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The Journals of Gerontology Series B: Psychological Sciences and Social Sciences logoLink to The Journals of Gerontology Series B: Psychological Sciences and Social Sciences
. 2022 Dec 23;78(2):220–229. doi: 10.1093/geronb/gbac179

Adverse Childhood Experiences, Inflammation, and Depressive Symptoms in Late Life: A Population-Based Study

Chao Li 1, Shiting Xiang ✉
Editor: Rodlescia Sneed
PMCID: PMC10215978  PMID: 36373839

Abstract

Objectives

This study investigated the association and dose–response relationship between adverse childhood experiences (ACEs) and depressive symptoms in late life and explored the mediating roles of inflammatory markers in the ACEs–depressive symptom association among Chinese older adults.

Methods

This study was conducted using 2014 life history survey data and 2015 follow-up data from the China Health and Retirement Longitudinal Study. Data on ACEs and depression, inflammatory markers of high-sensitivity C-reactive protein (CRP), and white blood cell were collected. The association between ACEs and depressive symptoms was examined using logistic regression, and the mediation effects of inflammatory markers were evaluated.

Results

A total of 6,518 individuals over 60 years were included in the analysis. Compared to no ACE exposure, the adjusted odd ratios ranged from 1.377 (95% confidence interval [CI], 1.133–1.673) when participants had been exposed to 2 ACEs to 1.809 (95% CI, 1.451–2.256) when participants were exposed to 4 or more ACEs. A significant dose–response relationship between cumulative ACE scores and depression was observed. Six of the 12 ACE exposures were related to increased odds of depressive symptoms. CRP appeared to partially mediate the ACE–depressive symptom association, and the proportion of the effect of ACEs on depression was 1.17% (P = 0.008).

Discussion

A dose–response association exists between ACEs and the prevalence of depressive symptoms among older Chinese adults. CRP partially mediated the ACE–depressive symptom association in late life. Emphasizing interventions targeting individuals with ACE exposure may minimize the burden of late-life depression in China.

Keywords: Adverse childhood experiences, Depression, Inflammatory markers, Mediation effects, Older adults

Late-life depression is a significant public health problem worldwide, with 1%–4% of older adults suffering from major depression and 4%–13% of older adults suffering from minor depression (Blazer, 2003). Depression is probably the most frequent cause of emotional suffering in late life and could decrease older adults’ quality of life (Blazer et al., 1991). Moreover, in older adults with chronic medical illnesses, depression could result in suffering, disability, family disruption, worse outcomes of many medical illnesses, and increased mortality (Alexopoulos, 2005). A meta-analysis showed that 25.55% of Chinese adults over 60 years of age had depression during the period 2010–2019 (Rong et al., 2020). Data from the National Bureau of Statistics of China in 2018 showed that the number of older adults aged ≥60 years reached 249.49 million, representing 17.9% of the total population, and it is predicted that the proportion of older adults aged over 60 years will reach 35.1% of the total Chinese population by 2050 (Zhou et al., 2018). Due to the accelerating pace of population aging, understanding the factors that may increase the risk of depression in older adults and their potential mechanisms has become an important public health priority (Rodda et al., 2011).

Adverse childhood experiences (ACEs) refer to various potentially stressful experiences that occur in childhood and can also be regarded as childhood adversities (Felitti et al., 1998). Currently, it is generally accepted that ACEs should include conventional ACEs (Anda et al., 1999), an additional set of expanded ACEs (Cronholm et al., 2015), and several new ACE indicators (Björkenstam et al., 2017; Rod et al., 2020). More specifically, the expanded view of ACEs includes household challenges, neglect, abuse, school bullying, unsafe communities, and somatic illness or death of family members. This set of ACEs is thought to adequately reflect perceived childhood adversity in populations that differ from those in the “Centers for Disease Control and Prevention, (CDC)-Kaiser Permanente Adverse Childhood Experiences (ACE) Study” (Lin et al., 2021). Evidence has shown that ACE exposure has negative effects on adults’ mental health and could be a potential risk for depression (Tian et al., 2019). However, the effects of ACE exposure on late-life depression are unclear because previous studies employed samples of mixed ages. Evidence from a recent study suggests that the detrimental influence of early-life stress experiences on mental health can persist until late life (Cheong et al., 2017).

Inflammation could be a potential mechanism underlying the association between ACEs and late-life depression; to be more specific, individuals with ACE exposure might present an increased low-grade inflammatory profile, leading to long-term neurobehavioral changes containing an aberrant stress response (Lo Iacono et al., 2015). Numerous studies have explored the association between inflammation and ACEs or depression and found that individuals exposed to various ACEs had increased concentrations of inflammatory markers such as C-reactive protein (CRP) in their plasma decades later. A recent investigation demonstrated that ACEs are related to high CRP levels in older adults with a mean age of 70 years old (Iob et al., 2020b). Meanwhile, meta-analyses have shown that depressed individuals tend to have increased levels of inflammatory markers in their plasma (Enache et al., 2019; Haapakoski et al., 2015). However, most previous studies have not tested the mediating role of inflammation in the ACE–depression association in late life. Only one study conducted among older English adults examined the longitudinal mediation effects of inflammation through ACEs and late-life depression (Iob et al., 2020a). Thus, there is a lack of evidence for older adults from developing countries such as China, where ACEs are more prevalent.

This study uses data from a representative national sample of the China Health and Retirement Longitudinal Study (CHARLS) to extract information on 12 ACE indicators among Chinese adults over 60 years old. We aim to investigate whether there is an association and a dose– response relationship between ACEs and depressive symptoms in late life and to further explore the mediating roles of inflammatory markers in the ACE–depressive symptom association among Chinese older adults.

Method

Study Design and Population

The analysis was conducted using the data from CHARL, whose design and sampling methods have been reported in a previous study (Zhao et al., 2014). In brief, the baseline survey included 17,708 participants from 28 provinces in China for an investigation conducted in 2011. Follow-up data were collected every 2 years from residents and their families through self-report questionnaires and interviews. Childhood experiences were collected in the 2014 life history survey of live participants who completed the surveys in 2011 and 2013. This study used data from the 2015 follow-up survey and the 2014 life history survey. A total of 20,544 participants completed the 2014 life history survey, and 13,264 participants with blood data were included in the 2015 follow-up survey (Figure 1). After conducting 1:1 matching of participants who had completed both surveys, a total of 12,430 individuals were included. A total of 5,912 participants aged <60 were excluded from the study, leaving a final sample of 6,518 individuals. Written informed consent was obtained from each participant, and CHARLS received ethical approval from the Peking University Institutional Review Board. This study was a secondary analysis conducted using established data sets.

Figure 1.

Figure 1.

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Flowchart of study participant selection. CHARLS = China Health and Retirement Longitudinal Study.

Definition of ACEs

According to a previous study (Lin et al., 2021), we extracted 12 ACEs from the 2014 life history survey, including seven conventional ACEs (domestic violence, physical abuse, household substance abuse, emotional neglect, incarcerated household members, household mental illness, and parental separation or divorce), three new ACEs (sibling death, parental death, and parental disability), and two expanded ACEs (bullying and unsafe neighborhood). The details of the 12 definitions of ACEs and the questionnaire items are shown in Supplementary Table 1. Participants’ responses to each ACE item were dichotomized and scored as 1 or 0, and the sum of the ACE scores ranged from 0 to 12. According to the sum of the ACE scores and a previous meta-analysis (Hughes et al., 2017), participants were categorized into five groups: 0, 1, 2, 3, and 4 or higher.

Definition of Depressive Symptoms

Depressive symptoms were assessed using the 10-item Center for Epidemiological Studies—Depression scale (CES-D) short form in the 2015 follow-up survey. CES-D is widely used all over the world (Andresen et al., 1994; Mohebbi et al., 2018). With a total score of 30, each positive item (e.g., “I felt hopeful about the future”) was scored from 0 (rarely or none of the time) to 3 (most or all the time), and each negative item (e.g., “I felt fearful”) was scored from 0 (most or all the time) to 3 (rarely or none of the time). Previous psychometric analyses demonstrated that the 10-item CES-D scale had good reliability and validity among older Chinese population (Chen & Mui, 2014). A score of 10 or higher was defined as having depression in this study according to the previous study because this cutoff score offers optimal discriminatory power in detecting participants from CHARLS with or without a risk of depression (Fu et al., 2022). According to previous studies (Frank et al., 2019; Ma et al., 2021), the participants were divided into two groups: those with or without depressive symptoms. Older adults’ depression scores were used as a continuous variable for the sensitivity analyses.

Assessments of High-Sensitivity CRP and White Blood Cell

The high-sensitivity CRP and white blood cells of the included participants were collected and tested following the “Blood Collection and Handling” CDC manual (Zhao et al., 2011). White blood cells were examined by automated analyzers within 141 minutes of collection. High-sensitivity CRP was measured using an immunoturbidimetric assay which had both intra- and interassay coefficients of variation of <1.3% and <5.7%, respectively. All test results for these quality control samples were within the target range (within 2 standard deviations [SDs] of the mean quality control concentrations; Zhao et al., 2011).

Assessments of Covariates

Contemporary confounders were divided into five groups based on the information collected by the participants answering the questionnaire: demographic factors, marital status, physical health, socioeconomic status, and lifestyle behaviors. Age, gender, and hukou were three demographic factors (Hukou is the registration system in China, created in 1955, which is used to restrict internal population movement, especially rural-to-urban migration. The Chinese participants were divided into two categories: agricultural hukou [rural hukou] and nonagricultural hukou [urban hukou]). Marital status was divided into two groups: married and unmarried (never married, divorced, separated, or widowed). Participants’ physical health was represented by their functional limitations and feelings of pain. The functional limitations were performed by six activities of daily living (ADLs; eating, using the toilet, bathing, continence, dressing, and getting in and out of bed) and six instrumental activities of daily living (IADLs; shopping, taking medicine, cooking, managing money, making phone calls, and doing housework). Each item was scored from 1 (I do not have difficulty) to 4 (I cannot do it). The sum scores of these items could represent ADL or IADL limitations and ranged from 6 to 24. Pain was divided into two groups: 0 (not often troubled with bodily pain) and 1 (often troubled with bodily pain). Socioeconomic status was assessed by educational level, childhood economic hardship, and annual per capita household expenditure. Educational level was divided into low (middle school or below) and high (high school or above). Participants were asked to answer the question “When you were a child before the age of 17 years, compared to the average family in the same community/village at that time, how was your family’s financial situation?” Participants who responded, “a lot worse” or “somewhat worse,” were regarded as having economic hardship during childhood. In contrast, those who answered, “a lot better,” “somewhat better,” or “same as them” were regarded as without hardship during childhood. In the current study, the annual per capita household expenditure was summed for a wide range of expenditures over the past 12 months, including household spending on food and eating out, communication and transportation, and clothing and durable goods. Lifestyle behaviors included current smoking or drinking and physical activity (vigorous, moderate physical activity, and walking), which were all defined as two groups: yes (smoking/drinking/engaging in physical activity) and no (not smoking/drinking/engaging in physical activity).

Statistical Analysis

Imputation methods based on the random forest were used for data included in this analysis (Hong & Lynn, 2020) using the “missForest package” (Stekhoven & Bühlmann, 2012) for R 3.5.1. Random forest analysis is a machine-learning method that can be used for singular or multiple imputations (Hong & Lynn, 2020). We have imputed a single complete data set for analyses. Continuous variables are described as mean and SD, and categorical variables are described as numbers and percentages. We used χ 2 tests to compare categorical characteristics across different ACE groups, and analysis of variance (ANOVA) tests were used for the continuous variables.

Logistic regression models were applied to assess the relationship between ACE groups and depressive symptoms. We applied Model 1 as a crude model, and Model 2 as an adjusted model, which was adjusted for age, gender, marital status, educational level, hukou status, smoking and drinking status, annual per capita household expenditure, physical activity, ADL scores, IADL scores, and often experiencing pain and childhood economic hardship. Trend tests were performed to examine whether dose–response associations were present. A sensitivity analysis was conducted with conventional ACEs and another sensitivity analysis was conducted with depressive symptoms scores (using negative binomial regression models).

Mediation analysis (Baron & Kenny, 1986) was conducted using the “mediation package” (Tingley et al., 2014) for R 3.5.1 to explore the mediating role of inflammatory markers (Supplementary Figure 1). Mediation analysis assumes that the relationship between the independent variable (ACEs) and dependent variable (late-life depressive symptoms) is mediated by a third variable (inflammatory markers). The total effect refers to the effect of ACEs on late-life depressive symptoms, while the indirect effect indicates the amount of mediation exerted through inflammatory markers on the relationship between an elevated number of ACEs and late-life depressive symptoms. The direct effect represents the effect of ACEs on late-life depressive symptoms while controlling for mediator variables (Rucker et al., 2011). Although a significant total effect was originally assumed to be necessary for establishing mediation (Baron & Kenny, 1986), it is widely agreed that significant mediation can be demonstrated in the absence of a total or direct effect (Goldsmith et al., 2018; Hayes, 2018). Mediation analyses were adjusted for age, sex, marital status, educational level, hukou status, and childhood economic hardship, and the ACEs were modeled as a continuous variable in the mediation test. Depressive symptoms were modeled as a categorical variable, so the outcome models were applied as a logistic model, with the mediator models applied to linear models because the mediating variables (inflammatory markers: CRP and white blood cell) were continuous variables. The indirect effects’ bias-corrected 95% confidence intervals (CIs) were contributed via 1,000 bootstrap samples, and the 95% CI did not include zero indicating significant statistical results. Bootstrapping was used in this mediation analysis to examine indirect effects because it does not assume normality in the data distribution. According to previous research (Iob et al., 2020a), lifestyle factors (body mass index, current smoking and drinking, physical activity), and adult socioeconomic status are possible intermediate variables. To avoid over adjustment bias (Schisterman et al., 2009), we did not set these as covariables in the mediation analysis. A sensitivity analysis was conducted using conventional ACEs and another sensitivity analysis was conducted with depressive symptoms scores (the outcome and mediator models were applied to linear models). Statistical analysis was performed using SPSS 22.0 and R 3.5.1 (www.r-project.org) with a p value of <.05, which was considered statistically significant.

Results

The descriptive characteristics of 6,518 including participants over 60 years of age are presented in Supplementary Table 2. A total of 48.8% of the participants were male, and the average age of the included population was 68.13 years old. In total, 15.9% of the participants had a high educational level. A total of 35.3% of the participants in this analysis had depressive symptoms. Overall, 86.9% of participants had been exposed to at least one of the 12 ACEs and 13.7% had been exposed to four or more ACEs. The detailed rates of ACE indicators are presented in Supplementary Table 1; 26.3% of participants had been exposed to physical abuse, and 33% had been exposed to emotional neglect in their childhood. Compared to participants without any ACE exposure, those with four or more ACE exposures tended to be male, unmarried, had a low educational level, had been exposed to childhood economic hardship, had higher CRP levels, and had depressive symptoms (Table 1). The results showed that an increase in the number of ACEs was related to an increase in the rate of depressive symptoms.

Table 1.

Characteristics of Participants by Number of ACE

ACEs (N = 6,518) p Value for trend
Characteristicsa 0 (n = 853) 1 (n = 1,892) 2 (n = 1,765) 3 (n = 1,112) ≥4 (n = 896) p Value
Gender
 Male 373 (43.7%) 900 (47.6%) 898 (50.9%) 569 (51.2%) 440 (49.1%) .004 .004
 Female 480 (56.3%) 992 (52.4%) 867 (49.1%) 543 (48.8%) 456 (50.9%)
Age, years 67.64 (6.42) 68.07 (6.40) 68.29 (6.62) 68.27 (6.78) 68.22 (6.48) .16 .052
Marital status
 Married 695 (81.5%) 1,527 (80.7%) 1,411 (79.9%) 878 (79.0%) 687 (76.7%) .079 .005
 Unmarried 158 (18.5%) 365 (19.3%) 354 (20.1%) 234 (21.0%) 209 (23.3%)
Educational level
 Middle school or below 679 (79.6%) 1,560 (82.5%) 1,492 (84.5%) 961 (86.4%) 791 (88.3%) <.001 <.001
 High school or above 174 (20.4%) 332 (17.5%) 273 (15.5%) 151 (13.6%) 105 (11.7%)
Hukou status
 Agriculture 711 (83.4%) 1,559 (82.4%) 1,484 (84.1%) 951 (85.5%) 776 (86.6%) .035 .004
 Nonagriculture 142 (16.6%) 333 (17.6%) 281 (15.9%) 161 (14.5%) 120 (13.4%)
Current smoking 623 (73.0%) 1,344 (71.0%) 1,250 (70.8%) 806 (72.5%) 644 (71.9%) .709 .984
Current drinking 241 (28.3%) 607 (32.1%) 580 (32.9%) 368 (33.1%) 318 (35.5%) .025 .003
Physical activity
 Vigorous physical activities (yes) 194 (22.7%) 475 (25.1%) 465 (26.3%) 291 (26.2%) 286 (31.9%) <.001 <.001
 Moderate physical activities (yes) 441 (51.7%) 948 (50.1%) 892 (50.5%) 550 (49.5%) 463 (51.7%) .812 .948
  Walking (yes) 704 (82.5%) 1,558 (82.3%) 1,456 (82.5%) 938 (84.4%) 760 (84.8%) .336 .066
Childhood economic hardship (yes) 246 (28.8%) 631 (33.4%) 743 (42.1%) 559 (50.3%) 533 (59.5%) <.001 <.001
Often feel pain (yes) 210 (24.6%) 533 (28.2%) 590 933.4%) 388 (34.9%) 408 (54.5%) <.001 <.001
ADL score 6.67 (1.72) 6.72 (1.74) 6.73 (1.60) 6.87 (1.87) 6.94 (1.83) .001 <.001
IADL score 7.35 (3.07) 7.47 (3.12) 7.56 (3.21) 7.57 (3.05) 7.71 (3.04) .15 .012
BMI (kg/m2) 23.84 (5.06) 23.86 (5.43) 23.52 (5.00) 23.62 (5.39) 23.51 (5.69) .228 .06
Annual per capita household expenditure (yuan) 56,807.05 (129,408.23) 58,327.62 (176,235.96) 62,859.92 (168,984.59) 67,275.83 (241,954.31) 68,301.97 (173,237.11) .447 .067
WBC (103) 6.02 (1.68) 6.06 (1.91) 5.99 (1.70) 5.92 (1.88) 5.91 (1.80) .162 .024
CRP (mg/l) 2.82 (4.58) 2.77 (4.43) 3.12 (6.74) 3.23 (7.07) 3.82 (10.60) .002 <.001
Depressive symptoms (yes) 229 (26.8%) 571 (30.2%) 635 (36.0%) 441 (39.7%) 422(47.1%) <.001 <.001
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Notes: ACE = adverse childhood experience; ADL = activities of daily living; BMI = body mass index (calculated as weight in kilograms divided by height in meters squared); CRP = C-reactive protein; IADL = instrumental activities of daily living; WBC = white blood cell. Bold values in Table 1 indicated p <.05.

aContinuous data are reported as the mean (SD), and categorical data are reported as the number (subsample) and percentage of participants.

In the analysis of the relationship between ACE groups and depressive symptoms, the results showed that exposure to four or more ACEs was related to a higher risk of depressive symptoms compared to participants without ACE exposure (Table 2). Compared to no ACE exposure, the adjusted odd ratios ranged from 1.377 (95% CI, 1.133–1.673) when participants were exposed to two ACEs to 1.809 (95% CI, 1.451–2.256) when participants had been exposed to four or more ACEs. A significant dose–response relationship between cumulative ACE scores and depressive symptoms was observed. Sensitivity analyses revealed associations between conventional ACEs and depressive symptoms (Supplementary Table 3). We found that 6 of the 12 ACEs (physical abuse, household, substance abuse, household mental illness, unsafe neighborhood, bullying, and parental disability) were associated with increased odds of depressive symptoms (Figure 2).

Table 2.

Association Between the Number of ACE and Subsequent Depressive Symptoms

OR (95% CI) by number of ACEs p Value for trend
Depression 0 1 2 3 ≥4
Crude model 1 [Reference]a 1.178 (0.983, 1.411) 1.531 (1.279, 1.833) 1.791 (1.476, 2.173) 2.426 (1.986, 2.964) <.001
Adjusted modelb 1 [Reference]a 1.130 (0.930, 1.373) 1.377 (1.133, 1.673) 1.552 (1.257, 1.915) 1.809 (1.451, 2.256) <.001
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Notes: ACE = adverse childhood experience; ADL = activities of daily living; CI = confidence interval; IADL = instrumental activities of daily living; OR = odds ratio.

aReference: no ACE exposure.

bThe model was adjusted for age, gender, marital status, educational level, hukou status, smoking and drinking status, annual per capita household expenditure level, physical activity, ADL scores, IADL scores, often feel pain, and childhood economic hardship.

Figure 2.

Figure 2.

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Association between individual ACE indicator and depressive symptoms. The model was adjusted for age, gender, marital status, educational level, hukou status, smoking and drinking status, annual per capita household expenditure level, physical activity, ADL scores, IADL scores, often experiencing pain, and childhood economic hardship. ACE = adverse childhood experience; ADL = activities of daily living; IADL = instrumental activities of daily living.

We examined the associations between the number of ACEs and depressive symptoms, and between inflammatory markers and depressive symptoms. The results (Supplementary Table 4) showed that the number of ACEs was associated with higher levels of high-sensitivity CRP (p < .001) and white blood cell counts (p = .0191). Higher levels of high-sensitivity CRP (p = .012) were associated with a higher prevalence of depressive symptoms, whereas white blood cell count (p = .353) was not significantly associated with the prevalence of depressive symptoms. Based on the above analysis, we examined the mediating role of high-sensitivity CRP in the ACE–depressive symptom association and found significant mediating effects (Table 3). High-sensitivity CRP appeared to partially mediate the ACE–depressive symptom association, and the proportion of the effect of ACEs on depressive symptoms was 1.17% (p = .008). We also tested the mediating effects of white blood cells on the ACEs–depressive symptom association but did not find nonsignificant mediating effects (Supplementary Table 5).

Table 3.

Mediating Role of High-Sensitivity CRP in ACE–Depressive Symptom Association

Estimate 95% CI lower 95% CI upper p Value
Indirect effect 0.0004 0.00008 0.0005 .008
Direct effects 0.0366 0.0299 0.04 <.001
Total effect 0.0370 0.0304 0.04 <.001
Proportion mediated 0.0117 0.0023 0.03 .008
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Notes: Adjusted by age, gender, marital status, educational level, hukou status, and childhood economic hardship. ACE = adverse childhood experience; CI = confidence interval; CRP = C-reactive protein.

The sensitivity analyses revealed that, according to the results in Supplementary Tables 6–8, high-sensitivity CRP appears to partially mediate the conventional ACE–depressive symptom association. The results of sensitivity analyses when depression was defined as a continuous variable were similar to the results when depression was defined as a binary variable, and we can see the sensitivity analyses results in detail in Supplementary Tables 9–16 in Supplementary File.

Discussion

In this cross-sectional study, we found that exposure to ACEs is related to depressive symptoms in late life among older Chinese adults. Moreover, a significant dose–response relationship between the number of participants exposed to ACEs and depressive symptoms was revealed. In addition, the number of ACE–depressive symptom association was partly affected by high-sensitivity CRP levels.

The finding of associations between ACEs and late-life depressive symptoms was consistent with previous studies (Hughes et al., 2017), and our results showed that older adults who experienced four or more ACEs were 1.8 times more likely to have depressive symptoms than those without ACE exposure. Results from a meta-analysis (Hughes et al., 2017) demonstrated that individuals with four or more ACEs exposures were three times more likely to develop depressive symptoms than those without ACE exposure. A possible reason for this may be that the impact of childhood adversity on older adults declines with age. A previous study (Hu, 2019) suggests that bullying victimization is a significant predictor of depressive symptoms among people aged 60–79 years, but there was no significant association between bullying and depressive symptoms in adults over the age of 80. Our study also showed that older adults who had experienced physical abuse, household substance abuse, household mental illness, unsafe neighborhoods, bullying, and parental disability in their childhoods were more likely to suffer from late-life depressive symptoms. Previous studies have also suggested that bullying (Hu, 2019) and abuse (Comijs et al., 2013) were possible causes of individuals’ late-life depression, and bullying was recognized as a significant predictor of late-life depression among older people aged 60–79 (Hu, 2019). Other studies (Alexopoulos, 2005; van Dijk et al., 2021) suggested that household mental illness, such as a family history of depression, is a possible risk factor for depression, possibly because ACEs may be associated with increased cortisol levels and chronic inflammation (Iob et al., 2020b). Furthermore, ACEs may be related to DNA methylation in key genes and telomere length shortening, which potentially results in a greater risk of developing age-related diseases (Herrmann et al., 2018; Jin & Liu, 2018; Lang et al., 2020). In this study, we further explored the mediating roles of inflammatory markers in the association between ACE and late-life depressive symptoms.

Several studies have examined the relationship between inflammation and ACEs or depression (Baumeister et al., 2016; Haapakoski et al., 2015; Iob et al., 2020a, 2020b). However, little research has examined these associations in older Chinese adults or formally examined the possible mediating role of inflammation. The results suggest that CRP has a potential mediating role in ACE–depressive symptom association in older Chinese adults, but the mediating effect is weak, which is in line with a previous study analyzing data from older English adults (Iob et al., 2020a). This finding may lend support to the idea that inflammation may be a possible psychobiological mechanism underlying the ACE–depressive symptom association, given that intense chronic stress in the early years of life may negatively affect immune system development and lead to a chronic hyper- and overreactive inflammatory response. Consequently, brain development and function may be impaired, making individuals more vulnerable to depression (Danese & Baldwin, 2017). The mediation effect of high-sensitivity CRP levels was generally small, and the mediation effect of white blood cells was not statistically significant. This suggests that other inflammatory markers such as tumor necrosis factor α (TNFα) and interleukin-6 (IL-6), or other markers of different biological mechanisms might play a role in the ACE–late-life depressive symptom association. Previous research has found that epigenetic changes, neurocognitive processes, neuroendocrine responses, and microbiome alterations are all likely to be related to the ACE–depressive symptom association and inflammation (Berens et al., 2017). Further research on how these biological processes could affect each other in depression etiology is necessary. Because the results showed that the mediating effect of the inflammatory marker CRP is weak, other nonbiomarker mediators could mainly mediate the relationship between ACEs and late-life depressive symptoms, including socialization, abuse, and adult social economic status (SES). A study (Fu & Chen, 2022) conducted on Japanese older adults indicated that elder abuse victimization mediated the relationship between the number of ACEs and depression due to the cumulative effects of exposure to trauma across one’s lifetime. Another study (Yazawa et al., 2022) found that the interaction between ACEs and adult SES could mediate the association between ACEs and late-life depression, and achieving high adult SES could mitigate the negative effect of ACEs on late-life depression. The impacts of ACEs can be cumulative, and this may increase the vulnerability of older adults and reduce their psychological resilience, which could, in turn, increase older adults’ susceptibility to depression. Further studies are necessary to test the mediating effects of biomarkers and nonbiomarkers.

Strengths and Limitations

A large population-based sample of older Chinese adults was used in this study and the participants were not selected based on ACE exposure or depressive symptoms. Therefore, the population included in our analysis was more representative of the general population. We were also able to directly assess the mediation effects of inflammatory markers in ACE–depressive symptom association. Furthermore, we defined ACEs as a cumulative score based on the total number of ACEs exposed to show the association of ACEs with late-life depression. Finally, we explored the associations between different ACEs experienced and late-life depressive symptoms.

This study has several limitations. First, recall bias existed because the data on ACE indicators were collected retrospectively. Moreover, measurement errors were possible because participants’ ACE indicators were collected at older ages, and the participants’ motivations, cognitive function, and memory biases could have influenced their recollections of their childhood experiences. Further studies are needed to determine whether prospective and retrospective ACEs have different associations with late-life depressive symptoms. Second, the chronicity, intensity, and frequency of ACE indicators were not available in this study, and it was assumed that the risk of each ACE indicator was equal. Although previous studies have shown that the results of relationships between weighted and unweighted ACEs scores and inflammation are similar, weighted scoring considering the chronicity, intensity, and frequency of ACE indicators might enhance measurement precision (Davis et al., 2014; Slopen et al., 2013). Third, due to the limited available data on inflammatory markers, inflammatory markers of high-sensitivity CRP and white blood cells as mediating roles were included in this study, but other important inflammatory factors such as TNFα and IL-6 also deserve further research. Fourth, because our study was based on data from a cross-sectional design, we could not establish causal relationships between ACE exposure and late-life depressive symptoms. Fifth, missingness in this study was handled with single imputation, which is less robust than multiple imputations for handling missingness.

Conclusion

The results of the analysis of the CHARLS data showed that a dose–response association exists between ACEs and the prevalence of depressive symptoms among older Chinese adults. CRP partially mediated the ACE–depressive symptom association in late life. These results suggest that inflammation may be one of the psychobiological mechanisms underlying the relationship between ACEs and depression. However, the mediating effects of CRP were generally small and susceptible to possible unmeasured confounding factors. Other inflammatory markers and stress-related biological processes should be considered to provide a more comprehensive understanding of the biological embedding of ACEs. These findings suggest a need to prevent ACEs and a life-course public health strategy to reduce the potential associated risks of late-life depression. Furthermore, emphasizing universal interventions that target individuals with ACE exposure may minimize the burden of the associated late-life depression in China.

Supplementary Material

gbac179_suppl_Supplementary_Material
Click here for additional data file. (201.7KB, pdf)

Acknowledgments

The authors thank all the participants in the survey design and data collection and the CHARLS research team for collecting high-quality, nationally representative data and making the data public. This study was not preregistered. We would like to thank Editage (www.editage.com) for English language editing.

Funding

This work was funded by the Central South University (grant number 2021zzts0965) and the Hunan Provincial Natural Science Foundation Youth Foundation (grant number 2021JJ40275). The funders had no role in study design, data collection, and analysis, decision to publish, or manuscript preparation. The data collection was supported by the Behavioral and Social Research Division of the National Institute on Aging of the National Institute of Health (grants 1-R21-AG031372-01, 1-R01-AG037031-01, and 3-R01AG037031-03S1), the Natural Science Foundation of China (grants 70773002, 70910107022, and 71130002), the World Bank (contracts 7145915 and 7159234), and Peking University. The sponsor had no role in the study design, data analysis, or article writing.

Conflict of Interest

The authors declare that they have no competing interests. C. Li wrote the first draft of the manuscript. No honorarium, grant, or other forms of payment was given to anyone to produce the manuscript.

Author Contributions

C. Li conceptualized and designed the study, developed the data extraction instrument, collected data and carried out the initial analysis, and drafted and revised the manuscript. S. Xiang conceptualized the study, supervised data collection, and critically screened important intellectual contents of the manuscript. All authors have read and approved the manuscript as submitted and agree to be accountable for all aspects of the work.

Statement of Ethics

All methods were carried out following the Declaration of Helsinki. Written informed consent was provided by each participant, and the China Health and Retirement Longitudinal Study received ethical approval from the Peking University Institutional Review Board. The IRB approval number for the main household survey, including anthropometrics, is IRB00001052-11015; the IRB approval number for biomarker collection was IRB00001052-11014. This study was a second analysis based on the data from the CHARLS study, so the ethics committee that approved the CHARLS study is noted to be Peking University.

Consent for publication: Not applicable.

Data Availability

All data generated or analyzed during this study are included in 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

gbac179_suppl_Supplementary_Material
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Data Availability Statement

All data generated or analyzed during this study are included in this article.

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