Skip to main content
Download PDF

Associations between cadmium exposure and whole-body aging: mediation analysis in the NHANES

BMC Public Health volume 23, Article number: 1675 (2023) Cite this article

Abstract

Introduction

Even though cadmium (Cd) exposure and cellular senescence (telomere length) have been linked in previous studies, composite molecular aging biomarkers are more significant and reliable factors to consider when examining the connection between metal exposure and health outcomes. The purpose of this research was to assess the association between urinary cadmium (U-Cd) and whole-body aging (phenotypic age).

Methods

Phenotypic age was calculated from chronological age and 9 molecular biomarkers. Multivariate linear regression models, subgroup analysis, and smoothing curve fitting were used to explore the linear and nonlinear relationship between U-Cd and phenotypic age. Mediation analysis was performed to explore the mediating effect of U-Cd on the association between smoking and phenotypic age.

Results

This study included 10,083 participants with a mean chronological age and a mean phenotypic age of 42.24 years and 42.34 years, respectively. In the fully adjusted model, there was a positive relationship between U-Cd and phenotypic age [2.13 years per 1 ng/g U-Cd, (1.67, 2.58)]. This association differed by sex, age, and smoking subgroups (P for interaction < 0.05). U-Cd mediated a positive association between serum cotinine and phenotypic age, mediating a proportion of 23.2%.

Conclusions

Our results suggest that high levels of Cd exposure are associated with whole-body aging.

Peer Review reports

Introduction

Cadmium (Cd) is a toxic heavy metal that is ubiquitous in the environment and poses a major public health challenge [1, 2]. The major sources of Cd exposure for the general population are tobacco smoke, diet, and workplace exposure [3,4,5]. Excessive exposure to Cd may lead to renal tubular dysfunction and abnormal bone metabolism [6,7,8]. In addition, there is evidence that Cd exposure induces oxidative stress, leading to elevated levels of inflammation and mitochondrial damage [9, 10].

Population aging is a global issue, with one-fifth of the world’s population expected to be 65 or older by 2030. Healthy life expectancy, on the other hand, is growing more slowly than total life expectancy [11, 12]. Although everyone ages, the pace at which biological aging occurs varies, and inequalities in aging rates between individuals manifest as differences in mortality and disease vulnerability [13]. Several aging metrics based on molecular factors, such as DNA methylation age, have been proposed [14], pro-inflammatory cytokines [15], and telomere length [16]. In addition, ‘phenotypic aging measures’ derived from clinical biomarkers have been shown to be better predictors of whole-body aging and outcomes than actual age in representative population data [17,18,19].

Cd exposure has emerged as a significant contributor to the development of several diseases of aging, including diabetes [20], cardiovascular disease [21, 22], and osteoarthritis [19], as a result of the toxic effects of Cd on various metabolic organs [23]. Evidence from epidemiological studies and animal studies suggests that Cd exposure contributes to oxidative stress and stimulates the production of cytokines [24, 25], even though the exact mechanisms underlying these associations are still unknown. A recent study investigating the association between urinary metals and telomere length in the US population showed a negative association between U-Cd and telomere length [26].

To the best of our knowledge, no population-based investigation has studied the relationship between Cd exposure and phenotypic age. As a result, we conducted a cross-sectional study using data from the National Health and Nutrition Examination Survey (NHANES) to examine the link between Cd exposure and phenotypic age in a typical U.S. population.

Methods

Study population

The National Center for Health Statistics (NCHS) conducts the well-known National Health and Nutrition Examination study (NHANES), a cross-sectional study that is nationally representative [27,28,29]. All research participants provided written agreement at the time of recruitment, and the NCHS Research Ethics Review Board approved the study’s methodology. Over 10 survey cycles in a period of twenty years (1999–2018), the survey was carried out. A total of 19,004 participants with U-Cd data were initially enrolled in the study, as were 8,915 participants without phenotypic age data and 6 participants with missing urinary creatine data. The study ultimately included 10,083 participants (Fig. 1).

Fig. 1
figure 1

Flow chart of participants selection. NHANES, National Health and Nutrition Examination Survey

Full size image

Cadmium exposure

In this study, urinary cadmium (U-Cd) levels were used as a Cd exposure assessment because it is considered a proxy for cumulative Cd exposure, reflecting the accumulation of Cd in the kidneys and other tissues [30]. U-Cd levels were measured by inductively coupled plasma mass spectrometry (ICP-MS), U-Cd concentration corrected by urinary creatinine [31, 32].

Phenotypic age

The phenotypic age was determined using chronological age and nine biomarkers: albumin, creatinine, glucose, C-reactive protein, lymphocyte percentage, mean cell volume, erythrocyte distribution width, alkaline phosphatase, and white blood cell count [18]. Laboratory methods for measuring these biomarkers are as follows:

  • Albumin: Measured using fluorescence immunoassay.

  • Creatinine: Assessed by Jaffe kinetic method.

  • Glucose: Measured using the glucose oxidase-peroxidase method.

  • C-reactive protein: Quantified using a high-sensitivity enzyme-linked immunosorbent assay (ELISA).

  • Percentage of lymphocytes: Calculated using flow cytometry.

  • Mean cell volume: assessed using an automated hematology analyzer.

  • Erythrocyte distribution width: Analyzed using an automated hematology analyzer.

  • Alkaline phosphatase: Measured using colorimetric assay.

  • Leukocyte count: Counted using an automated cell counter.

Covariates

Covariates for this study were identified based on the literature on metal exposure and biological aging [33,34,35], including age, sex, education level, race, PIR (ratio of family income to poverty), BMI, smoking status (ever/never), sleep disorder, serum cotinine, cancer status, klotho, waist circumference, diabetes status, triglycerides and LDL-C (low-density lipoprotein cholesterol).

Statistical analysis

All analyses were performed with R (version 4.2) and Empowerstats (version 5.0) [36, 37]. All statistical analyses were conducted weighted according to the NHANES guidelines. Missing covariate data were addressed using multiple imputation. To examine the demographic features of the individuals by U-Cd quartile, the chi-square test and t-test were utilized. Multivariate linear regression models were used to examine the linear associations between U-Cd and phenotypic age. The non-linear relationship between U-Cd and phenotypic age was investigated by smoothing curve fitting (penalized spline method) after logarithmic transformation of U-Cd [38]. Subgroup analyses and interaction tests were used to examine differences in the above correlations across gender, BMI, diabetes and age. Cd is a component of cigarette smoke [39], and Cd is also thought to be associated with aging [40]. Given that serum cotinine serves as an established biomarker for cigarette smoke exposure [41], to establish a foundation for mediation as described by Baron & Kenny [42], we first examined:

  • The association between cotinine (independent variable) and U-Cd (mediator).

  • The association between cotinine (independent variable) and phenotypic age (dependent variable).

  • The results of these foundational analyses are presented in Table S1 of the supplementary material. Following the establishment of these associations, we conducted a mediation analysis to ascertain the extent to which Cd exposure mediates the relationship between serum cotinine and phenotypic age. The proportion of the effect mediated by Cd exposure was calculated using the formula (mediated effect/total effect) × 100%.

Results

Baseline characteristics

The mean (SD) age and phenotypic age of the 10,083 participants were 42.24 (21.55) years and 42.34 (21.93) years, with 49.76% of male participants. The mean U-Cd and serum cotinine was 0.33 (0.44) ng/g. The characteristics of the study population according to the quartiles of the U-Cd are depicted in Table 1. Participants in the higher urinary Cd quartiles were more likely to be older, female, non-Hispanic black, and smokers; have higher rates of diabetes and cancer; have higher serum cotinine levels, triglyceride levels, LDL-C levels, BMI, and waist circumference; and have lower educational levels and household income.

Table 1 Basic characteristics of participants by urinary cadmium quartile
Full size table

Associations between cadmium exposure and biological aging

Table 2 presents the results of multivariate linear regression models between U-Cd (ng/g) and phenotypic age (year). In the crude model, there was a positive correlation between U-Cd and phenotypic age, and the effect values were much higher than in the other models [13.97 (12.90, 15.05)], which mainly stemmed from the fact that the chronological age was not adjusted in Model 1. In the fully adjusted model, each 1 ng/g increase in U-Cd phenotypic age was associated with a 2.13-year increase in phenotypic age [2.13 (1.67, 2.58)]. When U-Cd was subsequently converted to quartiles and trend was tested, participants in the top quartile had phenotypic ages that were 1.6 years older than participants in the bottom quartile in the fully adjusted model [1.60 (1.00, 2.21)]. Additionally, all models showed a significant linear trend (P for trend < 0.01).

Table 2 The associations between urinary cadmium (ng/g) and phenotypic age (year)
Full size table

The subgroup analysis revealed that the link between U-Cd levels and phenotypic age was not consistent, despite the fact that a positive relationship occurred in all categories (Table 3). The results of the interaction test indicated that gender, age, and smoking modified the association between U-Cd levels and phenotypic age (P for interaction < 0.05). For participants who smoked, this positive linear association was strong and significant [2.27 (1.75, 2.78)], whereas in non-smokers, the association was not significant [0.97 (-0.02, 1.95)].

Table 3 Subgroup analysis of the association between urinary cadmium (ng/g) and phenotypic age (year)
Full size table

In addition, smoothed curve fitting further confirmed the nonlinear relationship between U-Cd and phenotypic age (Fig. 2). After stratification by smoking status, the nonlinear relationship between U-Cd and phenotypic age for participants who smoked showed a trend toward covariance with the nonlinear relationship for all participants, while non-smokers showed significant differences. Considering that smoking is one of the main sources of Cd exposure and that serum cotinine levels are a valid indicator of an individual’s smoking behavior and exposure, mediation analysis was further used to explore the role of U-Cd in mediating the relationship between serum cotinine and phenotypic age. U-Cd had a significant indirect effect (mediation effect) with a mediation ratio of 23.2% (Table 4).

Fig. 2
figure 2

The nonlinear associations between urinary cadmium and phenotypic age. The solid red line represents the smooth curve fit between variables. Blue bands represent the 95% of confidence interval from the fit. A total participants; (B) Participants stratified by smoking

Full size image
Table 4 Urinary cadmium as a mediator in the associations between serum cotinine (ng/mL) and phenotypic age (year)
Full size table

Discussion

In past studies on biological aging, researchers have focused mainly on genetic factors and less on metal exposure. In this study, we provide two new findings based on a representative population in the United States. First, greater Cd levels were linked to biological aging, with each 1 ng/g rise in U-Cd related with a 2.13-year increase in phenotypic age. Also, Cd exposure had a mediating effect in the positive association of smoking on phenotypic age, with a 23.2% mediating proportion.

Phenotypic age is considered to be a valid indicator of whole-body aging [43], has been found to be associated with a range of health outcomes, including osteoarthritis [19], diabetes, and overall mortality [44]. These health outcomes may be due to changes in physiological and metabolic functions as a result of biological aging, including cellular senescence [45], decreased DNA repair capacity [46], and chronic inflammation [47]. In addition, phenotypic age may also be used as a risk assessment tool to identify individuals who are at high risk of developing health problems due to smoking or cadmium exposure [48]. For example, those with a higher phenotypic age than their actual age and with smoking or cadmium exposure may need to undergo more frequent health screenings or take more aggressive preventive measures. To our knowledge, this study is the first population-based study that investigated the association between Cd exposure and phenotypic age, and our results are consistent with many studies that have investigated other biological aging indicators [26, 49, 50]. Telomere shortening is an important mechanism of cellular senescence, and therefore telomere length is considered an important indicator of cellular senescence [51]. Patel et al. investigated the linear association of 461 variables, including environmental exposures, with telomere length, and their results showed that a total of eight of the 461 variables were associated with telomere shortening, including Cd exposure, C-reactive protein, and physical activity [49]. A cross-sectional study in China investigated the association of Cd and lead concentrations in the placenta with telomere length and demonstrated that telomere length was not connected with lead and was negatively associated with Cd concentration [50].

Furthermore, the results of the subgroup analysis of this study showed no association between U-Cd and phenotypic age in nonsmoking participants. In contrast, Demanelis et al. found an inverse association between Cd and biomarkers of aging in a non-smoking population [52]. These associations were significantly different from those in the smoking population, and these differences may stem from the increased levels of Cd exposure in humans due to smoking. The results of our mediation analysis showed that Cd exposure significantly mediated the positive association between smoking (serum cotinine) and phenotypic age, mediating a proportion of 23.2%. Cigarette smoke is thought to be an important factor in accelerating the aging process [53], and the association between nicotine metabolites and phenotypic age may be due in part to the presence of Cd in tobacco smoke. Even though our study was cross-sectional and we were unable to establish a causal link, this interpretation is supported by the findings of the mediation analysis and a number of experimental studies [54, 55]. In addition, the mediating effect of Cd exposure between smoking and telomere length was also demonstrated in a cross-sectional study by Zota et al. using mediation analysis, but their measure of smoking utilized questionnaire variables, years of smoking (30 years, 30–59 years, > 60 years) [56], whereas we investigated the mediating effect of smoking between Cd exposure and phenotypic age from a different perspective using the nicotine metabolite serum cotinine.

There are many biological aging mechanisms that have been linked to Cd exposure. It is believed that oxidative stress is the main cause of telomere shortening. High levels of guanine in telomeres, which are extremely vulnerable to reactive oxygen species, cause the production of 8-oxo-7,8-dihydrodeoxyguanosine, which can cause DNA strand breaks and telomere wear [57,58,59]. In addition, Cd exposure is associated with higher levels of inflammatory markers [60, 61], and inflammation may further induce oxidative stress to accelerate cellular senescence [62]. Finally, Cd has been shown to have the ability to interfere with the DNA repair system and can affect the stability of excision and mismatch repair systems [63].

Our research has several limitations. First, due to the cross-sectional study’s design, we were unable to establish a causal association between Cd exposure and biological aging. Furthermore, the variables related with biological aging are too complicated for us to account for all potential confounding factors, such as medication usage and food recall, which may significantly influence the results. Despite these shortcomings, our study provides a number of advantages. The current study is the first to investigate the association between Cd exposure levels and whole-body aging. In addition, a large representative sample size was included in this study, which allowed us to stratify the analysis across multiple variables and reduce the error in the results of the subgroup analysis.

Conclusion

Cd exposure is positively associated with whole-body aging (phenotypic age). In addition, U-Cd mediated a positive association between smoking and whole-body aging. These results suggest that phenotypic age may be used as a risk assessment tool to identify individuals who are at high risk of developing health problems due to smoking or cadmium exposure.

Availability of data and materials

The survey data are publicly available on the internet for data users and researchers throughout the world ( www.cdc.gov/nchs/nhanes/ ).

Abbreviations

U-Cd:

Urinary cadmium

NHANES:

National Health and Nutrition Examination Survey

NCHS:

National Center for Health Statistics

ICP-MS:

Inductively coupled plasma mass spectrometry

LDL-C:

Low-density lipoprotein cholesterol

BMI:

Body mass index

PIR:

Ratio of family income to poverty

References

  1. Min JY, Min KB. Blood cadmium levels and Alzheimer’s disease mortality risk in older US adults. Environ Health. 2016;15(1):69.

    PubMed  PubMed Central  Google Scholar 

  2. Chen L, Sun Q, Peng S, Tan T, Mei G, Chen H, Zhao Y, Yao P, Tang Y. Associations of blood and urinary heavy metals with rheumatoid arthritis risk among adults in NHANES, 1999–2018. Chemosphere. 2022;289: 133147.

    CAS  PubMed  Google Scholar 

  3. Faroon O, Ashizawa A, Wright S, Tucker P, Jenkins K, Ingerman L, Rudisill C. Agency for Toxic Substances and Disease Registry (ATSDR) Toxicological Profiles. In: Toxicological Profile for Cadmium. edn. Atlanta: Agency for Toxic Substances and Disease Registry (US); 2012.

  4. Wen X, Li T, Xu X. Cadmium exposure in US adults, research based on the National Health and Nutrition Examination Survey from 1988 to 2018. Environ Sci Pollut Res Int. 2022;29(15):22293–305.

    CAS  PubMed  Google Scholar 

  5. Schaefer HR, Dennis S, Fitzpatrick S. Cadmium: mitigation strategies to reduce dietary exposure. J Food Sci. 2020;85(2):260–7.

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Satarug S, Garrett SH, Sens MA, Sens DA. Cadmium, environmental exposure, and health outcomes. Environ Health Perspect. 2010;118(2):182–90.

    CAS  PubMed  Google Scholar 

  7. Zhang C, Lin T, Nie G, Hu R, Pi S, Wei Z, Wang C, Xing C, Hu G. Cadmium and molybdenum co-induce pyroptosis via ROS/PTEN/PI3K/AKT axis in duck renal tubular epithelial cells. Environ Pollut. 2021;272: 116403.

    CAS  PubMed  Google Scholar 

  8. Lu J, Lan J, Li X, Zhu Z. Blood lead and cadmium levels are negatively associated with bone mineral density in young female adults. Arch Public Health. 2021;79(1):116.

    PubMed  PubMed Central  Google Scholar 

  9. Park JH, Lee BM, Kim HS. Potential protective roles of curcumin against cadmium-induced toxicity and oxidative stress. J Toxicol Environ Health B Crit Rev. 2021;24(3):95–118.

    CAS  PubMed  Google Scholar 

  10. Zhang L, Yang F, Li Y, Cao H, Huang A, Zhuang Y, Zhang C, Hu G, Mao Y, Luo J, et al. The protection of selenium against cadmium-induced mitophagy via modulating nuclear xenobiotic receptors response and oxidative stress in the liver of rabbits. Environ Pollut. 2021;285: 117301.

    CAS  PubMed  Google Scholar 

  11. Partridge L, Deelen J, Slagboom PE. Facing up to the global challenges of ageing. Nature. 2018;561(7721):45–56.

    CAS  PubMed  Google Scholar 

  12. Costantino S, Paneni F, Cosentino F. Ageing, metabolism and cardiovascular disease. J Physiol. 2016;594(8):2061–73.

    CAS  PubMed  Google Scholar 

  13. Liu Z, Kuo PL, Horvath S, Crimmins E, Ferrucci L, Levine M. A new aging measure captures morbidity and mortality risk across diverse subpopulations from NHANES IV: a cohort study. PLoS Med. 2018;15(12): e1002718.

    PubMed  PubMed Central  Google Scholar 

  14. Horvath S. DNA methylation age of human tissues and cell types. Genome Biol. 2013;14(10):R115.

    PubMed  PubMed Central  Google Scholar 

  15. Jurado-Fasoli L, Amaro-Gahete FJ, Arias-Tellez MJ, Gil A, Labayen I, Ruiz JR. Relationship between dietary factors and S-Klotho plasma levels in young sedentary healthy adults. Mech Ageing Dev. 2021;194:111435.

    CAS  PubMed  Google Scholar 

  16. Benetos A, Okuda K, Lajemi M, Kimura M, Thomas F, Skurnick J, Labat C, Bean K, Aviv A. Telomere length as an indicator of biological aging: the gender effect and relation with pulse pressure and pulse wave velocity. Hypertension. 2001;37(2 Pt 2):381–5.

    CAS  PubMed  Google Scholar 

  17. Levine ME. Modeling the rate of senescence: can estimated biological age predict mortality more accurately than chronological age? J Gerontol A Biol Sci Med Sci. 2013;68(6):667–74.

    PubMed  Google Scholar 

  18. Levine ME, Lu AT, Quach A, Chen BH, Assimes TL, Bandinelli S, Hou L, Baccarelli AA, Stewart JD, Li Y, et al. An epigenetic biomarker of aging for lifespan and healthspan. Aging. 2018;10(4):573–91.

    PubMed  PubMed Central  Google Scholar 

  19. Chen L, Zhao Y, Liu F, Chen H, Tan T, Yao P, Tang Y. Biological aging mediates the associations between urinary metals and osteoarthritis among U.S. adults. BMC Med. 2022;20(1):207.

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Zhu K, Zhang Y, Lu Q, Geng T, Li R, Wan Z, Zhang X, Liu Y, Li L, Qiu Z, et al. Associations of exposure to lead and cadmium with risk of all-cause and cardiovascular disease mortality among patients with type 2 diabetes. Environ Sci Pollut Res Int. 2022;29(51):76805–15.

    CAS  PubMed  Google Scholar 

  21. Duan W, Xu C, Liu Q, Xu J, Weng Z, Zhang X, Basnet TB, Dahal M, Gu A. Levels of a mixture of heavy metals in blood and urine and all-cause, cardiovascular disease and cancer mortality: a population-based cohort study. Environ Pollut. 2020;263(Pt A): 114630.

    CAS  PubMed  Google Scholar 

  22. Xu C, Weng Z, Zhang L, Xu J, Dahal M, Basnet TB, Gu A. HDL cholesterol: a potential mediator of the association between urinary cadmium concentration and cardiovascular disease risk. Ecotoxicol Environ Saf. 2021;208: 111433.

    CAS  PubMed  Google Scholar 

  23. Matović V, Buha A, Ðukić-Ćosić D, Bulat Z. Insight into the oxidative stress induced by lead and/or cadmium in blood, liver and kidneys. Food Chem Toxicol. 2015;78:130–40.

    PubMed  Google Scholar 

  24. Ercal N, Gurer-Orhan H, Aykin-Burns N. Toxic metals and oxidative stress part I: mechanisms involved in metal-induced oxidative damage. Curr Top Med Chem. 2001;1(6):529–39.

    CAS  PubMed  Google Scholar 

  25. Dong W, Simeonova PP, Gallucci R, Matheson J, Flood L, Wang S, Hubbs A, Luster MI. Toxic metals stimulate inflammatory cytokines in hepatocytes through oxidative stress mechanisms. Toxicol Appl Pharmacol. 1998;151(2):359–66.

    CAS  PubMed  Google Scholar 

  26. Xia F, Li Q, Luo X, Wu J. Association between urinary metals and leukocyte telomere length involving an artificial neural network prediction: findings based on NHANES 1999–2002. Front Public Health. 2022;10: 963138.

    PubMed  PubMed Central  Google Scholar 

  27. Xie R, Zhang Y. Associations between dietary flavonoid intake with hepatic steatosis and fibrosis quantified by VCTE: evidence from NHANES and FNDDS. Nutr Metab Cardiovasc Dis. 2023;33(6):1179–89.

    PubMed  Google Scholar 

  28. Xie R, Zhang Y. Association between 19 dietary fatty acids intake and rheumatoid arthritis: results of a nationwide survey. Prostaglandins Leukot Essent Fatty Acids. 2023;188: 102530.

    CAS  PubMed  Google Scholar 

  29. Xie R, Liu X, Wu H, Liu M, Zhang Y. Associations between systemic immune-inflammation index and abdominal aortic calcification: results of a nationwide survey. Nutr Metabolism Cardiovasc Dis. 2023;33(7):1437–43.

    CAS  Google Scholar 

  30. Xie R, Liu Y, Wang J, Zhang C, Xiao M, Liu M, Zhang Y. Race and Gender Differences in the Associations Between Cadmium Exposure and Bone Mineral Density in US Adults. Biol Trace Elem Res. 2023;201(9):4254–61.

  31. Kim K, Melough MM, Vance TM, Kim D, Noh H, Koo SI, Chun OK. The relationship between zinc intake and cadmium burden is influenced by smoking status. Food Chem Toxicol. 2019;125:210–6.

    CAS  PubMed  Google Scholar 

  32. Park SK, Sack C, Sirén MJ, Hu H. Environmental cadmium and mortality from Influenza and Pneumonia in U.S. adults. Environ Health Perspect. 2020;128(12):127004.

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Ciesielski T, Bellinger DC, Schwartz J, Hauser R, Wright RO. Associations between cadmium exposure and neurocognitive test scores in a cross-sectional study of US adults. Environ Health. 2013;12: 13.

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Wang W, Schaumberg DA, Park SK. Cadmium and lead exposure and risk of cataract surgery in U.S. adults. Int J Hyg Environ Health. 2016;219(8):850–6.

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Aoki Y, Yee J, Mortensen ME. Blood cadmium by race/hispanic origin: the role of smoking. Environ Res. 2017;155:193–8.

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Xie R, Liu M. Relationship between non-alcoholic fatty liver disease and degree of hepatic steatosis and bone Mineral Density. Front Endocrinol (Lausanne). 2022;13:857110.

    PubMed  Google Scholar 

  37. Zhang Y, Wu H, Li C, Liu C, Liu M, Liu X, Yin Q, Li X, Xie R. Associations between weight-adjusted waist index and bone mineral density: results of a nationwide survey. BMC Endocr Disord. 2023;23(1):162.

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Xie R, Xiao M, Li L, Ma N, Liu M, Huang X, Liu Q, Zhang Y. Association between SII and hepatic steatosis and liver fibrosis: a population-based study. Front Immunol. 2022;13: 925690.

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Pappas RS, Fresquez MR, Martone N, Watson CH. Toxic metal concentrations in mainstream smoke from cigarettes available in the USA. J Anal Toxicol. 2014;38(4):204–11.

    CAS  PubMed  Google Scholar 

  40. Needham BL, Adler N, Gregorich S, Rehkopf D, Lin J, Blackburn EH, Epel ES. Socioeconomic status, health behavior, and leukocyte telomere length in the National Health and Nutrition Examination Survey, 1999–2002. Soc Sci Med. 2013;85:1–8.

    PubMed  PubMed Central  Google Scholar 

  41. Kim S. Overview of Cotinine Cutoff values for smoking status classification. Int J Environ Res Public Health. 2016;13(12):1236.

  42. Baron RM, Kenny DA. The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. J Pers Soc Psychol. 1986;51(6):1173–82.

    CAS  PubMed  Google Scholar 

  43. Sebastiani P, Thyagarajan B, Sun F, Schupf N, Newman AB, Montano M, Perls TT. Biomarker signatures of aging. Aging Cell. 2017;16(2):329–38.

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Chen L, Yin X, Zhao Y, Chen H, Tan T, Yao P, Tang Y. Biological ageing and the risks of all-cause and cause-specific mortality among people with diabetes: a prospective cohort study. J Epidemiol Community Health. 2022;76(9):771–8.

    PubMed  Google Scholar 

  45. Herranz N, Gil J. Mechanisms and functions of cellular senescence. J Clin Invest. 2018;128(4):1238–46.

    PubMed  PubMed Central  Google Scholar 

  46. Pieren DKJ, Smits NAM, Imholz S, Nagarajah B, van Oostrom CT, Brandt RMC, Vermeij WP, Dollé MET, Guichelaar T. Compromised DNA repair promotes the Accumulation of Regulatory T cells with an aging-related phenotype and responsiveness. Front Aging. 2021;2.

  47. Xie R, Ning Z, Xiao M, Li L, Liu M, Zhang Y. Dietary inflammatory potential and biological aging among US adults: a population-based study. Aging Clin Exp Res. 2023;35(6):1273–81.

    PubMed  Google Scholar 

  48. Wu Y, Wu Q, Pan R, Yi W, Li Y, Jin X, Liang Y, Mei L, Yan S, Sun X, et al. Phenotypic aging mediates the association between blood cadmium and depression: a population-based study. Environ Sci Pollut Res Int. 2023;30(15):44304–15.

    CAS  PubMed  Google Scholar 

  49. Patel CJ, Manrai AK, Corona E, Kohane IS. Systematic correlation of environmental exposure and physiological and self-reported behaviour factors with leukocyte telomere length. Int J Epidemiol. 2017;46(1):44–56.

    PubMed  Google Scholar 

  50. Lin S, Huo X, Zhang Q, Fan X, Du L, Xu X, Qiu S, Zhang Y, Wang Y, Gu J. Short placental telomere was associated with cadmium pollution in an electronic waste recycling town in China. PLoS ONE. 2013;8(4): e60815.

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Bernadotte A, Mikhelson VM, Spivak IM. Markers of cellular senescence. Telomere shortening as a marker of cellular senescence. Aging. 2016;8(1):3–11.

    CAS  PubMed  PubMed Central  Google Scholar 

  52. Demanelis K, Virani S, Colacino JA, Basu N, Nishijo M, Ruangyuttikarn W, Swaddiwudhipong W, Nambunmee K, Rozek LS. Cadmium exposure and age-associated DNA methylation changes in non-smoking women from northern Thailand. Environ Epigenet. 2017;3(2):dvx006.

    PubMed  PubMed Central  Google Scholar 

  53. Nicita-Mauro V, Lo Balbo C, Mento A, Nicita-Mauro C, Maltese G, Basile G. Smoking, aging and the centenarians. Exp Gerontol. 2008;43(2):95–101.

    CAS  PubMed  Google Scholar 

  54. Huang Z, Sun S, Lee M, Maslov AY, Shi M, Waldman S, Marsh A, Siddiqui T, Dong X, Peter Y, et al. Single-cell analysis of somatic mutations in human bronchial epithelial cells in relation to aging and smoking. Nat Genet. 2022;54(4):492–8.

    CAS  PubMed  PubMed Central  Google Scholar 

  55. Astuti Y, Wardhana A, Watkins J, Wulaningsih W. Cigarette smoking and telomere length: a systematic review of 84 studies and meta-analysis. Environ Res. 2017;158:480–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  56. Zota AR, Needham BL, Blackburn EH, Lin J, Park SK, Rehkopf DH, Epel ES. Associations of cadmium and lead exposure with leukocyte telomere length: findings from National Health and Nutrition Examination Survey, 1999–2002. Am J Epidemiol. 2015;181(2):127–36.

    PubMed  Google Scholar 

  57. Tellez-Plaza M, Jones MR, Dominguez-Lucas A, Guallar E, Navas-Acien A. Cadmium exposure and clinical cardiovascular disease: a systematic review. Curr Atheroscler Rep. 2013;15(10):356.

    PubMed  Google Scholar 

  58. Houben JM, Moonen HJ, van Schooten FJ, Hageman GJ. Telomere length assessment: biomarker of chronic oxidative stress? Free Radic Biol Med. 2008;44(3):235–46.

    CAS  PubMed  Google Scholar 

  59. Nomura SJ, Robien K, Zota AR. Serum folate, vitamin B-12, vitamin A, γ-Tocopherol, α-Tocopherol, and Carotenoids do not modify Associations between Cadmium exposure and leukocyte telomere length in the General US Adult Population. J Nutr. 2017;147(4):538–48.

    CAS  PubMed  PubMed Central  Google Scholar 

  60. Lee DH, Lim JS, Song K, Boo Y, Jacobs DR Jr. Graded associations of blood lead and urinary cadmium concentrations with oxidative-stress-related markers in the U.S. population: results from the third National Health and Nutrition Examination Survey. Environ Health Perspect. 2006;114(3):350–4.

    CAS  PubMed  Google Scholar 

  61. Colacino JA, Arthur AE, Ferguson KK, Rozek LS. Dietary antioxidant and anti-inflammatory intake modifies the effect of cadmium exposure on markers of systemic inflammation and oxidative stress. Environ Res. 2014;131:6–12.

    CAS  PubMed  PubMed Central  Google Scholar 

  62. O’Donovan A, Pantell MS, Puterman E, Dhabhar FS, Blackburn EH, Yaffe K, Cawthon RM, Opresko PL, Hsueh WC, Satterfield S, et al. Cumulative inflammatory load is associated with short leukocyte telomere length in the Health, Aging and Body Composition Study. PLoS ONE. 2011;6(5):e19687.

    PubMed  PubMed Central  Google Scholar 

  63. Giaginis C, Gatzidou E, Theocharis S. DNA repair systems as targets of cadmium toxicity. Toxicol Appl Pharmacol. 2006;213(3):282–90.

    CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We are grateful to the National Health and Nutrition Examination Survey for the data provided and to all participants for their selfless dedication; and to the China Scholarship Council for their support of Dr. Xie’s studying in Germany.

Funding

This study was funded by the Clinical Research Center of Hand and Foot Wound Repair and Functional Reconstruction in Hunan province (2021SK4030); Special Project of Hunan Provincial Health and Family Planning Commission (20201906); and Scientific Research Project of Hunan Health and Family Planning Commission (A2017018).

Author information

Authors and Affiliations

  1. Department of Gland Surgery, The Affiliated Nanhua Hospital, Hengyang Medical school, University of South China, Hengyang, 421002, China

    Ya Zhang

  2. Department of Hand & Microsurgery, The Affiliated Nanhua Hospital, Hengyang Medical school, University of South China, No.336 Dongfeng South Road, Zhuhui District, Hunan Province, Hengyang, 421002, China

    Mingjiang Liu & Ruijie Xie

  3. Hengyang Medical school, University of South China, Hengyang, 421002, China

    Ruijie Xie

Authors
  1. Ya Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mingjiang Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ruijie Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

RX and YZ designed the research. ML and YZ collected, analyzed the data, and drafted the manuscript. RX revised the manuscript. All authors contributed to the article and approved the submitted version.

Corresponding author

Correspondence to Ruijie Xie.

Ethics declarations

Ethics approval and consent to participate

The portions of this study involving human participants, human materials, or human data were conducted in accordance with the Declaration of Helsinki and were approved by the NCHS Ethics Review Board. The patients/participants provided their written informed consent to participate in this study.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1: Table S1. 

The associations between cotinine (ng/mL) with urinary cadmium (ng/g) and phenotypic age (year).

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, Y., Liu, M. & Xie, R. Associations between cadmium exposure and whole-body aging: mediation analysis in the NHANES. BMC Public Health 23, 1675 (2023). https://doi.org/10.1186/s12889-023-16643-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12889-023-16643-2

Keywords

BMC Public Health

ISSN: 1471-2458

Contact us