Abstract
Objective
This study used data from the China Health and Retirement Longitudinal Survey (CHARLS) to examine the association between adverse childhood experiences (ACEs) and accelerated aging in middle-aged and older Chinese, with implications for healthy aging interventions.
Methods
A total of 8327 participants aged 45 years and older were selected from the 2011 baseline survey. Accelerated aging was defined using biological age estimated from 2011 biomarker data. Information on ACEs was obtained from the 2014 life history questionnaire. Three multivariable logistic regression models were used to assess the association between ACEs and accelerated aging, and restricted cubic spline (RCS) analysis was used to test potential non-linear patterns. Exploratory subgroup analyses were conducted to examine whether the association differed across demographic, socioeconomic, lifestyle, and disease-status groups.
Results
Each additional ACE type was associated with a 5% increased odds of accelerated aging (OR = 1.05, 95% CI: 1.02–1.08). This association remained significant after adjustment for all confounders (OR = 1.05, 95% CI: 1.01–1.08). Individuals with ≥ 4 ACE types had significantly higher odds of accelerated aging (OR = 1.20, 95% CI: 1.01–1.44), showing a dose-response relationship (P for trend < 0.05). The RCS analysis did not show a significant non-linear relationship, but indicated that the overall odds increased with the number of ACEs types (p-overall < 0.05). Exploratory subgroup analyses showed no statistically significant interactions across subgroups (all P for interaction > 0.05), suggesting no clear evidence of effect modification.
Conclusion
Among the middle-aged and older population in China, adverse childhood experiences are positively associated with accelerated aging, showing a cumulative effect. Strengthening community support, social services and policy measures may help to mitigate the long-term adverse effects of ACEs on healthy aging.
Keywords: Adverse childhood experiences, ACEs, Accelerated aging, China health and retirement longitudinal study, CHARLS
Introduction
As the global aging process accelerates, age-related health issues are receiving widespread attention. According to the United Nations, by 2050 the proportion of the world’s population aged over 65 years will reach approximately 16% [1]. This trend poses a serious challenge to the health and social care systems of all countries. It is worth noting that aging is not a simple biological process, but is profoundly influenced by multiple factors such as social, psychological and environmental factors [2]. In recent years, researchers have paid increasing attention to the potential impact of early life experiences on the aging process, particularly the relationship between adverse childhood experiences (ACEs) and aging. Previous research has suggested that ACEs can have a profound impact on lifelong health and development through biological embedding effects on the nervous, endocrine, and immune systems, thereby increasing the risk of age-related diseases in adulthood [3].
Childhood is a critical period of rapid physical and mental development. If an individual suffers adverse experiences during this time, such as abuse, neglect or family dysfunction, this often has lasting effects on social interaction, mental health and physiological functioning. Existing studies have shown that ACEs are highly correlated with a range of health problems in adulthood, including lung dysfunction [4], cognitive decline [5] and new cardiovascular disease [6]. Notably, a study based on the UK Biobank suggests that childhood adversity has a significant impact on accelerated aging in adulthood [7].
However, there is still a lack of systematic research on the relationship between ACEs and accelerated aging in the middle-aged and older Chinese population. To fill this gap, this study analyzed the association between ACEs and accelerated aging among middle-aged and older Chinese adults using a large sample from the China Health and Retirement Longitudinal Study (CHARLS). Previous studies have suggested that the health consequences of ACEs may vary according to demographic characteristics, socioeconomic status, lifestyle behaviors, sleep patterns, and chronic disease status. In addition, factors such as age, sex, education, place of residence, body mass index (BMI), smoking, alcohol consumption, sleep duration, and chronic diseases are closely related to aging-related health outcomes. Therefore, exploratory subgroup analyses were conducted to assess whether the association between ACEs and accelerated aging was consistent across these groups. Through this study, we aimed to clarify the potential cumulative effect of childhood ACEs on aging and provide empirical evidence for improving the overall health of middle-aged and older Chinese adults.
Materials and methods
Study design
The data for this study come from the China Health and Retirement Longitudinal Study (CHARLS), which is hosted by the National School of Development at Peking University. The study uses a multi-stage stratified sampling design to ensure the representativeness of the sample. As a national cohort study, CHARLS continuously tracks socioeconomic and health indicators of people aged 45 and over. The baseline survey began in 2011 and is followed up every 2–3 years, covering 150 county-level regions and 450 village-level communities in mainland China, including approximately 17,000 participants from 10,000 households [8].
Participants
This study used baseline 2011 CHARLS data to assess biological age (and hence accelerated aging status) and incorporated the 2014 Adverse Childhood Experiences (ACEs) questionnaire. CHARLS is designed to survey Chinese residents aged 45 years and older; therefore, participants aged 45 years and above were included in this study. A total of 17,705 participants were included at baseline in 2011. After excluding 404 individuals younger than 45 years, 5781 individuals with missing biological age data, and 3193 individuals who did not have ACEs information from the 2014 life history questionnaire, 8327 participants were included in the final analysis. Biological age data were considered missing when the biomarkers required to calculate biological age were unavailable; thus, accelerated aging status could not be classified for these participants. The 3193 individuals without ACEs information in 2014 may have been due to attrition or missing questionnaire data between the 2011 baseline and the 2014 life history survey, including loss to follow-up, death, refusal, or incomplete questionnaire responses. Figure 1 shows the recruitment flow. Biological age and accelerated aging status were derived from biomarker data collected at the 2011 baseline survey, whereas ACEs were measured using the 2014 life history questionnaire.
Fig. 1.
Flow chart of participant inclusion. Note: Biological age and accelerated aging status were derived from biomarkers measured at the 2011 baseline survey. Adverse childhood experiences (ACEs) were assessed using the 2014 CHARLS Life History Survey
Variables
Outcome variable
The outcome variable of this study was accelerated aging status at baseline. Biological age was calculated using biomarker data from the 2011 baseline survey and was measured on the basis of eight biomarkers [9], including several domains representing bodily function: immune function, including high-sensitivity C-reactive protein and platelet count; cardiometabolic function, including total cholesterol, triglycerides, glycated haemoglobin, and systolic blood pressure; and renal function, including blood urea nitrogen and creatinine. Accelerated aging was defined as an estimated biological age exceeding the participant’s chronological age at the 2011 baseline survey [10, 11]. Participants whose biological age exceeded their chronological age at baseline were classified as having accelerated aging. Accelerated aging was assessed cross-sectionally by comparing biological age, calculated from biomarkers measured at the 2011 baseline, with chronological age at the same baseline. The 2014 wave was used only to collect ACE information.
Primary exposure variable
The primary exposure variable was adverse childhood experiences (ACEs). ACEs were defined as adverse experiences occurring during childhood, before the age of 18 years. Information on ACEs was obtained from the 2014 CHARLS Life History Survey. The construction of the ACE indicators has been described in detail in a previously published CHARLS study [12]. This study included 13 types of ACE indicators covering abuse and neglect (physical abuse and emotional neglect), family dysfunction (domestic violence, family member substance dependence, family member mental illness, family member incarceration, parental divorce or separation), family member death or serious illness (parental death, sibling death, parental disability), and adverse environment (bullying or unsafe neighbourhood). Each ACE indicator was coded as 0 or 1, and the indicator scores were summed to generate a cumulative ACE score. A higher score indicated exposure to a greater number of ACE types. In addition to treating the total ACEs score as a continuous variable, participants were also divided into five groups (0, 1, 2, 3, or ≥ 4) based on the number of ACEs to reflect cumulative ACE exposure [13].
Covariates
This study included demographic and socioeconomic covariates, including age, sex, ethnicity, marital status, educational level, and place of residence. Age was categorized as 45–59, 60–79, and ≥ 80 years in descriptive and subgroup analyses. Ethnicity was classified as Han or others. Han ethnicity is the majority ethnic group in China, while the “others” category included participants from ethnic minority groups other than Han. Because the number of participants in individual minority ethnic groups was relatively small, these groups were combined into one category for analysis. Marital status was classified as married or non-married. Place of residence was classified as urban or rural according to the residence/community classification recorded in CHARLS, reflecting whether participants lived in urban communities or rural villages at the time of the survey. Educational level referred to the highest level of education attained by participants and was categorized as elementary school and below, high school, and college and higher. Lifestyle-related covariates included smoking, alcohol consumption, BMI status, sleep duration at night, and sleep duration at noon. Smoking was defined as having smoked more than 100 cigarettes in a lifetime. BMI status was classified as underweight, normal, overweight, or obese according to WHO criteria [14]. Comorbidities included hypertension, diabetes mellitus, cancer, and cardiovascular disease.
Statistical analysis
All statistical analyses were performed using R version 4.4.2 (http://www.r-project.org), with statistical significance set at p < 0.05. Continuous variables in the baseline table are presented as means and standard deviations, and categorical variables are presented as sample sizes and percentages. All descriptive statistics and regression analyses were conducted using unweighted data; therefore, Table 1 reports actual sample sizes and unweighted percentages rather than population-weighted estimates.
Table 1.
Characteristics of participants by accelerated aging
| Variable | Total (n = 8327) |
Accelerated Aging (n = 3585) |
Non-accelerated aging (n = 4742) |
P value |
|---|---|---|---|---|
| Adverse childhood experiences (ACEs) | 2.08 ± 1.45 | 2.14 ± 1.47 | 2.04 ± 1.44 | < 0.01 |
| Adverse childhood experiences (ACEs) | 0.05 | |||
| 0 | 1081 (13.0) | 442 (12.3) | 639 (13.5) | |
| 1 | 2089 (25.1) | 861 (24.0) | 1228 (25.9) | |
| 2 | 2342 (28.1) | 1016 (28.3) | 1326 (28.0) | |
| 3 | 1458 (17.5) | 647 (18.1) | 811 (17.1) | |
| ≥4 | 1357 (16.3) | 619 (17.3) | 738 (15.6) | |
| Biological age (years) | 58.84 ± 9.97 | 62.39 ± 9.53 | 56.16 ± 9.44 | < 0.001 |
| Age (years) | 59.38 ± 9.22 | 59.12 ± 9.06 | 59.58 ± 9.35 | 0.02 |
| Age (years) | 0.08 | |||
| 45–59 | 4499 (54.0) | 1977 (55.2) | 2522 (53.2) | |
| 60–79 | 3627 (43.6) | 1533 (42.8) | 2094 (44.2) | |
| ≥80 | 201 (2.4) | 75 (2.1) | 126 (2.7) | |
| Sex | < 0.001 | |||
| Male | 3836 (46.1) | 2064 (57.6) | 1772 (37.4) | |
| Female | 4491 (53.9) | 1521 (42.4) | 2970 (62.6) | |
| Ethnicity | < 0.001 | |||
| Han | 7674 (92.2) | 3251 (90.7) | 4423 (93.3) | |
| Others | 580 (7.0) | 296 (8.3) | 284 (6.0) | |
| Missing | 73 (0.9) | 38 (1.1) | 35 (0.7) | |
| Marital status | 0.19 | |||
| Non-Married | 993 (11.9) | 408 (11.4) | 585 (12.3) | |
| Married | 7334 (88.1) | 3177 (88.6) | 4157 (87.7) | |
| Educational level | < 0.001 | |||
| Elementary school and below | 5942 (71.4) | 2452 (68.4) | 3490 (73.6) | |
| High school | 2295 (27.6) | 1091 (30.4) | 1204 (25.4) | |
| College and higher | 88 (1.1) | 41 (1.1) | 47 (1.0) | |
| Missing | 2 (0.0) | 1 (0.0) | 1 (0.0) | |
| Place of residence | < 0.001 | |||
| Rural | 5579 (67.0) | 2301 (64.2) | 3278 (69.1) | |
| Urban | 2748 (33.0) | 1284 (35.8) | 1464 (30.9) | |
| Body mass index status | < 0.001 | |||
| Underweight | 534 (6.4) | 164 (4.6) | 370 (7.8) | |
| Normal | 5083 (61.0) | 1966 (54.8) | 3117 (65.7) | |
| Overweight | 2168 (26.0) | 1162 (32.4) | 1006 (21.2) | |
| Obese | 403 (4.8) | 227 (6.3) | 176 (3.7) | |
| Missing | 139 (1.7) | 66 (1.8) | 73 (1.5) | |
| Smoking | < 0.001 | |||
| No | 5095 (61.2) | 1905 (53.1) | 3190 (67.3) | |
| Yes | 3224 (38.7) | 1677 (46.8) | 1547 (32.6) | |
| Missing | 8 (0.1) | 3 (0.1) | 5 (0.1) | |
| Alcohol consumption | < 0.001 | |||
| No | 5589 (67.1) | 2239 (62.5) | 3350 (70.7) | |
| Yes | 2731 (32.8) | 1343 (37.5) | 1388 (29.3) | |
| Missing | 7 (0.1) | 3 (0.1) | 4 (0.1) | |
| Sleep duration at night (hours) | 0.17 | |||
| ≤6 | 4181 (50.2) | 1766 (49.3) | 2415 (50.9) | |
| 6–8 | 1608 (19.3) | 714 (19.9) | 894 (18.9) | |
| ≥8 | 2408 (28.9) | 1057 (29.5) | 1351 (28.5) | |
| Missing | 130 (1.6) | 48 (1.3) | 82 (1.7) | |
| Sleep duration at noon (minutes) | 0.06 | |||
| <30 | 4623 (55.5) | 1954 (54.5) | 2669 (56.3) | |
| ≥30 | 3623 (43.5) | 1603 (44.7) | 2020 (42.6) | |
| Missing | 81 (1.0) | 28 (0.8) | 53 (1.1) | |
| Hypertension | < 0.001 | |||
| No | 6150 (73.9) | 2251 (62.8) | 3899 (82.2) | |
| Yes | 2118 (25.4) | 1314 (36.7) | 804 (17.0) | |
| Missing | 59 (0.7) | 20 (0.6) | 39 (0.8) | |
| Diabetes mellitus (DM) | < 0.001 | |||
| No | 7758 (93.2) | 3227 (90.0) | 4531 (95.6) | |
| Yes | 474 (5.7) | 316 (8.8) | 158 (3.3) | |
| Missing | 95 (1.1) | 42 (1.2) | 53 (1.1) | |
| Cancer | 0.22 | |||
| No | 8216 (98.7) | 3546 (98.9) | 4670 (98.5) | |
| Yes | 63 (0.8) | 21 (0.6) | 42 (0.9) | |
| Missing | 48 (0.6) | 18 (0.5) | 30 (0.6) | |
| Cardiovascular disease (CVD) | 0.08 | |||
| No | 7223 (86.7) | 3079 (85.9) | 4144 (87.4) | |
| Yes | 1084 (13.0) | 499 (13.9) | 585 (12.3) | |
| Missing | 20 (0.2) | 7 (0.2) | 13 (0.3) |
Among the 8327 participants, the amount of missing values for the covariates were 2 (0.02%) for educational level, 73 (0.9%) for ethnicity, 139 (1.7%) for BMI status, 8 (0.1%) for smoke, 7 (0.8%) for alcohol, 59 (0.7%) for hypertension, 95 (1.1%) for diabetes mellitus, 48 (0.6%) for cancer, 20 (0.2%) for cardiovascular disease, 130 (1.6%) for sleep duration at night, and 81 (1.0%) for Sleep duration at noon
“Others” in ethnicity refers to non-Han ethnic minority groups. Place of residence was classified as urban or rural according to the residence/community classification recorded in CHARLS. Values are actual sample sizes and unweighted percentages
Logistic regression models were used to examine the association between ACEs and accelerated aging, and restricted cubic spline (RCS) regression was used to explore the potential non-linear relationship. Three logistic regression models were constructed using sequential covariate adjustment: Model 1 was an unadjusted model; Model 2 was adjusted for demographic and socioeconomic characteristics, including age, sex, ethnicity, marital status, educational level, and place of residence; and Model 3 was further adjusted for lifestyle factors and comorbidities, including BMI status, smoking, alcohol consumption, sleep duration at night, sleep duration at noon, hypertension, diabetes mellitus, cancer, and cardiovascular disease.
Trend tests were performed to examine whether there was a dose-response relationship. Exploratory subgroup analyses were conducted according to age, sex, ethnicity, marital status, educational level, place of residence, BMI status, smoking, alcohol consumption, sleep duration at night, sleep duration at noon, hypertension, diabetes mellitus, cancer, and cardiovascular disease. These variables were selected because they represent demographic, socioeconomic, lifestyle, and health-related factors that may be associated with both ACEs exposure and aging-related outcomes. Interaction terms between ACEs and each subgroup variable were added to the models to test for potential effect modification. Because the subgroup analyses were exploratory, the results were interpreted cautiously.
To reduce the bias that may be caused by missing variables, we used dummy variables to indicate missing covariate values when the missing values exceeded 1%, as described in previous analyses [15, 16].
No formal correction for multiple comparisons was applied because the three logistic regression models represented sequential covariate adjustment rather than separate primary hypothesis tests. The main inference was based on the fully adjusted model. Subgroup analyses were exploratory and were interpreted mainly according to the P values for interaction.
Results
Baseline characteristics of research participants
A total of 8327 participants were included in the study, of whom 3585 had accelerated aging. Regarding the distribution of ACEs, 13.0% had 0 ACE types, 25.1% had 1 ACE type, 28.1% had 2 ACE types, 17.5% had 3 ACE types, and 16.3% had ≥ 4 ACE types.
Compared with the non-accelerated aging group, the accelerated aging group had a higher mean number of ACE types (2.14 ± 1.47 vs. 2.04 ± 1.44, p < 0.01), younger mean age (59.12 ± 9.06 vs. 59.58 ± 9.35 years, p = 0.02), a higher proportion of males (57.6% vs. 37.4%, p < 0.001), a lower proportion of Han ethnicity (90.7% vs. 93.3%, p < 0.001), a higher proportion of participants with a high school education or above (31.6% vs. 26.4%, p < 0.001), a higher proportion of urban residents (35.8% vs. 30.9%, p < 0.001), and a higher proportion of overweight or obese participants (38.7% vs. 24.9%, p < 0.001). The accelerated aging group also had a higher proportion of smokers (46.8% vs. 32.6%, p < 0.001) and alcohol consumers (37.5% vs. 29.3%, p < 0.001). In addition, the prevalence of hypertension (36.7% vs. 17.0%, p < 0.001) and diabetes mellitus (8.8% vs. 3.3%, p < 0.001) was higher in the accelerated aging group. There were no significant differences between the two groups in marital status, sleep duration, cancer, or cardiovascular disease (Table 1). The numbers and percentages reported in Table 1 are actual sample sizes and unweighted percentages.
Logistic regression analysis
The results of multivariable logistic regression analysis with baseline accelerated aging status as the dependent variable are shown in Table 2. The unadjusted model (Model 1) showed that each additional ACE type was associated with 5% higher odds of accelerated aging (OR = 1.05, 95% CI: 1.02–1.08). After adjustment for demographic and socioeconomic characteristics (Model 2), each additional ACE type was associated with 4% higher odds of accelerated aging (OR = 1.04, 95% CI: 1.01–1.08). After further adjustment for lifestyle factors and comorbidities (Model 3), each additional ACE type was associated with 5% higher odds of accelerated aging (OR = 1.05, 95% CI: 1.01–1.08).
Table 2.
Association of adverse childhood experiences with accelerated aging
| Exposure | Model 1 | Model 2 | Model 3 | |||
|---|---|---|---|---|---|---|
| OR (95% CI) | P value | OR (95% CI) | P value | OR (95% CI) | P value | |
| Adverse childhood experiences (ACEs) | 1.05 (1.02, 1.08) | 0.003 | 1.04 (1.01, 1.08) | 0.01 | 1.05 (1.01, 1.08) | 0.01 |
| ACEs (Frequency, Percentage) | ||||||
| 0 (1081, 13.0%) | Reference | Reference | Reference | |||
| 1 (2089, 25.1%) | 1.01 (0.87, 1.18) | 0.86 | 1.00 (0.86, 1.16) | 0.98 | 1.00 (0.85, 1.18) | 0.97 |
| 2 (2342, 28.1%) | 1.11 (0.96, 1.28) | 0.17 | 1.10 (0.95, 1.28) | 0.21 | 1.09 (0.92, 1.27) | 0.32 |
| 3 (1458, 17.5%) | 1.15 (0.98, 1.35) | 0.08 | 1.12 (0.95, 1.32) | 0.19 | 1.14 (0.96, 1.36) | 0.14 |
| ≥ 4 (1357, 16.3%) | 1.21 (1.03, 1.43) | 0.02 | 1.20 (1.02, 1.42) | 0.03 | 1.20 (1.01, 1.44) | 0.04 |
| P for trend | 0.002 | 0.01 | 0.01 | |||
Model 1 was adjusted for no covariates.
Model 2 was adjusted for age, sex, ethnicity, marital status, educational level, place of residence.
Model 3 was adjusted for covariates in Model 2 plus BMI status, smoking, alcohol consumption, sleep duration at night, sleep duration at noon, hypertension, diabetes mellitus, cancer, cardiovascular disease.
When ACEs were grouped according to cumulative exposure, a significant dose-response relationship was observed in all models (P for trend < 0.05). Compared with those who had never experienced ACE types (0 ACE types), those with ≥ 4 ACE types had a significantly increased risk of accelerated aging (model 1: OR = 1.21, 95% CI: 1.03–1.43; model 2: OR = 1.20, 95% CI: 1.02–1.42; model 3: OR = 1.20, 95% CI: 1.01–1.44).
Restricted cubic splines
Restricted cubic splines (RCS) (Fig. 2) showed that no significant non-linear relationship between ACEs and accelerated aging was observed in any of the three models (p-nonlinear > 0.05). However, overall, the odds of accelerated aging increased significantly with the number of ACEs (p-overall < 0.05).
Fig. 2.
Restricted cubic splines of adverse childhood experiences and accelerated aging. Note: Restricted cubic spline curves show the odds ratios (solid lines) and 95% confidence intervals (shaded areas) for accelerated aging according to the number of adverse childhood experiences (ACEs). Model 1 was unadjusted. Model 2 was adjusted for age, sex, ethnicity, marital status, educational level, and place of residence. Model 3 was adjusted for covariates in Model 2 plus BMI status, smoking, alcohol consumption, sleep duration at night, sleep duration at noon, hypertension, diabetes mellitus, cancer, and cardiovascular disease
Exploratory subgroup analysis
Exploratory subgroup analyses were conducted to examine whether the association between ACEs and accelerated aging differed across demographic, socioeconomic, lifestyle, and disease-status groups, including age, sex, ethnicity, marital status, educational level, place of residence, BMI status, smoking, alcohol consumption, sleep duration at night, sleep duration at noon, hypertension, diabetes mellitus, cancer, and cardiovascular disease (Fig. 3). No statistically significant interactions were observed across these subgroups, as all P values for interaction were greater than 0.05. These findings suggest no clear evidence of effect modification by the examined subgroup variables.
Fig. 3.
Association between each additional adverse childhood experience and accelerated aging across subgroups. Note: Points and horizontal lines represent odds ratios (ORs) and 95% confidence intervals (CIs), respectively, for accelerated aging per additional adverse childhood experience (ACE) within each subgroup. The displayed subgroup-specific estimates are unadjusted. The vertical reference line at OR = 1.00 indicates no association. P values for interaction were obtained by adding an ACE-by-subgroup interaction term to the corresponding unadjusted model
Discussion
Based on the large sample data of the China Health and Retirement Longitudinal Study (CHARLS), this study systematically examined the association between adverse childhood experiences (ACEs) and accelerated aging in the Chinese middle-aged and older population. The results showed that individuals who reported more ACE types had a significantly higher likelihood of accelerated aging as assessed using baseline biomarker data. This finding is consistent with similar results from studies of Western populations [7], suggesting that ACEs may have long-term adverse effects on adult health across different population settings.
There is increasing evidence that ACEs may be related to aging through several physiological and psychological pathways [17, 18]. Chronic inflammation and neuroendocrine dysregulation have been proposed as important mechanisms linking childhood adversity with later-life health outcomes [18, 19]. Previous studies have also suggested that early-life stress may be associated with biological aging processes, including telomere shortening and DNA methylation changes [20, 21]. Telomeres have been reported as important indicators of biological aging in specific disease populations, such as haemophilia patients [22], and DNA methylation has also been implicated in the aging process [23]. However, telomere length and DNA methylation were not directly measured in the present study. Therefore, these mechanisms should be interpreted as possible biological pathways suggested by previous literature rather than direct evidence from our data. Future studies incorporating molecular biomarkers are needed to further clarify how ACEs may contribute to accelerated aging.
Existing studies have continued to focus on the profound impact of adverse experiences such as abuse, neglect, or family dysfunction in childhood on social, psychological, and physical health [3]. Studies have found that individuals who have experienced ACEs are not only more likely to develop a variety of health problems such as lung dysfunction [4], chronic pain [24], and cardiovascular disease [6] in adulthood, but are also more likely to develop mental health problems such as depression, substance abuse, and suicide [5]. In addition, Chanlongbutra et al. [25] showed that ACEs were significantly associated with self-reported health status, chronic diseases, and mental health risks among rural residents in the United States. Evidence from the UK Biobank also suggested a close association between childhood adversity and accelerated aging in adulthood [7]. The results of the present study are consistent with these previous findings and further support the association between ACEs and accelerated aging among middle-aged and older Chinese adults. These findings provide empirical evidence for understanding the link between childhood adversity and later-life health in the context of population aging in China.
It is possible that the association between ACEs and accelerated aging may vary across populations because of differences in demographic characteristics, socioeconomic status, lifestyle factors, sleep patterns, and health conditions. Therefore, we conducted exploratory subgroup analyses to examine potential effect modification. The exploratory subgroup analyses showed no statistically significant interactions, suggesting that there was no clear evidence that the association between ACEs and accelerated aging differed across the examined subgroups. Specifically, the association between ACEs and accelerated aging was broadly consistent across age groups (45–59 years: OR = 1.049, 95% CI: 1.007–1.092; 60–79 years: OR = 1.044, 95% CI: 0.998–1.093; ≥80 years: OR = 1.067, 95% CI: 0.886–1.285) (Fig. 3), with no evidence of interaction (P for interaction = 0.969). However, because these subgroup analyses were exploratory, the findings should be interpreted cautiously and confirmed in future studies. Moreover, heterogeneity in ACE-related effects may also reflect the presence of protective factors across the life course. For example, evidence from CHARLS suggests that positive childhood social experiences such as childhood friendship may be associated with more favorable cognitive aging trajectories, potentially buffering the long-term consequences of early adversity [26]. This may partly explain why the magnitude of associations is not uniform across populations.
Although this study provides important empirical support for the relationship between ACEs and accelerated aging in the middle-aged and older population, there are several limitations that need to be noted. First, ACEs data were collected in a self-reported manner, which may be subject to potential recall bias; future studies should conduct prospective monitoring in conjunction with family, school or medical records to improve data accuracy. Second, although hs-CRP is commonly right-skewed, no additional log-transformation was applied to hs-CRP in this study because it was incorporated as one component of the established composite biological age measure rather than analyzed as an independent biomarker. Third, a relatively large number of participants were excluded because of missing biological age data or missing ACEs information. Missing biological age data were mainly due to unavailable biomarker information required for biological age estimation, while missing ACEs data may partly reflect attrition from the 2011 baseline survey to the 2014 life history survey. Future studies that include mortality follow-up, for example using survival analysis or inverse probability weighting, could further clarify how differential survival influences age-specific estimates of ACE-related accelerated aging. Additionally, differential survival related to higher ACE burden may further contribute to selection bias: individuals with ACE score ≥ 4 may be less likely to remain in older age strata due to earlier mortality, which could attenuate or bias the observed association with accelerated aging in the oldest groups. In addition, the present analyses were unweighted, and the demographic distribution of the analytic sample may not fully match the national demographic profile of middle-aged and older Chinese adults. Therefore, the generalizability of the findings should be interpreted with caution. Nevertheless, the skewed distribution of hs-CRP may have influenced the biological age calculation to some extent, and future studies could examine whether log-transformed hs-CRP yields more robust estimates. In addition, although this study controlled for a number of confounding factors, potential confounding effects or interactions cannot be completely excluded.
In conclusion, this study based on CHARLS data supports the association between childhood ACEs and accelerated aging among middle-aged and older Chinese adults and highlights the need for timely intervention for individuals exposed to ACEs. The long-term adverse effects of ACEs on health and aging in adulthood may be reduced through community-based psychological support, social services, and policy support. In the Chinese context, these measures may include governmental budgetary support for child protection and mental health services, integration of ACEs screening and psychological counselling into community health services, and improved referral pathways among schools, communities, and primary healthcare institutions. Future studies should further examine the mechanisms linking ACEs to accelerated aging in different cultural and social contexts. Research incorporating biomarkers and epigenetic indicators, such as DNA methylation and telomere length, may also help provide a more targeted scientific basis for intervention strategies.
Acknowledgements
Thanks to all the staff and the participants of the CHARLS study for their valuable contributions.
Authors’ contributions
Damei Ye and Shuchao Ye: Data curation (lead); project administration (lead); writing – original draft (lead). Meimei Zheng: Data curation; project administration, writing – review and editing (lead).
Funding
This study was supported by funding from Startup Fund for scientific research, Fujian Medical University [2022QH1255], Sanming City Joint Funding Project in the Medical and Health Field [2024-S-087] and Fujian Provincial Natural Science Foundation of China [2025J011552,2026J0011975].
Data availability
The survey data are publicly available on the internet for data users and researchers throughout the world. (https://charls.pku.edu.cn).
Declarations
Ethics approval and consent to participate
This study was approved by the Ethics Review Committee of Peking University, approval number [IRB 00001052–11014], and all CHARLS participants provided written informed consent. This study was conducted in accordance with the Declaration of Helsinki.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The survey data are publicly available on the internet for data users and researchers throughout the world. (https://charls.pku.edu.cn).