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. Author manuscript; available in PMC: 2022 Apr 1.
Published in final edited form as: Aging Ment Health. 2021 Jan 27;26(4):843–851. doi: 10.1080/13607863.2021.1876636

Early-life stress, depressive symptoms, and inflammation: the role of social factors

Julia S Nakamura a,b, Eric S Kim b, Kelly E Rentscher c, Julienne E Bower a,c, Kate R Kuhlman c,d,e
PMCID: PMC8313624  NIHMSID: NIHMS1712487  PMID: 33502257

Abstract

Objective:

To identify modifiable, social factors that moderate the relationship between early-life stress (ELS) and health outcomes as measured by depressive symptoms and inflammation.

Methods:

Data were from 3,416 adults (58.28% female), ages 36 - 97 (Mage = 68.41; SDage = 10.24) who participated in the 2006 wave of the Health and Retirement Study, a nationally representative sample of older adults in the United States. This study used hierarchical regression analyses to first test the main effects of ELS on depressive symptoms and inflammation (high-sensitivity C-reactive protein). Four social factors (perceived support, frequency of social contact, network size, and volunteer activity) were assessed as moderators of the ELS-depression and ELS-inflammation relationships.

Results:

There was a small, positive association between ELS and depressive symptoms (B = 0.17, SE = 0.05, p = .002), which was moderated by social contact and perceived support. Specifically, ELS was only associated with elevated depressive symptoms for participants with limited social contact (B = 0.24, SE = 0.07, p < .001) and low perceived support (B = 0.24, SE = 0.07, p < .001). These associations remained after accounting for potential confounds (age, body-mass index, adulthood stress, and marital status).

Conclusions:

Increased social contact and perceived support may be protective for individuals at a higher risk of developing depressive symptoms as a result of ELS. Future interventions may benefit from leveraging these social factors to improve quality of life in adults with ELS.

Keywords: Aging, stress, depression, inflammation, social factors

By 2060, the population of older adults ages 65 and older in the United States is projected to reach 98 million people, comprising nearly a quarter of the total population (Mather et al., 2015). While people are living longer, they are also living with a higher prevalence of chronic disease and lower self-rated health than previous generations (King et al., 2013). Approximately 80% of people over the age of 65 in the United States have ≥ 1 chronic condition, and 50% of people have ≥ 2 (CDC, 2011). Further, older adults often face a variety of factors (e.g. disability, bereavement, financial strain, etc.) that may increase risk of depression (Almeida, 2014). Up to a third of older adults report depressive symptoms, with the course of depression often worsening with increasing age (Wilkinson et al., 2018). Depression in older adults is also associated with greater morbidity and mortality (Blazer, 2003), decreasing quality of life and increasing health care costs (Unützer, 1997). Specifically, depression in older adults has been associated with several concurrent medical conditions, including frailty, cognitive impairment, dementia (vascular dementia and Alzheimer’s disease), various chronic conditions (e.g. diabetes, cardiovascular disease, stroke, cancer), and other health conditions in later life (Fiske et al., 2009; Hall & Reynolds III, 2014; Kok & Reynolds III, 2017). Thus, the prevention of depression and other chronic diseases of aging is an increasingly important public health concern.

Early-life stress (ELS) is one important risk factor for age-related morbidity in older adults. ELS is broadly defined as any negative, non-normative developmental experience in childhood (0 - 17 years) that is unpredictable or potentially traumatic (Felitti et al., 1998; Steptoe et al., 2019), including abuse by a caregiver, parental substance abuse, parent separation or divorce, family member incarceration, or poverty (Chung et al., 2016; Felitti et al., 1998). ELS is associated with adverse mental (e.g. depression) (Felitti et al., 1998) and physical (e.g. chronic inflammatory diseases) (Fagundes & Way, 2014; Felitti et al., 1998) health outcomes later in life. ELS has a well-established association with geriatric depressive symptoms (Beekman et al., 1995; Comijs et al., 2013; Ege et al., 2015; Kamiya et al., 2016; Kraaij et al., 1997; Kraaij & de Wilde, 2001; Kuhlman et al., 2013; Novelo et al., 2018; Ritchie et al., 2009) and poor general mental health (Maschi et al., 2013). In one sample of nearly 1,000 adults over the age of 65, exposure to ELS in childhood doubled the risk of depression in later-life (Ritchie et al., 2009). There is also evidence of a dose-dependent relationship between ELS and depression (Comijs et al., 2013; Novelo et al., 2018). One study of adults over the age of 60 found that individuals who reported no ELS had a 15% chance of having geriatric depression. However, those that reported experiencing five different types of ELS (physical abuse, emotional abuse, sexual abuse, physical neglect, and emotional neglect) had a 50% chance of having geriatric depression (Novelo et al., 2018). ELS has also been linked with several other chronic health conditions, including: cardiovascular disease, type 2 diabetes, and cancer (Fagundes et al., 2013; Fagundes & Way, 2014). While the underlying mechanisms are not fully understood, peripheral inflammation may play an important role in the association between ELS and adverse health outcomes throughout the lifespan (Kuhlman et al., 2017; Miller et al., 2011). According to the biological embedding model (Miller et al., 2011), ELS exacerbates proinflammatory processes over the life course. Indeed, ELS has been linked to pro-inflammatory gene expression (Levine et al., 2015) and elevated inflammatory biomarkers (e.g. C-reactive protein (CRP), interleukin-6, and tumor necrosis factor-alpha) in general (Baumeister et al., 2016), and that extends into older adulthood (Carroll et al., 2011; Kiecolt-Glaser et al., 2011; Lin et al., 2016).

A large and growing body of research has established the importance of social support in facilitating mental (Kessler & McLeod, 1985) and physical (Cohen, 2004; Uchino et al., 1999) well-being. Social support has an association with mortality risk that is comparable with smoking cessation and exceeds other well-established risk factors including obesity and physical inactivity (Holt-Lunstad et al., 2010). Yet, how social support confers health benefits remains unknown. According to the social support buffering hypothesis, social support can act as a “buffer” against the harmful effects of stressful events (Cohen & Wills, 1985). Social support has been shown to protect against the effects of stress on both mental (Carr et al., 2017; Chan et al., 2011; Cohen & Wills, 1985; Luo & Waite, 2011; Okun et al., 1990) and physical (Cohen, 2004; Mezuk et al., 2010) health outcomes. The most consistent support for the buffering hypothesis has come from studies involving older (age ranges 70 - 103, 57 - 85, and 60 - 89, respectively) (Chan et al., 2011; Luo & Waite, 2011; Okun et al., 1990) rather than younger participants (age ranges 45 - 75 and <40 - 70+, respectively) (Falcón et al., 2009; Kornblith et al., 2001). This suggests that social support interventions may be particularly beneficial in older adults. Further, there is a need to investigate individual social factors in the literature on social support (Gariépy et al., 2016; George et al., 1989; Krause 1986). Specifically, quality is more important than quantity of support (e.g. Vandervoort, 1999). In older adults, perceived support may be linked to lower rates of chronic disease development and better physical health outcomes (Uchino, 2009).

Populations with ELS may be particularly strong candidates for social support interventions, as ELS has been linked to decreased quality and quantity of social relationships (Miller et al., 2011; Sheikh, 2018). There is preliminary evidence of stress-buffering effects from social support in the ELS-depressive symptoms association in middle-aged (Bellis et al., 2017; Jaffee et al., 2017) and older adults (Cheong et al., 2017). However, the specific social factors that moderate the ELS-depressive symptoms association remain unclear. In addition, the role of social factors as moderators of the ELS-inflammation association in older adults has not been examined previously. Testing the potential stress-buffering effects of individual social factors will advance our understanding of potential intervention targets that may buffer against the adverse mental and physical outcomes associated with ELS.

The present study examined the association between ELS, depressive symptoms, and inflammation, and whether four social factors (perceived support, frequency of social contact, network size, and volunteer activity) moderated the ELS-depression and ELS-inflammation relationships. These four domains, perceived support (Cheong et al., 2017), social integration (e.g. social contact) (Holt-Lunstad et al., 2010), network size (Santini et al., 2015), and volunteering (Douglas et al., 2017), have all been associated with favorable health outcomes. We used data from the Health and Retirement Study (HRS, 2018), an ongoing, nationally representative panel study of older adults in the United States. To our knowledge, this study is the first to simultaneously assess perceived support, frequency of social contact, network size, and volunteer activity as moderators of the ELS-depression and ELS-inflammation relationships. We assessed individual social factors to inform the development of more precise psychosocial interventions. We hypothesized that (1) ELS would be associated with increased depressive symptoms; (2) ELS would be associated with increased inflammation, as measured by high-sensitivity C-reactive protein (hsCRP); (3) each of the four social factors would independently moderate the ELS-depressive symptoms link, such that higher ELS would be associated with elevated depressive symptoms increasingly for participants with lower scores on each social factor (e.g. low perceived support, low frequency of social contact, small network size, and no volunteer activity) and (4) each of the four social factors would independently moderate the ELS-inflammation link, such that higher ELS would be associated with elevated inflammation increasingly for participants with lower scores on each social factor.

Methods

Participants

Data for this study were obtained from 3,416 participants in the 2006 wave of the HRS, an ongoing, nationally representative panel study that has interviewed Americans over the age of 50 biennially since 1992 (Sonnega et al., 2014). Participants were excluded from the present study if they did not have (1) complete measures of ELS, all social variables, and depressive symptoms and (2) valid hsCRP values in 2006. A total of 7,180 participants were eligible in the 2006 wave of the HRS, and 5,241 participants met the aforementioned requirements. Based on current recommendations for exclusion criteria when measuring inflammatory biomarkers (O’Connor et al., 2009), participants were excluded if they reported being a current smoker (n = 686), resulting in a sample of 4,555 participants. The HRS included spousal partners in the dataset. For the purposes of these analyses, we excluded spousal partners (n = 1,139) to avoid issues of non-independence. If two participants from the same household were present, only the first enrolled participant was included. This resulted in a final sample of 3,416 participants.

Procedures

Starting in 2006, approximately 50% of HRS respondents were randomly selected for an enhanced face-to-face (EFTF) interview. All participants who completed the EFTF interview were asked to participate in biomarker collection (81% blood spot completion rate in 2006) (Crimmins et al., 2013). After the interview, participants completed a psychosocial questionnaire (Smith et al., 2017) which they mailed to the University of Michigan upon completion (88% response rate in 2006) (Smith et al., 2017).

The HRS website contains further documentation about the HRS study protocol (http://hrsonline.isr.umich.edu). The HRS is sponsored by the National Institute on Aging (NIA U01AG009740) and is conducted by the University of Michigan (Sonnega et al., 2014). This study used publicly available, de-identified data from the HRS (which was approved by the University of Michigan).

Measures

Early-life stress

ELS was assessed using a three-item self-report measure administered as part of the psychosocial questionnaire. Participants indicated the occurrence of three adverse events linked to health disparities: repeating a year of school (Kuhlman et al., 2018), parental substance abuse (Felitti et al., 1998), and parental physical abuse (Felitti et al., 1998) before the age of 18, and responses were summed (range 0-3). The items were selected from a longitudinal study assessing the consequences of trauma in older adults (Krause et al., 2004).

Perceived support

All social variables were collected via the psychosocial questionnaire. Perceived support was assessed using items from previous studies on social support (Schuster et al., 1990; Smith et al., 2017; Turner et al., 1983) for four relationship types: spouse/partner, child, family, and friends. Participants answered three questions for each relationship type (“How much do they really understand the way you feel about things?”, “How much can you rely on them if you have a serious problem?”, “How much can you open up to them if you need to talk about your worries?”) on a four-point Likert scale from 1 (a lot) to 4 (not at all). An index of support was created for each relationship type by reverse-coding and averaging items, with higher scores indicating more perceived support. Then, the values obtained for each relationship type were averaged to create an overall score (range 1 - 4; αs = .81). If one or more relationship type did not apply (e.g. those without a spouse), the scores for the applicable relationship categories were averaged (Wang & Matz-Costa, 2019).

Frequency of social contact

Social contact was measured as the frequency with which participants were in contact with their social networks. Participants answered nine questions indicating how often they “meet up” (including both arranged and chance meetings), “speak on the phone”, and “write or email” members of their social network across three relationship types (children, other family, and friends). Responses were given on a six-point Likert scale (1= <1x/year or never, 2 = 1–2x/year, 3 = every few months, 4 = 1-2x/month, 5 = 1x-2x/week, 6 = ≥3x/week), with higher scores indicating more contact. Responses were summed to assess the frequency of contact across all relationships and modes of contact (range 0 - 54) (αs = .67 across all relationships and modes of contact).

Network size

Network size was assessed by measuring the composition of each participant’s social network. Participants were asked if they have: “a husband, wife, or partner with whom you live?”, “any living children?”, “any other immediate family, for example, any brothers or sisters, parents, cousins or grandchildren?”, and “any friends?” on a dichotomous scale (1 = yes; 0 = no) and responses were summed (range 0 - 4) (Smith et al., 2017).

Volunteer activity

In 2006, participants were asked, “Have you spent any time in the past 12 months doing volunteer work for religious, educational, health-related, or other charitable organizations?” Volunteer activity was measured as a binary variable (volunteer or non-volunteer), as done in previous studies (Kim & Konrath, 2016).

Depressive symptoms

Depressive symptoms were measured with an eight-item version of the Center for Epidemiologic Studies Depression (CES-D) Scale (Radloff, 1977) during the EFTF interview. The CESD-8 is a commonly used measure of depressive symptoms and has been validated in several studies of older adults (Karim et al., 2015; Lewinsohn et al., 1997; Turvey et al., 1999). Participants indicated the presence of eight depressive symptoms during the previous week on a dichotomous scale (1 = yes; 0 = no), with six negative items (feeling depressed, feeling sad, feeling lonely, feeling everything was an effort, having trouble getting going, having restless sleep) and two positive items (feeling happy and enjoying life). Positive items were reverse scored. Responses were summed to range from 0 (no symptoms) to 8 (all symptoms) (αs = .79).

High-sensitivity C-reactive protein (hsCRP)

CRP was measured in whole blood via dried blood spots (DBS). Blood samples were taken from participants after cleaning the finger with an alcohol swab, pricking the finger with a sterile lancet, and then collecting blood droplets on a No. 903 filter paper blood spot card (Whatman, Piscataway, NJ), which was dried for 10 to 15 min, and then placed in foil envelopes with a desiccant packet. Dried blood spot cards were placed in a box that allowed airflow on all sides for at least two hours prior to shipment, and no temperature control was used to preserve the specimens. The samples were shipped to the University of Vermont for analysis. High-sensitivity C-reactive Protein (hsCRP) was measured in blood using the BNII nephelometer (Siemens, Inc., Deerfield, IL) with a particle enhanced immunonephelometric assay that used a monoclonal antibody to CRP. The lower limit of detection was 0.035 mg/L, with a within-assay coefficient of variability (CV) of 8.1% and a between-assay CV of 11.0%. CRP was measured in micrograms per milliliter (μg/mL). As recommended by the HRS, we utilized values of bloodspot hsCRP that were regressed onto the whole-blood equivalent hsCRP values from the National Health and Nutrition Examination Survey for analyses (Crimmins et al., 2013). A detailed description of biomarker collection procedures was published previously (Crimmins et al., 2013).

Covariates

Participants reported demographic information in the EFTF interview at study baseline in 2006 and they included: age, marital status (married/not married), adulthood stress, and body-mass index (BMI). BMI was calculated by dividing participant’s self-reported weight (kg) by the square of their self-reported height (m2). In the psychosocial questionnaire, adulthood stress was assessed by asking participants to indicate the occurrence of seven adverse events after the age of 18 (having: a child who died; been in a major fire, flood, earthquake, or natural disaster; fired a weapon in combat or having been fired upon in combat; a spouse, child, or partner addicted to drugs or alcohol; a life-threatening illness or accident; a spouse or child with a life-threatening illness or accident; and being the victim of a serious physical attack or assault) (Krause et al., 2004) and summing them (range 0 - 7) to create a composite score (Lin et al., 2016).

Data analysis

All variables met assumptions of normality except for hsCRP. The skew of the raw hsCRP variable was 12.21, and the kurtosis was 263.52. The hsCRP values of 46 participants were winsorized to three standard deviations above the mean. All hsCRP values were log-transformed to address skewness and kurtosis. After this, the skew of hsCRP was −0.03 and kurtosis was −0.17. All continuous predictors (besides ELS) were centered at the mean.

First, we used hierarchical regression analyses to test the main effects of ELS on (1) depressive symptoms and (2) inflammation in separate models. For both outcomes, we first conducted unadjusted bivariate analyses. In a subsequent set of analyses, we then controlled for potential confounds including adulthood stress (to isolate the effects of ELS), marital status (to test the effects of social moderators above and beyond marital status), and age. All models assessing inflammation additionally controlled for BMI (O’Connor et al., 2009).

In subsequent analyses, we tested whether social factors moderated the association between ELS and depressive symptoms using PROCESS Version 3.4 (Hayes, 2017). In four separate models, we independently tested each social factor (perceived support, social contact, network size, and volunteer activity) as a moderator of the ELS-depressive symptoms association in turn. When a significant interaction was found, we probed the interaction using the Johnson-Neyman approach (Preacher et al., 2006). We then repeated this series of analyses to test whether social factors moderated the ELS-inflammation association. For all analyses, the threshold for statistical significance was p < .05. All analyses were conducted in SPSS 26.0.

Results

Participants ranged in age from 36 - 97 years (Mage = 68.41; SDage = 10.24; 58.28% female) and were predominately married (60.45%) and Caucasian (84.60%). The most common educational background was a high school degree (54.49%), and 35.22% of participants were employed at the time of data collection. Over a quarter of participants (27.14%) reported exposure to ≥1 early-life adversities. Over half (51.96%) of participants reported at least one depressive symptom. Participants reported (on average) 3.38 relationship types in their social networks (SD = 0.73; range 0 - 4), “some” perceived support (M = 3.14; SD = 0.53; range 1 - 4), and social contact more often than “every few months” (M = 30.08; SD = 8.24; range 3 - 54); 39.20% of participants reported volunteering in the past year. See Table 1 for descriptive statistics and bivariate correlations between all continuous study variables.

Table 1.

Means, standard deviations, and correlations between all continuous study variables.

M (SD) 1 2 3 4 5 6 7 8 9 10
1. Age 68.41 (10.24) 1.0
2. BMI 28.54 (5.94) −0.22*** 1.0
3. Adult stress 1.24 (1.18) 0.07*** 0.05** 1.0
4. ELS 0.33 (0.60) −0.12*** 0.07*** 0.14*** 1.0
5. Network size 3.38 (0.73) −0.17*** 0.00 −0.04* 0.00 1.0
6. Social contact 30.08 (8.24) −0.06*** −0.04* −0.04* −0.05** −0.31*** 1.0
7. Volunteer activity (39.2%) −0.08*** −0.05* 0.03 −0.01 0.13*** 0.12*** 1.0
8. Perceived support 3.14 (0.53) 0.06*** −0.08*** −0.07*** −0.12*** 0.05** 0.29*** 0.07*** 1.0
9. Depressive symptoms 1.34 (1.87) 0.03 0.15*** 0.10*** 0.06*** −0.21*** −0.08*** −0.19*** −0.19*** 1.0
10. High-sensitivity CRP 0.69 (1.20) 0.00 0.34*** 0.01 −0.01 −0.05** −0.01 −0.07*** −0.03 0.14*** 1.0
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*

p ≤ 0.05

**

p ≤ 0.01

***

p ≤ 0.001.

ELS, social factors, and depressive symptoms

After adjusting for age, marital status, and adulthood stress, there remained a significant and positive association between ELS and depressive symptoms (B = 0.17, SE = 0.05, p = .002).

There was a significant interaction between ELS and frequency of social contact when predicting depressive symptoms (B = −0.01, SE = 0.01, p = .05). Specifically, higher ELS was associated with elevated depressive symptoms for participants with limited social contact (B = 0.24, SE = 0.07, p < .001), but not for participants with high social contact (B = 0.04, SE = 0.08, p = .59). See Figure 1a for the estimated associations between ELS and depressive symptoms for participants with low (−1 SD), moderate (mean), and high (+1 SD) social contact.

Figure 1.

Figure 1.

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Social contact and perceived support moderate the early-life stress-depressive symptoms association. The estimated associations between early-life stress and depressive symptoms for participants with low, medium, and high frequencies of (a) social contact and (b) perceived support.

There was also a significant interaction between ELS and perceived support when predicting depressive symptoms (B = −0.31, SE = 0.09, p < .001). Specifically, higher ELS was associated with elevated depressive symptoms for participants with low perceived support (B = 0.24, SE = 0.07, p < .001), but not for participants with high perceived support (B = −0.09, SE = 0.08, p = .28). See Figure 1b for the estimated associations between ELS and depressive symptoms for participants with low (−1 SD), moderate (mean), and high (+1 SD) perceived support. In subsequent analyses, network size (B = 0.01, SE = 0.07, p = .89) and volunteer activity (B = −0.02, SE = 0.11, p = .84) did not moderate the ELS-depressive symptoms association. See Table 2 for coefficient estimates predicting depressive symptoms from ELS, social factors, and their interactions.

Table 2.

Estimates predicting depressive symptoms from early-life stress, social factors, and their interactions.

Social contact
 Perceived support
 Network size
 Volunteer activity
R2 B (SE) p R2 B (SE) p R2 B (SE) p R2 B (SE) p
.06 <0.001 .09 <0.001 .06 <0.001 .08 <0.001
Intercept 1.77 (0.05) <0.001 1.75 (0.05) <0.001 1.55 (0.06) <0.001 1.97 (0.06) <0.001
ELS 0.14 (0.05) 0.01 0.08 (0.05) 0.15 0.16 (0.05) 0.00 0.16 (0.07) 0.02
Social factor −0.02 (0.00) <0.001 −0.52 (0.07) <0.001 −0.33 (0.06) <0.001 −0.65 (0.07) <0.001
ELS x Social factor −0.01 (0.01) 0.05 −0.31 (0.09) <0.001 0.01 (0.07) 0.89 −0.02 (0.11) 0.84
Covariates
Age 0.00 (0.00) 0.36 0.00 (0.00) 0.93 0.00 (0.00) 0.48 0.00 (0.00) 0.23
Adulthood stress 0.12 (0.03) <0.001 0.10 (0.03) <0.001 0.12 (0.03) <0.001 0.13 (0.03) <0.001
Marital status −0.79 (0.07) <0.001 −0.74 (0.06) <0.001 −0.44 (0.09) <0.001 −0.71 (0.06) <0.001
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Note: Early-life stress: ELS.

ELS, social factors, and inflammation

After adjusting for age, marital status, adulthood stress, and BMI, ELS was not associated with inflammation (B = −0.04, SE = 0.03, p = .25). In addition, social contact (B = 0.01, SE = 0.00, p = .13), network size (B = −0.04, SE = 0.04, p = .43), volunteer activity (B = 0.04, SE = 0.07, p = .54), and perceived support (B = −0.06, SE = 0.06, p =.27) did not moderate the ELS-inflammation link. See Table 3 for coefficient estimates predicting inflammation from ELS, social factors, and their interactions.

Table 3.

Estimates predicting inflammation from early-life stress, social factors, and their interactions.

Social contact
 Perceived support
 Network size
 Volunteer activity
R2 B (SE) p R2 B (SE) p R2 B (SE) p R2 B (SE) p
.12 <0.001 .12 <0.001 .12 <0.001 .13 <0.001
Intercept 0.76 (0.03) <0.001 0.76 (0.03) <0.001 0.74 (0.04) <0.001 0.80 (0.04) <0.001
ELS −0.03 (0.03) 0.31 −0.05 (0.03) 0.17 −0.04 (0.03) 0.24 −0.06 (0.04) 0.18
Social factor 0.00 (0.00) 0.37 0.01 (0.04) 0.84 −0.02 (0.04) 0.62 −0.12 (0.05) 0.01
ELS x Social factor 0.01 (0.00) 0.13 −0.06 (0.06) 0.27 −0.04 (0.04) 0.43 0.04 (0.07) 0.54
Covariates
Age 0.01 (0.00) <0.001 0.01 (0.00) <0.001 0.01 (0.00) <0.001 0.01 (0.00) 0.00
Adulthood stress −0.01 (0.02) 0.43 −0.01 (0.02) 0.44 −0.01 (0.02) 0.46 −0.01 (0.02) 0.54
Marital status −0.11 (0.04) 0.01 −0.10 (0.04) 0.01 −0.07 (0.05) 0.17 −0.10 (0.04) 0.02
Body-mass index 0.07 (0.00) <0.001 0.07 (0.00) <0.001 0.07 (0.00) <0.001 0.07 (0.00) <0.001
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Note: Early-life stress: ELS.

Post hoc analyses

Frequency of social contact and perceived social support moderated the ELS-depressive symptoms association. Frequency of social contact was comprised of three modes of social contact (meeting in person, calling, and writing or emailing) and three relationship types (children, other family, and friends). To further probe the modes of contact and types of relationships that may be driving the observed moderation, we conducted post hoc analyses to assess these different modes of social contact and relationship types as separate moderators of the ELS-depressive symptoms association. The protective role of social contact appeared to be driven by frequency of in-person meetings and calling relative to other forms of contact, as well as frequency of contact with children relative to other relationship types. There was a significant interaction between ELS and “meeting in person” when predicting depressive symptoms (B = −0.04, SE = 0.02, p = .02), such that higher ELS was only associated with elevated depressive symptoms for participants with a low frequency of in-person meetings (B = 0.27, SE = 0.07, p < .001). There was also an interaction between ELS and “calling” when predicting depressive symptoms (B = −0.04, SE = 0.02, p = .02), such that higher ELS was only associated with elevated depressive symptoms for participants with infrequent calling (B = 0.28, SE = 0.07, p < .001). Writing and emailing (B = −0.01, SE = 0.01, p = .52) did not moderate the ELS-depressive symptoms link. There was also a significant interaction between ELS and contact with children when predicting depressive symptoms (B = −0.04, SE = 0.02, p = .02), such that higher ELS was only associated with elevated depressive symptoms for participants with limited social contact with children (B = 0.24, SE = 0.07, p < .001). Social contact with family (B = −0.02, SE = 0.02, p = .22) and friends (B = −0.03, SE = 0.02, p = .06) did not moderate the ELS-depressive symptoms link.

Discussion

In a large, diverse, and nation-wide sample of older adults, we found a positive association between ELS and depressive symptoms, and social contact and perceived support moderated this association. The size of the protective association these two social factors appeared to have against depressive symptoms was small to medium, which is comparable to other commonly used and efficacious interventions (e.g. psychoeducation, physical exercise, supportive interventions) (Pinquart et al., 2007). Contrary to our hypotheses, ELS was not associated with inflammation, and no social factors moderated the ELS-inflammation association.

We found a positive association between ELS and depressive symptoms that is consistent with findings from previous studies in older adults (Beekman et al., 1995; Comijs et al., 2013; Ege et al., 2015; Kamiya et al., 2016; Kraaij et al., 1997; Kraaij & de Wilde, 2001; Kuhlman et al., 2013; Novelo et al., 2018; Ritchie et al., 2009), and social contact and perceived support moderated this association. This aligns with previous findings indicating that social integration has a substantial salubrious association with enhanced on health and longevity (Holt-Lunstad et al., 2010), and that perceived support may buffer against the influence of ELS on adverse mental health outcomes in older adults (Cheong et al., 2017). Social support interventions that reduce depressive symptoms facilitate social interaction among participants, and effective interventions vary in the strategies they use (group-based activities, peer support, skill building), often incorporating several strategies to foster interaction (Nagy & Moore, 2017). Interventions have also been implemented at different societal levels (interpersonal, network, and community), and thus have the potential to be scaled for large numbers of people (Nagy & Moore, 2017). Further research will need to disentangle the most effective modes of social contact and perceived support for those with a history of ELS, as older adults with ELS are a specific risk group that may receive substantial benefit from social support interventions.

Volunteer activity and social network size did not moderate the association between ELS and depressive symptoms. The HRS did not include information on type or frequency of volunteer activity beyond the past year, which could have made a difference in our results. For example, given our other findings, people who volunteer in social settings (e.g. with kids) frequently might derive greater benefits than people who volunteer in isolation (e.g. doing paperwork). The HRS also did not allow for measurement of social network size beyond four relationship types, precluding a more in-depth comparison of people with very large or small networks.

ELS was not associated with inflammation in our sample. ELS has been associated with inflammation in adults with small overall effect sizes (Baumeister et al., 2016). However, only a small number of studies assessing the relationship between ELS and inflammation using older adult samples have been published (e.g. Kiecolt-Glaser et al., 2011; Lin et al., 2016). The lack of an association between ELS and inflammation may be consistent with previous studies that have found the association between ELS and inflammation to be partially explained by health behaviors (Raposa et al., 2014; Slopen et al., 2010; Taylor et al., 2006) and adulthood stressors (Slopen et al., 2010). Alternatively, it is well-established that inflammation increases with age and predicts susceptibility to age-related morbidity as the immune system becomes less efficient at regulating proinflammatory tendencies over time (Franceschi et al., 2017). Thus, we might see elevated inflammation in younger samples for those with anomalous experiences (e.g. ELS), but not in older adults because of the generally elevated inflammation in this population.

These findings should be considered in the context of their limitations. First, these data are cross-sectional, and thus do not allow for causal inferences. Additionally, the ELS measure used in this study only included three types of adversity; thus, it did not allow for the measurement of ≥4 adverse events that have been linked to health disparities (Chapman et al., 2004; Dube et al., 2003). Our measure also did not assess the severity or duration of ELS. Rates of ELS exposure in this sample (27.1% reported ELS exposure) appear to be lower than the general population (around 50% of adults report ELS exposure) (Felitti et al., 1998). However, frequencies of individual types of ELS (repeating a year of school (Kuhlman et al., 2018), parental substance abuse (Felitti et al., 1998), and parental physical abuse (Felitti et al., 1998)) were similar to other studies. One item in the CESD-8 (feeling lonely) may have been confounded with our social factors; however, we retained this item in order to maintain the psychometric properties of the measure and to thoroughly represent the construct of depression. However, as a result, the association between our social factors and depression may have been inflated and therefore moderations may have been more difficult to detect in the data. Finally, the perceived support measure in this study assessed support averaged across the applicable relationships out of four possible relationship types, with the potential to equate an individual with high support in only one relationship category (e.g. children) with an individual who had high support in all four. Despite these limitations, this study also had several strengths, including the use of a large, nationally representative sample and an assessment of four unique social factors which provide an empirical basis upon which to build discourse on modifiable social factors that mitigate health risks among older adults.

Prevention of late life depression (LLD) is increasingly important in light of the aging population as it heightens risk of disability, morbidity, mortality, and also increases health care costs (Hall & Reynolds III, 2014; Unützer, 1997). The medical field is looking to integrated care as a way to care for complex patient populations (e.g. older adults with co-occurring long-term conditions) (Ramanuj et al., 2019; Vogel et al., 2017). Compared to usual care, collaborative management of depression in primary care is consistently more effective at reducing depression and results in substantially lower overall health care costs (Unützer & Park, 2012). Effective collaborative care may adhere to a stepped care approach, for example, in which various treatments (e.g. pharmacological, mental health referrals, psychosocial treatments (behavioral activation, cognitive behavioral therapy)) are modified and intensified if patients are not improving as expected (Unützer & Park, 2012). Social determinants of health further contribute to the complexities associated with LLD (Hall & Reynolds III, 2014), and our findings highlight how social factors contribute to the complexities associated with LLD and suggest the value of incorporating the assessment of social factors into clinical care and integrated health practices. There is a need to systematically identify populations with depression and implement an evidence-based collaborative care approach to prevent and treat LLD in primary care settings.

The population of older adults is rapidly increasing in the U.S., with as many as one third of older adults reporting depressive symptoms (Wilkinson et al., 2018). Thus, we need a comprehensive and multidisciplinary effort to meet the unique mental health needs of this growing demographic. Our findings suggest that specific social factors (i.e. enhancing frequency of social contact and increasing actual or perceived support) might serve as novel targets for prevention (e.g. a screener to identify individuals at risk for depressive symptoms in primary care settings) and multi-level intervention (e.g. integration across health care settings, social services, and community organizations) efforts aimed at enhancing the mental health of the aging population.

Acknowledgements

We would like to thank the Health and Retirement Study, which is conducted by the Institute for Social Research at the University of Michigan, with grants from the National Institute on Aging (U01AG09740) and the Social Security Administration.

Funding

This work was supported by the National Institute of Mental Health (grant: K08MH112773) and the University of California, Los Angeles (UCLA) Undergraduate Research Scholars Program (Undergraduate Research Center – Humanities, Arts, and Social Sciences). We would like to acknowledge and thank the Health and Retirement Study, which is conducted by the Institute for Social Research at the University of Michigan, with grants from the National Institute on Aging (U01AG09740) and the Social Security Administration.

Footnotes

Disclosure statement

Eric S. Kim has worked as a consultant with AARP and UnitedHealth Group.

Data availability statement

The data that support the findings of this study and more information about the Health and Retirement Study can be found at: https://hrs.isr.umich.edu/about.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The data that support the findings of this study and more information about the Health and Retirement Study can be found at: https://hrs.isr.umich.edu/about.

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