabolic-driven hepatic steatosis.
Covariates
In our analysis, we included key covariates identified in previous studies [18–28], such as age, gender, race/ethnicity, education level, marital status, family poverty income ratio (PIR), alcohol consumption, smoking habits, diabetes, and hypertension.
NHANES classifies race/ethnicity into categories such as Mexican American, other Hispanic, non-Hispanic white, non-Hispanic black, and a group that includes non-Hispanic Asians and multiracial individuals. Education levels range from below ninth grade to a college degree or higher. Marital status is categorized from married to unspecified, reflecting diverse family structures. PIR is calculated based on annual income relative to poverty thresholds, adjusted for family size.
Alcohol consumption is recorded if an individual consumes at least 12 alcoholic beverages annually. Smoking status is determined by whether an individual has smoked more than 100 cigarettes in their lifetime. Both diabetes and hypertension are self-reported but verified by medical professionals.
Statistical analysis
To account for the NHANES survey design, we applied sampling weights in our analysis. For statistical evaluation, we used the Kruskal-Wallis test for continuous variables and Fisher’s exact test for rare categorical data. All biological aging metrics have been Z-scored.
Initially, we employed a restricted cubic spline (RCS) model to explore the relationship between biological aging metrics and the prevalence of NAFLD. The number of spline knot was 4, and the median was the refered (OR = 1). Logistic regression was then used to calculate the odds ratios (ORs) and 95% confidence intervals (95% CIs) for NAFLD prevalence in relation to biological aging. Model I was unadjusted, while Model II included adjustments for all covariates as specified in Sect. 2.6. Additionally, we identified independent predictors through logistic regression models. Subgroup analyses were performed to evaluate the consistency of the associations across different groups.
All statistical analyses were adjusted for demographic variables and performed using R software version 4.3.3, with a significance level set at P < 0.05 [29, 30].
Result
General information
A total of 2786 participants were included in our study, with a NAFLD prevalence rate of 40.06%. When divided into two groups based on NAFLD prevalence, significant differences were observed between the groups in gender, age, race and ethnicity, education, marital status, cigarette use, hypertension, diabetes status, and all biological aging metrics (P < 0.05) (Table 1).
Table 1.
General information
| Non-NAFLD | NAFLD | P | |
|---|---|---|---|
| Population | 1670 (59.94%) | 1116 (40.06%) | |
| Gender | < 0.0001 | ||
| Male | 792 (47.43%) | 659 (59.05%) | |
| Female | 878 (52.57%) | 457 (40.95%) | |
| Age | 49.35 ± 17.93 | 52.45 ± 15.48 | < 0.0001 |
| Race and Ethnicity | 0.0001 | ||
| Mexican American | 187 (11.2%) | 162 (14.52%) | |
| Other Hispanic | 151 (9.04%) | 123 (11.02%) | |
| Non-Hispanic White | 884 (52.93%) | 556 (49.82%) | |
| Non-Hispanic Black | 297 (17.78%) | 216 (19.35%) | |
| Other Race - Including Multi-Racial | 151 (9.04%) | 59 (5.29%) | |
| Educational Background | < 0.0001 | ||
| Less than 9th grade | 96 (5.75%) | 84 (7.53%) | |
| 9-11th grade (Includes 12th grade with no diploma) | 173 (10.36%) | 106 (9.5%) | |
| High school graduate/GED or equivalent | 302 (18.08%) | 264 (23.66%) | |
| Some college or AA degree | 500 (29.94%) | 377 (33.78%) | |
| College graduate or above | 599 (35.87%) | 285 (25.54%) | |
| Marital Status | 0.0003 | ||
| Married | 905 (54.19%) | 666 (59.68%) | |
| Widowed | 109 (6.53%) | 78 (6.99%) | |
| Divorced | 197 (11.8%) | 138 (12.37%) | |
| Separated | 37 (2.22%) | 30 (2.69%) | |
| Never married | 294 (17.6%) | 125 (11.2%) | |
| Living with partner | 128 (7.66%) | 79 (7.08%) | |
| PIR | 2.95 ± 1.64 | 2.93 ± 1.6 | 0.7918 |
| Drinking | 0.0502 | ||
| No | 207 (12.4%) | 168 (15.05%) | |
| Yes | 1463 (87.6%) | 948 (84.95%) | |
| Smoking | 0.0012 | ||
| No | 927 (55.51%) | 549 (49.19%) | |
| Yes | 743 (44.49%) | 567 (50.81%) | |
| Hypertension | < 0.0001 | ||
| No | 1233 (73.83%) | 580 (51.97%) | |
| Yes | 437 (26.17%) | 536 (48.03%) | |
| Diabetes | < 0.0001 | ||
| No | 1575 (94.31%) | 933 (83.6%) | |
| Yes | 95 (5.69%) | 183 (16.4%) | |
| Biological Aging | |||
| AL Score | 0.19 ± 0.15 | 0.27 ± 0.16 | < 0.0001 |
| HD | 1.94 ± 0.46 | 2.22 ± 0.56 | < 0.0001 |
| KDM | 39.36 ± 25.44 | 40.57 ± 21.81 | < 0.0001 |
| PA | 59.86 ± 20.05 | 67.04 ± 17.88 | < 0.0001 |
Dose-response association between biological aging metrics and the prevalence of NAFLD
A restricted cubic spline (RCS) model was used to examine the dose-response relationship between biological aging metrics and the prevalence of NAFLD. The unadjusted model showed a positive association between the AL score and HD with NAFLD prevalence, an inverted U-shaped association between KDM and the prevalence of NAFLD and a J-shaped association between PA and the prevalence of NAFLD (P < 0.0001) (Figure S1). After adjusting for all covariates, the association remained significant (P < 0.0001) (Fig. 2).
Fig. 2.
Dose-response association between biological aging metrics and the prevalence of NAFLD (Adjusted) Note: a) AL score; b) HD; c) KDM; d) PA
Association between biological aging metrics and the prevalence of NAFLD
Logistic regression was applied to confirm the relationships between biological aging metrics and NAFLD prevalence. In the adjusted model, NAFLD prevalence was significantly associated with AL score (OR (95%CI) = 1.1932 (1.0597 ~ 1.3435), P = 0.0035), HD (OR (95%CI) = 1.2092 (1.0565 ~ 1.3839), P = 0.0058), and PA (OR (95%CI) = 1.7564 (1.1949 ~ 2.5818), P = 0.0042) (Table 2). AL score, HD, and PA were identified as independent predictors (Table 2). Among them, PA was most associated with the prevalence of NAFLD.
Table 2.
Association between biological aging metrics and the prevalence of NAFLD
| Variables | Single Factor | Multiple Factor | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| β | S.E | Z | P | OR (95%CI) | β | S.E | Z | P | OR (95%CI) | |
| Model I: Unadjusted | ||||||||||
| AL Score | 0.4965 | 0.0417 | 11.9082 | <.0001 | 1.6430 (1.5141 ~ 1.7829) | 0.2421 | 0.0539 | 4.4933 | <.0001 | 1.2740 (1.1463 ~ 1.4159) |
| HD | 0.5507 | 0.0441 | 12.4856 | <.0001 | 1.7345 (1.5908 ~ 1.8911) | 0.2982 | 0.0562 | 5.3106 | <.0001 | 1.3475 (1.2070 ~ 1.5042) |
| KDM | 0.045 | 0.0394 | 1.1435 | 0.2528 | 1.0461 (0.9684 ~ 1.1300) | -0.2181 | 0.0556 | -3.9237 | <.0001 | 0.8041 (0.7211 ~ 0.8966) |
| PA | 0.3745 | 0.0406 | 9.2213 | <.0001 | 1.4543 (1.3430 ~ 1.5748) | 0.2716 | 0.0635 | 4.2784 | <.0001 | 1.3121 (1.1586 ~ 1.4859) |
| Model II: Adjusted | ||||||||||
| AL Score | 0.4965 | 0.0417 | 11.9082 | <.0001 | 1.6430 (1.5141 ~ 1.7829) | 0.1766 | 0.0605 | 2.9177 | 0.0035 | 1.1932 (1.0597 ~ 1.3435) |
| HD | 0.5507 | 0.0441 | 12.4856 | <.0001 | 1.7345 (1.5908 ~ 1.8911) | 0.1899 | 0.0689 | 2.7581 | 0.0058 | 1.2092 (1.0565 ~ 1.3839) |
| KDM | 0.045 | 0.0394 | 1.1435 | 0.2528 | 1.0461 (0.9684 ~ 1.1300) | 0.2107 | 0.1306 | 1.6134 | 0.1067 | 1.2345 (0.9558 ~ 1.5946) |
| PA | 0.3745 | 0.0406 | 9.2213 | <.0001 | 1.4543 (1.3430 ~ 1.5748) | 0.5633 | 0.1965 | 2.8661 | 0.0042 | 1.7564 (1.1949 ~ 2.5818) |
Subgroup analysis
The association between each biological aging metric and NAFLD prevalence was consistent across all subgroups (Fig. 3).
Fig. 3.
Subgroup analysis. Note: a) AL score; b) HD; c) KDM; d) PA
Discussion
In this study, we found that all biological aging metrics were independent predictors and risk factors for the prevalence of NAFLD. To the best of our knowledge, this is the first study to explore the relationship between biological aging and NAFLD prevalence.
As life expectancy continues to rise, the focus on healthy aging is becoming more pronounced, influenced by physiological, psychological, social, and environmental factors. Numerous studies have suggested that biological age is a significant risk factor for various age-related diseases, including chronic, metabolic, and neurodegenerative conditions. Biological aging is a complex process involving multiple biological mechanisms across various organs and systems [31]. Over the past few decades, biological age has been assessed using a variety of biomarkers at the cellular level [32]. Emerging evidence highlights the role of epigenetic mechanisms such as DNA methylation, chromatin remodeling, and RNA modifications in aging. Different DNA methylation algorithms have been linked to inflammation, age-related health outcomes, and mortality. Currently, the most accurate measure of biological age is obtained by studying epigenetic changes, such as using epigenetic clocks in blood samples [32].
NAFLD is a common and potentially progressive liver disease strongly associated with an increased risk of both cardiovascular and liver-related mortality [33]. Inflammation plays a central role in the prognosis of NAFLD, contributing to its progression to NASH and liver fibrosis [34]. The potential benefits of anti-inflammatory treatments, such as vitamin E and pentoxifylline, in modulating inflammation and improving NAFLD outcomes are being explored [35]. The metabolic inflammation in NAFLD is a chronic, low-grade, sterile state where monocytes play a key role, producing cytokines that affect insulin signaling and promote NAFLD development [36, 37]. Coexisting diseases such as diabetes mellitus, hypertension and cardiovascular disease significantly exacerbate biological aging and NAFLD.NAFLD patients are often accompanied by insulin resistance, which is the pathological basis of type 2 diabetes mellitus.With aging, pancreatic β-cells function declines and insulin secretion decreases, which further aggravates insulin resistance and hyperglycemia, leading to the development of diabetes mellitus, which in turn promotes hepatocyte fatty degeneration and inflammatory responses through Oxidative stress, endoplasmic reticulum stress and other mechanisms promote hepatocyte steatosis and inflammatory response, accelerating the progression of NAFLD and biological aging(PMID: 35054837). In addition, the increased risk of cardiovascular disease in NAFLD patients may be related to chronic inflammation, endothelial dysfunction and atherosclerosis, and with biological aging, the endothelial function of the blood vessels naturally decreases, blood pressure regulation decreases, and the prevalence of hypertension increases, and high blood pressure further aggravates the burden on the heart and blood vessels, leading to cardiovascular diseases such as myocardial hypertrophy and myocardial infarction and exacerbating the NAFLD. These co-morbidities, through their mutual influence and synergistic effects, form a vicious circle that makes the relationship between aging and NAFLD more complex and difficult to control.
Aging is a multifactorial process that leads to the gradual decline of biological systems, increasing susceptibility to diseases and mortality [38]. Biological age, which reflects the functional and physiological status of an organism rather than its chronological age, can be estimated using biomarkers like clinical chemistry parameters and epigenetic signatures. Previous studies have shown that aging is linked to the prevalence and severity of NAFLD, as well as the risk of liver-related complications and mortality [39, 40]. Early intervention of biological aging indicators aims to regulate the biological processes associated with aging and thus is expected to play an active role in the prevention and management of NAFLD. From clinical practice, healthy lifestyle interventions, such as rational diet and moderate exercise, can not only slow down aging, but also effectively reduce the risk of NAFLD and improve its condition, while some drugs with anti-aging effects have also shown potential efficacy in NAFLD. In addition, monitoring of biological aging indicators can help in early identification of people at risk of NAFLD and assessment of disease progression. In the future, with the in-depth study of the relationship between aging and NAFLD, the development of precise monitoring technology and the formulation of personalised intervention strategies, early intervention of biological aging indicators will show a broader application prospect in the prevention and management of NAFLD, providing new ideas and methods for the clinical prevention and treatment of NAFLD.
In our study we found that PA was most associated with the prevalence of NAFLD. This might be because PA contained inflammation indicators which weren’t included in other 3 indicators. Prior research, including epidemiological investigations and meta-analyses, has established associations between inflammatory biomarkers and hepatic pathologies. For instance, a study involving 376 Chinese individuals with decompensated cirrhosis revealed that elevated NPAR levels independently predicted higher mortality risk after adjusting for covariates [HRQ3vs.Q1 = 1.92; 95% CI (1.04, 3.56)]. Specifically, each unit rise in NPAR was correlated with a 92% increase in the likelihood of death [41]. Additionally, Liu et al. [42], in their analysis of 2017–2018 NHANES datasets, reported that rising NLR and NPAR values were strongly associated with an elevated probability of MASLD development.
It is important to acknowledge the limitations of this study. First, due to its cross-sectional nature, no causal relationships can be established [43]. Although causality could not be established in this study, the results of this study combined with the availability of a large sample of gwas data provide the necessary evidence base for subsequent identification of causal associations through methods such as Mendelian randomization and RCT experiments. Second, the sample size of participants included in the final analysis was relatively small.
Conclusion
A higher prevalence of NAFLD was linked to increased biological aging metrics, underscoring the importance of addressing the potential health risks related to biological aging.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Acknowledgements
We thank Wenqi Yang, Haitao Xu and Ruijie Xiao from xiangya school of medicine central south university for their statistical guidance on this manuscript.
Author contributions
QS.M. conducted the formal analysis and wrote the manuscript. YZ.K. critically reviewed the manuscript. JR.H, YK.Z., CW.Z., XL.T. and G.L. prepared all tables and figures. All authors reviewed the manuscript.
Funding
Not applicable.
Data availability
The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.
Declarations
Ethics approval and consent to participate
Informed consent was obtained from all participants. Ethic approval received from NCHS Ethics Review Board (Protocol #2011-17 and Protocol #2005-06).
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.