Graphical abstract
Keywords: Marijuana, Phenotypic age, Klemera–Doubal biological age, Blood cadmium
Abstract
Background
Global marijuana use has risen markedly in recent decades. Although prior research suggests that marijuana use is associated with epigenetic alterations, its relationship with biological aging remains unclear. This study aimed to examine the association between marijuana use and accelerated aging and explore the mediating role of metal exposure.
Methods
Using data from 12,806 U.S. adults (NHANES 2005–2018), biological age (BA) was calculated using two validated algorithms, phenotypic age (PhenoAge) and Klemera–Doubal biological age (KD-BioAge), with aging acceleration defined as the residuals from linear regression of BA on chronological age (CA). Marijuana use status was ascertained via standardized interviews. Analyses included survey-weighted multivariable linear regression, stratified subgroup analyses, joint exposure assessments, and mediation analyses.
Results
Current marijuana users exhibited significantly accelerated aging versus never users: PhenoAge acceleration (β = 0.72, 95% CI: 0.41–1.02, P < 0.001) and KD-BioAge acceleration (β = 0.36, 95% CI: 0.14–0.59, P = 0.002), after full adjustment. Subgroup and joint exposure analyses showed consistent associations, with evidence of additive associations among dual users of marijuana and tobacco. Mediation analyses identified blood cadmium as a partial mediator, explaining 15.6% and 8.3% of aging acceleration for PhenoAge and KD-BioAge, respectively.
Discussion
This study provides robust epidemiological evidence linking marijuana use to accelerated biological aging, with blood cadmium identified as a potential mechanistic link. These findings highlight the public health importance of understanding the long-term physiological correlates of marijuana use.
1. Introduction
Marijuana is one of the most widely used psychoactive substances globally, with an estimated 200 million past-year users in 2019, representing roughly 4% of the population aged 15–64 [1]. While marijuana may offer therapeutic benefits for certain conditions such as chronic pain and epilepsy, concerns remain about addiction, cognitive impairment, cardiovascular risk, and psychotic disorders [2,3]. Although previous studies link marijuana to age-related health outcomes, its associations with biological aging remain poorly understood.
Aging is a multisystem process marked by progressive physiological dysregulation and functional decline, with biological aging providing greater insight into individual differences by directly capturing physiological decline. Various measures of biological aging have been proposed, including blood-based biomarker–derived biological age (BA) [4], multi-omics–based BA [5], and epigenetic clocks [6]. Emerging evidence from epigenetic studies suggests that marijuana use may induce distinct alterations in DNA methylation patterns [[6], [7], [8]], potentially linking it to accelerated biological aging. However, due to limited sample sizes, further research is needed to confirm this association.
Beyond its psychoactive properties, marijuana may introduce environmental toxicants, including heavy metals [9] and organic pollutants [10], all linked to adverse health outcomes [11]. Notably, heavy metal exposure has been associated with accelerated biological aging [12], but its role in the relationship between marijuana use and biological aging remains understudied.
This study investigates the association between marijuana use and accelerated biological aging using National Health and Nutrition Examination Survey (NHANES) data and examines whether heavy metal exposure mediates this relationship, providing insights for public health policy.
2. Methods and materials
2.1. Data source and study population
We analyzed data from seven NHANES cycles (2005–2018), a nationally representative, multistage survey of U.S. adults conducted by the National Center for Health Statistics (NCHS). Of 70,190 participants, after excluding those <20 years, missing marijuana or biomarker data, and those with incomplete covariates, 12,806 adults aged 20–59 years were included in the final analysis (Supplementary Figure S1). All participants provided written informed consent under protocols approved by the NCHS Ethics Review Board. The study followed STROBE guidelines.
2.2. Definition of marijuana use
Marijuana use was self-reported among adults aged 18–59 years. Participants reporting use within the past 30 days were defined as current users, those with prior but not recent use as former users, and those denying lifetime use as never users.
2.3. Assessment of accelerated biological aging
BA was estimated using two validated algorithms, PhenoAge and KD-BioAge, which combine chronological age (CA) with clinical biomarkers to capture age-related physiological decline. PhenoAge follows Levine’s method [4], while KD-BioAge uses the Klemera–Doubal method with biomarkers selected from prior all-subsets regression optimizing mortality prediction [13]. CRP was incorporated into the primary biological age measures when available, and CRP-excluded versions were used only in sensitivity analyses to accommodate NHANES cycles with missing CRP data. The biomarkers included in each algorithm are listed in Supplementary Table S1. Biological aging acceleration was defined as the residuals obtained from linear regression of BA on CA, with positive residuals indicating accelerated aging [14]. These residual-based metrics are hereafter denoted as PhenoAgeAccel and KD-BioAgeAccel. Supplementary Figure S2 shows BA versus CA scatterplots and distributions of biological aging acceleration.
2.4. Assessment of metal exposure
Whole blood and spot urine specimens were collected, frozen at −20 °C, and shipped to the CDC’s Division of Laboratory Sciences, National Center for Environmental Health (NCEH) for analysis. Five blood and 18 urinary metals were quantified across relevant survey cycles. All metal concentrations were measured using inductively coupled plasma dynamic reaction cell mass spectrometry (ICP-DRC-MS) in accordance with CDC laboratory procedures. Urinary metal concentrations were adjusted for creatinine to account for urine dilution. Metal concentrations below the detection limit were imputed as LOD/√2, and all values were log-transformed before analysis. Detailed laboratory methods are described in the Supplementary Methods.
2.5. Assessment of covariates
Sociodemographic characteristics (age, sex, race/ethnicity, marital status, education, and family income), lifestyle factors (smoking, alcohol consumption, illicit drug use, and physical activity), and disease information were obtained via standardized questionnaires, physical examinations, and laboratory tests conducted in the mobile examination center. Detailed definitions and categorization of all covariates are provided in the Supplementary Methods.
2.6. Statistical analysis
Analyses accounted for NHANES complex survey design using sample weights, stratification, and clustering. Participants were grouped by marijuana use status. Baseline characteristics were summarized with weighted means (SD), medians (IQR), or proportions, and between-group differences were assessed via ANOVA, Kruskal–Wallis, or Fisher’s exact tests as appropriate. Age-standardized prevalence was calculated using the 2000 U.S. Census, and temporal trends were examined with multivariable-weighted logistic regression adjusted for age, sex, and race/ethnicity.
Associations between marijuana use and accelerated biological aging (Age residuals) were assessed using multivariable survey-weighted linear regression across four progressively adjusted models. Regression coefficients (β) and 95% CIs reflected changes in BA metrics, and least-squares geometric means (LSGMs) were estimated to compare marijuana groups. Subgroup analyses were performed for PhenoAge and KD-BioAge, and sensitivity analyses used modified BA algorithms. To account for overlap with tobacco use, marijuana exposure was reclassified into a binary variable (current vs. non-current), and a four-level joint exposure variable (neither, marijuana only, tobacco only, both) was used to isolate the independent effect of current marijuana use.
Mediation analyses evaluated the role of blood and urinary metals using the R mediation package with 5,000 bootstrap resamples to estimate indirect effects and bias-corrected CIs. Two-sided P < 0.05 indicated significance, with FDR correction for multiple comparisons.
3. Results
3.1. Characteristics of participants
A total of 12,806 participants from NHANES 2005–2018 were included (median age 40 years, IQR 29–50), with similar proportions of men and women. Overall, 14.7% reported marijuana use in the past 30 days, increasing from 12.5% in 2005/2006 to 18.9% in 2017/2018 (PTrend < 0.001, Supplementary Figure S3). A detailed comparison of demographic and clinical characteristics across marijuana use categories is presented in Table 1.
Table 1.
Characteristics of participants according marijuana use in US adults in NHANES 2005-2018 (n = 12,806).
| Overall | Never | Ever | Current | P for groups | |
|---|---|---|---|---|---|
| Unweighted sample size | 12,806 | 5,833 | 5,119 | 1,854 | |
| Age (years, median [IQR]) | 40 [29, 50] | 41 [31, 50] | 41 [31, 50] | 33 [25, 44] | <0.001 |
| Age category (%) | <0.001 | ||||
| 20−39 years | 6,553 (49.2) | 2,773 (47.2) | 2,546 (46.2) | 1,234 (64.2) | |
| 40−59 years | 6,253 (50.8) | 3,060 (52.8) | 2,573 (53.7) | 620 (35.8) | |
| Sex (%) | <0.001 | ||||
| Women | 6,617 (50.0) | 3,366 (55.5) | 2,514 (49.0) | 737 (38.7) | |
| Men | 6,189 (50.0) | 2,467 (44.5) | 2,605 (51.0) | 1,117 (61.3) | |
| Race/ethnicity (%) | <0.001 | ||||
| non-Hispanic White | 5,423 (66.8) | 1,784 (56.1) | 2,764 (75.7) | 875 (67.9) | |
| Hispanics | 3,638 (15.2) | 2,321 (23.0) | 1,015 (10.0) | 302 (10.5) | |
| Non-Hispanic Black | 2,504 (10.6) | 969 (10.4) | 1,010 (9.2) | 525 (15.5) | |
| Other | 1,241 (7.4) | 759 (10.6) | 330 (5.1) | 152 (6.1) | |
| Education levels (%) | <0.001 | ||||
| <9 years | 926 (3.8) | 726 (6.9) | 156 (1.8) | 44 (1.8) | |
| 9−12 years | 4,716 (32.8) | 2,039 (31.1) | 1,813 (30.9) | 864 (43.3) | |
| >12 years | 7,164 (63.4) | 3,068 (62.0) | 3,150 (67.4) | 946 (54.8) | |
| Marital status (%) | <0.001 | ||||
| Never married | 2,932 (21.3) | 1,138 (19.5) | 1,081 (18.4) | 713 (35.3) | |
| Married or cohabiting | 8,008 (65.6) | 3,950 (69.6) | 3,213 (67.2) | 845 (49.7) | |
| Widowed, divorced, or separated | 1,866 (13.1) | 745 (10.9) | 825 (14.5) | 296 (15.0) | |
| Annual family income (%) | <0.001 | ||||
| ≤$20,000 per year | 2,689 (14.8) | 1,215 (15.1) | 925 (11.7) | 549 (23.5) | |
| >$20,000 per year | 10,117 (85.2) | 4,618 (84.9) | 4,194 (88.3) | 1,305 (76.5) | |
| Physical activity (%) | <0.001 | ||||
| Inactive | 5,725 (39.1) | 2,849 (42.1) | 2,121 (37.2) | 755 (36.9) | |
| Moderate | 3,099 (26.1) | 1,401 (26.5) | 1,281 (26.2) | 417 (24.9) | |
| Vigorous | 3,982 (34.8) | 1,583 (31.4) | 1,717 (36.6) | 682 (38.1) | |
| Body mass index (%) | <0.001 | ||||
| <25 kg/m2 | 3,777 (31.4) | 1,574 (29.6) | 1,464 (30.0) | 739 (40.4) | |
| 25−30 kg/m2 | 4,074 (31.6) | 1,924 (30.9) | 1,611 (32.8) | 539 (30.0) | |
| ≥30 kg/m2 | 4,955 (37.0) | 2,335 (39.6) | 2,044 (37.2) | 576 (29.6) | |
| Alcohol current (yes, %) | 9,684 (80.2) | 3,530 (64.8) | 4,461 (89.4) | 1,693 (92.6) | <0.001 |
| Tobacco current (yes, %) | 3,985 (30.2) | 896 (14.6) | 1,869 (33.7) | 1,220 (61.3) | <0.001 |
| Illicit drug abuse (yes, %) | 336 (2.4) | 30 (0.4) | 113 (1.7) | 193 (9.8) | <0.001 |
| Cardiovascular diseases (yes, %) | 507 (3.4) | 208 (3.1) | 228 (3.6) | 71 (3.2) | 0.441 |
| Hypertension (yes, %) | 2,809 (21.3) | 1,254 (21.6) | 1,202 (22.0) | 353 (18.1) | 0.025 |
| Diabetes (yes, %) | 853 (5.5) | 464 (6.8) | 323 (5.2) | 66 (3.1) | <0.001 |
| Arthritis (yes, %) | 2,056 (17.0) | 802 (15.1) | 971 (19.3) | 283 (15.0) | <0.001 |
| Airway diseases (yes, %) | 2,285 (17.7) | 792 (13.8) | 1,038 (19.3) | 455 (23.0) | <0.001 |
| Liver conditions (yes, %) | 457 (3.3) | 185 (2.8) | 209 (3.8) | 63 (3.4) | 0.111 |
| Weak kidneys (yes, %) | 196 (1.4) | 87 (1.4) | 79 (1.3) | 30 (1.5) | 0.916 |
| Thyroid problems (yes, %) | 868 (7.6) | 409 (7.6) | 383 (8.5) | 76 (4.6) | <0.001 |
| Cancer (yes, %) | 501 (4.7) | 194 (3.8) | 237 (5.5) | 70 (4.8) | 0.015 |
| Prescription medicines use (yes, %) | 5,616 (48.0) | 2,399 (45.2) | 2,516 (52.9) | 701 (39.9) | <0.001 |
| SBP (mmHg, mean (SD)) | 119.56 (14.59) | 119.5 (14.84) | 119.41 (14.46) | 120.16 (14.29) | 0.418 |
| eGFR (ml/min/1.73 m2, mean (SD)) | 104.53 (18.06) | 105.17 (18.51) | 102.78 (17.76) | 107.36 (17.04) | <0.001 |
| HbA1c (%, mean (SD)) | 5.56 (0.84) | 5.64 (0.94) | 5.52 (0.81) | 5.4 (0.59) | <0.001 |
| TC (mg/dL, mean (SD)) | 194.23 (40.43) | 195.04 (39.46) | 195.54 (39.94) | 188.08 (43.90) | <0.001 |
| UA (mg/dL, mean (SD)) | 5.3 (1.39) | 5.19 (1.39) | 5.39 (1.39) | 5.43 (1.38) | <0.001 |
| Depressive symptoms (median [IQR]) | 2 [0,4] | 1 [0,3] | 2 [0,4] | 2 [0,5] | <0.001 |
NHANES: National Health and Nutrition Examination Survey; SD: standard deviation; IQR: interquartile range; SBP: systolic blood pressure; eGFR: estimated glomerular filtration rate; HbA1c: glycated hemoglobin A1c; TC: total cholesterol; UA: uric acid.
3.2. Marijuana use and accelerated biological aging
Current marijuana use was significantly associated with accelerated biological aging measured by both PhenoAge and KD-BioAge, with associations remaining robust across four progressively adjusted models controlling for demographic, socioeconomic, lifestyle, and clinical factors (Supplementary Table S2). Sensitivity analyses using CRP-excluded BA measures yielded similar results (Supplementary Table S3). Based on the fully adjusted model, least squares geometric means (LSGM) of residuals indicated greater acceleration among current users compared to never users (Fig. 1): PhenoAgeAccel, 0.49 (SE 0.28) vs. –0.22 (SE 0.25) years (P < 0.001); KD-BioAgeAccel, 0.21(SE 0.19) vs. –0.16 (SE 0.20) years (P = 0.005). Ever users showed no significant difference.
Fig. 1.
Least squares geometric mean and standard error of biological aging acceleration by marijuana use status among US adults in NHANES 2005–2018 (n = 12,806).
LSGM (SE) values were derived from fully adjusted regression models (see Supplementary Table S2). To minimize marginal prediction bias due to imbalanced covariate distributions, selected disease variables (e.g., hypertension, cardiovascular disease, diabetes, arthritis, cancer) were explicitly fixed to the non-disease state when defining the reference population. Pairwise comparisons between marijuana use groups were performed using LSGM, with P values adjusted for multiple testing using the false discovery rate (FDR) method. LSGM: least squares geometric mean; SE: standard error.
Subgroup analyses showed generally consistent positive associations across most strata (Supplementary Figure S4). Significant interactions with age group were observed for both aging measures (Both P < 0.001), with stronger associations among adults aged ≥40 years, whereas no significant interaction with sex was detected (Supplementary Table S4). Joint exposure analysis revealed additive interaction of marijuana and tobacco use: for PhenoAge, β = 0.824 (0.405–1.242) for marijuana only and 1.500 (1.159–1.840) for dual users; for KD-BioAge, β = 0.439 (0.116–0.761) and 0.824 (0.588–1.061), respectively (all P < 0.001) (Supplementary Table S5).
3.3. Mediation effects of blood cadmium
Among all metals examined, only blood cadmium showed significant mediation for both biological aging measures (Supplementary Table S6, S7). Its indirect effect on PhenoAgeAccel was 0.089 (95% CI: 0.057–0.126), accounting for 15.6% of the total effect (FDR P = 0.003) (Fig. 2A), and for KD-BioAgeAccel, 0.026 (95% CI: 0.010–0.044), representing 8.3% of the total effect (FDR P = 0.003) (Fig. 2B).
Fig. 2.
Mediation effects of blood cadmium on association between marijuana use (current vs never) and accelerated biological aging in US adults.
Proportion of mediation = ACME / (ACME + ADE) * 100%. Cd: Cadmium; ACME: average causal mediation effect; ADE: average direct effect; TE: total effect.
4. Discussion
This study demonstrates a significant positive association between marijuana use and accelerated biological aging in a nationally representative sample of U.S. adults. We further propose a novel “marijuana–metals–aging” pathway, suggesting that cadmium exposure may partially mediate this association and serve as a plausible biological mechanism.
Previous studies have reported associations between marijuana use and epigenetic age acceleration, though sample sizes were often limited. For instance, a prospective longitudinal study by Allen et al. (n = 154, aged 13–30 years) found that marijuana use predicted epigenetic changes associated with accelerated aging, as measured by DNAmGrimAge and DunedinPoAm [7]. Similar trends were observed in sub-cohorts of the Coronary Artery Risk Development in Young Adults (CARDIA) Study (n = 1023) [8] and the Canadian Cohort of Obstructive Lung Disease (CanCOLD) (n = 93) [6]. As reported in our previous study [15], marijuana use has also been linked to decreased levels of the anti-aging protein α-Klotho. In the present study, we addressed prior limitations by conducting a nationally representative analysis with standardized data and rigorous methods to clarify the relationship between marijuana use and biological aging. Both aging measures consistently showed that current marijuana use was associated with accelerated biological aging, independent of tobacco exposure. Subgroup and joint exposure analyses further confirmed that marijuana and tobacco exert independent, additive interaction, suggesting that marijuana may link to aging through mechanisms distinct from those of tobacco.
Although marijuana and tobacco smoke share numerous toxicants, quantitative differences in specific constituents may explain their distinct biological effects [16,17]. Marijuana smoke contains higher levels of nitrogen oxides, hydrogen cyanide, and aromatic amines, as well as comparable or greater particulate matter and tar, but lower concentrations of polycyclic aromatic hydrocarbons [17,18]. These compositional differences may differentially influence oxidative stress, DNA damage, and inflammatory responses, contributing to accelerated aging.
Our findings align with evidence that marijuana may promote molecular and cellular hallmarks of aging. Prior studies indicate that cannabis modulates DNA methylation-based biomarkers associated with lifespan [19] and may accelerate aging processes [20]. Compounds derived from Cannabis sativa can affect mitochondrial function through complex signaling pathways [21], representing a key aspect of cannabinoids’ toxicological profile with potential implications for pathological conditions. For example, cannabidiol (CBD) has been shown to induce oxidative stress and apoptosis in human monocytes in a time- and concentration-dependent manner [22] and to increase proinflammatory factor expression in mice by altering gut microbiota, including Akkermansia muciniphila [23,24]. Collectively, these molecular and cellular alterations contribute to accelerated systemic biological aging.
Metal contamination of marijuana can occur during cultivation, processing, and consumption, posing health risks. NHANES analyses revealed that exclusive marijuana users exhibited elevated cadmium and lead levels in blood and urine compared to non-users [9]. Cadmium, in particular, has been shown to promote cellular aging via oxidative stress [25], chronic inflammation [26], and mitochondrial dysfunction [27]. Epidemiological studies further support this link, reporting cadmium-associated epigenetic age acceleration [28] and associations with both PhenoAge and KD-BioAge [29]. Our study advances this field by including a larger and more diverse sample and, for the first time, proposing a “marijuana-cadmium-biological aging” pathway, quantifying cadmium as a significant mediator in the association between marijuana use and accelerated biological aging.
Several limitations should be considered. First, the cross-sectional design precludes causal inference. Second, although our study expands the age range of prior research, findings may not generalize to adults aged 60 and older, who were not assessed for marijuana use. Third, marijuana use was self-reported, which may introduce recall and social desirability biases. Additionally, NHANES did not capture detailed information on use modality, type, or potency, which could influence biological responses [30]. Future longitudinal studies should clarify the biological mechanisms linking cannabis exposure to accelerated aging and examine whether different constituents or modes of use have distinct effects on aging trajectories.
5. Conclusion
This study investigated the association between marijuana use and accelerated biological aging, revealing a significant positive relationship between these variables. Importantly, our mediation analysis identified serum cadmium as a key mechanistic factor linking cannabis use to biological aging. These findings offer empirical evidence to inform public health strategies regarding marijuana consumption and its potential health consequences.
CRediT authorship contribution statement
Xiaotong Chen: Writing – original draft, review & editing, Conceptualization, Funding acquisition. Kai Wei: Writing –original draft, review & editing, Formal analysis, Methodology, Conceptualization, Funding acquisition.
Declaration of Generative AI and AI-assisted technologies in the writing process
The authors used ChatGPT (GPT-5, OpenAI, San Francisco, CA, USA) to assist with language editing (e.g., improving grammar, readability, and phrasing). The study design, data analysis, interpretation of results, and all scientific conclusions were entirely developed by the authors. All AI-assisted text was carefully reviewed and revised by the authors to ensure accuracy and appropriateness.
Funding
This work was supported by Shanghai Jing'an District Health Research Project (2024QN01), Jing'an District Health "Ten Hundred Thousand" Talent Fund (2025SBX-YQ01), Guizhou Provincial People’s Hospital Talent Fund ([2022]-15), Guizhou Provincial Science and Technology Program (ZK[2024]General 467), and Science and Technology Fund Project of Guizhou Provincial Health Commission (gzwkj2024-345).
Data availability
The data that support the findings of this study are openly available on the NHANES website and can be accessed at https://wwwn.cdc.gov/nchs/nhanes/Default.aspx.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could influence the work reported in this study.
Acknowledgments
We are grateful to the participants involved in the National Health and Nutrition Examination Survey and the US Centers for Disease Control and Prevention.
Footnotes
Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j.jnha.2026.100778.
Appendix A. Supplementary data
The following is Supplementary data to this article:
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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 data that support the findings of this study are openly available on the NHANES website and can be accessed at https://wwwn.cdc.gov/nchs/nhanes/Default.aspx.