Adverse childhood experiences increase the risk of dementia among older Chinese adults

Abstract

Background

Adverse childhood experiences have a lasting negative effect on both mental and physical health and likely increase the risk of dementia.

Objective

Our study aims to investigate the association between adverse childhood experiences (ACEs) and dementia in Chinese older adults, and to explore whether sociodemographic variables and health status influence this association.

Methods

This study utilized data from the China Health and Retirement Longitudinal Study (CHARLS) and included 5092 participants. Logistic regression models were employed to evaluate the association between ACEs and dementia. Stratified and interaction analyses were conducted to examine the influence of demographic and lifestyle characteristics on this association. Two sensitivity analyses were further performed.

Results

Compared to individuals without ACE exposure, those who experienced three (OR: 1.292, 95% CI: 1.025–1.627) or four or more ACEs (OR: 1.363, 95% CI: 1.070–1.737) had a significantly higher risk of dementia, demonstrating a clear dose-response relationship. Subgroup analyses revealed that ACEs were significantly associated with dementia among adults aged 60–79 years, females, rural residents, highly educated, married, and reported lifetime history of drinking, no lifetime history of smoking, or short sleep duration. No significant interaction was observed between subgroup variables and ACEs in the interaction analysis.

Conclusions

Our findings demonstrated that Chinese older adults experiencing ACEs were at higher risk of developing dementia compared to those without. Preventing and mitigating ACEs will enhance neurocognitive health and promote successful aging.

Keywords

adverse childhood experiences Alzheimer's disease CHARLS dementia early malnutrition older adults

Introduction

The rapidly aging population and increasing life expectancy in China indicate that age-related neurological illnesses have emerged as a severe public health concern.¹ As a common age-related clinical syndrome characterized by progressive cognitive decline, the main symptoms of dementia include cognitive dysfunction, sleep disorders, and in severe cases, loss of independent living ability in the later stages, which leads to a serious disease burden and an increased risk of higher mortality.^2,3 The results of a national research study indicated that the general prevalence of dementia among Chinese people is 6.0%, suggesting that 15.07 million adults aged 60 and older are affected.⁴ However, the efficacy of symptomatic therapies for this illness is quite limited.⁵ Therefore, recognizing modifiable risk factors for dementia in later life is critical for preventing dementia and reducing the disease burden it entails.

Adverse childhood experiences (ACEs) refer to potentially traumatic experiences caused by negative events such as violence and abuse during childhood, which may be directly related to chronic health problems and mental disease.⁶ In a recent study of adults from 25 U.S. states, 60.9% reported experiencing at least one ACE, while 15.6% reported experiencing four or more ACEs.⁷ In recent years, an increasing number of studies have emphasized the harmful effects of ACEs on health throughout the life cycle. For example, a United States study reported a significant association between ACEs and cardiovascular diseases in adults.⁸ Research from Germany identified elevated risks of perinatal pain and pre-existing back pain among women with ACEs.⁹ Another study found that ACEs may increase the risk of obesity in Canadian adults.¹⁰ Previous empirical research results also show that ACEs often occur simultaneously, and this cumulative risk increases the likelihood of adverse health outcomes both in childhood and adulthood.⁶ Substantial evidence from the U.S. Centers for Disease Control and Prevention (CDC) pointed out that ACEs prevention interventions could prevent up to 1.9 million cases of heart disease and 21 million cases of depression.¹¹ Reviewing relevant literature, a large amount of data on ACEs has been accumulated in developed countries (e.g., North American and European populations). However, the generalizability of these associations to developing countries, particularly China, remains understudied and requires long-term and in-depth exploration.

In the context of cognitive decline and dementia, certain epidemiological studies indicate that elements of ACEs, such as parental loss and early-life famine, may exert detrimental effects on cognitive function in later life.^12,13 Some studies have also focused on the cumulative impact of ACEs on cognitive function. However, the evaluation of ACEs in relevant research is often not comprehensive. For instance, a Japanese study examining the relationship between experiencing ACEs during and after World War II and the incidence of Alzheimer's disease considered only seven specific ACEs.¹⁴ Similarly, a US study found that ACEs could be a risk factor for dementia symptoms and adverse neuropsychiatric health in retired professional football players, although it focused solely on ten common ACEs.¹⁵ The most widely used measure is the Adverse Childhood Experiences Questionnaire (ACEQ), which consists of 10 items covering experiences of emotional, sexual, and physical assault and dysfunctional families.¹⁶ However, this scale was primarily developed using data from White, middle-to-upper-class participants, focusing predominantly on intra-familial experiences.¹⁷ Concerns about the possibility that typical ACEs do not fairly represent perceived hardships in childhood have grown in recent years. To quantify stressors at the community level, like bullying and neighborhood violence, Finkelhor et al. recommended extending the ACEQ.¹⁸ Furthermore, some studies suggested that physical disease or the loss of a family member should be taken into account, which were both extremely common ACEs,^19,20 especially for low- and middle-income countries where medical resources and social security are relatively scarce.

Although ACEs are significantly related to adverse outcomes in adulthood, an individual's background and life experiences may influence the way ACEs affect health status. Several studies have revealed that the influence of ACEs differs according to demographic characteristics (such as rural or urban residence, gender) and behavioral profile, suggesting these factors may serve as moderators in the ACE-health relationship.²¹ Research also demonstrated that ACEs are connected with lower financial and educational outcomes in adulthood, and that low income and academic achievement serve as mediators between ACEs and bad health.^22,23 A prospective cohort study found that adults who had experienced childhood maltreatment and emotional abandonment had significantly lower education levels and possessions than reference groups.²⁴ However, a UK study revealed no variations in the relationship between ACEs and academic achievement or medical conditions throughout different socioeconomic categories.²⁵ A large amount of research has also shown that unhealthy lifestyles serve as a link between ACEs and unfavorable physical outcomes, and has advocated for the use of lifestyle medicine interventions to minimize the effects of negative experiences in childhood.^26,27 However, the association between ACEs, socio-demographic, and lifestyle factors with dementia in older people is understudied.

It is worth noting that ACEs have lasting effects on both mental and physical health, and their negative health outcomes are widespread and far-reaching.^28,29 However, only a limited number of studies have examined this association, particularly in developing countries like China, in which ACEs prevalence is relatively high.³⁰ No systematic studies of ACEs and later dementia in Chinese older adults are currently available. Utilizing data from the China Health and Retirement Longitudinal Study (CHARLS), we examined the link between 12 ACE indicators and dementia in Chinese adults aged 60 and above. We also explored how demographic and lifestyle factors may impact this relationship.

Methods

Study design and population

The CHARLS database adopted a multi-stage cluster random sampling method and conducted baseline surveys through face-to-face Computer-Assisted Personal Interviewing (CAPI) interviews in 2011, collecting demographic and lifestyle variables from residents aged 45 and above in 450 villages/urban communities across 28 provinces of China.³¹ Given China's linguistic diversity, the CHARLS project team recruited interviewers fluent in local dialects or temporarily hired local translators to ensure the accuracy and reliability of the interview data. Follow-up surveys have been conducted every two years, with new volunteers recruited for each wave. So far, four follow-up polls have been conducted in 2013, 2015, 2018, and 2020. The 2014 life history survey also included questions on ACEs. All face-to-face interviews were conducted by uniformly trained investigators. They employed standardized instructions and maintained neutral expressions to foster supportive, nonjudgmental environments that encouraged respondents to share their real-life experiences, thereby minimizing the influence of inducement or social expectation bias. Data from CHARLS 2014 and 2018 were used in this research. CHARLS obtained ethical approval from the Institutional Review Board of Peking University.³² Written informed consent was obtained from all participants who agreed to take part in the survey. The present study adheres to the EQATOR guidelines for reporting research using the “Strengthening the Reporting of Observational Studies in Epidemiology” (STROBE) checklist (Supplemental Table 1).

This study is a large national cross-sectional survey, utilizing data from the 2014 CHARLS life history survey (20,544 participants) and the 2018 CHARLS follow-up survey (19,816 participants). The exclusion criteria for this study were: (1) individuals lacking information on 12 types of ACEs in the 2014 life history survey of CHARLS; (2) individuals missing dementia assessment data and covariate data in the 2018 CHARLS; (3) individuals lost to follow-up or deceased in 2018; (4) individuals aged < 60 years. Finally, 5092 eligible individuals were included in this analysis to assess the association between ACEs and dementia. The participant selection flowchart is shown in Figure 1. Supplemental Table 2 lists the number and proportion of deletions for each variable.

Figure 1.

Flowchart for the study design and participants.

To address the issue of sample representativeness, a supplementary calculation was conducted for the sample size required in cross-sectional studies. The formula [n = (Z²_α/2pq)/δ²] was used. (1) n represents sample size; (2) p represents the prevalence of dementia in the elderly; (3) q is equivalent to (1-p); (4) Two-tailed test was performed with Z_α/2 set at 1.96 and α set at 0.05. (5) δ represents the allowable error and is calculated as 0.1p. Considering that the prevalence of dementia in China's elderly population is 18.3%, a total of 3781 participants were necessary to achieve the required sample size. Consequently, this study's sample met the minimum requirement for adequate representation.

Measurement of dementia

The Community Screening Instrument-Dementia (CSI-D) is a dementia screening tool specifically designed for cross-cultural community research. The CSI-D comprehensively assesses the daily function and general health status of participants by combining their cultural and educational backgrounds and using cognitive tests and informant interviews.³³ Originally developed and validated among the Kri American Indian population, the CSI-D has since been extensively validated in numerous low- and middle-income countries, demonstrating strong reliability and validity across diverse cultural contexts. The seven cognitive test items and six informant report items of the CSI-D have significant hierarchical characteristics, with a clear difficulty gradient, effectively meeting the practical needs of dementia screening. To clarify the assessment methodology, it is important to emphasize that the CSI-D incorporates both self-reported and informant-reported components. The self-report component measures seven cognitive test items, ranked in order of difficulty as follows: hammer use, elbow naming, pointing to the window and then the door, finding the nearest store, season orientation, day of the week orientation, and delayed recall of three words, with a total score of 0 to 9. Furthermore, six questions were answered in the informant-report components of CSI-D, which include decreased ability to speak, think, and comprehend, frequent forgetting where they put things away, what happened the day before, difficulty recognizing their current location, and dressing (on a scale of 0 to 6). The detailed questionnaire content and scoring scheme are provided in Supplemental Table 3. A CSI-D cognitive score < 5 was classified as dementia. When the self-reported cognitive total score was 5 to 6, dementia was also deemed to be present if the composite score resulting from subtracting the informant-reported cognitive score from the total score was less than 5.³⁴

Definition of adverse childhood experiences

ACEs were assessed using the modified Adverse Childhood Experiences Scale. Information about adolescents’ ACEs before 17 from the CHARLS 2014 was collected through face-to-face interviews in our research. We extracted 12 ACEs and classified them into three types: Conventional ACEs, Expanded ACEs, and New ACEs. Seven Conventional ACEs include physical abuse, emotional neglect, family substance abuse, family mental illness, family violence, incarcerated family members, and parental separation or divorce.³⁵ Based on the recommendation of David Finkelhor et al. and China's unique social and cultural background and its possible impact on children's adversity cognition, we additionally included two Expanded ACEs indicators (including unsafe neighborhood and bullying),¹³ and three New ACEs indicators (including parental death, sibling death, and parental disability).¹⁶ The adjusted ACE scale is presented in Supplemental Table 4, which provides a detailed overview of the questionnaire items and definitions for each ACE measure. Each ACE indication was classified as either absent or present. Cumulative scores for ACEs were calculated as the sum of the number of ACEs, ranging from 0 to 12. Participants were further divided into five groups with total scores of 0, 1, 2, 3, and ≥ 4.³⁶

Covariate

Covariates included: sex, age, education level, marital status, residence, lifetime history of smoking, lifetime history of drinking, sleep duration, social participation, health self-assessment, hypertension, diabetes, heart disease, physical function, and depressive symptoms. All covariates were assessed through face-to-face interviews. The covariate selection rationale is detailed in the Supplemental Methods. The detailed variable measurement and assignment schemes are shown in Supplemental Table 5.

Statistical analysis

To compare the characteristics across groups classified by the number of ACEs, the χ² test was used for categorical variables. The Mantel-Haenszel statistic was used for the assessment of changing trends across different ACEs number groups.

Logistic regression models were established to assess the association between dementia and ACEs number groups. Model 1 was a crude model. Age, sex, residence, education level, marital status, lifetime history of drinking, lifetime history of smoking, sleep duration, social participation, health self-assessment, hypertension, diabetes, heart disease, physical function, and depressive symptoms were all considered while adjusting for Model 2. Trend tests were used to determine whether a dose-response connection existed. Subsequently, Logistic regression model was used to evaluate the association between each individual ACE and dementia, with adjustments made for the same covariates listed in Model 2. For each regression model, the odds ratios (ORs) and 95% confidence intervals (CIs) of all regression models were reported.

To identify potential moderating variables, stratified and subgroup analyses were performed on sex, age, marital status, education level, residence, lifetime history of smoking, lifetime history of drinking, and sleep duration. To enhance the robustness of our findings, we conducted two sensitivity analyses. First, multiple imputation by chained equations was applied to address missing data, and the association between ACEs and dementia was reexamined. Second, we repeated the analysis using only seven conventional ACEs to evaluate the consistency of the association under a more restricted definition. All statistical analyses were completed using R 4.3.0 and SPSS 27.0. p-values were bilateral, with significance set at p < 0.05.

Results

Characteristics of participants

Among the 5092 participants in our research, 2086 (40.97%) were female, and 3006 (59.03%) were male, with 95.05% of the participants aged between 60 and 79 years (Table 1). The prevalence rates of the various components of ACEs ranged from 0.45% (incarcerated family member) to 41.95% (emotional neglect) (Supplemental Table 4). As shown in Supplemental Figure 1, 20.78% of the participants had not experienced any ACEs, and 15.46% of the participants were included in the study due to experiencing New ACEs and Expanded ACEs (unsafe neighborhood, bullying, parental death, sibling death, and parental disability). About 79.22% of participants had experienced at least one of the 12 ACEs, and 11.86% of the participants had experienced four or more. There were significant differences in the distribution of sex, residence, education level, sleep duration, health self-assessment, heart disease, physical function, and depressive symptoms among different numbers of ACEs (Table 1).

Table 1.

Characteristics of participants by numbers of ACEs.

Variables	Total (n = 5092)	Numbers of ACEs					Statistic	p	p for trend
Variables	Total (n = 5092)	0(n = 1058)	1(n = 1512)	2(n = 1173)	3(n = 745)	≥4(n = 604)	Statistic	p	p for trend
Age, n (%)							χ²=5.531	0.237	0.078
60–79	4840 (95.05)	1015 (95.94)	1445 (95.57)	1105 (94.20)	702 (94.23)	573 (94.87)
≥80	252 (4.94)	43 (4.06)	67 (4.43)	68 (5.80)	43 (5.77)	31 (5.13)
Sex, n (%)							χ²=18.79	<0.001	0.003
Female	2086 (40.97)	494 (46.69)	597 (39.48)	471 (40.15)	285 (38.26)	239 (39.57)
Male	3006 (59.03)	564 (53.31)	915 (60.52)	702 (59.85)	460 (61.74)	365 (60.43)
Residence, n (%)							χ²=11.002	0.027	<0.001
Rural	3592 (70.54)	719 (67.96)	1050 (69.44)	830 (70.76)	542 (72.75)	451 (74.67)
Urban	1500 (29.46)	339 (32.04)	462 (30.56)	343 (29.24)	203 (27.25)	153 (25.33)
Education level, n (%)							20.880	0.007	<0.001
Illiteracy	361 (7.09)	55 (5.20)	92 (6.08)	90 (7.67)	66 (8.86)	58 (9.60)
Primary school or below	2901 (56.97)	560 (52.93)	839 (55.49)	676 (57.63)	441 (59.19)	385 (63.74)
Above Primary school	1830 (35.94)	443 (41.87)	581 (38.43)	407 (34.70)	238 (31.95)	161 (26.66)
Marital status, n (%)							χ²=2.229	0.694	0.516
Separated/divorced/widowed/ never married	814 (15.99)	167 (15.78)	244 (16.14)	179 (15.26)	116 (15.57)	108 (17.88)
Married	4278 (84.01)	891 (84.22)	1268 (83.86)	994 (84.74)	629 (84.43)	496 (82.12)
Lifetime history of drinking, n (%)							χ²=6.099	0.192	0.121
No	3212 (63.08)	700 (66.16)	939 (62.10)	738 (62.92)	456 (61.21)	379 (62.75)
Yes	1880 (36.92)	358 (33.84)	573 (37.90)	435 (37.08)	289 (38.79)	225 (37.25)
Lifetime history of smoking, n (%)							χ²=0.603	0.963	0.557
No	4880 (95.84)	1015 (95.94)	1451 (95.97)	1126 (95.99)	711 (95.44)	577 (95.53)
Yes	212 (4.16)	43 (4.06)	61 (4.03)	47 (4.01)	34 (4.56)	27 (4.47)
Sleep duration, n (%)							χ²=21.909	0.005	<0.001
0–7h	2919 (57.33)	569 (53.78)	831 (54.97)	694 (59.16)	446 (59.87)	379 (62.75)
7–9h	1949 (38.28)	431 (40.74)	612 (40.48)	435 (37.08)	270 (36.24)	201 (33.28)
>9h	224 (4.40)	58 (5.48)	69 (4.56)	44 (3.75)	29 (3.89)	24 (3.97)
Social participation, n (%)							χ²=6.347	0.175	0.161
No	2438 (47.88)	482 (45.56)	712 (47.09)	592 (50.47)	366 (49.13)	286 (47.35)
Yes	2654 (52.12)	576 (54.44)	800 (52.91)	581 (49.53)	379 (50.87)	318 (52.65)
Health self-assessment, n (%)							χ²=82.045	<0.001	<0.001
Good	1092 (21.45)	283 (26.75)	348 (23.03)	245 (20.89)	139 (18.66)	77 (12.75)
Common	2565 (50.37)	538 (50.85)	776 (51.32)	583 (49.70)	373 (50.07)	295 (48.84)
Bad	1435 (28.18)	237 (22.40)	388 (25.66)	345 (29.41)	233 (31.28)	232 (38.41)
Hypertension, n (%)							χ²=0.891	0.926	0.978
No	4515 (88.67)	940 (88.85)	1337 (88.43)	1039 (88.58)	667 (89.53)	532 (88.08)
Yes	577 (11.33)	118 (11.15)	175 (11.57)	134 (11.42)	78 (10.47)	72 (11.92)
Diabetes, n (%)							χ²=5.801	0.215	0.095
No	4780 (93.87)	1002 (94.71)	1415 (93.58)	1110 (94.63)	696 (93.42)	557 (92.22)
Yes	312 (6.13)	56 (5.29)	97 (6.42)	63 (5.37)	49 (6.58)	47 (7.78)
Heart disease, n (%)							χ²=11.477	0.022	0.010
No	4697 (92.24)	992 (93.76)	1397 (92.39)	1075 (91.65)	693 (93.02)	540 (89.40)
Yes	395 (7.76)	66 (6.24)	115 (7.61)	98 (8.35)	52 (6.98)	64 (10.60)
Physical function, n (%)							χ²=48.427	<0.001	<0.001
No	4076 (80.05)	880 (83.18)	1250 (82.67)	935 (79.71)	585 (78,52)	426 (70.53)
Yes	1016 (19.95)	178 (16.82)	262 (17.20)	238 (20.29)	160 (21.48)	178 (29.47)
Depressive symptoms, n (%)							χ²=79.886	<0.001	<0.001
No	3531 (69.34)	775 (73.25)	1124 (74.34)	806 (68.71)	486 (65.23)	340 (56.29)
Yes	1561 (30.66)	283 (26.75)	388 (25.66)	367 (31.29)	259 (34.77)	264 (43.71)

χ²: Chi-square Test, SD: standard deviation.

Among the participants, 1232 (24.19%) older adults had dementia (Table 2). There were significant differences in the distribution of age, residence, education level, lifetime history of drinking, sleep duration, social participation, self-rated health, physical function, and depressive symptoms between those with and without dementia. The prevalence of dementia increased gradually with the number of ACEs among individuals under the age of 80 and in the overall sample stratified by sex (Figure 2). Among those who experienced the same number of ACEs, the prevalence of dementia was higher in females than in males. Additionally, dementia was much more prevalent in those over 80 compared to those under 80.

Figure 2.

Prevalence of dementia by number of adverse childhood experiences in the overall study population stratified by age and sex.

Table 2.

Characteristics of participants by dementia.

Variables	Total (n = 5092)	Normal (n = 3860)	Dementia (n = 1232)	Statistic	p
Age, n (%)				29.548	<0.001
60–79	4840 (95.05)	3705 (95.98)	1135 (92.13)
≥80	252 (4.95)	155 (4.02)	97 (7.87)
Sex, n (%)				3.545	0.060
Female	2086 (40.97)	1553 (40.23)	533 (43.26)
Male	3006 (59.03)	2307 (59.77)	699 (56.74)
Residence, n (%)				185.862	<0.001
Rural	3592 (70.54)	2533 (65.62)	1059 (85.96)
Urban	1500 (29.46)	1327 (34.38)	173 (14.04)
Education level, n (%)				255.110	<0.001
Illiteracy	361 (7.09)	195 (5.05)	166 (13.47)
Primary school or below	2901 (56.97)	2069 (53.60)	832 (67.53)
Above Primary school	1830 (35.94)	1596 (41.35)	234 (18.99)
Marital status, n (%)				3.534	0.60
Separated/divorced/widowed/never married	814 (15.99)	596 (15.44)	218 (17.69)
Married	4278 (84.01)	3264 (84.56)	1014 (82.31)
Lifetime history of drinking, n (%)				10.977	<0.001
No	3212 (63.08)	2386 (61.81)	826 (67.05)
Yes	1880 (36.92)	1474 (38.19)	406 (32.95)
Lifetime history of smoking, n (%)				0.141	0.707
No	4880 (95.84)	3697 (95.78)	1183 (96.02)
Yes	212 (4.16)	163 (4.22)	49 (3.98)
Sleep duration, n (%)				130.812	<0.001
0–7h	2919 (57.33)	2208 (57.20)	711 (57.71)
7–9h	1949 (38.28)	1508 (39.07)	441 (33.36)
>9h	224 (4.40)	144 (3.73)	80 (6.49)
Social participation, n (%)				52.041	<0.001
No	2438 (47.88)	1738 (45.03)	700 (56.82)
Yes	2654 (52.12)	2122 (54.97)	532 (43.18)
Health self-assessment, n (%)				1399.873	<0.001
Good	1092 (21.45)	890 (23.06)	202 (16.40)
Common	2565 (50.37)	1979 (51.27)	586 (47.56)
Bad	1435 (28.18)	991 (25.67)	444 (36.04)
Hypertension, n (%)				0.226	0.635
No	4515 (88.67)	3418 (88.55)	1097 (89.04)
Yes	577 (11.33)	442 (11.45)	135 (10.96)
Diabetes, n (%)				3.399	0.065
No	4780 (93.87)	3637 (94.22)	1143 (92.78)
Yes	312 (6.13)	223 (5.78)	89 (7.22)
Heart disease, n (%)				0.005	0.944
No	4697 (92.24)	3560 (92.23)	1137 (92.29)
Yes	395 (7.76)	300 (7.77)	95 (7.71)
Physical function, n (%)				81.386	<0.001
No	4076 (80.04)	3200 (82.90)	876 (71.10)
Yes	1016 (19.95)	660 (17.10)	356 (28.90)
Depressive symptoms, n (%)				45.764	<0.001
No	3531 (69.34)	2772 (71.81)	759 (61.61)
Yes	1561 (30.66)	1088 (28.19)	473 (38.39)

χ²: Chi-square Test, SD: standard deviation.

Association between the numbers of ACEs and dementia

Table 3 shows the association between ACEs and dementia. After adjusting for all covariates, we found that compared with those not exposed to ACEs, experiencing three ACEs (OR: 1.292, 95% CI: 1.025–1.627) and four or more ACEs (OR: 1.363, 95% CI: 1.070–1.737) were significantly associated with a higher risk of dementia in later life. There was a significant dose-response relationship between the number of ACEs and dementia. We also observed that after controlling for all covariates, older adults who experienced family mental illness, bullying, and sibling death in childhood had a significantly higher risk of dementia than those who did not (Supplemental Figure 2).

Table 3.

Association between the numbers of ACEs and dementia.

Model	OR (95%CI) by numbers of ACEs					p for trend
Model	0	1	2	3	≥4	p for trend
Model 1ª	Ref.^c	1.103 (0.910,1.337)	1.215 (0.994,1.485)	1.531 (1.231,1.905)***	1.765 (1.406,2.216)***	<0.001
Model 2^b	Ref.^c	1.047 (0.856,1.282）	1.065 (0.862,1.315)	1.292 (1.025,1.627)*	1.363 (1.070,1.737)*	0.004

Note: Abbreviations: OR: odds ratio; CI: Confidence Interval; ACEs: adverse childhood experiences.

*p < 0.05, ***p < 0.001.

Model 1 was the crude model.

Model 2 was adjusted for age, sex, residence, marital status, education level, sleep duration, lifetime history of smoking, lifetime history of drinking, social participation, health self-assessment, hypertension, diabetes, heart disease, physical function, and depressive symptoms.

Ref.: No ACEs exposure.

Subgroup analysis of the association between ACEs and dementia

We considered eight subgroups (age, sex, residence, marital status, education level, lifetime history of smoking, lifetime history of drinking, and sleep duration) in the subgroup and interaction analyses but did not observe significant interactions between subgroups and ACEs. Significant associations of ACEs with dementia were observed in females aged 60–79 years, particularly among those living in rural areas, with higher education levels, married, lifetime history of drinking, no lifetime history of smoking, and short sleep duration (Table 4).

Table 4.

Association between the numbers of ACEs and dementia among subpopulations.

Model ^a	OR (95%CI) by numbers of ACEs					p for trend	p for interaction
Model ^a	0	1	2	3	≥4	p for trend	p for interaction
Age, n (%)							0.686
60–79	Ref.^b	1.038 (0.843,1.279)	1.078 (0.866,1.341)	1.270 (0.999,1.613)	1.395 (1.087,1.792)**	0.003
≥80	Ref.^b	0.976 (0.396,2.403)	0.896 (0.361,2.222)	1.622 (0.594,4.431)	0.995 (0.331,2.987)	0.630
Sex, n (%)							0.626
Female	Ref.^b	1.046 (0.772,1.419)	1.171 (0.852,1.610)	1.563 (1.101,2.220)*	0.390 (0.960,2.013)	0.009
Male	Ref.^b	1.023 (0.779,1.343)	1.018 (0.765,1.356)	1.135 (0.832,1.548)	1.327 (0.959,1.837)	0.118
Residence, n (%)							0.194
Rural	Ref.^b	1.016 (0.814,1.269)	1.041 (0.825,1.313）	1.223 (0.949,1.576)	1.312 (1.005,1.712)*	0.014
Urban	Ref.^b	1.170 (0.707,1.937)	1.242 (0.736,2.096)	1.673 (0.954,2.933)	1.736 (0.954,3.160)	0.081
Education level, n (%)							0.479
Illiteracy	Ref.^b	0.899 (0.434,1.866)	1.239 (0.597,2.573)	2.005 (0.925,4.348)	1.402 (0.633,3.107)	0.056
Primary school or below	Ref.^b	1.052 (0.820,1.350)	0.992 (0.762,1.291)	1.251 (0.941,1.663)	1.415 (1.055,1.897)*	0.014
Above Primary school	Ref.^b	1.092 (0.732,1.630)	1.239 (0.816,1.881)	1.216 (0.744,1.990)	1.152 (0.667,1.990)	0.577
Marital status, n (%)							0.540
Separated/divorced/widowed/never married	Ref.^b	0.930 (0.565,1.531)	0.931 (0.548,1.583)	1.561 (0.888,2.743)	1.437 (0.806,2.560)	0.059
Married	Ref.^b	1.054 (0.844,1.317)	1.088 (0.862,1.372)	1.243 (0.963,1.604)	1.339 (1.023,1.753)*	0.024
Lifetime history of drinking, n (%)							0.082
No	Ref.^b	0.965 (0.756,1.233)	1.019 (0.789,1.316)	1.099 (0.826,1.462)	1.220 (0.907,1.641)	0.077
Yes	Ref.^b	1.249 (0.863,1.808)	1.280 (0.869,1.886)	1.779 (1.184,2.674)**	1.67 (1.081,2.581)*	0.029
Lifetime history of smoking, n (%)							0.823
No	Ref.^b	1.018 (0.828,1.251)	1.046 (0.843,1.299)	1.309 (1.034,1.656)*	1.328 (1.036,1.702)*	0.005
Yes	Ref.^b	1.520 (0.486,4.755)	2.632 (0.811,8.547)	0.754 (0.185,3.067）	2.335 (0.617,8.839）	0.513
Sleep duration, n (%)							0.262
0–7h	Ref.^b	1.026 (0.777,1.354)	1.110 (0.835,1.476)	1.310 (0.963,1.784)	1.486 (1.082,2.041)*	0.005
7–9h	Ref.^b	1.075 (0.779,1.483)	1.030 (0.729,1.455)	1.278 (0.874,1.868)	1.225 (0.810,1.854)	0.213
>9h	Ref.^b	0.999 (0.429,2.324)	1.215 (0.480,3.075)	1.088 (0.380,3.114)	0.956 (0.316,2.892)	0.929

OR: odds ratio; CI: confidence interval; ACEs: adverse childhood experiences.

*p < 0.05, **p < 0.01.

adjusted for age, sex, residence, marital status, education level, sleep duration, lifetime history of smoking, lifetime history of drinking, social participation, health self-assessment, hypertension, diabetes, heart disease, physical function, and depressive symptoms.

Ref.: No ACEs exposure.

Sensitivity analysis of the association between ACEs and dementia

The first sensitivity analysis addressed missing data using multiple imputation by chained equations (Supplemental Table 6), while the second analysis restricted the exposure definition to seven conventional ACEs (Supplemental Table 7). Both sensitivity analyses demonstrated the robustness of the primary model from different perspectives. The results consistently indicated that individuals exposed to ACEs had a significantly higher risk of developing dementia in later life compared to those without ACE exposure. Furthermore, trend tests confirmed a cumulative effect of ACEs, with a graded increase in dementia risk corresponding to higher numbers of ACEs.

Discussion

In our study, older Chinese adults with more than three ACEs were found to be more likely to develop dementia compared to those without ACE exposure. A dose-response relationship was also observed, with the prevalence of late-life dementia increasing alongside the number of ACEs. This trend was evident in the overall sample, within the younger age group, and across both genders. Notably, the number of ACEs was associated with a higher prevalence of dementia in women than in men, and older individuals had a significantly higher risk of dementia compared to younger individuals. Subgroup analysis revealed a pattern similar to the overall findings. Additionally, demographic characteristics and lifestyle factors did not significantly modify the association between ACEs and dementia.

The widely used 10-item ACEQ, originally developed using data from White, upper-middle-class populations, demonstrates limited generalizability in accurately assessing childhood adversity across diverse sociodemographic groups.¹⁷ To address this limitation, our study incorporated several novel ACEs to reduce false-negative ACEs in population-based measures, while also considering prior studies’ suggestions and experiences. This approach is supported by a recent study in China that documented the associations between 12 ACEs and chronic diseases among older adults, noting that 14.2% of middle-aged and older individuals were included in the study due to the newly considered ACEs (Expanded ACEs and New ACEs).³⁶ If solely the effect of conventional ACEs on dementia had been taken into account in our research, about 15.46% of Chinese older adults’ exposure to ACEs would have been ignored. The study also noted the high prevalence of new ACEs and their strong association with dementia. This indicates that further research is necessary to explore and refine the ACE scale, particularly in developing countries.

Our results further demonstrated that individuals who experienced four or more ACEs had elevated risk of dementia compared to those who did not experience ACEs, consistent with prior research.³⁷ The observed association may be explained by two primary pathways. One possible explanation is that ACEs directly influence physiological processes in the development of dementia. Traumatic early life experiences alter stress regulation, leading to later dysregulation of the stress response, with high levels of stress exacerbating amyloid loads, which accelerates cognitive decline and consequently dementia.^38,39 Previous research has also shown that chronic stress induced by ACEs may result in permanent stimulation of the hypothalamic-pituitary-adrenal (HPA) axis, increasing misinform load and impairing neuroendocrine, immune, and autonomic regulation, all of which can negatively impact cognitive function.⁴⁰ Furthermore, the hippocampus, a crucial region for memory formation and reconstruction, contains an enormous amount of glucocorticoid receptors.⁴¹ Chronic stress caused by ACEs and hyperactivation of the HPA axis raises glucocorticoid levels,⁴² whereas chronic exposure to high levels of glucocorticoids can cause dendritic atrophy, neuronal inhibition, and volume loss in the hippocampus.⁴³ Another possible explanation involves behavioral manifestations of life after exposure to ACEs. Previous research has found various risk factors for developing dementia, including advanced age, rural housing, a lower level of education, lifetime history of smoking, hypertension, and diabetes.⁴ Furthermore, ACEs may enhance the likelihood of acquiring dementia by magnifying the potential factors for dementia.^14,44 Furthermore, our analysis discovered that only one or two ACEs were not connected with dementia prevalence, suggesting that minimal exposure to ACEs had no meaningful impact on dementia. The cumulative model of the association between ACEs and adult diseases may provide support for our findings.⁴⁵

When analyzing individual ACE, we found that bullying, parents having mental illness, and sibling death were significantly associated with the onset of dementia in later life. Psychological stress caused by fear in bullied children activates the amygdala, which disrupts the hippocampus's ability to encode and store memories. This severe stress leads to hippocampal atrophy and memory deficits and further leads to memory deficits in bullied children, thereby increasing the risk of dementia.^46,47 In addition, bullying-induced stress triggers overactivation of glucocorticoid receptors in the hippocampus, which suppresses neurotrophic factors and contributes to atrophy of the dentate gyrus (the cortical region in which the hippocampus forms), thereby impairing long-term memory.⁴⁸ The possible reasons for the increased risk of dementia in old age due to parents’ mental illness are as follows: Adolescents with mentally ill parents often struggle to receive adequate emotional support. They are likely to experience parental divorce, social bullying, substance misuse, and violence from their parents. These ACEs often lead to multiple negative outcomes, including persistent feelings of hopelessness, impaired self-esteem, reduced social support, decreased life satisfaction, neurobiological alterations in stress response systems, structural and functional brain abnormalities. Consequently, these individuals demonstrate elevated dementia risk in later life.⁴⁹ Notably, parental mental health disorders may be heritable, and the association between this genetic mechanism and the development of dementia requires further research. Our analysis results show that the death of siblings impacts dementia risk. Previous studies have reported a substantial increase in the probability of mental illness in individuals who experienced sibling deaths, particularly those exposed during adolescence,⁵⁰ which may go some way to explaining the significant association between sibling deaths and dementia in later life. Another research result has shown that after the death of a child, parents tend to provide diminished emotional support to surviving children, which may lead to long-term emotional deprivation during critical developmental periods and hinder the formation of cognitive reserve.⁵¹ These pathways may collectively contribute to the increased risk of dementia in old age. In the future, it is essential to investigate the biological and psychosocial mechanisms underlying this phenomenon. This exploration aims to systematically elucidate how various types of ACEs influence the risk of dementia in later life through distinct pathways. Furthermore, it seeks to uncover the specific mechanisms that contribute to the cumulative effects of ACEs.

In our study, the prevalence of dementia was significantly higher in older adults over 80 years of age who had experienced ACEs than others. The dangers of advanced age for dementia have long been recognized.⁵² Subgroup analyses showed that sociodemographic characteristics and lifestyle interactions with ACE exposure were not statistically significant, that is, these variables may not be significant modifiers of the association. However, there were variations in the association between ACEs and dementia between different subgroups. On the age side, a strong association between elevated ACE and the risk of dementia was observed among older adults under the age of 80. However, we did not observe a significant association between increased numbers of ACEs and dementia risk in people aged 80 years and older. This might be because those who overcome adversity early in life are resilient people who will live longer and be more resistant to dementia. In terms of gender, we observed a significant association of ACEs with late-life dementia in elderly females but not males. This disparity may stem from several factors. From a biological perspective, males and females exhibit fundamental differences in genetic predisposition, neuroendocrine system regulation (particularly in hypothalamic-pituitary-adrenal axis responsiveness), and immune system modulation. These systems also demonstrate sexually dimorphic temporal dynamics in their interactions, jointly contributing to differential patterns of brain development and subsequent sex-specific vulnerability to dementia.⁵³ On the other hand, in Chinese society in the last century, the idea of preferring sons over daughters was quite common. Females might have experienced more forms of ACEs (such as physical or emotional abuse, neglect, domestic violence, etc.) since childhood. Meanwhile, studies have shown that compared with males, females who have experienced ACEs are more likely to adopt unhealthy lifestyles, thereby leading to more serious health outcomes.^53,54 These might account for sex disparities in the association of ACEs with dementia. Based on the findings of this study, it is evident that both the elderly population and females are at an elevated risk of developing dementia following exposure to ACEs. Future research should further investigate the moderating effects of age and gender on this relationship, particularly focusing on how to formulate personalized prevention and intervention strategies tailored to specific gender and age characteristics. Finally, our findings revealed that ACEs did not appear to be correlated with the development of dementia among participants who lived in cities, had a higher level of education, no lifetime history of drinking, and slept 7–9 h per night, implying that better physical living conditions and lifestyle habits may alter the risk of developing dementia, even after experiencing a certain number of ACEs. A follow-up survey in Japan found that ACEs were related to an elevated risk of dementia only among people with low social capital. No such link was seen among people with significant social capital,⁵⁵ which supports our assumption. Future research should further investigate the role of protective factors, such as social support, economic resources, and health behaviors. Additionally, it is essential to explore the development of targeted early intervention strategies for individuals with a history of ACEs by enhancing the social environment and promoting healthy lifestyles.

The main analysis of this study found that experiencing three or more ACEs significantly increased the risk of dementia in later life compared to having no ACEs. Two sensitivity analyses, which used multiple chain imputation for missing data and focused on the seven traditional ACEs, also showed a significant increase in late-life dementia risk for individuals with two or three ACEs compared to those with no ACEs. After imputing missing values using chained equations, the main analysis included samples with complete variable data, while sensitivity analyses utilized these imputed values. This led to changes in sample composition. The sample size increased from N = 5092 to N = 5622 after imputation, potentially causing minor fluctuations in odds ratios (OR) and their confidence intervals. Although the association for ≥ 4 ACEs did not reach significance post-imputation (p < 0.05), both the direction (>1) and magnitude of OR remained consistent with the main model.Different processing of data sets can introduce bias, which is common in practical studies. In a sensitivity analysis focusing on the seven traditional ACEs, reducing the number of ACE items from 12 to 7 altered the definition of “exposure,” leading to a systematic reclassification of an individual's ACE count. For instance, someone classified as “ ≥ 4” in the main analysis due to experiencing 4 out of 12 ACEs may be counted as “2” if 2 of those were not among the traditional 7 in the sensitivity analysis. Thus, directly comparing significance differences for counts like “2,” “3,” or “ ≥ 4” based on these definitions is inappropriate. Both sensitivity analyses conclude that experiencing ACEs significantly raises dementia risk later in life compared to not having any ACEs. Trend analysis further indicates that dementia risk increases with more ACEs.

At present, dementia prevention and control in China remains challenging. The long-term impact of ACEs on cognitive health is an important yet underrecognized risk factor that requires more attention. To address this issue, we recommend the following: First, healthcare services should incorporate ACEs into dementia risk assessments at primary care facilities and geriatric clinics. For elderly individuals with a history of ACE exposure, regular cognitive screening and follow-up mechanisms should be established for early detection and intervention. Additionally, comprehensive programs combining psychological support, cognitive training, and social participation should be implemented to enhance resilience and slow cognitive decline. Second, public health policies must prioritize early interventions for children by establishing screening mechanisms for high-risk populations and promoting education on ACE prevention. For example, standardized ACE screening tools should be implemented in schools, community health centers, and maternal and child health institutions. Mechanisms for early identification and referral of ACEs among children and adolescents must be established. Public health institutions should integrate psychologists, social workers, educators, and medical staff to provide comprehensive intervention services for high-risk children and their families, helping them cope with early trauma and promoting healthy social behavior development. We urge the government and public health agencies to include ACE prevention in the national dementia action plan, enhance policy and financial support for related interventions, and foster multi-sectoral cooperation to ensure continuous health support from childhood to old age. This approach aims to reduce the long-term disease burden caused by ACEs.

Limitation

First, many participants were eliminated due to missing data, which may result in less representative conclusions. However, the vital link between ACEs and dementia was maintained even after numerous interpolations for missing data, demonstrating the study's findings’ robustness. Furthermore, the data in this study were acquired only from individuals’ self-reports. Therefore, the findings may have been influenced by subject recall bias. Despite the possibility of recollection bias, earlier research has shown that retrospective measures are reliable.⁵⁵ Third, due to the limited availability of detailed information on ACEs in the CHARLS database, we were unable to assess the frequency, intensity, and chronicity of ACEs—factors that are known to be associated with various health outcomes. Finally, while exploratory analyses were conducted within subgroups of patients aged 80 years or older, those who smoked, and individuals who slept more than 9 h per day, the relatively small sample sizes in these specific groups may have somewhat diminished the statistical power of our tests. Nevertheless, such analyses provide valuable insights, particularly in identifying high-risk populations and their underlying risk characteristics. In future research endeavors, we will consider designing larger population-based studies to further validate our findings.

Conclusion

This is the first study to demonstrate a link between the risk of dementia in the Chinese population and the accumulation of ACEs. The higher the number of people experiencing ACEs, the higher the prevalence of dementia among older people in China. Consequently, preventing and minimizing the occurrence of ACEs, as well as identifying individuals who have experienced them, may improve brain health, offer lifelong benefits to those affected, and reduce the risk of dementia in later life.

Supplemental Material

sj-docx-1-alz-10.1177_13872877251406624 - Supplemental material for Adverse childhood experiences increase the risk of dementia among older Chinese adults

Supplemental material, sj-docx-1-alz-10.1177_13872877251406624 for Adverse childhood experiences increase the risk of dementia among older Chinese adults by Xiaobing Xian, Shiwei Cao, Wanting Tang, Jie Xiang, Yandi Fu, Kun Shen and Qian Wang in Journal of Alzheimer's Disease

Footnotes

Acknowledgements

We would like to express our sincere gratitude to Peking University for providing.

ORCID iDs

Xiaobing Xian

Kun Shen

Ethical considerations

CHARLS was approved by the Biomedical Ethics Review Committee of Peking University (approval number: IRB00001052-11015).

Consent to participate

Written informed consent was obtained from all participants who agreed to take part in the survey.

Consent for publication

Not applicable

Author contribution(s)

Xiaobing Xian: Data curation; Methodology; Visualization; Writing – original draft.

Shiwei Cao: Software; Visualization; Writing – original draft.

Wanting Tang: Methodology; Writing – original draft.

Jie Xiang: Software; Writing – original draft.

Yandi Fu: Methodology; Writing – original draft.

Kun Shen: Visualization; Writing – original draft.

Qian Wang: Supervision; Writing – review & editing.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data availability statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Supplemental material

Supplemental material for this article is available online.

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