Adverse Childhood Experiences and Adult Disease: Examining Mediating Pathways in the Hispanic Community Health Study/Study of Latinos Sociocultural Ancillary Study

Abstract

Objectives:

Adverse childhood experiences (ACEs) have been linked to adulthood chronic diseases, but there is little research examining the mechanisms underlying this association. We tested pathways from ACEs to adult disease mediated via risk factors of depression, smoking, and body mass index (BMI).

Methods:

Prospective data from adults 18 to 74 years old from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) and Sociocultural Ancillary Study (SCAS) were used. Retrospectively reported ACEs and hypothesized mediators were measured at Visit 1 (2008-2011). Outcomes of disease prevalence were assessed at Visit 2, approximately 6 years later. The analytic sample includes 5,230 Hispanic/Latino participants with ACE data. Statistical mediation was examined using structural equation modeling on cardiometabolic and pulmonary disease prevalence and reported probit regression coefficients with 95%CIs.

Results:

We found a significant association between ACEs and prevalence of asthma/chronic obstructive pulmonary disorder (COPD) (standardized β=0.07, 95%CI [0.02, 0.12]). In the mediational model, the direct association was non-significant (β=0.02, [−0.04,0.07]) but was mediated by depressive symptoms (β=0.03, [0.02, 0.04]). There were no associations between ACEs and prevalence of diabetes and self-reported coronary heart disease (CHD) or cerebrovascular disease. However, a small indirect effect was identified via depressive symptoms and CHD (β=0.02, [0.01, 0.03]).

Conclusions:

In this diverse Hispanic/Latino sample, depressive symptoms were found to be a pathway linking ACEs to self-reported cardiopulmonary diseases, although the effects were of small magnitude. Future work should replicate pathways, confirm the magnitude of effects, and examine cultural moderators that may dampen expected associations.

Introduction

Exposure to trauma early in life can have a significant impact on future health and has consistently been linked to multiple adverse health outcomes in adulthood (Felitti et al., 1998; Hughes et al., 2017). Adverse childhood experiences (ACEs) include physical, emotional, or sexual abuse or neglect, and encompass environmental stressors, such as living in a dysfunctional household with caregiver mental illness or substance abuse. ACEs can negatively impact developmental processes, conferring increased risks for negative psychological consequences, behavioral outcomes, and diseases (Anda et al., 1999; Hughes et al., 2017; Shevlin et al., 2015; Sugaya et al., 2012). Studies have replicated a dose-response relationship where an increase in the number of exposures to ACEs can lead to higher odds of adulthood chronic diseases (Felitti et al., 1998). Multiple meta-analyses have demonstrated an overall positive association between ACEs and chronic diseases, such as diabetes (Odds Ratio [OR]= 1.32, 95%CI [1.16, 1.51]; Huang et al., 2015) cardiovascular disease (CVD; OR= 1.46, [1.33, 1.61]; Jakubowski et al., 2018) myocardial infarction (MI; OR=1.88, [1.40, 2.53]; Jacquet-Smailovic et al., 2021) and chronic lung diseases (i.e., chronic obstructive pulmonary disorder (COPD)/Asthma; OR=1.41, [1.28, 1.54]; Lopes et al., 2020).

In a recent American Heart Association (AHA) scientific statement, Suglia et al. (2018) note the research gap and paucity of empirical studies testing mediational models to understand the mechanisms underlying this ACEs-diseases association. The authors proposed mental health, behavioral, and biological pathways that could potentially explain how ACEs in childhood can become biologically embedded (Berens et al., 2017). Persons exposed to ACEs at a young age can experience chronic stress, placing them at higher risk of mental health disorders (Sahle et al., 2021) and engaging in unhealthy coping behaviors (e.g., smoking, overeating, etc.; Anda et al., 1999; Hughes et al., 2017). In addition, the repeated and excessive activation of the stress response resulting from ACEs can have a negative impact on the neuroendocrine, cardiovascular, and immune systems (Agorastos et al., 2018; Glaser & Kiecolt-Glaser, 2005; Schury & Kolassa, 2012). Adults who have experienced ACEs show increased dysregulation of the hypothalamic-pituitary-adrenal axis, elevated inflammation levels, and decreased volume in the prefrontal cortex and hippocampus when compared to those who have not experienced maltreatment (Danese & McEwen, 2012).

A meta-analysis including both cross-sectional and prospective studies has found that people who experienced ACEs had an increased risk of mental disorders and suicide (Sahle et al., 2021). When compared to those who reported no ACEs, adults with at least one ACE (OR=2.01, 95%CI [1.86, 2.32]; Sahle et al., 2021) and those with four or more ACEs (OR=4.40, [3.54, 5.46]; Hughes et al., 2017) had much higher odds of depression. In addition, meta-analyses using prospective cohort studies showed that those with depression had a 30% increased risk of coronary heart disease (CHD; Relative Risk [RR]=1.30, [1.22, 1.40]) and MI (RR=1.30, [1.18, 1.44]; Gan et al., 2014). Depression was also associated with an increased risk for stroke (Hazard Ratio [HR]=1.45, [1.29, 1.63]; Pan et al., 2011) and Type 2 diabetes (RR=1.32, [1.18, 1.47]; Yu et al., 2015) Consequently, depression has been hypothesized as a potential mediating pathway between ACEs and chronic diseases (Deschênes et al., 2018; Deschênes et al., 2021; Suglia et al., 2018). The few prospective studies examining depressive symptoms as mediators of the association between ACEs and cardiometabolic diseases have consistently found significant indirect effects (Deschênes et al., 2018; Deschênes et al., 2021; Ho et al., 2020; Liu et al., 2022).

The same studies have also found that obesity is another potential biological pathway between ACEs and chronic diseases, specifically diabetes and CHD (Deschênes et al., 2018; Deschênes et al., 2021; Ho et al., 2020). Most studies exploring the linkages between ACEs and obesity use body mass index (BMI), a function of weight and height, versus other adiposity measures. To date, BMI is still the standard criteria (Centers for Disease Control and Prevention [CDC] and the World Health Organization) for diagnosing and classifying obesity and is consistently linked with adverse health and disease outcomes. Experiencing chronic and severe stress during childhood can impact both psychological and neuroendocrine processes, which may lead to obesity through biological changes that impact appetite and self-regulation leading to compulsive overeating and preference for “comfort foods” (Mason et al., 2016; Razzoli et al., 2017; Sinha & Jastreboff, 2013). In addition, children exposed to ACEs also consume fewer fruits and vegetables and engage in less physical activity, which places them at a higher risk of becoming obese (Russell et al., 2016; Su et al., 2015). Exposure to ACEs is linked to a 46% increase in the odds of obesity (OR=1.46, 95%CI [1.28, 1.64]; Wiss & Brewerton, 2020) with numerous cross-sectional and longitudinal studies replicating this positive relationship between ACEs and obesity (Llabre et al., 2017; Schroeder et al., 2021). Obesity is related to Type 2 diabetes, CVD, and COPD through complex pathogenic mechanisms (Al-Talabany et al., 2018; Fuller-Thomson et al., 2018).

Similar to obesity, smoking is a hypothesized maladaptive coping strategy in response to the psychological distress related to ACEs exposures (Kassel et al., 2003; Suglia et al., 2018). Persons exposed to 5 or more ACEs had a 5-fold increased risk of early initiation of smoking (OR=5.4, 95%CI [4.1, 7.1]) and a 3-fold increased risk of ever smoking (OR=3.1, [2.6, 3.8]; Anda et al., 1999). These observed relationships support findings that persons experiencing negative affect and moods following exposure to ACEs may find the psychoactive properties of nicotine to temporarily improve their affective states despite the health risks that may ensue (Edwards et al., 2007). It is well-established that smoking is a major cause of CVD, CHD, stroke, chronic lung diseases, and diabetes (Services, 2014). To date, there have been a handful of studies testing this behavioral pathway. Morton et al. (2014) found that smoking mediated the relationship between ACEs and acute MI while Lopes et al. (2020) reported the mediating effect of smoking with chronic lung disease. While there has been some ACEs research on ethnic minoritized groups, mostly African Americans (Hampton-Anderson et al., 2021), the majority of studies included in the aforementioned meta-analyses (Huang et al., 2015; Hughes et al., 2017) demonstrating the ACEs associations with risk factors and diseases have been conducted among predominantly White, educated samples (Felitti et al., 1998) with few studies comprised of Hispanic/Latino samples (Llabre et al., 2017). Further, the few existing studies exploring mediational pathways also used predominantly white samples from the United Kingdom (Deschênes et al., 2018; Deschênes et al., 2021; Ho et al., 2020) and United States (Dong et al., 2004) and have yet to include studies using a Hispanic/Latino sample.

In a prior paper using the Sociocultural Ancillary Study (SCAS) of the Hispanic Community Health Study/Study of Latinos (HCHS/SOL), Llabre et al. (2017) showed significant associations between retrospectively reported ACEs and disease-risk factors and some adult diseases measured at Visit 1/baseline. However, mediation was not tested in this cross-sectional, baseline paper (Llabre et al., 2017), so the present study aims to test the mediational pathways underlying ACEs and adult disease outcomes at Visit 2, on average ~5 +years later, in the HCHS/SOL sociocultural sample. We focused on Visit 2 disease outcomes to increase the number of disease events and provide the temporality component needed for mediation. We tested the mediating effects of disease risk factors, specifically depression symptoms, BMI, and smoking at Visit 1 for the relationship between retrospectively reported ACEs at Visit 1 and disease outcomes at Visit 2. Models of disease mechanisms linking ACEs to adult chronic illness implicate the mental health, behavioral, and biological pathways that themselves are risk factors or that lead to disease (Suglia et al., 2018). Our primary aim is to test indirect effects from ACEs to adult disease mediated via risk factors that have been related to both ACEs and chronic conditions of interest in prior research, specifically depressive symptoms, BMI, and smoking. Based on the extant literature, we hypothesized that ACEs retrospectively reported at baseline would be positively associated with Visit 2 prevalence of diabetes and self-reported CHD, cerebrovascular disease, and asthma/COPD mediated via depressive symptoms, BMI, and smoking measured at Visit 1. A secondary aim was to assess whether ACEs predicted Visit 2 incidence of diabetes and self-reported CHD, cerebrovascular disease, and asthma/COPD and if there were mediated effects via the above-mentioned disease risk factors at Visit 1.

Methods

Participants

The Hispanic Community Health Study/Study of Latinos (HCHS/SOL) is a multi-center, epidemiological cohort study collecting information related to CVD, other chronic health conditions, and related risk and protective factors among participants who self-identify as Mexican, Puerto-Rican, Cuban, Central American, Dominican, South American, and other/mixed backgrounds. A two-stage area probability sampling plan was used where census blocks were randomly chosen during the first stage, then the second stage involved selecting households within each census block group. The study oversampled the 45-74 years age group, Hispanic/Latino concentration, and proportion of high/low socioeconomic status. HCHS/SOL involved 16,415 participants between the ages of 18 to 74 years of age who were recruited from community areas in four field centers: Bronx, Chicago, Miami, and San Diego. Obtained between March 2008 and June 2011, baseline exam and data collection included anthropometric assessment (e.g., height and weight), electrocardiogram to identify evidence of past MI, fasting blood draw, behavioral measures (i.e., tobacco use), and sociodemographic measures. Participants underwent Visit 2 and clinical examination ~ 5+ years later between the years 2014 and 2017. More details on the study design, sample, methods, and implementation can be found elsewhere (LaVange et al., 2010).

The current study was a secondary data analysis from the Sociocultural Ancillary Study (SCAS), a sub-sample of HCHS/SOL participants who agreed to attend an additional visit within 9 months of HCHS/SOL baseline visit. The purpose of SCAS was to assess additional information related to socioeconomic, sociocultural, and psychosocial factors (Gallo et al., 2014). A total of 5,313 participants were enrolled in SCAS. The ACE data were obtained during the SCAS visit. The current study excluded those with missing data on ACEs yielding an analytic sample of 5,230. We used Visit 1 baseline data for sociodemographic, anthropometric (i.e., BMI), psychological (i.e., CESD-10), and behavioral (i.e., smoking) variables. The prevalence of all disease outcomes of interest was determined if present by Visit 2 although for many participants, disease was already present at Visit 1. All participants provided written informed consent and Institutional Review Boards of all participating institutions and HCHS/SOL coordinating center approved HCHS/SOL and SCAS.

Measures

Adverse Childhood Experiences (ACE).

Developed by the CDC and Kaiser Permanente, the original ACE scale (Felitti et al., 1998) was used to determine the number of adverse events experienced during childhood. Participants endorsed a series of possible adverse events they may have been exposed to before the age of 18, such as physical or sexual abuse, or having lived with a mentally ill household member. A total score was calculated by summing the number of events experienced ranging from 0 to 10. The ACEs score was included as a continuous variable.

Control variables.

Age (continuous), sex (i.e., male or female), years in the U.S. (continuous), nativity (i.e., U.S. born or not U.S. born), language preference (Spanish or English), income (less than $10,000, $10,000 to $15,000, $15,001-20,000, $20,001-25,000, $25,001-29,999, $30,000-40,000, $40,001-50,000, $50,000-75,000, $75,001-100,000, more than $100,000), education (no, at most, or greater than a high school degree/GED), Hispanic/Latino background (Mexican, Puerto Rican, Cuban, Dominican, Central American, South American, and other) and field center (Bronx, Chicago, Miami, and San Diego) from self-reported demographic questionnaires were included as control variables.

Mediator Variables

Depression Symptoms.

The Center for Epidemiologic Studies Depression Scale (CESD-10; Andresen et al., 1994) is a short-form measure that assesses multiple areas of depressive symptomatology such as depressed affect, interpersonal relations, and positive affect. During baseline, participants responded to 4-point Likert-type questions (0=rarely or none of the time to 3=all of the time). The total score (continuous) ranged from 0 to 30 with higher scores indicating higher levels of depressive symptoms (α_{English &Spanish}=0.82).

Body mass index (BMI).

Weight and height were measured during baseline and recorded to the nearest 1.0 cm and 0.1kg. BMI (continuous) was calculated by taking body weight in kilograms and dividing by the square of height in meters (kg/m²).

Smoking.

During baseline, participants responded to the following questions on the Tobacco Use Questionnaire: “Have you ever smoked at least 100 cigarettes in your entire life?” and “Do you NOW smoke daily, some days or not at all?” Responses to these questions categorized participants as “current/past” vs. “never” smokers.

Disease Outcome Variables

Diabetes.

Fasting blood samples were obtained for each participant. Using a hexokinase enzymatic method (Roche Diagnostics Corporation, Indianapolis, IN), plasma glucose was assessed, and glycosylated hemoglobin (A1C) was measured in EDTA whole blood using a Tosoh G7 automated high-performance liquid chromatography analyzer (Tosoh Bioscience Inc, San Francisco, CA). Participants with plasma glucose less than 150 mg/dL and who did not report being previously diagnosed with diabetes also underwent a two-hour oral glucose tolerance test. Participants self-reported using glucose-lowering medications. Use of medication was also ascertained via scanning of Universal Product Code bar codes when available or centralized manual coding. Diabetes was defined as a binary variable indicating whether a participant had a diagnosis by Visit 2 using the American Diabetes Association criteria (Association, 2013): fasting plasma glucose 126 mg/dL or greater, two-hour oral glucose tolerance test glucose level 200 mg/dL or greater, A1C level 6.5% or greater, scanned or transcribed glucose-lowering medication use, and/or self-report of medication use or previous diabetes diagnosis by a doctor.

Coronary Heart Disease (CHD).

All participants received a digital electrocardiogram during Visit 1. The ECG results were electronically transmitted to a Central ECG Reading Center (EPICARE, Wake Forest University School of Medicine, Winston-Salem, NC) and Harvard University ascertained for old MI using the Minnesota Code classification system. At annual follow-up visits and Visit 2, participants were asked to self-report any new or additional MI events where medical records were then obtained to confirm self-report by two trained physician reviewers and a third to adjudicate and confirm diagnosis using ECG records. Additionally, at Visit 2, participants self-reported any previous angina, heart attack, and coronary procedures (angioplasty, stent, or bypass surgery to the arteries of the heart) via standard questionnaire and interview. Prevalent CHD by Visit 2 was defined as a binary variable with either an ECG report of old MI and/or a self-report of angina, heart attack, or coronary procedure (Prineas et al., 2009).

Cerebrovascular Disease.

Cerebrovascular disease was defined as a binary variable using self-report for medical history of previous stroke, mini-stroke or transient ischemic attack, or cerebrovascular procedures (balloon angioplasty or surgery to the arteries of the neck) as obtained from participants via standard questionnaire and interview at Visit 2.

Asthma and Chronic Obstructive Pulmonary Disease.

Chronic obstructive pulmonary disease (COPD) was self-reported by answering the prompt, “Has a doctor ever told you that you had COPD or emphysema and/or chronic bronchitis?” Asthma was defined as a self-report of ever having had asthma, whether diagnosed by a health professional or not. Prevalent asthma/COPD was defined as a binary variable if present at Visit 2.

Statistical Analysis

Weighted descriptive statistics were performed using SPSS version 28. We conducted path analysis using a structural equation modeling framework in Mplus 8.2. All models used sampling weights, clustering, and stratification features of HCHS/SOL study design. We examined statistical mediation where BMI, smoking, and depression symptoms were included in the model as outcomes and as predictors to test for possible mediation by estimating indirect effects from ACEs to disease prevalence outcomes at Visit 2 via the parallel mediators (depressive symptoms, BMI, and smoking). Additional analyses were run on disease incidence outcomes at Visit 2. All mediation models were adjusted for sociodemographic covariates: age, sex, years in the U.S., nativity, language preference, income, education, Hispanic/Latino background, and field center. We estimated probit regression coefficients using a mean and variance adjusted weighted least squares with robust SE estimation (WLMSV) (Muthén et al., 2017; Muthén & Muthén, 2012). Unstandardized and standardized path coefficients, indicator of effect size, and their 95% confidence intervals were calculated. The fit indices for model evaluation were determined by chi-square test of model fit (p>0.05, acceptable), comparative fit index (CFI; ≥ 0.90, acceptable) and root mean square error of approximation (RMSEA; ≤ 0.08, acceptable).

Results

At baseline, the target population was on average 42.5 years old, more than half were female (54.85%), most had an annual household income of less than $30,000 (70.10%), the majority were overweight (defined by CDC criteria as BMI 25.0 to <30), the overall average BMI was 29.64, and more than half were never smokers (61.22%) (Table 1). The largest Hispanic/Latino background group was of Mexican heritage (36.53%). Roughly a quarter of individuals had diabetes and asthma/COPD at Visit 2, 25.33% and 25.03%, respectively. At Visit 2, seven percent of individuals had CHD and 2.53% had a stroke.

Table 1.

Descriptive Statistics for Target Population Demographic Characteristics and Outcomes

Weighted Statistics	N=5,006 N (%) or Mean (SD)
Demographics
Age (years)	42.48 (15.03)
Female, %	2,746 (54.85)
Years in U.S.	20.54 (14.84)
Born in U.S., %	1,097 (21.91)
Language Preference Spanish	3,776 (75.42)
Income <$30,000, %	3,203 (66.21)
Education %
<High School (HS)/GED	1,627(32.50)
At most a HS/GED	1,403(28.02)
>HS/GED	1,973(39.41)
Hispanic/Latino Background %
Central American	379 (7.57)
Cuban	1,017 (20.31)
Dominican	585 (11.69)
Mexican	1,829 (36.53)
Puerto Rican	789 (15.75)
South American	239 (4.78)
Other/More than one heritage	166 (3.31)
Center %
Bronx	1,513 (30.23)
Chicago	783 (15.63)
Miami	1,459 (29.15)
San Diego	1,251 (24.99)
Mediators
BMI	29.64 (6.26)
CESD-10	7.27 (6.23)
Smoking Status %
Never	3,065 (61.22)
Former/Current	1,937 (38.69)
Visit 2 Prevalence/Incidence %
Diabetes	1,268 (25.33)/417 (8.34)
CHD	344 (6.86)/63 (1.26)
Cerebrovascular Disease	127 (2.53)/46 (0.91)
Asthma/COPD	1,253 (25.03)/139 (2.78)

Note: SD=Standard Deviation; CHD=Coronary Heart Disease; COPD=Chronic Obstructive Pulmonary Disease; BMI= Body Mass Index (kg/m²; continuous)

After adjusting for covariates, ACEs were positively and significantly associated with mediators of depression symptoms, BMI, and smoking at baseline (Table 2). ACEs were positively and significantly related to asthma/COPD (standardized β=0.07, [0.02, 0.12]) prevalence at Visit 2 (Table 3).

Table 2.

Direct Effects of ACE and Covariates on Mediators (N=5,006)

Main Predictor Variable and Covariates	Depression	BMI	Smoking
	Beta (95% CI)	Beta (95% CI)	Beta (95% CI)
ACE	0.20 (0.16, 0.23)	0.05 (0.02, 0.09)	0.19 (0.13, 0.24)
Age, yrs	0.03 (−0.03, 0.09)	0.10 (0.04, 0.15)	0.19 (0.13, 0.26)
Female	−0.13 (−0.17, −0.09)	−0.10 (−0.14, −0.06)	0.25 (0.20, 0.30)
Years in the U.S.	0.07 (0.01, 0.13)	0.08(0.02, 0.14)	−0.03 (−0.10, 0.05)
Nativity status	0.05 (−0.05, 0.14)	0.03 (−0.03, 0.08)	0.12 (0.05, 0.19)
U.S. Born
Language preference
English	0.01 (−0.05, 0.07)	0.08 (0.02, 0.13)	0.09 (0.02, 0.16)
Spanish	-	-	-
Hispanic background
Central American	0.04 (−0.00, 0.09)	0.02 (−0.04, 0.07)	−0.04 (−0.10, 0.03)
Cuban	0.14 (0.07, 0.21)	−0.01 (−0.11, 0.09)	0.08 (−0.01, 0.17)
Dominican	0.04 (−0.02, 0.11)	0.02(−0.07, 0.10)	−0.03 (−0.12, 0.06)
Mexican	-	-	-
Puerto Rican	0.12 (0.06, 0.18)	0.05 (−0.01, 0.12)	0.08 (0.01, 0.16)
South American	0.04(−0.00, 0.07)	−0.00 (−0.05,1, 0.05)	0.02 (−0.03, 0.07)
More than one	0.03 (−0.03 0.08)	0.00 (−0.05, 0.05)	0.10 (0.04, 0.16)
Study site
Bronx	0.04 (−0.02, 0.11)	−0.01(−0.09, 0.06)	−0.06 (−0.15, 0.03)
Chicago	0.05 (−0.01, 0.10)	−0.02 (−0.09, 0.04)	−0.04 (−0.11, 0.03)
Miami	-	-	-
San Diego	0.06 (−0.02, 0.14)	−0.06 (−0.16, 0.04)	−0.01 (−0.11, 0.09)
Education
No High School (HS)	0.09 (0.04, 0.13)	0.06 (0.02, 0.11)	0.12 (0.07, 0.17)
Diploma or GED
At most a HS diploma or GED	0.03(−0.01, 0.08)	0.03 (−0.02, 0.08)	0.04 (−0.01, 0.09)
> HS/GED	-	-	-
Income
<$10,000	-	-	-
$10,001-15,000	−0.08 (−0.12, −0.04)	0.02 (−0.04, 0.07)	−0.06 (−0.13, 0.01)
$15,001-20,000	−0.09 (−0.13, −0.04)	0.03 (−0.02, 0.07)	−0.02 (−0.08, 0.04)
$20,001-25,000	−0.11(−0.15, −0.07)	0.01 (−0.03, 0.05)	−0.04 (−0.10, 0.02)
$25,001-29,999	−0.13 (−0.17, −0.08)	0.02 (−0.03, 0.07)	−0.10 (−0.16, −0.04)
$30,000-40,000	−0.20 (−0.25, −0.14)	0.05 (0.00, 0.10)	−0.07 (−0.13, −0.01)
$40,001-50,000	−0.14 (−0.19, −0.10)	−0.03 (−0.07, 0.01)	−0.04 (−0.09, 0.01)
$50,000-75,000	−0.15(−0.21, −0.10)	−0.01 (−0.06, 0.04)	−0.08 (−0.14, −0.03)
$75,001-100,000	−0.10 (−0.15, −0.04)	−0.00 (−0.04, 0.04)	−0.08 (−0.14, −0.01)
more than $100,000	−0.10 (−0.18, −0.00)	−0.02 (−0.07, 0.03)	−0.08 (−0.12, −0.04)

Note. Beta= Standardized Probit Regression Coefficient; CI=Confidence; BMI= Body Mass Index (kg/m²)

Table 3.

Direct effects of ACE and Covariates on Disease Visit 2 Prevalence Outcomes

Main Predictor Variable and Covariates	Diabetes (n=4,043)	CHD (n=3,916)	Cerebrovascular disease (n=3,865)	Asthma/COPD (n=4,008)
	Beta (95% CI)
ACE	0.03 (−0.02, 0.08)	0.05 (−0.01, 0.11)	−0.03 (−0.13, 0.06)	0.07 (0.02, 0.12)
Age, yrs	0.46 (0.39, 0.53)	0.39 (0.30, 0.48)	0.35 (0.22, 0.47)	0.03 (−0.05, 0.10)
Female	0.07 (0.01, 0.12)	0.07 (0.01, 0.14)	0.01 (−0.10, 0.11)	−0.12 (−0.17, −0.07)
Years in the U.S.	−0.02 (−0.10, 0.06)	0.10 (0.00, 0.19)	0.10 (−0.04, 0.23)	0.04 (−0.05, 0.12)
Nativity status	0.07(−0.01, 0.16)	0.07 (−0.06, 0.19)	0.01 (−0.18, 0.19)	0.07 (−0.01, 0.15)
U.S. Born
Language preference
English	−0.03(−0.11, 0.06)	−0.05 (−0.15, 0.05)	0.08 (−0.09, 0.25)	0.11 (0.02, 0.19)
Spanish	-	-	-	-
Hispanic background
Central American	−0.02 (−0.08, 0.05)	−0.05 (−0.13, 0.02)	0.03 (−0.09, 0.15)	0.05 (−0.02, 0.13)
Cuban	−0.03 (−0.14, 0.08)	0.04 (−0.10, 0.18)	0.05 (−0.14, 0.23)	0.24 (0.11, 0.36)
Dominican	−0.00 (−0.09, 0.09)	0.02 (−0.08, 0.11)	0.10 (−0.05, 0.24)	0.10 (−0.02, 0.22)
Mexican	-	-	-	-
Puerto Rican	0.08 (−0.02, 0.17)	0.02 (−0.08, 0.12)	0.09 (−0.01, 0.20)	0.29 (0.19, 0.40)
South American	−0.03 (−0.09, 0.03)	0.01 (−0.05, 0.08)	0.03 (−0.08, 0.14)	0.03 (−0.04, 0.10)
More than one	0.02 (−0.03, 0.08)	0.08 (0.01, 0.15)	0.07 (−0.04, 0.17)	0.09 (0.04, 0.15)
Study site
Bronx	0.02 (−0.08, 0.12)	0.03 (−0.10, 0.16)	−0.07 (−0.25, 0.11)	−0.01 (−0.13, 0.11)
Chicago	0.10 (0.02, 0.17)	0.02 (−0.09, 0.13)	0.04 (−0.12, 0.20)	−0.05 (−0.13, 0.04)
Miami	-	-	-	-
San Diego	0.09 (−0.04, 0.21)	−0.12 (−0.28, 0.03)	0.05 (−0.17, 0.26)	−0.01 (−0.15, 0.13)
Education
No High School (HS)	0.07 (0.02, 0.13)	0.01 (−0.07, 0.09)	−0.03 (−0.12, 0.07)	−0.02 (−0.08, 0.05)
Diploma or GED
At most a HS diploma or GED	0.03(−0.03, 0.08)	0.04 (−0.04, 0.12)	0.01 (−0.13, 0.14)	0.01 (−0.07, 0.08)
>HS or GED	-	-	-	-
Income
<$10,000	-	-	-	-
$10,001-15,000	−0.05 (−0.11, 0.01)	−0.03 (−0.11, 0.05)	−0.04 (−0.14, 0.06)	0.02 (−0.06, 0.09)
$15,001-20,000	−0.05 (−0.11, 0.01)	0.01 (−0.07, 0.08)	−0.03 (−0.13, 0.07)	−0.03 (−0.10, 0.04)
$20,001-25,000	−0.04 (−0.09, 0.02)	−0.06 (−0.13, 0.01)	−0.17 (−0.28, −0.07)	−0.04 (−0.11, 0.03)
$25,001-29,999	−0.05 (−0.11, 0.02)	−0.04 (−0.13, 0.04)	−0.15 (−0.27, −0.03)	0.03 (−0.04, 0.09)
$30,000-40,000	−0.03 (−0.09, 0.03)	−0.12 (−0.20, −0.04)	−0.16 (−0.30, −0.03)	−0.05 (−0.12, 0.02)
$40,001-50,000	−0.08 (−0.14, −0.02)	−0.12 (−0.22,−0.03)	−0.08 (−0.18, 0.03)	−0.05 (−0.12, 0.01)
$50,000-75,000	−0.07 (−0.14, 0.00)	−0.03 (−0.13, 0.08)	−0.14 (−0.26, −0.02)	−0.01 (−0.07, 0.04)
$75,001-100,000	−0.01 (−0.08, 0.06)	0.05 (−0.03, 0.13)	−0.16 (−0.28, −0.03)	−0.02 (−0.09, 0.03)
more than $100,000	−0.06 (−0.13, 0.00)	−0.02 (−0.11, 0.08)	−0.11(−0.22, 0.00)	−0.05 (−0.12, 0.02)

Beta= Standardized Probit Regression Coefficients CI=Confidence Interval; CHD= Coronary Heart Disease; COPD=Chronic Obstructive Pulmonary Disease; BMI= Body Mass Index (kg/m²)

Mediational Models

For all outcomes, the model fit was acceptable (χ²(2)=1.84, p=0.40; CFI=1.00; RMSEA=0.00, 95%CI [0.00, 0.03]).

Disease Prevalence Outcomes at Visit 2.

Depression symptoms, BMI, and smoking at baseline were simultaneously specified as parallel mediators to test indirect effects for the association between ACEs and disease prevalence outcomes at Visit 2 (see Table 4), while adjusting for covariates. Parameter estimates showed that the direct effect (p=0.86) from ACEs to diabetes was non-significant. There were no significant indirect effects.

Table 4.

Total, Total Indirect, Specific Indirect, and Direct Effects of ACE on Visit 2 Prevalence Outcomes

	Diabetes	CHD	Cerebrovascular disease	Asthma/COPD
	Beta (95% CI)
Direct Effects	0.01 (−0.05,0.06)	0.01 (−0.05,0.08)	−0.05 (−0.15,0.04)	0.02 (−0.04,0.07)
Total Effects	0.03 (−0.02,0.08)	0.05 (−0.01, 0.11)	−0.03 (−0.13,0.06)	0.07 (0.02,0.12)
Total Indirect Effect	0.03 (0.00, 0.05)	0.04 (0.02,0.06)	0.02 (−0.00,0.05)	0.05 (0.03,0.07)
ACE via Depression	0.01 (0.00, 0.02)	0.02 (0.01, 0.03)	0.02 (0.00, 0.03)	0.03 (0.02, 0.04)
ACE via BMI	0.02 (0.00,0.03)	0.00 (0.00, 0.01)	0.00(−0.00, 0.01)	0.01 (0.00, 0.01)
ACE via Smoking	0.00 (−0.01,0.01)	0.01 (−0.00, 0.03)	0.00 (−0.02, 0.02)	0.02 (0.00, 0.04)

Note: All models control for covariates; Beta= Standardized Probit Regression Coefficients, CI=Confidence Interval; CHD= Coronary Heart Disease; COPD=Chronic Obstructive Pulmonary Disease; BMI= Body Mass Index (kg/m²)

For self-reported CHD, parameter estimates showed that the direct effect from ACEs to CHD was non-significant (standardized β =0.01, 95%CI [−0.05,0.08]) in the mediation model. ACEs had a significant indirect effect to CHD via depression symptoms (standardized β =0.02, [0.01, 0.03]) such that increases in ACEs were associated with an increased probability of having CHD at Visit 2 through increased depression at baseline.

For self-reported cerebrovascular disease, parameter estimates showed that the direct effect (p=0.28) from ACEs to cerebrovascular disease was non-significant. There were no significant indirect effects.

The direct effect from ACEs to self-reported asthma/COPD was not significant (standardized β =0.02, 95%CI [−0.02, 0.09]) in the mediational model. ACEs had a significant indirect effect to asthma/COPD via depression symptoms (standardized β =0.03, [0.02, 0.04]) such that increases in ACEs were associated with an increased probability of having asthma/COPD at Visit 2 through increased depression at baseline.

Disease Incidence Outcomes.

Adjusted models showed that ACEs were not significantly associated with new Visit 2 cases of diabetes (standardized β =0.04, 95%CI [−0.02, 0.10]), self-reported CHD (standardized β =−0.08, [ −4.66, 4.50]), self-reported cerebrovascular disease (standardized β =−0.10, [−2.26, 2.06]) or self-reported asthma/COPD (standardized β =0.01, [−0.07, 0.08]). For mediational analyses, the model showed good fit (χ²(2)=1.84, p=0.40; CFI=1.00; RMSEA=0.00, 95%CI [0.00, 0.03], but there were no significant indirect effects for any of the mediators (Table 5).

Table 5.

Total, Total Indirect, Specific Indirect, and Direct Effects of ACE on Visit 2 Incidence of Disease Outcomes

	Diabetes	CHD	Cerebrovascular disease	Asthma/COPD
	Beta (95% CI)
Direct Effects	0.04 (−0.03,0.11)	−0.11 (−0.40, 0.17)	−0.13(−1.82, 1.57)	−0.02 (−0.71, 0.67)
Total Effects	0.04 (−0.02,0.11)	−0.08(−0.32, 0.15)	−0.10(−1.46, 1.26)	0.01 (−0.25, 0.26)
Total Indirect Effect	0.00 (−0.02,0.02)	0.03 (−0.04, 0.10)	0.03 (−0.32,0.37)	0.03(−0.90, 0.96)
ACE via Depression	−0.00 (−0.01, 0.01)	0.01 (−0.02, 0.03)	0.02 (−0.21, 0.24)	0.02 (−0.53, 0.56)
ACE via BMI	0.01 (0.00, 0.02)	−0.00 (−0.02, 0.01)	0.00(−0.04, 0.05)	−0.00 (−0.06, 0.06)
ACE via Smoking	−0.01 (−0.02, 0.01)	0.03 (−0.04, 0.09)	0.01 (−0.07, 0.08)	0.01 (−0.43, 0.45)

Note: All models control for covariates, Beta= Standardized Probit Regression Coefficients, CI=Confidence Interval; CHD= Coronary Heart Disease; COPD=Chronic Obstructive Pulmonary Disease; BMI=Body Mass Index (kg/m²)

Discussion

The goal of the current study was to test for whether depression symptoms, BMI, and smoking at baseline are mediators of the association between retrospective report of ACEs and prevalence and incidence of adult disease 6 years later, using data from the HCHS/SOL Sociocultural Ancillary Study. Our prior paper (Llabre et al., 2017) suggested that there were weaker associations between ACEs and adult disease in Hispanic/Latino populations than had been previously reported; however, whether these associations were weaker due to the age of the sample had not been explored until this current study. Our results suggest that the additional 6 years of follow-up have not substantially increased the magnitude of relationships between ACEs and diseases, but instead showed weakened relationships. The prospective nature of our data allowed us to address our aim of testing mental, behavioral, and biological pathways that may be responsible for linking ACEs to disease in adulthood in the Hispanic/Latino population. We found some evidence that: (1) depression symptoms mediated the association between ACEs and CHD prevalence and asthma/COPD prevalence. However, both associations were of small magnitude.

There were no significant associations or mediational pathways found between ACEs and the incidence of disease outcomes. To our knowledge, this is the first study to simultaneously examine these three pathways noted in the recent AHA statement (Suglia et al., 2018) among Hispanic/Latino sample. This study also adds to the small body of literature that comprehensively tests these pathways and does so for multiple disease outcomes.

At the 6 years follow-up, ACEs were significantly associated with the prevalence of asthma/COPD, but not diabetes, CHD, or cerebrovascular disease. In the context of other individual and meta-analytic studies, the relationships between ACEs and disease observed in our Hispanic/Latino sample were either weaker in comparison to other samples or non-significant (Huang et al., 2015; Hughes et al., 2017; Jacquet-Smailovic et al., 2021; Jakubowski et al., 2018; Lopes et al., 2020). The existing literature shows significant relationships between ACEs and metabolic diseases, such as diabetes, with stronger effects for CVD (Jakubowski et al., 2018). While we did not observe these same significant associations for CHD and cerebrovascular disease, our non-significant results for diabetes and significant association with chronic lung disease are relatively congruent with this finding and those of Hughes et al. (2017) meta-analysis assessing the impact of 4+ ACEs on health outcomes, which found that diabetes had the lowest pooled effect size and observed the highest pooled effect for respiratory diseases. Our non-significant results for CHD do, however, contrast that of Jacquet-Smailovic et al. (2021), which found a stronger relationship between ACEs and MI (OR=1.88, 95% CI [1.40, 2.53]) and an even stronger pooled effect for studies that included more than half of participants over the age of 55 (OR=2.34, [1.70, 3.23]). Given our target population is young (56.5% were 18-44 years old range at baseline), the number of cardiovascular events we observed in the follow-up is small and reduced the power.

When assessing potential pathways for the ACEs-diabetes association, our results did not align with a study by Deschênes et al. (2018). The authors found a significant, yet small association between ACEs and diabetes (OR=1.11, 95%CI [1.00,1.24]), but we did not observe this association. Their study also found that ACEs were indirectly associated with both depression (indirect effect 0.03, [0.02, 0.04]) and cardiometabolic dysregulations, including obesity (indirect effect 0.03, [0.01, 0.05]). In their mediation model, the ACEs-diabetes association became non-significant, indicating full mediation through the mental health and biological pathways. Whereas our mediational results were non-significant, we observed trends in the expected direction for both BMI and depression.

Our findings for coronary heart disease showed partial mediation through depression symptoms. Although BMI and smoking were not found to be significant mediators, our findings fit with mediational results from the original Kaiser and CDC’s ACEs study sample, which was comprised of mostly white participants. Dong et al. (2004) found the association between ACEs and ischemic heart diseases was mediated more strongly by psychological risk factors (i.e., depressed affect) versus more traditional risk factors’ indirect effects (i.e., obesity and smoking). In another study assessing behavioral, mental health, and biological pathways for CHD among a predominantly White and male sample, Deschênes et al. (2021) found depressive symptoms and cardiometabolic dysregulation (i.e., obesity) partially mediated the relationship between ACEs and CHD, which somewhat aligns with our results showing significant pathways through depression symptoms but not BMI. Both our results and those from Deschenes and colleagues found smoking to be a non-significant behavioral pathway while another study by Morton et al. (2014) found that smoking mediated the relationship between ACEs and acute MI.

In the original ACEs study, Ho et al. (2020) also observed a significant indirect effect of ACEs and CVD via smoking (14.7%) but to a much lesser extent when compared to depressive symptoms (56.2%), which mediated most of the association. Similar to our findings for CHD, Ho et al. (2020) did not find obesity to be a significant mediator. Another study testing solely the psychological pathway from ACEs to CVD also found depression to be a significant mediator among a Chinese sample (Liu et al., 2022). For the association between ACEs and cerebrovascular disease, our results did not show any significant pathways. There were limited studies investigating mediating pathways for this relationship.

For our asthma/COPD mediational model, we demonstrated mediation through depression symptoms, which does not align with other studies. Lietzén et al. (2021) tested several mediators and found that both BMI and smoking partially mediated the relationship between ACEs and asthma. The proportion of the total effect of ACEs found that smoking and obesity accounted for 15% and 3%, respectively. The mediating effects of depression symptoms were not tested in that study. In a meta-analysis assessing the relationship between ACEs and chronic lung diseases (combination of asthma and COPD), Lopes et al. (2020) calculated an overall effect (OR=1.41, 95%CI [1.28-1.54]), but when the mediating effect of smoking was tested separately, then the association was attenuated (OR=1.06, [1.02, 1.10]).

It is noteworthy that our results showed depression symptoms were a significant mediator for cardiopulmonary disease outcomes, specifically CHD and asthma/COPD. Taken together with the strong and significant indirect effects for depressive symptoms from Ho et al. (2020), Dong et al. (2004), Deschênes et al. (2018), Deschênes et al. (2021), depression is a potential pathway for the ACEs and adulthood chronic disease associations. The existing studies found significant biological and behavioral pathways for cardiometabolic diseases but to a lesser extent than the mental health pathway (Deschênes et al., 2018; Dong et al., 2004). Future studies should replicate these mediational findings as mental pathways could be potential malleable targets for future interventions for those who experienced ACEs. By focusing on ameliorating depressive symptoms for those at-risk, there may be the potential to prevent multiple chronic diseases.

Moreover, future research should elucidate this replicated mental health pathway by exploring the hypothesized biopsychological mechanisms underlying the relationship between ACEs and depressive symptomatology (Iob et al., 2020; Li & Xiang, 2023). Meta-analyses show that ACEs are predictive of low-grade inflammation in adulthood, such as C-reactive protein, interleukin-6, and tumor necrosis factor-alpha (Baumeister et al., 2016; Coelho et al., 2014) although most of the included studies were cross-sectional. Prospective research implicates inflammatory markers in the development of depression (Gimeno et al., 2009; Miller & Raison, 2016). Researchers hypothesize that exposure to adverse childhood experiences could lead to systemic inflammatory responses, which in turn, can contribute to impaired brain development and function during critical developmental periods of childhood and adolescence (Danese & Baldwin, 2017). As a result, those exposed to adverse childhood experiences could be more vulnerable to depression. Testing this biopsychological mechanism, large prospective studies have found significant mediating effects of inflammation for the relationship between ACEs and depressive symptoms (Iob et al., 2020; Li & Xiang, 2023). Future research should replicate this inflammatory link between ACEs and depression and test other proposed socioemotional mediators found in the literature including social information processing (i.e., rejection sensitivity) and emotional regulation (i.e., emotional reactivity) (Brodbeck et al., 2022).

In addition to replicating and elucidating mediational findings, future research should confirm the magnitude of effects. The current study’s effects were smaller than expected considering the extant literature’s effect sizes for ACEs-diseases and ACEs-risk factors associations; however, as previously mentioned, many of the meta-analyses were based on studies with predominantly white samples (Lopes et al., 2020; Zhang et al., 2011). Interestingly, although it has been consistently confirmed across studies that minoritized populations, including Hispanic/Latino individuals, experience significantly higher levels of exposure to ACEs, (Goldstein et al., 2021; Llabre et al., 2017; Merrick et al., 2018) there is some evidence showing that non-Hispanic White people tend to experience greater vulnerability to ACEs’ deleterious effects (Elkins et al., 2019; Goldstein et al., 2021; Schilling et al., 2007; Vásquez et al., 2019). Some research assessing for racial/ethnic differences has shown that non-Hispanic White individuals are more greatly impacted by ACEs versus minoritized racial and ethnic groups.

In one of the few diverse studies assessing ethnic and racial differences, Vásquez et al. (2019) examined the association between ACEs and multimorbidity. For middle-aged adults between the years 55-64 years old, the results showed that Hispanic/Latino adults had significantly fewer somatic multimorbidity (i.e., heart disease, diabetes, chronic lung problems, etc.) when compared to non-Hispanic White and non-Hispanic Black adults. Hispanic/Latino older adults (65+ years old) also reported fewer chronic medical conditions when compared to both their racial counterparts. In addition to ACEs differential impact on physical health, these ethnic and racial differences were also observed for mental health outcomes. Schilling et al. (2007) found that the adverse mental health effects were consistently stronger among non-Hispanic White adults versus Black and Hispanic adults when assessing for ACEs both cumulatively and for individual ACEs. For example, witnessing a murder or severe injury was strongly and significantly associated with depression and antisocial behavior in non-Hispanic white adults, whereas the associations were not significant for either outcome in those of Hispanic ethnicity.

Similarly, Elkins et al. (2019) also saw differential mental health effects. Using a nationally representative adolescent sample, the researchers examined whether race and ethnicity moderated the relationship between ACEs and PTSD and concluded that non-Hispanic white youth had a substantially higher likelihood of experiencing lifetime PTSD versus Hispanic/Latino and non-Hispanic adolescents. These racial and ethnic differences were also observed much earlier in the life course. Goldstein et al. (2021) found moderating effects by race and ethnicity where Black and Hispanic/Latino children exposed to high-risk levels of adverse childhood experiences tended to function better versus White children on the outcome of child flourishing. Both Schilling et al. (2007) and Goldstein et al. (2021) concluded that a possible explanation for minority groups being able to endure adverse experiences better than their white peers may be due to a “steeling or hardening effect” in ethnic minorities who have experienced protracted negative experiences, which could help them cope more successfully with exposures to ACEs and stress. Our results showed a significant mental health pathway via depressive symptomatology similar to prior mediational studies, but even our significant indirect effects were attenuated in comparison to the stronger mental health indirect effects reported in prior studies with predominantly white samples (Ho et al., 2020). This aligns with the aforementioned literature showing racial and ethnic differences.

Over the years, there have been many other explanations put forth and investigations into cultural values or resources that could account for the differential health outcomes for Hispanic/Latino populations. Gallo et al. (2009) proposed the Reserve Capacity Model to help contextualize the socioeconomic and cultural level factors that shape some of the psychosocial resilient processes. The processes include stressors, emotions, interpersonal and intrapersonal resources (e.g., supportive social relationships, social support, perceived control, etc.), and culturally specific resources, including language (Llabre, 2021), that can beneficially impact health risk factors and outcomes in the U.S. Hispanic/Latino populations. In our prior paper (Llabre et al., 2017), social support, an interpersonal resource that could potentially serve as a buffer to ACEs and stress, did not significantly moderate the ACEs-disease associations. Future research should investigate other reserve capacity resources, such as support or nurture from extended family members or community/institutional resources (church), that help people of Hispanic/Latino ethnicity buffer the deleterious health impacts of ACEs and dampen associations. Testing these and other culturally-specific moderating factors can help to elucidate these differential magnitudes of effects.

Our current study has some limitations. First, our ACE measure was assessed retrospectively, which could impact participants’ recall or bias in their reporting of ACEs. However, the extant literature is overwhelmingly retrospectively assessed in accordance with the original ACE study (Felitti et al., 1998). Future studies should include a prospective design and follow patients through their life courses to confirm the ACEs-disease associations. These long-term studies are necessary to more fully elucidate the multiple pathways that lead from ACEs to chronic diseases during adulthood. In addition, the recognized approach for the types and total number of ACEs uses a conventional sum score; however, it may not fully account for potential variations or combinations that may predict the manifestation of disease.

We also note the current study’s design limitation regarding the temporal aspect being present only for incidence disease outcomes but not for prevalence outcomes in our mediational analyses, which is due to our prevalence outcomes at Visit 2 being conceptualized as cumulative disease events at this follow-up visit but many cases were already present at Visit 1. Our mediators of interest were also measured at Visit 1. While there exists limited literature showing a reciprocal nature of disease outcomes and risk factors, most prospective studies show that the onset of risk factors occurs beforehand or precedes the onset of disease (Mezuk et al., 2008). For example, a meta-analysis including 192 epidemiological studies found that a quarter showed depressive symptoms before the age of 17 with a median age of 20.5, which is well before the onset of later adulthood diseases (Solmi et al., 2022). Similarly, about ninety percent of adults who smoke cigarettes do so by the age of 18 and ninety-nine percent of smokers by the age of 26 (General et al., 2012). After reviewing the available scientific evidence, the AHA identified obesity as a major risk factor that contributes directly to disease outcomes (Powell-Wiley et al., 2021). In addition, the existing mediational studies tested these risk factors as mediators with the recognition that they temporally precede the onset of diseases (Deschênes et al., 2018; Deschênes et al., 2021; Dong et al., 2004; Ho et al., 2020).

Despite the additional several years of observing disease events, we acknowledge the low incidence of disease at Visit 2 as a limitation. This could be due in part to age, but our results should also be interpreted in the context of the Hispanic paradox. This phenomenon has been observed among Hispanic/Latino populations that tend to have decreased risk for disease and increased longevity than their non-Hispanic White counterparts despite having a high prevalence of disease risk factors (mediators) and on average having lower socioeconomic status (Medina-Inojosa et al., 2014). The current study (see Table 2) and Llabre et al. (2017) reported on the observed significant associations between ACEs and our mediators of interest in the HCHS/SOL study. With exception to our significant mediational findings for depression, we did not find our other mediators, smoking and BMI, to be statistically significant mediators for the ACEs-disease association. As mentioned earlier, further examination of culturally-specific moderating or buffering factors is needed to elucidate the Hispanic paradox, which shows that disease-risk factors, like BMI, are not significantly predictive of disease or mortality rates among Hispanic/Latino populations (Fontaine et al., 2012) running contrary to the findings from other ethnic/racial populations.

Another limitation is our measure of BMI as it does not measure body fat content, muscle mass, bone density, or visceral fat, which are important components of body composition. Although BMI is still the standard criterion (CDC), future studies should use more accurate adiposity measures.

Despite these noted limitations, the current study contributes to the existing limited literature by simultaneously examining the proposed pathways in which ACEs can lead to multiple chronic diseases. The HCHS/SOL study is also a prospective cohort study that adheres to rigorous and standardized collection procedures, thus stronger inferences can be made versus other studies using a convenience sample. Another strength of this study is the large and diverse sample of Hispanics/Latino adults with a wide range of income. Because most of the ACEs research has been conducted among predominantly white samples, future work should include more ethnic minorities to confirm the magnitude of our associations. There exists very limited research on ACEs-disease associations among Hispanics/Latinos samples (Llabre et al., 2017; Vásquez et al., 2019). Moreover, additional research is needed to fully confirm the dampened associations in this population and mediational pathways between ACEs and diseases in both the general population and the Hispanic/Latino population.

Public Significance Statement.

• This is the first study to simultaneously examine mental health, behavioral, and biological pathways for the ACEs-disease associations among a Hispanic/Latino sample.

• We found depressive symptomatology to be a significant mediator for self-reported cardiopulmonary disease outcomes.

• In the context of other individual and meta-analytic studies, the relationships between ACEs and disease observed in our Hispanic/Latino sample were either weaker in comparison to other samples or non-significant.