Heavy drinking amplifies the risk of cardiometabolic multimorbidity attributed to adverse childhood experiences in middle-aged and older Chinese adults

Abstract

To examine longitudinal associations between adverse childhood experiences (ACEs) and cardiometabolic multimorbidity (CMM) and to assess whether unhealthy lifestyles modify these associations. This study utilized data from 5 waves of the China Health and Retirement Longitudinal Study (CHARLS). The study population comprised 11 200 participants aged 45 years or older, with available data on CMM and all 12 ACE indicators. The longitudinal associations between ACEs and CMM were assessed by generalized estimating equation (GEE) regression models. A mediation analysis examined the potential causal chain in which unhealthy lifestyle scores mediate the relationship between ACEs and CMM. A total of 11 200 participants were eligible for our analysis (mean (SD) age, 58.1 (8.8) years; 5839 (52.1%) were females), and 949 (8.47%) reported four or more ACEs. Compared with those without ACE exposure, participants who experienced four or more ACEs had an increased risk of CMM (odds ratio (OR), 1.40; 95% confidence interval (CI), 1.08–1.83). Drinking status significantly modified the associations between ACE groups and CMM (P for interaction = 0.034). Mediation analyses showed that unhealthy lifestyles partially explained 8.59% (95% CI, 0.56%–16.62%) of the relationship between ACEs and CMM. Childhood adverse events contribute to the development of CMM, and this effect is more significant in people who drink alcohol. Unhealthy lifestyles can aggravate the health inequity of ACEs on CMM.

Keywords

adverse childhood experiences / cardiometabolic multimorbidity / unhealthy lifestyle / health inequity / China Health and Retirement Longitudinal Study

1 Introduction

As the global population ages, the prevalence of cardiometabolic multimorbidity (CMM), defined herein as a history of ≥ 2 of the following: diabetes, heart disease, and stroke, is increasing rapidly [1,2]. Individuals with CMM experience significantly higher mortality rates compared to those with a single condition, leading to a substantial reduction in life expectancy and a considerable socioeconomic burden on families and society [3].

More than half of the global population is exposed to at least one adverse childhood experience (ACE), which has cascading effects on children’s health, development, and well-being across their lifespan [4]. ACEs include physical, sexual, and emotional maltreatment and markers of household dysfunction (e.g., parental mental health problems and parental divorce) [5]. Early exposure to adverse experiences in life has been linked to negative long-term health outcomes [6]. Previous studies have indicated that ACE count has a dose–response relationship with many health conditions, including heart rate responses to stress and chronic diseases such as diabetes, heart disease, and stroke [7–9]. Furthermore, most studies suggest that exposure to ACEs contribute to the development of cardiometabolic disease throughout the life course [8,10]. The association between ACEs and CMM in Chinese populations warrants further investigation, given the unique social context [7].

Furthermore, there is also growing evidence that lifestyle factors such as obesity, smoking, alcohol consumption, and sleeping status are associated with CMM [11,12]. However, the majority of these studies were cross-sectional, only included single assessments of lifestyle factors, ACEs, and CMM, and did not formally test the plausible role of lifestyle factors as mediators or in interaction pathways in the Chinese population [12,13].

This study aimed to assess the longitudinal associations between unhealthy lifestyles and ACEs with CMM occurrence based on the China Health and Retirement Longitudinal Study (CHARLS) data. These data are representative of the middle-aged and older population in China, including over 337 million people [14]. By examining the interplay among these factors, we hope to shed light on the mechanisms driving CMM and inform interventions to address health inequities.

2 Materials and methods

2.1 Study design and population

This study was conducted using CHARLS data, an ongoing prospective and nationally representative population-based cohort study. The study design and sampling methods have been previously reported [15]. In brief, participants in CHARLS were randomly selected using a multistage probability sampling strategy. The total sample size of the CHARLS baseline survey was 17 708 individual respondents from 150 counties or districts and 450 villages in 28 provinces in China between June 2011 and March 2012. Respondents were followed up every two years, and a small number of new participants were recruited in each survey. Four follow-up surveys have been conducted in 2013, 2015, 2018, and 2020. Information on childhood experiences was collected from the life history surveys of all surviving respondents in the 2011 and 2013 surveys. Ethical approval for the CHARLS was obtained from the institutional review board at Peking University. Each respondent who agreed to participate in the survey provided written informed consent. The study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.

A total of 17 708 individuals participated in CHARLS 2011. The study retained 11 200 participants after excluding 648 participants without age information or who were younger than 45 years, 5781 individuals with missing values on any ACEs indicator, and 64 individuals without baseline CMM information. To assess the association between ACEs and CMM, we further excluded 15 participants without any CMM information in the four follow-ups, leaving 11 200 individuals for this statistical analysis. The detailed participant selection process is shown in Fig. S1.

2.2 Ascertainment of CMM

CMM was defined as the presence of at least two cardiometabolic conditions, including diabetes, heart disease, and stroke [1]. Diabetes was defined as fasting plasma glucose ≥ 7.0 mmol/L, glycated hemoglobin (HbA1c) ≥ 6.5%, current use of glucose-lower drugs/insulin treatment, or a self-reported history of diabetes. Venous blood samples were collected from each participant to determine the plasma glucose and HbA1c levels using hexokinase and high-performance liquid chromatography, respectively. Information on heart disease and stroke relied on self-reported data during follow-up surveys (“Have you been diagnosed with diabetes or high blood sugar by a doctor?”; for heart attack, coronary heart disease, or other heart problems, “Have you been diagnosed with heart attack, coronary heart disease, congestive heart failure, angina, or other heart problems by a doctor?”; for stroke, “Have you been diagnosed with stroke by a doctor?”). If the participant answered with a “Yes,” they were considered to have a cardiometabolic condition.

2.3 Assessment of ACEs

We identified 12 ACEs from the CHARLS data set, including seven conventional ACEs (physical abuse, emotional neglect, household substance abuse, household mental illness, domestic violence, incarcerated household members, and parental separation or divorce) [16]. Two ACEs from the expanded set (unsafe neighborhood and bullying) [17] and three new ACEs that have been previously reported (parental death, sibling death, and parental disability) [17,18]. The detailed questionnaire items and definitions of each ACE indicator are shown in Table S1. Responses to each item were dichotomized and summed to generate a cumulative ACE score for each participant, ranging from 0 to 12. We further categorized participants into five groups based on the cumulative ACE scores: 0, 1, 2, 3, and ≥ 4.

2.4 Lifestyle factors and other measures

Since multiple lifestyle factors are correlated, we constructed a healthy lifestyle score (0–5) comprising cigarette smoking, alcohol consumption, physical activity, sleep, and body shape, according to previous studies [11]. All lifestyle factors were obtained through structured questionnaires. Dietary information was not gathered in prior surveys conducted as part of the earlier CHARLS assessments [11]. Former/current smoking and drinking habits were considered unhealthy lifestyle factors. Inactive physical activity was defined as less than three times per week and less than 30 min each time of vigorous (i.e., heavy lifting, plowing, cycling with a heavy load, aerobics, etc.) or moderate (i.e., bicycling at a regular pace, performing tai-chi, walking at a quick pace, etc.) activities [19]. Insufficient sleep was defined as less than 7 h of sleep per night [19]. Unhealthy body shape was defined as any body mass index (BMI) outside the range of 18.5 kg/m² ≤ BMI < 24.0 kg/m², with BMI calculated as weight (kg)/height squared (m²). For each lifestyle factor, we assigned one point to a healthy level and zero to an unhealthy level. Thus, healthy lifestyle score is the sum of the points and ranges from 0 to 5, with lower scores indicating an unhealthy lifestyle [20]. Other covariates were also collected via questionnaires, including age, sex, ethnicity, marital status (married or not), educational level, residence (rural or urban), childhood economic hardship, and annual per capita household expenditure.

2.5 Statistical analysis

For comparisons of characteristics across different CMM groups, analysis of variance was used for continuous variables, and χ² tests were applied for categorical variables. Multiple imputations using the chained equation [21] were applied to acquire appropriate values for variables with an absent percentage of less than 20%. Sensitivity analysis was performed to determine the effect of missing-value imputation in Table S2.

To handle the correlation across repeated measures, we utilized a multivariable logistic generalized estimating equations (GEE) model with exchangeable correlation structure to account for the temporal effects of five CHARLS waves and calculated odds ratios (ORs) and 95% confidence intervals (CIs) to investigate the association of ACEs with CMM. The model was adjusted for baseline age, sex, ethnicity, marital status, educational level, rural or urban residence, annual per capita household expenditure, childhood economic hardship, follow-up times, and five unhealthy lifestyles. Trend tests were performed to assess whether a dose–response association was present.

We performed stratified and multiplicative interaction analyses by some covariates as modifiers to assess the potential difference in the effect of ACEs across different subgroups. The GEE models were repeated after stratifying by cigarette smoking, alcohol consumption, physical activity, sleep, and body shape. The product terms of ACEs and those covariates were constructed to assess the multiplicative interaction in the model.

Indirect associations acting through unhealthy lifestyles as mediating variables and direct associations not mediated by unhealthy lifestyles were quantified to explain the association between ACEs and CMM (diabetes, heart disease, and stroke). The mediation proportion was calculated by dividing the indirect effect by the total effect.

All statistical analyses were conducted using SAS version 9.4 (SAS Institute Inc., Cary, NC, USA) and R version 4.2.2 (R Foundation). All P-values were two-sided, and statistical significance was set at P < 0.05.

3 Results

Of the 11 200 participants, 5839 (52.1%) were females, and 5361 (47.9%) were males; the mean (SD) age was 58.1 (8.8) years at baseline. Table 1 showed the baseline characteristics of the participants. This study identified 1336 individuals (12%) as having CMM. Participants with CMM were more likely to be older, less educated, had a higher BMI, higher unhealthy lifestyle scores, had insufficient sleep, and an unhealthy body shape, than those without CMM. Overall, across all four epidemic waves, the number of observed CMM cases was highest in 2017 (N = 1307), followed by 2020 (N = 1085) (Fig. S1).

Table 2 showed that 75.48% of the participants were exposed to at least one of the 12 ACEs, and 8.47% had experienced four or more ACEs. Individuals with four or more ACEs were more likely to be younger, male, of Han ethnicity, less educated, married, and without childhood economic hardships than those without ACEs (Table S3). The prevalence of each ACE component ranged from 0.04% (incarcerated household members) to 33.71% (emotional neglect) (Table S1).

We observed that an increase in the number of ACEs was associated with an increasing trend in the risk of CMM, diabetes, heart disease, and stroke (Table 2). In the adjusted model, when considering outcomes associated with higher cumulative ACE scores, we found that participants with four or more ACEs had an approximately 1.4-fold increased risk of CMM (OR, 1.40; 95% CI, 1.08–1.83) compared with those with no history of ACEs. Moreover, we further found that three ACEs (including household mental illness, domestic violence, and parental disability) were associated with increased odds of CMM (Fig. S2).

Fig. 1 showed the association between the number of ACEs and CMM in unhealthy lifestyle subgroups. The results showed that the effect of ACEs on CMM was modified by drinking status (P for interaction < 0.05). Smoking status, unhealthy body shape, physical activity, and sleep did not significantly modify the associations between ACE groups and CMM (P for interaction > 0.05). In addition, different age groups in the follow-up waves (2013, 2015, 2018, and 2020) were associated with increased CMM prevalence in the different numbers of ACEs (Fig. 2A), and unhealthy lifestyle scores groups (Fig. 2B). Individuals aged 65 years or older who experienced three ACEs exhibited a higher propensity for developing CMM. The prevalence of CMM increases with unhealthy lifestyle scores.

The total, direct, and indirect associations between the number of ACEs and CMM (diabetes, heart disease, and stroke) are presented in Table 3. The proportion of the association between ACEs and CMM mediated by unhealthy lifestyle scores was 8.59% (95% CI, 0.56%–16.62%), and for heart disease, it was 6.57% (95% CI, 0.10%–13.05%). Data across major strata defined by former/current smoking, former/current drinking, inactive physical activity, insufficient sleep, and unhealthy body shape showed comparable results (Table S4). By analyzing unhealthy lifestyle subgroups, we observed that the association of ACEs with CMM incidence is mediated by unhealthy physical activity (−11.53%; 95% CI, −20.41% to −2.65%) and insufficient sleep (5.79%; 95% CI, 0.68%–10.89%) (Table S4).

4 Discussion

This study contributed to the currently limited studies establishing the longitudinal association of ACEs and CMM, and our findings provided additional evidence based on a nationally representative sample of 11 200 middle-aged and older Chinese individuals. Our investigation revealed a notable link between ACEs and increased risk of CMM, with unhealthy lifestyle factors competitively mediating 8.59% of the association. In addition, drinking status and ACEs coordinate to promote the risk of CMM. This finding underscores the enduring impact of ACEs on the health trajectory of individuals in middle and older adulthood and healthy lifestyles will be an effective strategy to prevent and manage CMM.

To our knowledge, this study is the first to report an association between ACEs and CMM, independent of confounding factors. Existing literature has predominantly explored the correlation between ACE and individual health outcomes, notably cardiovascular disease, but has limited its scope to single outcomes [22]. This shift in focus assumes paramount significance in China’s demographic landscape, where an aging population has made multimorbidity a norm rather than an exception [2]. An in-depth analysis of each outcome indicator clearly showed that the risk of CMM is significantly higher. The findings highlight the need for integrated healthcare strategies that consider the cumulative impact of multiple health conditions, rather than treating each one in isolation. By identifying a significant link between ACEs and an increased risk of CMM, our research underscores the importance of early interventions and holistic care models to mitigate long-term health risks. This deeper understanding is essential for developing targeted public health policies and improving health outcomes in middle-aged and older Chinese adults.

In addition, the study investigates the impact of ACEs on CMM within subgroups characterized by unhealthy lifestyles. The findings highlight a significant interaction between ACEs and alcohol consumption, indicating that individuals who have experienced ACEs and consume alcohol may be at a higher risk for developing CMM. However, other unhealthy lifestyle factors, such as smoking status, body shape, physical activity, and sleep, did not significantly modify the associations between ACE groups and CMM. Alcohol consumption is known to have several detrimental effects on cardiovascular and metabolic health, including increasing blood pressure, contributing to weight gain, and impairing glucose metabolism [23]. These factors can further compound the stress and physiologic damage initiated by ACEs, creating a synergistic effect that elevates the risk of developing CMM. Therefore, individuals with a history of ACEs who also consume alcohol may face a heightened cumulative burden on their health, emphasizing the importance of addressing both behavioral and psychological factors in healthcare strategies. Notably, individuals aged 65 years or older who experienced three ACEs were particularly vulnerable to CMM. This finding underscores the importance of considering age in the analysis of ACEs and CMM, as older adults may have accumulated more risk over time, leading to a higher burden of disease.

One notable aspect of this study is the exploration of unhealthy lifestyles as mediators of the ACEs-CMM relationship. A previous study demonstrated that ACEs could affect the development of CMM in several ways, including influencing personal access to health resources (knowledge, wealth, power, prestige, and favorable social connections) and protective agents (healthy lifestyles and healthcare services) [24]. However, few studies have focused on the contribution of unhealthy lifestyles to the causal pathway from ACEs to CMM occurrence. This study showed that unhealthy lifestyle factors account for only 8.59% of the association between ACEs and CMM incidence in middle-aged and older Chinese adults. A plausible explanation for these findings is the potential effect of ACEs on the development of brain regions associated with coping, planning, learning, self-regulation, and management [25]. Individuals with ACEs might be more predisposed to behavioral issues in adulthood, such as heavy smoking, alcohol misuse, and sleep disturbances, all of which are well-established contributors to the risk of physical and mental illness [26]. This observation highlights the role of modifiable risk factors in the pathway from childhood adversities to CMM development, underscoring the importance of adopting and maintaining healthy lifestyles, such as refraining from smoking, moderating alcohol consumption, engaging in regular physical activity, ensuring adequate sleep, and maintaining a healthy body shape. However, the mediating role of the lifestyle score might be underestimated due to limitations in the dietary data. Future studies should further investigate the potential mediation of diet in the relationship between ACEs and CMM. The finding indicates that a modest reduction in health inequity in ACEs can be achieved by facilitating a healthy lifestyle. Public health policies should aim to reduce health inequalities through individual intervention.

5 Strengths and limitations

One strength of this study is the large sample size used to explore the association between ACEs and CMM. Specifically, we defined CMM using self-reported physician diagnoses and health examination data, which allowed us to identify undiagnosed cases. Furthermore, we operationalized ACEs as both a cumulative score derived from the total number of ACEs encountered and as individual components, thereby allowing us to elucidate the association between ACEs and CMM. More importantly, we explored whether unhealthy lifestyle modifies the association between ACEs and CMM, which has been underexamined.

This study has some limitations. First, ACE data were collected by asking questionnaires, which may lead to recall bias that cannot be entirely eliminated. This was also suggested by the lower multimorbidity odds ratios for emotional neglect. Second, we did not consider the frequency, intensity, or chronicity of ACEs, all of which are associated with poor health outcomes [27]; however, we were unable to assess these factors owing to data unavailability. Third, although the study included a wide range of ACE indicators, some well-established ACE indicators, such as sexual abuse and living in foster care [7], were not considered because of data unavailability. These unmeasured factors may have additional relevance to the findings of the present study.

6 Conclusions

This population-based cohort study showed a dose–response association between ACE count and an increased risk of CMM among middle-aged and older individuals in China, and this effect was more significant in people who drink alcohol. Our findings additionally indicate that healthy lifestyles interventions have shown promise in the association between ACEs and CMM. These findings emphasize the need for healthy lifestyle interventions to ameliorate inequalities in healthy aging.

6.0.0.0.1 Acknowledgements

We thank the CHARLS team for sharing the data sets. The study was supported by the Nursing Development Program, Shanghai Jiao Tong University School of Medicine (No. SJTUHLXK2024), the Three-Year Action Plan to Promote Clinical Skills and Innovation in Municipal Hospitals, Shanghai Hospital Development Center (No. SHDC2022CRS009B), the Key Projects of Science and Technology Foundation of Xinhua Hospital, Shanghai Jiao Tong University School of Medicine (No. xhhlcx2023-05), and the Xinhua Hospital’s Discipline Climbing Program Project (No. XKPF2024C301). The sponsors had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or decision to submit the manuscript for publication.

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