Abstract
Aim and background
In recent years, the dietary inflammation index (DII) has become an important tool widely used to assess the inflammatory potential of an individual’s diet. The aim of this study was to investigate the relationship between the DII and the biological aging of multiple organs (heart, liver, and kidneys) in American adults.
Methods
A cross-sectional study was performed using data from the National Health and Nutrition Examination Survey of American adults between 2003 and 2018. The DII was calculated using 28 nutrients from daily dietary intake. The biological age (BA) of the heart, liver, and kidneys was calculated using the Klemera–Doubal method. ∆ age was determined by assessing the difference between an individual’s estimated BA and their actual age.
Results
A total of 14,873 individuals were included; the mean (SD) age was 45.59 (16.54) years, and 7,639 (51.44%) were female. In the fully adjusted final model, the highest tertile of the DII was significantly correlated with the Δ age of each organ (cardiovascular Δ age: β = 0.87, liver Δ age: β = 2.86, kidney Δ age: β = 0.80; all p values ≤ 0.01). The DII was positively correlated with the cardiovascular (r = 0.06) (p ≤ 0.01), liver (r = 0.16), and kidney (r = 0.03) Δ age (all p ≤ 0.01). However, sensitivity analyses confirmed only significant positive associations of the DII with the heart and liver Δ age. The log-transformed and standardized (z score) values of either C-reactive protein or high-sensitivity CRP and white blood cell count were demonstrated to mediate the relationships between the DII and heart, liver, and renal Δ age.
Conclusion
Our analyses demonstrated significant associations between an elevated DII and accelerated biological aging in both the cardiovascular and hepatic systems, albeit with modest effect sizes that may reflect both genuine biological relationships and inherent limitations of cross-sectional dietary assessment. Prospective studies with repeated measures are warranted to validate these associations and elucidate the underlying physiological mechanisms.
Supplementary Information
The online version contains supplementary material available at 10.1186/s41043-025-01080-1.
Keywords: Dietary inflammatory index, Aging, Biological age, NHANES
Significance
What is new?
• Initially, in this study, the relationships between the DII and biological aging processes across different organs were investigated.
• Higher DIIs were significantly associated with accelerated biological aging in both the heart and liver.
• The Z-CRP and WBC count were demonstrated to mediate the relationships between the DII and the heart, liver, and renal Δ age.
What are the clinical implications?
• Higher DIIs were significantly associated with biological aging in both the heart and liver. Adjusting dietary patterns and increasing the consumption of anti-inflammatory foods may serve as potential strategies to delay the biological aging of these organs.
Supplementary Information
The online version contains supplementary material available at 10.1186/s41043-025-01080-1.
Introduction
Aging is a complex process resulting from interactions among various biological mechanisms and is characterized by an irreversible decline in physiological functions and increased vulnerability to age-related diseases over time [1, 2]. Epidemiological studies have shown that the global population aged 60 years and above currently constitutes 11% of the total population, with projections suggesting that it will increase to 22% by 2050 [3]. Because of variations in genetic material, living environments, and individual habits, aging is not a uniform process [4–6]. Solely relying on chronological age to measure aging is overly simplistic. Biological age (BA), quantified through biomarkers such as blood indicators [7], DNA methylation patterns [8], and telomere length [9], better captures physiological decline and disease susceptibility. Critically, BA acceleration varies across organ systems [10], underscoring the need for organ-specific investigations to elucidate modifiable aging drivers.
The DII is an emerging metric that evaluates dietary inflammatory potential by quantifying pro- and anti-inflammatory nutrient intake [11]. Substantial evidence links dietary-induced inflammation to chronic diseases in the cardiovascular, hepatic, and renal systems. Proinflammatory diets promote cardiovascular aging through oxidized low-density lipoprotein-cholesterol (ox-LDL)-induced endothelial dysfunction [12–14] and nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB)-mediated vascular inflammation, leading to hypertension [15, 16]. Cardiovascular aging leads to diastolic dysfunction, reduced cardiac output, and increased fibrosis, directly impairing oxygen and nutrient delivery to all organs while promoting multiorgan senescence through hemodynamic dysregulation [17]. The age-standardized mortality rate of cardiovascular diseases (CVDs) is estimated to be 235.2 per 100,000 people worldwide on the basis of data from the Global Burden of Disease (GBD) 2021 study [18]. As the body’s primary metabolic organ, the liver is particularly vulnerable to pro-inflammatory and pro-oxidative dietary components that trigger inflammatory cascades and oxidative stress responses. These processes lead to hepatocyte damage, manifested by abnormal aspartate aminotransferase (AST) and alanine aminotransferase (ALT) levels, which may subsequently induce DNA damage, a recognized hallmark of cellular senescence [19–21]. Furthermore, emerging evidence suggests that globulin levels may serve as biomarkers reflecting the inflammatory status, oxidative stress, and aging processes [22, 23], thereby mediating hepatic senescence. Hepatic aging impairs detoxification capacity and metabolic homeostasis, leading to the accumulation of pro-aging toxins, which subsequently accelerates systemic cellular damage [24]. In the renal system, chronic inflammation is strongly correlated with a creatinine/urea nitrogen imbalance, which contributes significantly to chronic kidney disease (CKD) progression and renal aging [25–27]. Renal aging results in glomerulosclerosis and reduced filtration capacity, permitting uremic toxin buildup, which exacerbates cardiovascular and neurological decline [28]. Recent GBD data report approximately 2 million deaths annually from liver diseases [29] and 1.2 million from CKD globally [30].
Numerous recent studies have demonstrated a significant correlation between an increased DII and the risk of several diseases, such as nonalcoholic fatty liver disease [31], depression [32], and cardiovascular conditions [33]. Investigating the relationships between dietary quality and the biological aging of the cardiac, liver, and renal systems is therefore crucial. This study not only identifies novel modifiable risk factors but also provides a scientific basis for developing early intervention strategies for heart, liver, and kidney failure.
Methods
Study population
The National Health and Nutrition Examination Survey (NHANES) stands as one of the most extensive national health surveillance initiatives in the United States. In addition to the National Center for Health Statistics (NCHS), a division of the Centers for Disease Control and Prevention, the NHANES uses a meticulous stratified multistage probability sampling approach to gather and analyze foundational data on health conditions, dietary habits, and disease prevalence among the U.S. population [34]. The related technical documentation and original datasets are available for access via the official website [35]. Our analysis included participants aged ≥ 18 years and < 80 years from the NHANES 2003–2018 cycles to minimize missing covariate data. To ensure cross-cycle comparability, we implemented standardized protocols recommended by the Centers for Disease Control and Prevention. Specifically, (1) dietary component variables were aligned across survey cycles using the USDA Food Patterns Equivalents Database coding system, and (2) survey weights were integrated across cycles using the normalized weighting approach (1/n × original weight, where n represents the number of included cycles). Individuals who met any of the following exclusion criteria were excluded from the analysis: (1) pregnant, nonadult, or aged ≥ 80 years; (2) lacking biomarkers of biological age; or (3) lacking components of the DII. The final analysis included 14,873 participants. The detailed participant selection process is illustrated in a flowchart (Fig. 1). Before the survey, all protocols were approved by the NCHS Institutional Review Board, and informed consent was obtained from all participants.
Fig. 1.
Flow chart of the process of selecting participants for the analysis of dietary inflammatory indices and biological aging
Assessment of the DII
Daily dietary intake was assessed on the basis of the average of two consecutive 24-hour dietary recall interviews. On the basis of existing studies, 45 dietary elements known to influence inflammation have been selected for incorporation into the DII calculation [36]. Owing to the lack of consistent measurements for several spices and less commonly assessed nutrients in the NHANES database, our study utilized 28 dietary components for DII computation. Previous studies have demonstrated that the use of these specific food parameters ensures the predictive stability of the DII [37, 38]. For detailed information on the dietary components and the DII calculation method, please refer to Table S1. A higher DII score indicates greater intake of proinflammatory components, whereas a lower DII score reflects greater intake of anti-inflammatory components.
Calculation of biological age
In this study, the Klemera–Doubal method (KDM) was employed using the R “BioAge” package to estimate organ-specific BA for the cardiovascular, liver, and renal systems. The selection of cardiovascular, liver, and renal biomarkers was based on the validated biomarkers identified by Nie et al. [10, 39], whose research demonstrated that cardiovascular BA together with CA significantly predict cardiac-related mortality. Furthermore, comparative analyses revealed that organ-specific BA measurements exhibited superior predictive capability for mortality risk than CA measurements alone did, highlighting the clinical utility of multisystem biological aging assessments. Xing et al. employed the KDM to calculate the biological ages of the cardiovascular, liver, and renal systems and reported that dietary flavonoid intake was significantly associated with attenuated aging in these organ systems [40].
Cardiovascular biological age was derived from several metrics, including blood pressure (systolic and diastolic), cholesterol levels (low-density lipoprotein and high-density lipoprotein), triglycerides, and fasting glucose. The physiological age of the liver was assessed by integrating the levels of four key biomarkers: AST, ALT, albumin, and gamma-glutamyl transferase. The physiological age of the kidney was determined through a comprehensive evaluation of four key biomarkers: creatinine, uric acid, blood urea nitrogen, and albumin. The data mentioned above can be obtained from the laboratory and physical examination sections of the NHANES. Additionally, the Δ age was determined by subtracting participants’ chronological age from their biological age as follows: Δ age = BA - CA.
Assessment of covariates
The statistical analysis accounted for several confounding variables, such as sex, age, ethnicity, education level, body mass index (BMI), the family poverty income ratio (PIR), smoking status, drinking status, physical activity, and several chronic conditions (including diabetes, hypertension, and chronic kidney disease). The participants were divided into three age groups: under 30 years old, aged 30–59 years old, and 60 years old or above. Ethnicity was categorized into five groups: white, black, Mexican, other Hispanic, and other. We adopted the World Health Organization (WHO)-recommended cutoff of 25 kg/m² to classify participants into normal weight (BMI < 25) and overweight/obese (BMI ≥ 25) groups [41, 42]. Smoking status was classified into current smokers, former smokers (those who smoked at least 100 cigarettes in their lifetime but had quit), and nonsmokers [43, 44]. Drinking status was divided into current drinkers, former drinkers (those who had more than 12 drinks in their lifetime but not in the past year), and nondrinkers [45]. The diabetes diagnosis criteria included self-reported diabetes, medication or insulin use, HbA1c > 6.5, and fasting glucose ≥ 7.0 mmol/L [46]. The criteria for determining hypertension included the patient’s self-reported medical history, current use of antihypertensive medications, or measurement results showing a systolic blood pressure ≥ 130 mmHg or a diastolic blood pressure ≥ 80 mmHg [47]. According to the KDIGO 2021 Clinical Practice Guidelines for the Management of Glomerular Diseases, the diagnostic criterion for CKD is a glomerular filtration rate of less than 60 mL/min/1.73 m² or an albumin‒creatinine ratio of 30 mg/g or higher [48]. The estimated Glomerular Filtration Rate was determined using the Chronic Kidney Disease Epidemiology Collaborative equation (CKD-EPI 2009). CVDs encompass five major conditions: heart failure, coronary heart disease, myocardial infarction, angina pectoris, and stroke. All conditions, as well as the diagnosis of liver disease, were ascertained on the basis of self-reported medical history.
Statistical analysis
Given the complex survey design and sampling methodology of the NHANES, sample weights were incorporated into the study to increase the national representativeness of the findings. The baseline characteristics of the participants are expressed as the means (SDs) or counts (percentages) and were categorized on the basis of tertiles of the DII.
To address missing data in our study, we employed multiple imputation by chained equations to estimate missing covariates. All covariates in this study had missing data proportions less than 9% (Table S2).
Chi-square tests and one-way analysis of variance (ANOVA) were employed to evaluate the relationships between the DII, organ biological age, and Δ age. Multivariate linear regression was conducted after adjusting for covariates to investigate the relationship between the DII and Δ age across organs. The crude model was unadjusted and omitted all possible covariates. Model 1 was adjusted for age, sex, ethnicity, BMI, educational level, and PIR. Model 2 was additionally adjusted for smoking, drinking, physical activity, dyslipidemia, diabetes mellitus, chronic kidney disease, and hypertension. Furthermore, restricted cubic splines (RCSs) featuring three knots at the 10%, 50%, and 90% percentiles, along with scatter plots, were employed to illustrate the associations between the DII and Δ ages. For potential nonlinear relationships, we systematically evaluated all possible inflection points to identify threshold effects by selecting the value with the highest likelihood. The association between the DII and accelerated organ biological aging was then examined using piecewise linear regression models.
The multicollinearity among the selected independent variables included in the model was assessed using the generalized variance inflation factor (GVIF). In accordance with the recommendations of Fox and Monette (1992), an adjusted GVIF threshold of 2 was established [49]. Variables exceeding this adjusted threshold were considered to exhibit significant multicollinearity and were consequently excluded from subsequent model construction.
To evaluate the potential mediating effects of baseline inflammatory biomarkers, we conducted mediation analyses examining log-transformed and standardized (z score) values of either C-reactive protein or high-sensitivity CRP (z-CRP) [50] and white blood cell (WBC) count in the association between the DII and biological age. These biomarkers were selected because they represent well-established indicators of systemic inflammation and directly reflect low-grade chronic inflammation. Subgroup analyses were conducted on the basis of sex, BMI, smoking status, drinking status, physical activity, hypertension, chronic kidney disease, and diabetes.
To evaluate the robustness of DII-associated biological aging in the cardiac, liver, and renal systems, we performed the following sensitivity analyses. First, the NHANES employs age truncation for participants aged ≥ 80 years, which may introduce bias in analyses requiring exact age data. To address this limitation, we used an actuarial table to assign imputed ages of 88 years for males and 89 years for females aged ≥ 80 years in the NHANES 2003–2018 cycles. A total of 15,836 participants were included in this sensitivity analysis. Second, to minimize reverse causality, we excluded participants with preexisting cardiovascular diseases, liver disease, renal disorders, or diabetes mellitus. Finally, to minimize potential confounding from extreme values, this sensitivity analysis excluded participants who met any of the following criteria: Class II obesity (BMI ≥ 35 kg/m²) or severe thinness (BMI ≤ 16 kg/m²) according to WHO standards [51, 52]; critical blood pressure values (systolic pressure ≥ 200 or ≤ 80 mmHg, diastolic pressure ≥ 130 or ≤ 50 mmHg); heavy alcohol consumption, defined as > 3 standard drinks per day for males or > 2 for females; binge drinking episodes (≥ 5 drinks per occasion) [53]; and implausible energy intake (< 500 or > 5000 kcal/day).
R software was used for all statistical analyses (version 4.3.2, Alcatel-Lucent, Paris, France), and a p value < 0.05 was considered statistically significant.
Results
Basic clinical characteristics of the study participants
A total of 14,873 participants were enrolled in this study, and their baseline characteristics are provided in Table 1. The mean chronological age of the participants was 45.59 (SD: 16.54) years, and the average DII was 1.30 (SD: 1.83). Those in the highest DII tertile were more likely to be overweight, current smokers, and physically inactive but less likely to be current drinkers than those in the lowest tertile were. Hypertension, chronic kidney disease, and diabetes were also more prevalent in the highest DII group. The detailed baseline characteristics, including education and ethnicity, are presented in Table 1.
Table 1.
Baseline population characteristics by DII tertiles (weighted)
| Variable | Total | Q1 (≤ 0.76) | Q2 (0.76–2.47) | Q3 (≥ 2.47) | P-value |
|---|---|---|---|---|---|
| Age (years) | 45.59 (16.54) | 46.52 (15.64) | 45.36 (16.73) | 44.72 (17.32) | < 0.01 |
| DII | 1.30 (1.83) | -0.70 (1.09) | 1.65 (0.48) | 3.32 (0.56) | < 0.01 |
| Female, n (%) | 7639 (51.44) | 1963 (39.15) | 2545 (52.86) | 3131 (64.46) | < 0.01 |
| Race, n (%) | < 0.01 | ||||
| mexican american | 2,541 (8.53) | 881 (8.63) | 885 (8.77) | 775 (8.15) | |
| non-hispanic black | 3,184 (10.97) | 791 (7.69) | 1064 (10.90) | 1,329 (14.94) | |
| non-hispanic white | 6,334 (67.67) | 2,247 (70.54) | 2,095 (67.69) | 1,992 (64.25) | |
| other hispanic | 1,366 (5.33) | 444 (5.21) | 436 (5.03) | 486 (5.78) | |
| other race | 1,448 (7.50) | 595 (7.92) | 476 (7.60) | 377 (6.88) | |
| Educational level, n (%) | < 0.01 | ||||
| college or higher | 8,043 (61.53) | 3,195 (72.17) | 2,616 (60.55) | 2,232 (49.98) | |
| high school | 3,408 (23.34) | 916 (17.46) | 1,182 (24.13) | 1,310 (29.45) | |
| less than high school | 3,422 (15.13) | 847 (10.37) | 1,158 (15.32) | 1,417 (20.57) | |
| PIR, n (%) | < 0.01 | ||||
| < 1 | 3,179 (14.98) | 813 (10.48) | 1,020 (14.68) | 1,346 (20.62) | |
| >= 1 | 11,694 (85.02) | 4,145 (89.52) | 3,936 (85.32) | 3,613 (79.38) | |
| BMI (kg/m2) | < 0.01 | ||||
| < 25 | 4,480 (32.13) | 1,671 (36.10) | 1,445 (30.60) | 1,364 (29.06) | |
| >= 25 | 10,393 (67.87) | 3,287 (63.90) | 3,511 (69.40) | 3,595 (70.94) | |
| Smoking, n (%) | < 0.01 | ||||
| former | 3,469 (24.36) | 1,301 (28.08) | 1,150 (23.60) | 1,018 (20.78) | |
| never | 8,284 (54.22) | 2,901 (57.59) | 2,790 (55.19) | 2,593 (49.19) | |
| now | 3,120 (21.42) | 756 (14.33) | 1,016 (21.21) | 1,348 (30.03) | |
| drinking, n (%) | < 0.01 | ||||
| former | 2,350 (13.34) | 679 (11.67) | 774 (13.15) | 897 (15.54) | |
| never | 2,081 (11.06) | 555 (8.84) | 658 (11.28) | 868 (13.47) | |
| now | 10,442 (75.59) | 3,724 (79.50) | 3,524 (75.57) | 3,194 (70.99) | |
| Physical activity, n (%) | < 0.01 | ||||
| no | 4,673 (26.25) | 1,187 (19.45) | 1,536 (26.04) | 1,950 (34.52) | |
| yes | 10,200 (73.75) | 3,771 (80.55) | 3,420 (73.96) | 3,009 (65.48) | |
| History of hypertension, n (%) | 5,802 (35.65) | 1,831 (34.06) | 1,896 (35.33) | 2,075 (37.87) | 0.05 |
| History of DM, n (%) | 2,708 (13.89) | 771 (11.80) | 913 (14.23) | 1,024 (15.98) | < 0.01 |
| History of CKD, n (%) | 2,136 (11.96) | 571 (9.68) | 699 (11.72) | 866 (14.93) | < 0.01 |
DII: dietary inflammatory index; CKD: chronic kidney disease; DM: diabetes mellitus; BMI: body mass index; PIR: poverty income ratio
Differences between biological age and chronological age in the study population
The means and standard deviations of organ biological ages and Δ ages across various categorical variables are presented in Table 2. When the DII was used as a continuous variable, the cardiovascular biological age was 44.98 (19.89) years, accompanied by a Δ age of -0.61 (11.58) years. The liver biological age was 43.26 (28.48) years, accompanied by a Δ age of -2.33 (23.26) years, and the kidney biological age was 45.09 (19.09) years, accompanied by a Δ age of -0.50 (11.20) years. DII tertile stratification revealed significant intertertile differences in cardiovascular and hepatic Δ age (all p < 0.01) but not in renal Δ age (p = 0.34). Compared with males, females generally exhibited a lower Δ age in the cardiovascular and renal systems but showed greater age differences in the liver. Participants with a college education or higher demonstrated lower cardiovascular and liver biological ages, as well as lower Δ ages, than those with lower educational attainment. Those with a BMI below 25 kg/m² often had a lower biological age and Δ age than those with a BMI > 25 kg/m². Biological age and Δ age were also impacted by health habits such as smoking, drinking, and exercising. For example, those who currently smoked and drank alcohol had a greater liver Δ age, whereas those who were physically active generally had lower Δ ages and biological ages for the kidneys, liver, and cardiovascular system (p < 0.01). Across all three organs, those with diabetes, hypertension, or chronic kidney illness presented analogous trends of increased Δ age and biological age (p < 0.01).
Table 2.
The averages of biological age (BA) and its differences with chronological age (CA) by tertile of the dietary inflammatory index (DII), NHANES 1999–2018
| Variables | Heart BA | Heart BA difference (BA-CA) | Liver BA | Liver BA difference (BA-CA) | Kidney BA | Kidney BA difference (BA-CA) | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Mean (SD) | P | Mean (SD) | P | Mean (SD) | P | Mean (SD) | P | Mean (SD) | P | Mean (SD) | P | ||||||
| DII | 44.98(19.89) | -0.61(11.58) | 43.26(28.48) | -2.33(23.26) | 45.09(19.09) | -0.50(11.20) | |||||||||||
| DIIQ | 0.97 | < 0.01 | < 0.01 | < 0.01 | 0.04 | 0.34 | |||||||||||
| Q1 | 44.97(18.42) | -1.55(11.34) | 39.79(27.22) | -6.74(21.93) | 45.83(17.58) | -0.70(10.06) | |||||||||||
| Q2 | 45.06(19.97) | -0.30(11.37) | 43.76(28.59) | -1.60(23.09) | 45.10(18.89) | -0.26(10.75) | |||||||||||
| Q3 | 44.91(21.44) | 0.19(12.00) | 46.88(29.35) | 2.16(24.04) | 44.18(20.94) | -0.54(12.84) | |||||||||||
| Age (years) | < 0.01 | < 0.01 | < 0.01 | < 0.01 | < 0.01 | < 0.01 | |||||||||||
| < 30 | 22.73(9.14) | -0.62(8.59) | 19.39(23.93) | -3.96(23.54) | 25.15(9.07) | 1.80(8.59) | |||||||||||
| 30–60 | 44.91(14.91) | -0.05(11.80) | 43.56(24.60) | -1.39(23.45) | 43.64(12.77) | -1.32(10.27) | |||||||||||
| ≥ 60 | 65.91(13.92) | -1.91(13.26) | 64.80(22.86) | -3.02(22.41) | 67.08(15.54) | -0.73(14.62) | |||||||||||
| Sex | 0.12 | < 0.01 | < 0.01 | < 0.01 | < 0.01 | < 0.01 | |||||||||||
| female | 44.64(21.10) | -1.67(11.43) | 50.51(25.69) | 4.19(22.63) | 43.42(18.64) | -2.90(10.60) | |||||||||||
| male | 45.34(18.51) | 0.52(11.63) | 35.58(29.27) | -9.24(21.88) | 46.85(19.40) | 2.03(11.25) | |||||||||||
| Education lever | < 0.01 | < 0.01 | < 0.01 | < 0.01 | 0.17 | 0.09 | |||||||||||
| college or higher | 43.52(18.93) | -1.57(11.03) | 41.54(27.61) | -3.55(22.52) | 44.75(18.54) | -0.34(10.85) | |||||||||||
| high school | 46.85(21.10) | 0.72(11.83) | 45.05(29.10) | -1.07(24.00) | 45.54(19.33) | -0.58(11.30) | |||||||||||
| below high school | 48.06(21.14) | 1.27(12.89) | 47.50(30.36) | 0.71(24.63) | 45.76(20.84) | -1.04 (12.35) | |||||||||||
| Race | < 0.01 | < 0.01 | < 0.01 | < 0.01 | < 0.01 | < 0.01 | |||||||||||
| Mexican | 40.17(19.70) | 0.83(11.03) | 37.67(28.62) | -1.67(22.76) | 38.11(17.94) | -1.23(10.85) | |||||||||||
| black | 44.36(21.20) | 1.83(12.62) | 52.17(29.61) | 9.64(25.50) | 43.93(21.04) | 1.40(14.18) | |||||||||||
| white | 46.23(19.55) | -1.26(11.38) | 43.26(28.00) | -4.23(22.34) | 46.81(20.60) | -0.68(10.65) | |||||||||||
| other Hispanic | 42.32(20.06) | 0.32(11.92) | 41.45(28.07) | -0.56(22.56) | 41.57(17.79) | -0.44(10.68) | |||||||||||
| other | 41.97(19.68) | -0.57(11.51) | 37.83(28.09) | -4.72(23.63) | 41.64(18.17) | -0.90(11.57) | |||||||||||
| PIR | < 0.01 | < 0.01 | 0.19 | < 0.01 | < 0.01 | 0.23 | |||||||||||
| < 1 | 40.55(21.33) | 0.85(12.06) | 42.05(32.06) | 2.35(26.02) | 38.89(19.64) | -0.81(11.87) | |||||||||||
| ≥ 1 | 45.76(19.52) | -0.86(11.47) | 43.47(27.80) | -3.15(22.64) | 46.18(18.79 | -0.45(11.07) | |||||||||||
| BMI (kg/m2) | < 0.01 | < 0.01 | < 0.01 | < 0.01 | < 0.01 | < 0.01 | |||||||||||
| < 25 | 38.15(19.88) | -3.13(11.20) | 33.74(28.85) | -8.23(22.53) | 38.84(19.04) | -3.13(11.20) | |||||||||||
| ≥ 25 | 48.22(19.06) | 0.74(10.98) | 47.77(27.16) | 0.46(23.08) | 48.05(18.39) | 0.74(10.98) | |||||||||||
| Smoking | < 0.01 | < 0.01 | < 0.01 | < 0.01 | < 0.01 | < 0.01 | |||||||||||
| former | 52.35(18.75) | -0.57(11.98) | 48.83(28.03) | -4.09(22.99) | 52.90(19.37) | -0.02(12.96) | |||||||||||
| never | 42.69(19.97) | -0.98(11.22) | 41.09(27.98) | -2.58(22.75) | 43.52(18.92) | -0.16(11.11) | |||||||||||
| now | 42.40(18.88) | 0.30(11.96) | 42.42(29.40) | 0.31(24.57) | 40.17(16.42) | -1.93(10.09) | |||||||||||
| Drinking | < 0.01 | 0.47 | < 0.01 | < 0.01 | < 0.01 | 0.06 | |||||||||||
| former | 52.65(19.85) | -0.29(13.41) | 51.51(27.20) | -1.42(23.03) | 51.65(20.36) | -1.28(13.16) | |||||||||||
| never | 45.33(22.81) | -0.33(11.74) | 46.06(29.57) | 0.40(23.30) | 44.81(22.67) | -0.84(14.44) | |||||||||||
| now | 43.58(19.11) | -0.70(11.22) | 41.39(28.25) | -2.89(23.26) | 43.97(18.03) | -0.31(10.23) | |||||||||||
| Physical activity | < 0.01 | < 0.01 | < 0.01 | < 0.01 | < 0.01 | 0.73 | |||||||||||
| no | 49.49(21.30) | 0.27(12.70) | 51.10(29.77) | 1.88(24.71) | 48.64(21.34) | -0.58(13.29) | |||||||||||
| yes | 43.38(19.11) | -0.92(11.14) | 40.47(27.48) | -3.83(22.53) | 43.82(18.06) | -0.47(10.35) | |||||||||||
| Hypertension | < 0.01 | < 0.01 | < 0.01 | < 0.01 | < 0.01 | < 0.01 | |||||||||||
| no | 37.04(16.24) | -3.08(9.31) | 36.92(26.94) | -3.20(22.37) | 39.00(15.93) | -1.12(9.21) | |||||||||||
| yes | 59.32(17.73) | 3.85(13.74) | 54.71(27.60) | -0.75(24.70) | 56.08(19.41) | 0.62(14.02) | |||||||||||
| CKD | < 0.01 | < 0.01 | < 0.01 | < 0.01 | < 0.01 | < 0.01 | |||||||||||
| no | 43.03(18.75) | -1.04(10.66) | 40.87(27.21) | -3.20(22.43) | 42.63(16.05) | -1.43(8.83) | |||||||||||
| yes | 59.35(22.04) | 2.55(16.53) | 60.85(31.33) | 4.06(27.82) | 63.14(27.96) | 6.35(20.54) | |||||||||||
| DM | < 0.01 | < 0.01 | < 0.01 | < 0.01 | < 0.01 | < 0.01 | |||||||||||
| no | 41.91(18.47) | -1.68(10.37) | 40.47(27.68) | -3.12(22.70) | 42.70(17.58) | -0.88(10.01) | |||||||||||
| yes | 64.06(17.64) | 6.02(15.75) | 60.58(27.23) | 2.54(25.94) | 59.91(21.30) | 1.87(16.58) | |||||||||||
BA: biological age; CA: chronological age; DII: dietary inflammatory index; CKD: Chronic kidney disease; DM: Diabetes mellitus; BMI: Body mass index; PIR: Poverty income ratio
Association between the DII and biological age
When treated as a continuous variable, after adjusting for all covariates, the DII was significantly correlated with the cardiovascular, liver, and kidney Δ age (β values of 0.19, 0.76, and 0.20, all p ≤ 0.01) (Table 3). When the DII was further categorized into tertiles (Q1–Q3), higher DII levels (Q3) were significantly associated with a greater Δ age than those in the lowest tertile (Q1) for the cardiovascular (β = 0.87, 95% CI: 0.28–1.46), liver (β = 2.86, 95% CI: 1.34–4.39), and renal systems (β = 0.80, 95% CI: 0.17–1.43). The RCS models were used to assess potential nonlinear relationships between the DII and biological age acceleration in the cardiovascular, liver, and renal systems. The analyses revealed linear associations for both the liver Δ age (nonlinear p = 0.16) and kidney Δ age (nonlinear p = 0.10), whereas a significant nonlinear relationship was observed between the DII and cardiovascular Δ age (nonlinear p = 0.01) (Fig. 2). Subsequent piecewise linear regression analysis of the association between the DII and cardiovascular age acceleration revealed no statistically significant threshold effect (log-likelihood ratio test p = 0.14) (Table S3). As shown in Fig. 2, the association between the DII and its results was further validated by scatter plots. The DII was positively correlated with the Δ age of the cardiovascular system (r = 0.06), liver (r = 0.16), and kidneys (r = 0.03) (all p ≤ 0.01).
Table 3.
The results of multiple linear regression analysis for the association between DII and differences in biological age (BA) with chronological age (CA)
| Variables | Crude model | Model 1 | Model 2 | |||
|---|---|---|---|---|---|---|
| Heart BA differences | β (95%CI) | P | β (95%CI) | P | β (95%CI) | P |
| DII | 0.41 (0.26,0.56) | < 0.01 | 0.29 (0.15, 0.43) | < 0.01 | 0.19 (0.06, 0.32) | 0.01 |
| DII (categories)S | ||||||
| Q1 | Ref | Ref | Ref | |||
| Q2 | 1.23 (0.50,1.96) | < 0.01 | 0.97 (0.27, 1.68) | 0.01 | 0.80 (0.15, 1.46) | 0.02 |
| Q3 | 1.79 (1.13,2.44) | < 0.01 | 1.37 (0.75, 1.99) | < 0.01 | 0.87 (0.28, 1.46) | < 0.01 |
| p for trend | < 0.01 | < 0.01 | < 0.01 | |||
| Liver BA differences | ||||||
| DII | 2.14 (1.79,2.49) | < 0.01 | 0.97 (0.63,1.31) | < 0.01 | 0.76 (0.42, 1.10) | < 0.01 |
| DII (categories) | ||||||
| Q1 | Ref | Ref | Ref | |||
| Q2 | 5.12 (3.75, 6.50) | < 0.01 | 2.49 (1.20,3.78) | < 0.01 | 2.07 (0.80, 3.35) | < 0.01 |
| Q3 | 8.70 (7.09,10.30) | < 0.01 | 3.82 (2.29,5.35) | < 0.01 | 2.86 (1.34, 4.39) | < 0.01 |
| p for trend | < 0.01 | < 0.01 | < 0.01 | |||
| Kidney BA differences | ||||||
| DII | 0.08 (-0.06,0.23) | 0.26 | 0.21 (0.07, 0.36) | < 0.01 | 0.20 (0.07, 0.33) | < 0.01 |
| DII (categories) | ||||||
| Q1 | Ref | Ref | Ref | |||
| Q2 | 0.54 (-0.07,1.15) | 0.08 | 0.77 (0.20,1.34) | 0.01 | 0.73 (0.19,1.27) | 0.01 |
| Q3 | 0.37 (-0.32,1.06) | 0.29 | 0.96 (0.29, 1.64) | 0.01 | 0.80 (0.17, 1.43) | 0.01 |
| p for trend | 0.27 | 0.01 | 0.01 | |||
Crude model: no variables were adjusted
Model 1: adjusted for age30, sex, race, BMI, educational levels, and PIR
Model 2: additionally adjusted for smoking, drinking, physical activity, hypertension, dyslipidemia, DM, and CKD,
BA: Biological age; CA: Chronological age; DII: Dietary inflammatory index; CKD: Chronic kidney disease; DM: Diabetes mellitus; BMI: Body mass index; PIR: Poverty income ratio; 95% CI: 95% confidence interval
Fig. 2.
DII and Δ age relationships revealed by RCS and scatter plot analyses for various organs. All models were adjusted for age, sex, ethnicity, education level, PIR, BMI, smoking status, drinking status, physical activity, hypertension, DM, and CKD. A, the RCS of the DII and heart Δ age; B, the RCS of the DII and liver Δ age; C, the RCS of the DII and renal Δ age; D, scatter plot of the DII and heart Δ age; E, scatter plot of the DII and liver Δ age; F, scatter plot of the DII and renal Δ age. BA: biological age; DII: dietary inflammatory index; CKD: Chronic kidney disease; DM: Diabetes mellitus; BMI: Body mass index; PIR: Poverty income ratio; 95% CI: 95% confidence interval; RCS: restrictive cubic spline
Association of inflammation biomarkers with biological age
After full adjustment for covariates, weighted multiple linear regression analyses revealed that the Z-CRP and WBC count were significantly positively associated with the heart Δ age (β = 0.81, 95% CI: 0.46–1.16, p < 0.01; β = 0.21, 95% CI: 0.08–0.33, p < 0.01, respectively). Both the liver and renal biological Δ age also exhibited uniformly positive associations with all the inflammatory biomarkers (all p < 0.01) (Table 4).
Table 4.
The associations of inflammation biomarkers with differences in biological age (BA) with chronological age (CA)
| Variables | Crude model | Model 1 | Model 2 | |||
|---|---|---|---|---|---|---|
| Heart BA differences | β (95%CI) | P | β (95%CI) | P | β (95%CI) | P |
| Z-CRP | 1.59 (1.24,1.94) | < 0.01 | 1.11 (0.73, 1.48) | < 0.01 | 0.81 (0.46, 1.16) | < 0.01 |
| WBC | 0.55 (0.39,0.71) | < 0.01 | 0.41 (0.37,0.54) | < 0.01 | 0.21 (0.08,0.33) | < 0.01 |
| Liver BA differences | ||||||
| Z-CRP | 7.84 (6.94,8.74) | < 0.01 | 6.37 (5.48, 7.27) | < 0.01 | 6.16 (5.29, 7.03) | < 0.01 |
| WBC | 1.21 (0.93,1.48) | < 0.01 | 1.05 (0.80, 1.30) | < 0.01 | 0.83 (0.58, 1.08) | < 0.01 |
| Kidney BA differences | ||||||
| Z-CRP | 2.32 (2.00,2.65) | < 0.01 | 2.35 (1.99, 2.71) | < 0.01 | 2.18 (1.82, 2.53) | < 0.01 |
| WBC | 0.41 (0.30,0.52) | < 0.01 | 0.35 (0.24, 0.45) | < 0.01 | 0.32 (0.22, 0.42) | < 0.01 |
Z-CRP: log-transformed and standardized (z score) values of either C-reactive protein or high-sensitivity CRP; WBC: white blood cell; 95% CI: 95% confidence interval
Mediation analysis
Figure 3 shows that the Z-CRP and WBC count mediated the associations between the DII and heart Δ age by 10.73% and 6.89%, respectively; liver Δ age by 37.60% and 8.35%, respectively; and renal Δ age by 43.95% and 10.98%, respectively.
Fig. 3.
Mediation effects of the Z-CRP and WBC count on the associations of the DII with heart, liver, and renal Δ age. Z-CRP: log-transformed and standardized (z score) values of either C-reactive protein or high-sensitivity CRP; WBC: white blood cell count; DII: dietary inflammatory index
Sensitivity analysis
First, on the basis of actuarial tables, estimated ages of 88 years and 89 years were assigned for male and female participants aged ≥ 80 years, respectively. Sensitivity analyses demonstrated that the significant associations between the DII and both the cardiovascular Δ age and hepatic Δ age remained robust (Table S4). Second, after excluding participants with preexisting cardiovascular disease, liver disease, kidney disease, or diabetes, our study included 10,132 eligible individuals in the final analysis. Multivariate linear regression analysis revealed that the DII remained significantly associated with both the liver Δ age and cardiovascular Δ age (Table S5). Third, after exclusion of extreme BMI, blood pressure, alcohol consumption values and implausible energy intake, the associations between the DII and Δ age remained robust for both the cardiovascular and hepatic systems (Table S6). These sensitivity analyses collectively demonstrate the robustness of our findings.
Subgroup analysis results
In this study, a hierarchical linear regression model was employed to systematically investigate the relationship between the DII and Δ age. The findings revealed that the DII is significantly correlated with accelerated biological age in both the heart and liver. This impact remained consistent across various subgroups, categorized by factors such as sex, smoking habits, alcohol intake, physical activity levels, BMI, hypertension history, chronic kidney disease, and diabetes (Fig. S1). Importantly, the baseline DII did not exhibit significant interaction effects with any of the stratified variables.
Discussion
Major findings
In this cross-sectional study involving 14,873 participants, we determined that a higher DII was correlated with biological aging in the heart and liver. Intriguingly, both the Z-CRP and WBC count significantly mediated the relationships between the DII and all three organ-specific Δ age measures (heart, liver, and kidneys). These findings underscore the detrimental effects of proinflammatory dietary patterns in accelerating both population and biological aging. In subgroup analyses, the baseline DII showed no significant interactions with any stratification variables. Notably, the sensitivity analyses consistently supported the robustness of the study results.
In our research, although the cardiovascular Δ age increase was 0.87 years in the high-DII group compared with that in the low-DII group, this modest difference may have substantial clinical implications. First, such seemingly minor annual changes could accumulate over decades to produce clinically meaningful consequences, analogous to pathological progression, where sustained minor elevations in blood pressure or glucose levels eventually lead to target organ damage. Previous studies have demonstrated that individuals whose cardiac biological age exceeds their chronological age by > 4 years are at twice the risk of fatal and nonfatal cardiovascular events compared with those with normal aging trajectories [54]. Thus, while the short-term individual-level effects may appear limited, the long-term cumulative impact of the DII on aging processes and its potential value in public health interventions warrant serious consideration. Second, while it cannot be definitively determined in this study whether the observed Δ ages across the cardiac and hepatic systems are fully independent or purely additive, our mediation analyses provide preliminary evidence supporting an additive model. If an individual has a Δ age of 0.87 for each organ, their total risk would be 2.61, excluding other organs for which the Δ age cannot be calculated. Thus, systemic inflammation induced by a high-DII diet may significantly increase the overall disease burden through the cumulative effect of multiorgan aging. In future investigations, comprehensive multiorgan modeling approaches should be employed to precisely evaluate the preventive potential of dietary interventions against system-wide aging processes. Finally, dietary intake in this study was assessed using 24-hour dietary recall, which introduces inherent measurement limitations in DII calculations, including recall bias and intraindividual dietary variability, which may attenuate the observed effect sizes. Although previous validation studies confirmed that 24-hour recalls provide reasonable population-level estimates, they systematically underreported proinflammatory foods, particularly among obese individuals, potentially underestimating true dietary inflammatory effects. This measurement error may explain the modest effect sizes observed (β = 0.19 for cardiovascular aging), suggesting that our estimates likely represent the lower bound of actual biological associations. While the lack of explicit seasonal stratification could theoretically affect the DII precision for seasonally variable anti-inflammatory components, the year-round data collection of the NHANES mitigates this bias, and although the United States Department of Agriculture automated multiple-pass method improves reporting accuracy, systematic misreporting remains possible without biomarker validation. Nevertheless, three factors support the robustness of our findings: (1) the nationally representative sampling of the NHANES minimizes seasonal bias, (2) sensitivity analyses excluding extreme energy reporters (< 500 or > 5000 kcal/day) yielded consistent results, and (3) prior studies have validated NHANES 24-hour recall data for DII calculation.
Furthermore, our primary analysis demonstrated that individuals in the highest DII tertile exhibited significantly accelerated renal aging (β = 0.80, p ≤ 0.01), suggesting that pro-inflammatory diets may contribute to faster kidney aging. However, sensitivity analyses indicated that this association was less robust than those observed for cardiac and hepatic aging, possibly due to unmeasured confounding factors. Notably, mediation analysis provided deeper mechanistic insight, revealing that systemic inflammation, particularly reflected by Z-CRP (mediating 43.95% of the effect) and white blood cell count, played a significant role in linking DII to renal aging. This supports the concept that dietary inflammation likely influences kidney aging primarily through indirect, inflammatory pathways rather than direct biological effects. Future studies should employ longitudinal designs and more rigorous confounding control to clarify the causal nature of this relationship.
While systemic inflammation represents a plausible mediator of the relationship between a higher DII and accelerated biological aging, several alternative explanations warrant careful consideration. First, the cross-sectional nature of our data precludes causal inference, and reverse causation cannot be ruled out. For example, individuals with subclinical organ dysfunction may unconsciously adopt more proinflammatory dietary patterns because of fatigue, metabolic alterations, or medical advice. Second, despite extensive covariate adjustment, residual confounding from unmeasured factors, such as the gut microbiome composition or chronic psychosocial stressors, could partially account for the observed associations.
Comparison with other studies
A cross-sectional study by Xuanyang Wang et al. demonstrated that a proinflammatory diet could increase four bioimaging indicators, thereby accelerating biological aging [55]. In a study of U.S. adults by Ruijie Xie et al., the DII was positively associated with biological age and phenotypic age but negatively associated with telomere length in certain subgroups [56]. Nevertheless, how the DII and the biological age of the body’s organs are related to one another is still unknown. We calculated the biological age and Δ age for the cardiovascular system, liver, and kidneys individually and explored the relationships between the DII and the biological aging of these organs. Consistent with previous findings, this study revealed a significant positive association between the DII and biological aging of the heart and liver. Although the KDM method was used as a well-validated phenotypic aging measure in this study, emerging aging clocks, including epigenetic clocks and metabolomic aging scores, provide molecular-level insights into biological aging. Multiple studies have demonstrated strong associations between DNA methylation age (GrimAge) and cardiovascular disease [57, 58]. Notably, the DII has been linked to epigenetic age acceleration of blood [59, 60], which is consistent with our observed phenotypic aging effects. However, whether the DII similarly influences metabolomic aging signatures remains unexplored, highlighting the need for future multiomics approaches. These findings provide further evidence that dietary management can be a simple and effective method to slow biological aging in individuals, as well as in their heart and liver. Notably, we observed a nonlinear relationship between the restrictive cubic spline of the DII and cardiovascular Δ age. Initially, the relationship between the DII and cardiovascular Δ age becomes stronger as the DII increases. This correlation, however, tends to stabilize or even slightly decline as the DII increases. Furthermore, the scatter plot revealed a definite positive association between the DII and heart biological aging. In the RCS, the relationship between the DII and cardiovascular Δ age may not be completely linear because, at high levels of inflammation, the immune system and repair mechanisms may play key roles, thereby reducing the sustained impact of inflammation on cardiovascular biological aging [61, 62]. It is also possible that as inflammation levels increase, other potential confounding factors (such as medication and lifestyle changes) may weaken or influence the relationship between inflammation and the cardiovascular Δ age [63, 64].
Potential mechanisms
Although the precise mechanisms by which the DII induces multiorgan aging remain incompletely understood, acute and chronic inflammatory stimuli likely play a central role. Under conditions of endothelial dysfunction, low-density lipoprotein cholesterol accumulates in the arterial walls and undergoes oxidation to form ox-LDL, triggering a chronic inflammatory response characterized by excessive secretion of IL-1β and IL-18, which accelerates atherosclerosis [65]. In the chronic inflammatory microenvironment, vascular smooth muscle cells exhibit DNA damage (↑γ-H2AX) and telomere shortening, leading to senescence-like phenotypic changes [66]. Inflammation promotes oxidative stress (mediated by a NOX2/4-induced ROS burst) through immune cell activation (monocytes/neutrophils) and adhesion molecules (ICAM-1/VCAM-1) [67, 68] while forming a positive feedback loop with the renin‒angiotensin‒aldosterone system (Ang II/aldosterone). These processes collectively contribute to endothelial dysfunction and vascular remodeling, ultimately promoting cardiovascular aging [69, 70]. In liver disease, innate immune cells activate inflammasomes via Toll-like receptors and NF-κB, releasing proinflammatory cytokines (IL-1β, IL-18, TNF-α, and IL-6) [71]. Subsequently, adaptive immune cells infiltrate and exacerbate inflammation, potentially inducing gasdermin D-mediated pyroptosis in hepatocytes [72]. As the disease progresses, a type 2 inflammatory response promotes tissue repair but also drives fibrosis, impairing liver function [73, 74]. As demonstrated by Ulf Panzer et al., inflammatory cells can directly or indirectly mediate renal injury through tissue infiltration [75]. NF-κB, a key inflammatory regulator, is sensitive to elevated ROS levels [76] and mitochondrial dysfunction [77]. It forms a positive feedback loop with inflammatory cytokines, leading to tissue dysfunction, fibrosis, stem cell depletion, and cellular senescence [76, 78]. The aforementioned evidence suggests that persistent or elevated inflammatory states in the body can adversely affect the physiological structure and function of various organs, potentially serving as a pathway through which the DII induces cardiovascular, hepatic, and renal biological aging.
Strengths and limitations
To the best of our knowledge, this is the first study to explore the relationship between the DII and the biological aging of the cardiovascular system, liver, and kidneys, confirming the role of the DII in promoting biological aging across multiple organs. Second, the study utilized data from the NHANES, which features a complex design and a large, representative study population. We also adjusted for several confounding factors to increase the reliability of the results. However, this particular research has several notable shortcomings. First, given the cross-sectional nature of this study, we cannot establish causal relationships between the DII and biological aging across different organ systems. Second, we cannot rule out reverse causation, such as the possibility that declining organ function may lead to dietary changes. Third, our investigation did not capture all relevant factors influencing both the DII and biological aging, such as gut dysbiosis and socioeconomic stressors, leaving potential residual confounding. In addition, there is a chance of recall bias in the dietary intake information gathered from the 24-hour food diaries, which may attenuate the observed effect sizes.
Conclusion
While our analyses revealed statistically significant correlations between elevated DII levels and accelerated biological aging in the cardiovascular system and liver, these findings require cautious interpretation given the methodological constraints of the study. The observed associations, although consistent across organ systems, demonstrate modest effect sizes that may reflect both true biological relationships and the inherent limitations of cross-sectional dietary assessment. Mediation analyses involving the Z-CRP and WBC count suggest plausible inflammatory pathways, although the partial mediation effects underscore the likelihood of additional mechanisms not captured in our models. The generalized statement that proinflammatory diets accelerate organ aging, while biologically plausible, should be tempered by the recognition that 24-hour recall-derived DII measures may systematically underestimate true dietary inflammation effects and that longitudinal designs are needed to establish temporal relationships. Future research incorporating repeated dietary measures, objective inflammation biomarkers, and multiomics approaches will be essential to validate these associations and elucidate the underlying physiological mechanisms.
Supplementary Information
Below is the link to the electronic supplementary material.
Acknowledgements
The authors thank all the participants and staff in the National Health and Nutrition Examination Survey for their substantial contributions.
Abbreviations
- NHANES
National Health and Nutrition Examination Survey
- BA
Biological age
- CA
Chronological age
- DII
Dietary inflammatory index
- DM
Diabetes mellitus
- BMI
Body mass index
- CKD
Chronic kidney disease
- PIR
Poverty income ratio
- HR
Hazard ratio
- 95%
CI 95% confidence interval
- GFR
Glomerular filtration rate
- ACR
Albumin-to-creatinine ratio
- RCS
Restricted cubic spline
Author contributions
L-L: Methodology, Software, Formal Analysis, Data Curation, Writing - Original Draft. J-C: Methodology, Investigation, Data curation. Y.F-Z: Methodology, Investigation, Data curation. P-Y: Methodology, Investigation, Q-L: Writing - Review & Editing. X-L: Conceptualization, Methodology, Data curation, Writing - Review & Editing, Supervision, Project administration. J.X-L: Conceptualization, Methodology, Writing - Review & Editing, Supervision, Project administration.
Funding
This work was supported by the Second Affiliated Hospital of Nanchang University Funded Clinical Research Projects (No.2022efyA03 to J.X-L).
Data availability
The dataset generated and analyzed in this study can be obtained from the official website of the NHANES database at https://www.cdc.gov/nchs/nhanes/.
Declarations
Ethics approval and consent to participate
The NHANES protocol received approval from the Institutional Review Board of the Centers for Disease Control and Prevention, and written informed consent was obtained from all participants.
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.
Lu Long and Jiang Cheng are Co-first author.
Contributor Information
Xiao Liu, Email: liux587@mail.sysu.edu.cn, Email: kellyclarkwei@vip.qq.com.
Juxiang Li, Email: ljx912@126.com.
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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 dataset generated and analyzed in this study can be obtained from the official website of the NHANES database at https://www.cdc.gov/nchs/nhanes/.