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. Author manuscript; available in PMC: 2022 Jun 9.
Published in final edited form as: Fam Process. 2021 Aug 14;61(2):659–673. doi: 10.1111/famp.12715

Intervention effects on self-control decreases speed of biological aging mediated by changes in substance use: A longitudinal study of African American youth

Man-Kit Lei 1, Gene H Brody 2, Steven R H Beach 3
PMCID: PMC8841568  NIHMSID: NIHMS1764185  PMID: 34389984

Abstract

Biological aging is a common root for multiple diseases causing morbidity and mortality, and trajectories of aging may start early in life. This study was designed to examine whether a universal family-based substance use preventive intervention to enhance self-control and reduce substance use would also result in reductions in biological aging among Black youth from the rural South. The Adults in the Making (AIM) program is a randomized controlled trial with six 2-hour sessions for Black youth. The 216 youths agreeing to provide blood at age 22 included 114 who had received the AIM intervention and 102 assigned to the control group. We examined accelerated DNA methylation (DNAm) based aging using a recently developed measure, “GrimAge,” that has been shown to predict the risk of early mortality and that is known to be more strongly affected by substance use than other DNAm-based aging indices. Relative to those randomly assigned to the control group, those receiving the intervention demonstrated significantly enhanced self-control, slower increases in substance use, and reduced Grim aging at age 22. Using a bootstrapping method with 1,000 replications, we found a significant indirect effect of AIM on reduced Grim aging through its effect on self-control and substance use. Sensitivity analyses examined effects using other indices of DNAm-based aging. These findings suggest that a family-based program designed to enhance rural Black youth’s self-control can have beneficial effects on self-control, enhancing young adult health and health behavior, and ultimately decreased mortality risk.

Keywords: intervention program, self-control, substance use, biological aging, late adolescence

Introduction

More than half of Black children in the rural South live in economically distressed households (Mattingly & Bean, 2010). Difficult economic conditions and the ongoing stress this engenders can take a toll on youth’s health, leading to health disparities that extend into adulthood (Hartley, 2004). These disparities are especially relevant for black residents of the rural South who, on average, experience shorter life expectancy than their urban counterparts. For many black youth transitioning to adulthood in the rural South, lack of a pathway to good jobs and satisfying adult roles leads to the initiation or escalation of unhealthy behaviors (Probst et al., 2020) such as substance use. For this reason, preparation for the transition to adulthood in low resource communities may be particularly consequential for emerging adult populations in many rural Black communities.

Despite the need for prevention programs that focus on the transition from adolescence to emerging adulthood among disadvantaged Black youth living in rural areas, few have been developed or examined. One exception is a randomized, controlled trial of the Adults in the Making (AIM) program. Given that better self-control may contribute to many positive outcomes in later life, the AIM program is a universal family-based substance use prevention program that is specifically designed to enhance developmentally appropriate and planful self-control skills to navigate stressors associated with financial hardship, community disadvantage, and racial discrimination (Brody et al., 2015a). Initial analyses (Brody et al., 2012) have shown that, among African American youths confronting higher levels of contextual risk, the AIM program resulted in slower increases in substance use and in better self-control (reduced risk-taking) during the transition to adulthood compared with control subjects. In addition, they also found that reduced risk-taking, a component of the impulsivity/risky taking scale, was associated with the beneficial change in substance use trajectory. However, left unaddressed was whether these changes would lead to measurable changes in long-term health outcomes and cellular level changes in biological aging.

Research suggests that individuals who have greater self-control tend to be make short-term sacrifices to pursue more valued long-term goals. Thus, they are more attentive to their health and exhibit more positive attitudes toward healthy behavior (Moffitt et al., 2011). Consonant with this view, Richmond-Rakerd et al. (2021) recently presented longitudinal data showing that individuals with good self-control exhibited slower biological aging. However, another study found that, among youth growing up in disadvantaged rural circumstances, greater self-control was associated with greater biological aging (Miller et al., 2015). Importantly, these studies are not contradictory because they examined different facets of self-control that are unrelated to each other and have different effects. The facet of self-control examined by Moffit et al. (2011) and Richmond-Rakerd et al. (2021) is characterized by less impulsivity and risk-taking, is generally helpful across socioeconomic status contexts, and is the facet typically targeted in prevention programs such as AIM. It is this facet that we examine in the current study. The facet examined by Miller et al (2015) is persistence, which often has positive effects, but may also create negative side effects in low SES environments, as pointed out in the Miller et al. (2015) study. Accordingly, given that AIM demonstrated a positive effect on self-control (impulsivity/risk-taking), it seemed possible that AIM might also, indirectly, prevent adverse health outcomes in adulthood.

According to self-control theory (Gottfredson & Hirschi, 1990), self-control is a behavioral and personality trait that has both immediate and long-term effects on well-being. Individuals with high self-control can control their impulses, take into account the likely consequences of their behavior for themselves and others, and delay immediate gratification. Research has shown that a lack of self-control makes individuals, especially during the transition to adulthood, more vulnerable to use substances when they are in situations that allow them to do so (Conner, Stein, & Longshore, 2009; Fergus & Zimmerman, 2005).

There is also strong evidence that substance use (e.g., smoking, binge drinking, and marijuana use) is associated with harmful effects in multiple organs and body systems, leading to accelerated speed of biological aging (Lei et al., 2020a; Navarro‐Mateu et al., 2021). For example, studies revealed that substance use is associated with an elevated risk of coronary heart disease and diabetes; and accelerated aging (Mills et al., 2019). Accordingly, individuals who are higher in self-control are anticipated to be more resist temptations and persist in achievement-oriented activity, creating a causal pathway from a lack of self-control to an increased probability of substance use, and ultimately greater speed of aging later in life.

It has been noted that accelerated aging can be viewed as a common root for multiple diseases causing morbidity and mortality (Harper, 2014). Indeed, this observation led to a recent National Institutes of Health (NIH) emphasis on the prevention of age-related diseases by intervening on the basic process of aging (Alvidrez et al., 2019). The use of accelerated aging as an indicator of health in the social science literature is also consistent with literature emphasizing its consequences, such as physiological weathering (Geronimus et al., 2020) and cellular and molecular changes (Lopez-Otın et al., 2013), including changes in deoxyribonucleic acid methylation (DNAm) that can be used to quantify DNAm-based aging. DNAm is a biological process that occurs when a methyl group attaches to a segment of DNA at a CpG site (i.e., a DNA region where a cytosine nucleotide is positioned next to a guanine nucleotide separated by one phosphate). DNAm results in either up-regulation or downregulation of the affected genes.

More recently, researchers have become aware of the strong association between DNAm and aging (Koch & Wagner, 2011). Certain CpG sites scattered across the human genome gradually become less methylated, whereas others become more methylated as a person ages. That is, some genes are being turned-off as we age while others are being turned on. Horvath’s pioneering work in this area focused on how DNAm patterns across tissues followed a regular pattern of change reflecting chronological age (see Horvath, 2013). This allowed Horvath and colleges to identify an optimal set of DNAm-based predictors of chronological age. Using a similar approach but focused on peripheral blood only, Hannum and colleagues devised an additional DNAm-based age focused on chronological age prediction (Hannum et al., 2013).

The Hannum and Horvath aging measures were designed based on the relationship between DNAm-based age and chronological age; however, these measures were not consistently associated with the early onset of chronic illness (Ryan et al., 2020). To overcome these limitations, the PhenoAge was developed by associating morbidity (Levine et al., 2018). A limitation of these DNAm-based indices is that most of the validation studies have been on non-Hispanic white Americans (Simons et al., 2021). Recently, a new DNAm-based age was developed (Lu et al., 2019), based on methylation predictors of seven plasma protein predictors of morbidity and mortality, plus pack years of smoking. Because an accelerated score on this measure is grim news, this new DNAm-based age is labeled GrimAge. Differences between GrimAge and chronological age is a robust predictor of time-to-death, time-to-coronary heart disease, time-to-cancer, fatty liver/excess visceral fat, and age-at-menopause (Lu et al., 2019). Further, GrimAge has been validated with a Black sample (Crimmins et al., 2021; Simons et al., 2021) and is superior to other widely available biological clocks or specific biomarkers in terms of predicting morbidity and mortality (see Hillary et al., 2020; McCrory et al., 2021). Grim age is also more strongly associated with substance use than earlier clocks (Lei et al., 2020a). Finally, an advantage of GrimAge relative to self-reported health is that it is an objective measure that avoids inflation of association patterns due to shared method variance and human memory limitations (Lei et al., 2020b). Because of these advantages, the current investigation focuses on prediction of GrimAge to examine long-term health benefits of participating in the AIM intervention. Given that another prevention program for preadolescents (Strong African American Families Program) showed prevention effects on Horvath age assessed nine years after participation in the intervention (Brody et al., 2015b), we provide supplemental analyses using other DNAm-based indices of biological aging, examining the theoretical indirect pathway.

The current study investigates the extent to which the AIM intervention influences DNAm-based biological aging, as measured by GrimAge, by enhancing self-control and, in turn, decreasing substance use. Summarizing, Figure 1 illustrates the theoretical model tested in the present study. Using an experimental design and the measures from multiple sources of data (e.g., self-reported and DNAm-based measures), we tested the following hypotheses:

  • H1a: AIM participants compared to those in the control group will show changes in self-control (Pathway a).

  • H1b: Increased self-control will be significantly associated with reduced biological aging (Pathway b).

  • H2: There will be a significant indirect pathway from participation in the AIM intervention to reduced biological aging through changes in self-control (Pathways a and b).

  • H3a: There will be a significant association between change in self-control and substance use (pathway c), as well as a significant association between substance use and accelerated biological aging (Pathway d).

  • H3b: Based on Hypotheses H1 and H3a, there will be a significant indirect pathway from participation in the AIM intervention to biological aging through changes in self-control and substance use (Pathways a, c, and d). In addition, a direct effect from the intervention to biological aging will not be significant.

  • H4: Prior research reported that GrimAge is more strongly associated than other age indices (e.g., Horvath, Hannum, and PhenoAge) with substance use. Thus, we hypothesize that H3b will not be supported for other age indices as outcomes.

Figure 1.

Figure 1.

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Theoretical model linking AIM program to accelerated Grim aging. The model indicates that the theoretical link is mediated by self-control and substance use. Δ = change from age 17 to age 19

Method

Sample

In the current study, rural Black youth participated in the AIM randomized prevention trial (Brody et al., 2015a). Initially, 740 black families from rural parts of Georgia were contacted through school lists and advertisements, and screening interviews were conducted by phone. Of these, 180 families were not eligible for participation because the child was not within the specified age range or the child was not African American. In addition, 178 declined to participate. Baseline data were collected from 367 families, and they were randomly assigned to the intervention (n = 187) or control (n = 180) condition. The families had an average of 2.4 children. Of the youth in the sample, 59.1% were female, and 63.6% lived in single-mother-headed households. The youth’s mean age was 17.01 years (SD = 0.75) at baseline assessment. A majority of the youths’ caregivers (78.7 %) had completed high school or earned a GED. The median family gross income was $2,012 per month. The first follow-up took place an average of 17 months after the baseline (youths at age 18.39, SD = 0.91). The second follow-up assessments (28 months after the baseline, youth at age 19.26, SD = 0.87) were collected from 308 (89.1%) youths. Two years after the second follow-up (5 years after the baseline), we attempted to contact the study participants to obtain their blood for methylation analysis. After excluding participants who were unreachable (n = 58) and hard refusals (n = 92), 217 youths (70.5%) remained in the study at age 22. Participants provided blood samples at approximately the same rate in both intervention (n = 114, 53%) and control (103, 47%) conditions. Figure 2 presents a CONSORT diagram of the flow of participants through the study.

Figure 2.

Figure 2.

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Consort diagram of the study.

After eliminating participants with missing data, 216 youths (114 intervention; 102 control) were available in the current study. To examine potential differential attrition, we conducted a two-factor multivariate analysis of variance of baseline demographics and major study variables for participants with or without complete data by intervention versus control groups. Comparison of individuals excluded from the current analyses (n = 151) with those retained in the analyses (n = 216) by intervention and control groups did not identify any significant differences related to gender, poverty, self-control, and substance use at the baseline assessment, indicating no differential attrition on these variables (see Table S1).

Intervention Implementation

The AIM prevention program consisted of six consecutive weekly meetings held at community facilities (detail provided in Brody et al., 2015a). Group size ranged from 4 to13 families, with an average of 8 families and 16 individuals attending each session. Each of the six meetings included separate, concurrent training sessions for parents and youth, followed by a joint parent-youth session during which the families practiced the skills they learned in the separate sessions. Concurrent and family sessions each lasted 1 hour; thus, parents and youth received 12 hours of prevention programming. All AIM program leaders were African Americans from participants’ local communities who had received 12 hours of training in three training sessions over a 4-day period in administering the protocol. Both the field researchers and the project staff who assigned families for them to visit were unaware of whether families were assigned to the intervention or control group.

Group leaders presented the prevention curriculum, organized role-playing activities, guided discussions among group members, and answered participants’ questions. The curriculum included didactic information, group discussion, and skill-building activities. To develop a future orientation and improve individuals’ self-control, the prevention topics for participants addressed setting goals and developing courses of action to meet them; using time productively and keeping commitments; identifying coping strategies to use when confronting family, financial, or discrimination-related stress; applying nonavoidant, problem-focused coping strategies to various stressful situations; forming educational and occupational goals with step-by-step plans for attaining them; and dealing with institutional racism. Finally, youth learned the importance of forming goals, resistance efficacy, and controlling impulses.

At each wave of data collection, one home visit lasting two hours was made to each family. At the home visit, self-report questionnaires were administered to caregivers and youth in an interview format. Each interview was conducted privately, with no other family members present or able to overhear the conversation. Each family was paid $100 at each assessment. The University of Georgia Institutional Review Board approved all study procedures. Of the pretested families, 72% took part in four or more sessions, with 35% attending all six of them. To preserve the group assignments’ random nature, the analyses reported here included all participants who completed the pretest regardless of the number of prevention sessions they attended (an intent-to-treat analysis). These families were retained in the analysis to preclude the introduction of self-selection bias into the findings.

Measures

DNAm-based aging.

Methylation profiling was conducted by the University of Minnesota’s Genome Center, following the manufacturer’s protocol for the Illumina HumanMethylation 450 BeadChip. We randomized samples with respect to slide and position on arrays to minimize potential batch effects as recommended by the Illumina Infinium Protocol Guide. The resulting data were inspected for complete bisulfite conversion, and average β values for each targeted CpG residue were determined using the Illumina Genome Studio Methylation Module, Version 3.2; β values were calculated as the ratio of methylated probes to the sum of methylated and unmethylated probes, ranging from 0 (entirely unmethylated) to 1 (fully methylated). A replicated sample of DNA was included in each plate to assess batch variation and ensure correct handling of specimens. The replicate sample was examined for the average correlation of beta values between plates and was found to be greater than 0.99. Quantile normalization methods were used, with separate normalization of Type I and Type II assays. This approach has been found to produce marked improvement for the Illumina array in detecting relationships by correcting distributional problems inherent in the manufacturer’s default method for calculating the beta value.

The GrimAge index (Lu et al., 2019) was analyzed using the online “New Methylation Age Calculator” (https://dnamage.genetics.ucla.edu/) and using the Advanced Analysis option and the normalize data option. We formulated a measure of accelerated Grim aging using the unstandardized residual scores from the regression of GrimAge on chronological age and it was denoted as “AgeAccelGrim.” These residuals had a mean of zero and represented both positive and negative deviations from chronological age (in years), with positive scores indicating accelerated aging (i.e., a worse outcome). To examine performance of other indicators of Age Acceleration, describe below, we computed several additional clocks, including AgeAccelHorvath, AgeAccelHannum, and AgeAccelPheno.

Substance use.

Youths reported the number of days during the previous month that they drank alcohol, had 3 or more drinks of alcohol at one time, smoked cigarettes, or smoked marijuana. The items regarding substance use were, “In the past month, how many days have you: drunk beer, wine, wine coolers, whiskey, gin, or other liquor; had three or more drinks of alcohol at one time; smoked cigarettes, smoked marijuana?” These four items were rated on a six-point scale; responses were summed to form a past-month substance use index. We averaged scores between ages 18 and 19 to form a substance use composite at follow-up. The alpha coefficient was .64 at age 17 (baseline) and .84 at ages 18 to 19 (follow-up).

Self-control.

At ages 17 to 19, we assessed self-reported behavioral/personality characteristics relevant to self-control using Eysenck and Eysenck’s scale (Eysenck & Eysenck, 1975; see online supplementary material). This scale consists of 7 items about short-sightedness and impulsive behavior and includes the 6 items using by Brody et al. (2012) to assess risk-taking. The relationship between risk-taking and AgeAccelGrim is significant (r = −.174, p = .011). Thus, this facet of self-control is responsive to the AIM intervention (Brody et al., 2012). The response format for these items ranges from (1) very true to (5) not at all true, with higher scores indicating greater self-control. Scores for age 18 and age 19 were averaged to form a self-control composite at follow-up. The alpha coefficient was .81 at age 17 (baseline) and .87 at ages 18 to 19 (follow-up). A different facet of self-control, perseverance, was used in some prior research with this sample (e.g., Miller et al., 2015), indicating that it has negative health effects for youth growing up in disadvantaged contexts. However, because this facet of self-control was not affected by the AIM intervention (Miller et al., 2015: 10327), it is not relevant to examining potential mediation of AIM intervention effects and was not examined in the current study.

Control variables.

Gender and socioeconomic status (SES) risk have been linked to health-related measures and so were included to minimize risk of confounding in the associations of interest. Six dichotomous variables formed a cumulative SES risk index (Brody et al., 2015a). A score of 1 was assigned to each of the following characteristics: family poverty based on federal guidelines, primary caregiver unemployment, receipt of Temporary Assistance for Needy Families, primary caregiver single parenthood, primary caregiver education level less than high school graduation, and caregiver-reported inadequacy of family income. The scores were summed to form an index that has been found to forecast biomarkers of stress in Black adolescents (M = 1.926, SD = 1.368, range = 0 to 6). In addition to cumulative SES risk, we also controlled for cell-type variation to adjust for cellular heterogeneity that can affect methylation-based scores. Cell-type composition was estimated using the “EstimateCellCounts” function in the minfi Bioconductor package, which is based on the reference-based method developed by Houseman and colleagues (2012). Using this approach, we estimated cell-type proportions for CD4+ T cells, CD8+ T cells, Natural Killer cells, B cells, and monocytes.

Analytic Strategy

The analyses were designed to test an indirect effect model proposing that AIM-induced improvement in self-control and changes in substance use would carry forward and serve as a mediator linking participation in AIM at age 17 to smaller increases in DNAm-based aging at age 22 years. Such indirect models are increasingly common in psychiatric and behavioral science prevention and intervention research (O’Rourke & MacKinnon, 2018). We first checked for the model assumptions. The results revealed that assumptions for normality (Shapiro–Wilk W test = .99, p = .22) and homoscedasticity (Breusch–Pagan/Cook–Weisberg test χ(1)2 = .99, p = .32) were met. To ensure results were not driven entirely by outliers, sensitivity analyses excluded outliers on the dependent variable defined by the 1.5 × IQR criterion. We utilized path modeling with maximum likelihood estimation to examine our hypotheses, conducted with Mplus (Version 8; Muthén & Muthén 2017). To determine whether intervention gains maintained over time, we first averaged scores for self-control and substance use measures across ages 18 – 19 years and then calculated change scores (Δ) for study variables using the unstandardized residuals from the regression of follow-up scores (ages 18–19) on baseline scores (age 17). To assess the goodness-of-fit of the model, we used Steiger’s root-mean-square error of approximation (RMSEA < .05) and the comparative fit index (CFI > .90). Finally, the 95% confidence interval (CI) estimated with bias-corrected and accelerated bootstrapping with 1,000 resamples was used to assess the significance of hypothesized indirect effects. The effect size of an indirect effect is defined as the ratio of the indirect effect to the total effect (Lee, Lei, & Brody, 2015). To test our hypotheses, variables were entered in the path models in the following steps: (a) the simple indirect effect model, which was used to test an indirect effect of AIM on aging through self-control (Hypotheses 1 and 2); (b) the simple indirect effect model with substance use, which tested a significant indirect effect of AIM on aging through self-control and substance use (Hypothesis 3). Finally, we repeated the analysis in the final indirect effect model, for the alternative indicators of accelerated aging (Hypothesis 4).

Results

The mean self-control scores were 3.899 (SD = .610) at baseline and 4.159 (SD = .571) at follow-up. As expected, the independent t-test revealed a significant mean difference (t = 2.761, p = .006, Cohen’s d = .376) between adolescents who participated in AIM (M = 4.258, SD = .584) and in the control group (M = 4.047, SD = .536) on self-control at follow-up. Then, average substance use was 4.440 (SD = 1.037) at baseline but increased to 5.208 (SD = 1.977) at follow-up. Finally, the mean biological age, calculated as the weighted sum of the 1,030 CpG sites identified by Lu et al. (2019), was 35.094 (SD = 3.701) relative to a mean chronological age of 22.054. About 49 percent of respondents had a biological age greater than their chronological age. It is worth noting that there was no significant direct association of AIM intervention with either substance use or AgeAccelGrim (see Table S2). However, this does not rule out a possible significant indirect effect, as these may be present even in the absence of a significant intervention effect on the outcome (see O’Rourke & MacKinnon, 2018: 178).

As shown in Table 1, change in self-control was significantly related to change in substance use from ages 17 to 19 (r = −.351) (hypothesis 1a) and AgeAccelGrim at age 22 (r = −.182) (hypothesis 1b), respectively. Participates who experienced greater increases in self-control from ages 17 to 19 showed a significant slower rate of increase in substance use than those with less change in self-control (see Figure S1). In addition, change in substance use was correlated with AgeAccelGrim at age 22 (r = .329). As can also be seen, gender, CD 8+ T cells, CD 4+ T cells, B cells, and monocytes were significantly associated with AgeAccelGrim, suggesting the value of retaining them as controls in the analyses.

Table 1.

Correlations, means, and standard deviations among study variables (N = 216)

1 2 3 4 5 6 7 8 9 10 11
1. AIM intervention —
2. Δ Self-control (ages 17–19) .183** —
3. Δ Substance use (ages 17–19) .010 −.351** —
4. Accelerated Grim aging (age 22) −.020 −.182** .329** —
5. Females .120 .057 −.283** −.402** —
6. Cumulative SES risk (age 17) −.017 .044 .148* .017 −.014 —
7. CD8+ T cells −.028 −.024 −.002 −.175* −.012 −.067 —
8. CD4+ T cells .050 .054 −.122* −.389** .242** −.101 .208** —
9. Natural killer cells −.046 −.022 .077 .076 −.389** .006 −.382** −.292** —
10. B cells .043 .032 .056 −.142* .130 .006 .015 .051 −.170* —
11. Monocytes .068 −.076 .064 .161* −.063 .028 −.316** −.313** .108 .039 —
Mean .528 .000 .000 .000 .620 1.926 .147 .277 .072 .152 .151
SD .500 .483 1.758 3.613 .486 1.369 .060 .078 .071 .051 .077
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Note: Δ = change from age 17 to age 19; Cumulative SES risk = Cumulative socioeconomic risk index; point-biserial correlations are used to test the dichotomous variables AIM intervention and females.

*

p ≤ .05;

**

p ≤ .01 (two-tailed tests).

To examine the indirect effects of AIM on AgeAccelGrim, path modeling was used. As shown in Figure 3, the model provided a good fit to the data (χ2 = 2.469, df = 5, p = .781; CFI = 1.000; RMSEA = .000). As expected in hypothesis 2, AIM participants showed significantly enhanced self-control at follow-up (age 17) compared to those did not receive the program. Furthermore, as can be seen in Figure 3, change in self-control from ages 17 to 19 was negatively associated with AgeAccelGrim at age 22, indicating that intervention-based improvement in self-control was associated with lower Grim aging scores suggesting better health (less morbidity or mortality). Using a bootstrapping method with 1,000 replications we found that the indirect effect of AIM on AgeAccelGrim through enhancement of self-control was significant [indirect effect = −.029, 95% CI (−.066, −.006), with a large effect size (ES) of .32]. As hypothesized, the finding suggested that AIM deterred youth’s AgeAccelGrim by improving their self-control.

Figure 3.

Figure 3.

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Effects of AIM on accelerated Grim aging through change in self-control. N = 216. Values are standardized parameter estimates and standard errors are in parentheses. Gender, cumulative SES risk, and cell-type compositions are controlled in the analyses.

*p < .05, **p < .01 (two-tailed tests).

Turning to our hypothesis 3, Figure 4 presents the results of entering change in substance use from ages 17 to 19 as a mediator. As expected, the fit of the theoretical model was good (χ2 = 5.763, df = 10, p = .834; CFI = 1.000; RMSEA = .000). As can be seen in Figure 4, the AIM intervention was positively associated with enhanced self-control from ages 17 to 19, which, in turn, was correlated with changes in substance use from ages 17 to 19. Substance use, in turn, showed a significant association with AgeAccelGrim. As hypothesized, using a bootstrapping procedure to test the indirect effect from AIM intervention to enhanced self-control to changes in substance use to AgeAccelGrim, we found a significant indirect effect [indirect effect = −.013, 95% CI (−.034, −.003), with a medium-large ES of .23]. To address the robustness of our results, we performed a sensitivity analysis omitting six outliers. The results showed no change in the pattern of effects (see Figure S2). To test our assumption that self-control led to substance use and not vice versa, we examined this possibility directly. As shown in online supplemental Figure S3, the alternative model, that change in substance use led to change in self-control was not supported.

Figure 4.

Figure 4.

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Effects of AIM on accelerated Grim aging through both change in self-control and substance use. N = 216. Values are standardized parameter estimates and standard errors are in parentheses. Gender, cumulative SES risk, and cell-type compositions are controlled in the analyses.

*p < .05, **p < .01 (two-tailed tests).

Finally, in view of recent reports that AgeAccelGrim performed better than other DNAm-based aging indices in predicting morbidity, mortality, and substance use (Hillary et al. 2020; Lei et al., 2020a; McCrory et al. 2020), we examined the indirect effect of AIM on three other aging outcomes: AgeAccelHorvath, AgeAccelHannum, and AgeAccelPheno. As shown in online supplementary Table S3, there were no significant effects of AIM through self-control and substance use on these three aging indices, reflecting the lack of association between substance use and the other measures of DNAm-based aging in this sample.

Discussion

The AIM program is a universal, family-based, substance-use preventive intervention focusing on enhancing self-control processes (Brody et al., 2012). Young adults able to demonstrate good self-control, have been shown to have better health (Adler, 2015), and it was hypothesized that Black youth with better self-control would be less likely to become involved with substance use and other risky behaviors across the transition to adulthood than would similar youths who evinced lower levels of self-control. In particular, after leaving high school and home, if youth confront difficulties related to reduced economic opportunities and resources for employment or continuing education, youth with high self-control may persist in achievement-oriented activity, whereas others who see no pathway to future attainment of life goals may respond with increased drinking, marijuana use, or other substance use, and may increasingly affiliate with deviant peers who reinforce this pattern. This may, in turn, lead to greater health consequences among those with poorer self-control and thus account for the association between self-control and accelerated aging or chronic illness. It was expected that AIM would interrupt this pattern, increasing self-control and decreasing risk for substance use, ultimately manifesting as slower AgeAccelGrim.

To test this model, we examined a sample of participants living in disadvantaged areas of the rural south, using multiple sources of data (e.g., self-reported and blood-based health measures) to avoid inflation due to shared method variance. Given that self-rated health may be confounded by individual characteristics and psychological well-being, we utilized a relatively new DNAm-based index of biological aging (Lu et al., 2019) that was developed to be an improved predictor of all-cause mortality.

As hypothesized, participating in the AIM program improved self-control relative to changes observed in the control group. Further, individuals with greater self-control had less substance use and reduced Grim aging. These associations were robust to controls for gender, cumulative SES risk, and individual differences in cell-type compositions. It is worth noting that individual differences in cell-types may confound, or account for, associations with DNAm-based aging (Andersen et al., 2020). Our results indicated no substantial differences in patterns of associations depending on whether we controlled for cell-type variation, suggesting that individual differences in cell-type variations were not accounting for the observed effects (see Figure S4). Accordingly, the AIM program’s focus on enhancing self-control appears to be important in understanding prevention effects on the Grim aging. These relationships also suggest that AIM-induced improvements in self-control may endure into young adulthood, affecting longer-term outcomes for youth.

It has been shown previously that high self-control exerts a protective effect and reduces the risk of substance use (Conner, Stein, & Longshore, 2009; Fergus & Zimmerman, 2005). Further, medical research has emphasized the importance of lifestyle factors related to substance use, smoking, alcohol consumption, and marijuana use as determinants of speed of aging (Bachi et al., 2017; Prochaska, Spring, & Nigg, 2008). The current results supported the theoretical model undergirding the development of the AIM program and extended it by showing that there was a significant medium-to-large effect of indirect pathway connecting AIM to speed of aging, with a positive change in self-control, leading to slower increases in substance use and finally to reduced Grim aging. This supports the theoretical model positing a pathway whereby AIM-induced improvement in self-control decreases speed of aging mediated by slower increases in substance use.

In contrast to AgeAccelGrim, there was no significant indirect effect of AIM on other DNAm-based aging indices. Given that AIM is a preventive intervention for initiation of substance use, this result is consistent with recent studies documenting that GrimAge is more strongly associated than other aging indices with substance use (Crimmins et al., 2021; Lei et al., 2020a). Accordingly, it may be that GrimAge is a particularly appropriate index to use in examining outcomes of substance use initiation prevention programs. It is also possible that unlike other indices (Philibert et al., 2020; Simons et al., 2021), GrimAge was trained on data containing multiple ethnic data, allowing to examine minorities. Indeed, Crimmins and colleagues (2021) revealed larger racial differences in GrimAge.

There are several limitations to the current study that temper the findings but also provide opportunities for future research. First, the sample was limited to Black youth from the rural South, which may limit the generalizability of the findings to other ethnic or racial groups and places of residence (e.g., urban or other countries). However, it is important to stress that Black youth suffer greater health-related morbidity and mortality than do other ethnic groups in the US (Howard et al., 2017). Understanding mechanisms related to accelerated biological aging among Black youth is of considerable interest and importance. That said, clearly, there is a need to replicate our findings with other marginalized groups of youth who may be confronting challenges associated with financial hardship, limited opportunity, and discrimination. Second, our study assessed DNA methylation status at only the final time. The lack of GrimAge at baseline limits the confidence that can be placed in conclusions about temporal priority. For example, GrimAge may have been lower at baseline in adolescents who responded to the intervention, or adolescents who had lower GrimAge may have been less likely to initiate substance use. Mitigating concerns somewhat, there were no differences between groups on self-control or substance use at baseline. Future studies focusing on longitudinal change in the degree of acceleration in Grim aging will be needed to confirm the causal impact of the intervention and the role of the preventive program in “decelerating” biological aging. Third, self-control comprises multiple dimensions and different facets are not always strongly related to each other, resulting in different patterns of association with aging and health outcomes (e.g., Miller et al., 2015; vs. Richmond-Rakerd et al., 2021). Consonant with self-control theory (Gottfredson & Hirschi, 1990), the present study used a short version of Eysenck and Eysenck’s scale (Eysenck & Eysenck, 1975) comprising 13 items that focused on risk-taking and impulsive behavior. We found that this theoretically important dimension of self-control was influenced by the intervention. By contrast, a different facet of self-control, perseverance, that was used by Miller et al. (2015) in research with this sample, was not affected by the intervention. Accordingly, the important caveats raised by Miller et al. (2015) about the differential effect of persistence on health depending on broader social conditions, do not qualify the results obtained in the current investigation. Future studies could address the potentially divergent impact of different facets of self-control in more detail using different self-control measures. Finally, our findings do not rule out possible additional pathways to biological aging process. Although research consistently finds that substance use has a strong effect on GrimAge (Lei et al., 2020a; Ryan et al., 2020), several additional unmeasured factors may relate to DNAm-based aging, including hypothalamic–pituitary–adrenal axis dysregulation (Davis et al., 2017) and elevated stress hormones (Brody et al., 2021). Future studies could extend this study by examining the way these additional potential pathways contribute to DNAm-based aging beyond their association with substance use initiation.

The current study constitutes the first test of which we are aware of the impact of a prevention program’s promotion of self-control on biological aging mediated by slower increases in substance use. Our findings provide support for family-based theories suggesting that improving self-control in the form of decreased impulsivity and risk-taking may be one psychosocial mechanism helping explain the impact of supportive family context on long-term health outcomes. These findings suggest that health risk behaviors are linked to increased morbidity and mortality (Bodai et al., 2018), and that the family is an important context for learning and developing the type of self-control that discourages unhealthy lifestyle choices, which in turn leads to better health. Future research will be important to identify ways to strengthen each of the links in the proposed model, strengthening the overall indirect effect. Of relevance to family clinical practice, efficacious family-based programs designed to strengthen the family process are available for rural African Americans that positively affect personality development and youth protective factors, substance use, and healthy aging. The current research underscores the potential for these programs to improve long-term health outcomes for youth growing up in challenging circumstances.

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References

  1. Adler NE (2015). Disadvantage, self-control, and health. Proceedings of the National Academy of Sciences, 112, 10078–10079. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Andersen AM, Lei MK, Beach SR, Philibert RA, Sinha S, & Colgan JD (2020). Cigarette and cannabis smoking effects on GPR15+ helper T cell levels in peripheral blood: Relationships with epigenetic biomarkers. Genes, 11, 149. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Alvidrez J, Castille D, Laude-Sharp M, Rosario A, & Tabor D (2019). The national institute on minority health and health disparities research framework. American Journal of Public Health, 109(S1), S16–S20. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Bachi K, Sierra S, Volkow ND, Goldstein RZ, & Alia-Klein N (2017). Is biological aging accelerated in drug addiction? Current Opinion in Behavioral Sciences, 13, 34–39. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Bodai BI, Nakata TE, Wong WT, Clark DR, Lawenda S, Tsou C, … & Campbell TM (2018). Lifestyle medicine: a brief review of its dramatic impact on health and survival. The Permanente Journal, 22, 17–25. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Brody GH, Yu T, Chen YF, Kogan SM, & Smith K (2012). The Adults in the Making program: long-term protective stabilizing effects on alcohol use and substance use problems for rural African American emerging adults. Journal of Consulting and Clinical Psychology, 80, 17–28. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Brody GH, Yu T, Beach SRH, & Philibert RA (2015a). Prevention effects ameliorate the prospective association between nonsupportive parenting and diminished telomere length. Prevention Science, 16, 171–180. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Brody GH, Yu T, Chen E, Beach SRH, & Miller GE (2015b). Family-centered prevention ameliorates the longitudinal association between risky family processes and epigenetic aging. Journal of Child Psychology and Psychiatry, 57, 566–574. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Brody GH, Yu T, Chen E, Kobor M, Beach SRH, Lei M-K, … & Miller GE (2021). Risky family climates presage increased cellular aging in young adulthood. Psychoneuroendocrinology, 130, 105256. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Conner BT, Stein JA, & Longshore D (2009). Examining self-control as a multidimensional predictor of crime and drug use in adolescents with criminal histories. The Journal of Behavioral Health Services & Research, 36, 137–149. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Crimmins EM, Thyagarajan B, Levine ME, Weir DR, & Faul J (2021). Associations of age, sex, race/ethnicity, and education with 13 epigenetic clocks in a nationally representative US sample: The health and retirement study. The Journals of Gerontology: Series A, 76, 1117–1123. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Davis EG, Humphreys KL, McEwen LM, Sacchet MD, Camacho MC, MacIsaac JL, … & Gotlib IH (2017). Accelerated DNA methylation age in adolescent girls: associations with elevated diurnal cortisol and reduced hippocampal volume. Translational Psychiatry, 7, e1223–e1223. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Eysenck HJ, & Eysenck SBG (1975). The Eysenck personality questionnaire. London: London University Press. [Google Scholar]
  14. Fergus S, & Zimmerman MA (2005). Adolescent resilience: A framework for understanding healthy development in the face of risk. Annual Review of Public Health, 26, 399–419. [DOI] [PubMed] [Google Scholar]
  15. Geronimus AT, Pearson JA, Linnenbringer E, Eisenberg AK, Stokes C, Hughes LD, & Schulz AJ (2020). Weathering in Detroit: Place, race, ethnicity, and poverty as conceptually fluctuating social constructs shaping variation in allostatic load. The Milbank Quarterly, 98, 1171–1218. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Gottfredson MR, & Hirschi T (1990). A General Theory of Crime. Stanford University Press. [Google Scholar]
  17. Hannum G, Guinney J, Zhao L, Zhang L, Hughes G, Sadda S, … & Zhang K (2013). Genome-wide methylation profiles reveal quantitative views of human aging rates. Molecular Cell, 49, 359–367. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Harper S (2014). Economic and social implications of aging societies. Science, 346, 587–591. [DOI] [PubMed] [Google Scholar]
  19. Hartley D (2004). Rural health disparities, population health, and rural culture. American Journal of Public Health, 94, 1675–1679. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Hillary RF, Stevenson AJ, McCartney DL, Campbell A, Walker RM, Howard DM, … & Porteous DJ (2020). Epigenetic clocks predict prevalence and incidence of leading causes of death and disease burden. Clinical Epigenetics, 12, 115. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Horvath S (2013). DNA methylation age of human tissues and cell types. Genome Biology, 14, 1–20. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Houseman EA, Accomando WP, Koestler DC, Christensen BC, Marsit CJ, Nelson HH, … & Kelsey KT (2012). DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinformatics, 13, 1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Howard G, Kleindorfer DO, Cushman M, Long DL, Jasne A, Judd SE, … & Howard VJ (2017). Contributors to the excess stroke mortality in rural areas in the United States. Stroke, 48, 1773–1778. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Koch CM, & Wagner W (2011). Epigenetic-aging-signature to determine age in different tissues. Aging (Albany NY), 3, 1018–1027. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Lee S, Lei MK, & Brody GH (2015). Constructing confidence intervals for effect size measures of an indirect effect. Multivariate Behavioral Research, 50, 600–613. [DOI] [PubMed] [Google Scholar]
  26. Lei MK, Gibbons FX, Simons RL, Philibert RA, & Beach SRH (2020a). The effect of tobacco smoking differs across indices of DNA methylation-based aging in an African American sample: DNA methylation-based indices of smoking capture these effects. Genes, 11, 311. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Lei MK, Berg MT, Simons RL, Simons LG, & Beach SRH (2020b). Childhood adversity and cardiovascular disease risk: An appraisal of recall methods with a focus on stress-buffering processes in childhood and adulthood. Social Science & Medicine, 246, 112794. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Levine ME, Lu AT, Quach A, Chen BH, Assimes TL, Bandinelli S, … & Horvath S (2018). An epigenetic biomarker of aging for lifespan and healthspan. Aging (Albany NY), 10, 573–591. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. López-Otín C, Blasco MA, Partridge L, Serrano M, & Kroemer G (2013). The hallmarks of aging. Cell, 153, 1194–1217. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Lu AT, Quach A, Wilson JG, Reiner AP, Aviv A, Ray K, … & Horvath S (2019). DNA methylation GrimAge strongly predicts lifespan and health span. Aging (Albany NY), 11, 303–327. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Mattingly MJ, & Bean JA (2010). The Unequal Distribution of Child Poverty: Highest Rates among Young Blacks and Children of Single Mothers in Rural America (Carsey Institute Issue Brief, No. 18). Durham: University of New Hampshire. [Google Scholar]
  32. McCrory C, Fiorito G, Hernandez B, Polidoro S, O’Halloran AM, Hever A, … & Kenny RA (2021). GrimAge outperforms other epigenetic clocks in the prediction of age-related clinical phenotypes and all-cause mortality. The Journals of Gerontology: Series A, 76, 741–749. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Miller GE, Yu T, Chen E, & Brody GH (2015). Self-control forecasts better psychosocial outcomes but faster epigenetic aging in low-SES youth. Proceedings of the National Academy of Sciences, 112, 10325–10330. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Mills JA, Beach SR, Dogan M, Simons RL, Gibbons FX, Long JD, & Philibert R (2019). A direct comparison of the relationship of epigenetic aging and epigenetic substance consumption markers to mortality in the Framingham heart study. Genes, 10, 51. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Moffitt TE, Arseneault L, Belsky D, Dickson N, Hancox RJ, Harrington H, … & Sears MR (2011). A gradient of childhood self-control predicts health, wealth, and public safety. Proceedings of the National Academy of Sciences, 108, 2693–2698. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Muthén LK, & Muthén BO (2017). 1998–2017. Mplus User’s Guide. Los Angeles, CA: Muthén & Muthén. [Google Scholar]
  37. Navarro‐Mateu F, Husky M, Cayuela‐Fuentes P, Álvarez FJ, Roca‐Vega A, Rubio‐Aparicio M, … & Sánchez‐Meca J (2021). The association of telomere length with substance use disorders: a systematic review and meta‐analysis of observational studies. Addiction. 116, 1954–1972. [DOI] [PubMed] [Google Scholar]
  38. O’Rourke HP, & MacKinnon DP (2018). Reasons for testing mediation in the absence of an intervention effect: A research imperative in prevention and intervention research. Journal of Studies on Alcohol and Drugs, 79, 171–181. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Philibert R, Beach SRH, Lei MK, Gibbons FX, Gerrard M, Simons RL, & Dogan MV (2020). Array-based epigenetic aging indices may be racially biased. Genes, 11, 685. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Prochaska JJ, Spring B, & Nigg CR (2008). Multiple health behavior change research: an introduction and overview. Preventive Medicine, 46, 181–188. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Probst JC, Zahnd WE, Hung P, Eberth JM, Crouch EL, & Merrell MA (2020). Rural-urban mortality disparities: Variations across causes of death and race/ethnicity, 2013–2017. American Journal of Public Health, 110, 1325–1327. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Ryan J, Wrigglesworth J, Loong J, Fransquet PD, & Woods RL (2020). A systematic review and meta-analysis of environmental, lifestyle, and health factors associated with DNA methylation age. The Journals of Gerontology: Series A, 75, 481–494. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Richmond-Rakerd LS, Caspi A, Ambler A, d’Arbeloff T, de Bruine M, Elliott M, … & Moffitt TE (2021). Childhood self-control forecasts the pace of midlife aging and preparedness for old age. Proceedings of the National Academy of Sciences, 118, e2010211118. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Simons RL, Lei M-K, Klopack E, Beach SRH, Gibbons FX, & Philibert RA (2021). The effects of social adversity, discrimination, and health risk behaviors on the accelerated aging of African American: Further support for the weathering hypothesis. Social Science and Medicine, 282. 113169. [DOI] [PMC free article] [PubMed] [Google Scholar]

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