Abstract
Purpose:
Positive dimensions of psychological well-being in adolescence may help youth preserve cardiometabolic health (CMH) as they age, but little is known about which aspects of well-being matter most and for whom. This study examines the differential impact of five dimensions of adolescent psychological well-being on CMH maintenance in adulthood and considers social patterning in both their distribution and respective health benefits.
Methods:
Data were from the National Longitudinal Study of Adolescent Health (N=3,464), five dimensions of psychological well-being were identified at baseline (1994–1995; mean age=15y): happiness, optimism, self-esteem, belonging, and feeling loved. CMH was measured using 7 biomarkers related to chronic disease risk in 2008 (mean age=28y) and 2016–2018 (mean age=38y): high-density lipoprotein (HDL) cholesterol, non-HDL cholesterol, systolic blood pressure (BP), diastolic BP, hemoglobin A1c, C-reactive protein, and body mass index. CMH maintenance in adulthood was characterized as having healthy levels of ≥6 biomarkers at each follow-up.
Results:
Youth who reported higher levels of belonging in the teen years were more likely to maintain CMH across young adulthood than those who reported lower levels, regardless of one’s social standing (ORper 1-SD=1.23, 95% CI=1.03, 1.46). Associations with other dimensions of well-being were heterogeneous by sex and race and ethnicity, while differences by socioeconomic factors were less apparent.
Discussion:
Fostering belonging through supportive social environments may help set youth on positive health trajectories and prevent chronic disease across the lifespan.
Keywords: psychological well-being, belonging, cardiometabolic health, health assets, health disparities, life course
Although mental health encompasses both positive and negative aspects of psychological functioning,1 most mental health research among adolescents focuses on adverse conditions like depression and anxiety. While understandable, this emphasis on ill-being led to a paucity of research on psychological well-being, or distinctly positive thoughts and emotions youth derive from their experiences and reflections on life.2 A growing body of research among adults finds that different facets of psychological well-being may help prevent cardiometabolic disease over time,3,4 but less is known about how early these processes are set into motion and which aspects of well-being matter most and for whom. To address these gaps, this study compares the impact of different dimensions of adolescent well-being on cardiometabolic health (CMH) over time and considers heterogeneities in both the distribution and impact of positive psychological factors across different segments of society.
Few studies have directly compared dimensions of psychological well-being in relation to cardiometabolic outcomes, but recent evidence suggests such an approach may be worthwhile. Among older adults in England,5 feelings of autonomy, control, pleasure, and self-realization were found to confer cumulative protective effects on cardiometabolic disease incidence, with more of these factors providing greater health benefits in an additive fashion. However, when examining which factors were the most protective, the strongest independent associations were observed with feelings of autonomy and control, while feelings of pleasure and self-realization were less impactful. A similar phenomenon was observed in work among English children, which found four positive psychological factors (executive functioning skills, prosocial behaviors, and low levels of internalizing and externalizing behaviors) conferred cumulative protective associations with CMH over time, but the strongest independent health benefits were observed with executive functioning skills.6 Taken together, these studies suggest that positive psychological factors may not function as interchangeable health assets, but rather may operate synergistically.
To date, we are only aware of one study that investigated the cardiometabolic impact of multiple well-being dimensions among socially diverse youth. In a study we conducted among participants in the National Longitudinal Study of Adolescent Health (Add Health), we found that feelings of happiness, optimism, self-esteem, belonging, and love in adolescence were cumulatively related to CMH maintenance in young adulthood.7 Interestingly, associations were strongest among Black individuals compared to those from other groups. The present study aims to extend our past work by comparing associations in Add Health by individual dimensions of well-being and considering differences by other indicators of social standing. We considered patterns by four axes of marginalization: sex, race and ethnicity, family income, and parent education. Informed by research on psychological well-being,3,8,9 we hypothesized that higher levels of each dimension would be associated CMH maintenance in adulthood. We also expected these distinct factors would serve as health assets, promoting more favorable health states across diverse populations. Since different theories exist regarding whether and how health assets function differently across social strata,10 we also anticipated that these benefits would vary by group.
METHODS
Participants
Add Health is a nationally representative sample of in-school youth in the US.17 In 1994–1995 (Wave 1), 20,745 adolescents (mean age=16y, range=11–21y) were enrolled from 145 schools, and baseline information on health and development was collected from participants and their parents through in-person interviews. In 2008 (Wave 4; mean age=28y, range=24–33y) and 2016–2018 (Wave 5; mean age=38y, range=33–44y), clinical measurements of cardiometabolic biomarkers were collected during in-home exams. Add Health was approved by the institutional review board of the University of North Carolina, Chapel Hill, and all participants provided written informed consent.
A study flow chart is provided in Supplemental Figure 1. Starting in Wave 5, the mode of survey administration changed from in-person to mixed in-person, telephone, and web-based, resulting in a sizeable drop in participants who completed Wave 5 biomarker collection. To account for differences between the initial sample and those who remained the Wave 5 biomarker sample and to ensure that results were nationally representative, analyses used Wave 5 biomarker weights derived by Add Health that account for design effects and differential attrition. More information on how non-response weights were generated is published elsewhere.11–13 Our analytic sample is limited to participants with complete cardiometabolic data in Waves 4 and 5 (n=3,597). Those with missing sampling weights (n=83), a history of chronic health conditions at Wave 1 (i.e., heart conditions, asthma, or diabetes; n=23), missing self-reported race and ethnicity data (n=13), or missing exposure information on psychological well-being dimensions at baseline (n=14) were excluded, resulting in a final sample of 3,464 participants. Since 60% of participants had incomplete data on covariates, missing covariate data was multiply imputed in 60 datasets.15 For a comparison of our final analytic sample to the full Wave 5 biomarker sample, see Supplemental Table S1.
Measures
Adolescent Psychological Well-Being.
In Wave 1, participants self-reported information on five dimensions of psychological well-being. We selected factors using prior epidemiologic work linking psychological well-being and cardiovascular-related outcomes,8,16 research on developmental assets among youth,17,18 and available data. Measures of optimism and happiness were created using questions from a modified version of the Center for Epidemiologic Studies Depression (CES-D) Scale,19 which asked how often participants felt hopeful about the future (1 item) and happy (2 items) in the past 7 days. Self-esteem was assessed using three positively-framed items adapted from the Rosenberg Self-Esteem Scale.20 Using survey items specially designed for Add Health, participants also reported their feelings of belonging by indicating if they felt socially accepted (1 item) and the extent to which they felt loved (1 item). To facilitate comparison across exposures, all analyses used standardized scores (Mean=0, SD=1) for well-being dimensions, with greater values indicating higher levels. Correlations between dimensions ranged from r=0.17–0.59 (see Supplemental Table S2).
Adult Cardiometabolic Health (CMH).
Detailed information on methods used to assess cardiometabolic biomarkers was published previously.7 Seven biomarkers13–18 were collected by study staff during in-home in Waves 4 and 5 using standardized study protocols: (1) high-density lipoprotein cholesterol (HDL-C), (2) non-HDL-C (calculated as total cholesterol [TC] – HDL-C), (3) systolic blood pressure (SBP), (4) diastolic blood pressure (DBP), (5) Hemoglobin A1c [HbA1c], (6) C-reactive protein (CRP), and (7) body mass index (BMI). In Wave 4, blood samples were collected via capillary finger prick and stored as dried blood spots. In Wave 5, venous blood samples were collected via phlebotomy.
Following prior research in populations with low levels of clinical disease6,7,21,22 we created composite CMH scores (range=0–7) using continuous values of each biomarker in Waves 4 and 5 respectively. At each assessment, participants’ total number of biomarkers in the recommended range for optimal health were defined conservatively using either decile ranks or clinical thresholds. Healthy levels of HDL-C and non-HDL-C were defined as ≥20th and ≤80th percentile of the sample distribution respectively,6 SBP and DBP as <120 mmHg and <80 mmHg respectively,32 HbA1c as <6.5,33 CRP as <3.0 mg/dL,21 and BMI as values between 18.5 kg/m2 and 25 kg/m2.13 Composite measures of CMH (yes/no) were then created for each follow-up assessment. In Wave 4, CMH was defined as receiving no cardiometabolic diagnoses (self-reported hypercholesterolemia, high blood pressure, diabetes, or heart disease) since the study baseline and meeting healthy criteria for ≥6 biomarkers. At Wave 5, data were not available on participants’ new diagnoses; therefore, CMH was characterized using biomarker values only. Our primary outcome of interest was CMH maintenance (yes/no), defined as achieving and sustaining CMH over time; i.e., having CMH at both Waves 4 and 5. Additional information on participants’ medication use history was not used in the derivation of our outcome measure since our prior study in Add Health found that combined information on self-reported conditions and biomarker levels provided the most comprehensive CMH measure possible in this sample.7
Social Structural Factors.
Four social structural factors were assessed using self-reported data in Wave 1: sex (male, female), race and ethnicity (White, Black, Latinx, other races and ethnicities [i.e., those with that made up <5% of the sample; namely, Native American and Asian youth), parent-reported annual family income (<$24,999, $25,000–49,999, $50,000-$74,999, ≥$75,000), and parental education (college or higher, less than college; defined as the highest level of education of either parent).
Covariates.
All covariates were self- or parent-reported in Wave 1 unless otherwise specified. Sociodemographic characteristics included age (years) and household structure (single parent household, two-parent household). Health-related covariates included parent history of obesity or diabetes (yes, no), youth BMI (kg/m2; measured in Wave 2), and pubertal timing (normal, abnormal [early or late]). Youth BMI scores were classified as underweight (<18.5 kg/m2), normal weight (18.5–24.9 kg/m2), and overweight or obese (≥25 kg/m2). Pubertal transitions are related to both youth mental health and cardiometabolic functioning, so we accounted for pubertal timing22 using female participants’ self-reported age of menarche and male participants’ puberty-related bodily changes. Early puberty was defined as 1-SD below sex-specific mean and late puberty was defined as 1-SD above the mean. Since psychological distress is associated with both well-being and cardiometabolic health,23 we also included negative affect as a covariate. A composite negative affect score was constructed using 4 items from the CES-D Scale18 assessing whether participants felt sad or lonely in the past week.
Statistical Analyses
Descriptive statistics were calculated using population-weighted means and proportions. Mean levels of each psychological well-being dimension were summarized by social structural factors, then associations were tested using linear regression models adjusting first for age, then additionally for household structure and all social structural factors. Associations between standardized levels of each well-being dimension and CMH maintenance were then tested in five different sets of logistic regression models that sequentially adjusted for relevant covariates. Model 1 adjusted for age only. Model 2 additionally included household structure, and all four social structural factors. Model 3 added family health history and BMI at Wave 2. Model 4 further accounted for negative affect. Finally, Model 5 included all study covariates and well-being dimensions simultaneously to examine their independent influence. Effect modification by social structural factors was evaluated using interaction terms and described via stratification. All analyses were conducted in Stata MP 17.0 and used the svy command to account for Add Health’s complex survey design.
RESULTS
A sample description is provided in Table 1. Only 12% of adolescents reached adulthood with CMH and maintained it over time. Differences in CMH maintenance were noted by sex (17% female vs. 7% male), race and ethnicity (13% white, 8% Black, 9% Latinx, 13% other groups), family income (≤$24,999: 9%, $25,000-$49,999: 11%, $50,000-$74,999: 15%, ≥$75,000: 16%), and parental education (college or higher: 17%, less than college: 11%).
Table 1.
Sample description (n=3,464).a
| n (%) | Mean (Range) | |
|---|---|---|
|
| ||
| Age at baseline | 15.5 (11, 21) | |
| Sex | ||
| Female | 2,103 (51.5) | |
| Male | 1,375 (48.5) | |
| Race and ethnicity | ||
| White | 2,183 (67.3) | |
| Black | 597 (14.8) | |
| Latinx | 430 (11.5) | |
| Other | 268 (6.4) | |
| Annual family income | ||
| ≤$24,999 | 641 (26.8) | |
| $25,000-$49,999 | 956 (31.8) | |
| $50,000-$74,999 | 689 (22.8) | |
| ≥$75,000 | 459 (18.6) | |
| Parent education | ||
| Less than college | 2,159 (73.7) | |
| College or higher | 895 (26.3) | |
| Household structure | ||
| Single parent household | 803 (26.2) | |
| Two-parent household | 2,264 (73.8) | |
| Parent obesity or diabetes | ||
| Yes | 842 (28.2) | |
| No | 2,636 (71.8) | |
| Body mass index | ||
| Underweight | 364 (14.3) | |
| Healthy | 1,705 (58.1) | |
| Overweight or obese | 585 (27.6) | |
| Pubertal maturation | ||
| Normal | 2,434 (72.5) | |
| Abnormal | 819 (27.5) | |
| Negative affect | ||
| Low or moderate | 1,961 (58.1) | |
| High | 1,514 (41.9) | |
| Psychological Well-being Dimensions | ||
| Happiness | 3.1 (1, 4) | |
| Optimism | 2.8 (1, 4) | |
| Self esteem | 10.6 (1, 15) | |
| Belonging | 4.1 (1, 5) | |
| Feeling loved | 4.3 (1, 5) | |
| Cardiometabolic Health (CMH) | ||
| Wave 4 | 840 (25.6) | |
| Wave 5 | 490 (14.1) | |
| Maintained from Wave 4 to 5 | 450 (12.0) | |
n’s may vary due to missing values. Percentages and means are nationally representative, population-weighted estimates obtained using imputed data.
Differences in Individual Dimensions of Psychological Well-Being
Mean levels of psychological well-being domains (per 1-SD) are illustrated by social structural factors in Figure 1. Adjusting for age, girls were happier than boys (β=0.12, 95% CI=0.02, 0.22), but reported less self-esteem (β=−0.36, 95% CI=−0.45, −0.27) and belonging (β=−0.13, 95% CI=−0.21, −0.05). Racial and ethnic differences were also evident, but the direction of associations was not uniform. Compared to white youth, Black youth reported more self-esteem (β=0.34, 95% CI=0.21, 0.47), belonging (β=0.20, 95% CI=0.09, 0.31), and love (β=0.23, 95% CI=0.11, 0.36), Latinx youth reported less happiness (β=−0.24, 95% CI=−0.44, −0.04) and optimism (β=−0.28, 95% CI=−0.45, −0.12), and those from other groups had less self-esteem (β=−0.23, 95% CI=−0.39, −0.08). Differences were not apparent by family income, but youth with college-educated parents reported greater feelings of happiness (β=0.19, 95% CI=0.09, 0.30), optimism (β=0.16, 95% CI=0.04, 0.27), and love (β=0.10, 95% CI=−0.00, 0.20).
Figure 1.
Mean standardized levels of individual psychological well-being dimensions, by sub-group.
Results from fully adjusted models are provided in Table 2. When all social structural factors were included in a single model, substantial sex differences remained apparent with respect to happiness, self-esteem, and belonging. Racial and ethnic differences were also robust to adjustment for other social structural factors, but differences by parental education were no longer evident.
Table 2.
Adjusted associations between social structural factors and standardized levels of psychological well-being dimensions (N=3,464).
| Happiness | Optimism | Self-esteem | Belonging | Feeling Loved | |
|---|---|---|---|---|---|
|
| |||||
| β (95% CI) | β (95% CI) | β (95% CI) | β (95% CI) | β (95% CI) | |
|
| |||||
| Sex | |||||
| Ref. Male | 0.11 (0.00, 0.21)* | 0.04 (−0.05, 0.13) | −0.37 (−0.46, −0.28)*** | −0.14 (−0.22, −0.06)*** | −0.07 (−0.16, 0.02) |
| Race and ethnicity | |||||
| White | Reference | Reference | Reference | Reference | Reference |
| Black | −0.07 (−0.22, 0.08) | −0.05 (−0.19, 0.08) | 0.37 (0.24, 0.49)*** | 0.23 (0.12, 0.34)*** | 0.27 (0.16, 0.39)*** |
| Latinx | −0.21 (−0.40, −0.02)* | −0.25 (−0.41, −0.09)** | 0.00 (−0.17, 0.16) | −0.03 (−0.21, 0.14) | −0.14 (−0.34, 0.06) |
| Other | −0.18 (−0.40, 0.03) | −0.08 (−0.29, 0.14) | −0.24 (−0.40, −0.09)** | −0.02 (−0.18, 0.14) | −0.15 (−0.34, 0.04) |
| Annual family income | |||||
| ≤$24,999 | Reference | Reference | Reference | Reference | Reference |
| $25,000-$49,999 | −0.03 (−0.17, 0.10) | −0.03 (−0.18, 0.12) | −0.10 (−0.23, 0.04) | −0.00 (−0.13, 0.13) | 0.03 (−0.11, 0.18) |
| $50,000-$74,999 | 0.00 (−0.16, 0.16) | 0.09 (−0.07, 0.26) | 0.05 (−0.11, 0.20) | 0.01 (−0.22, 0.20) | 0.02 (−0.15, 0.19) |
| ≥$75,000 | 0.03 (−0.15, 0.21) | 0.08 (−0.12, 0.28) | 0.01 (−0.18, 0.16) | 0.07 (−0.11, 0.24) | −0.04 (−0.23, 0.15) |
| Parent education | |||||
| Ref. Less than college | 0.15 (0.04, 0.27)** | 0.09 (−0.02, 0.21) | 0.09 (−0.02, 0.19) | 0.02 (−0.09, 0.13) | 0.10 (−0.00, 0.20) |
Differences in individual dimensions of psychological well-being were assessed using five separate models for each asset adjusting for age, household structure, and all social structural factors included in the model simultaneously.
p≤0.05,
p≤0.01,
p≤0.001
Associations Between Psychological Well-being Dimensions and CMH Maintenance
Associations between individual dimensions of psychological well-being and CMH maintenance are provided in Table 3. Adjusting for age, associations with all dimensions were in the expected direction, but the strongest were observed with self-esteem (OR=1.18, 95% CI=1.02, 1.36) and belonging (OR=1.26, 95% CI=1.07, 1.48). After adjusting for sociodemographic and health-related factors, substantive associations were only observed with belonging (OR=1.23, 95% CI=1.03, 1.46) and the point estimate was virtually unchanged after further adjusting for other well-being dimensions (OR=1.22, 95% CI=0.99, 1.50).
Table 3.
Associations between psychological well-being dimensions in adolescence and the likelihood of maintaining cardiometabolic health (CMH) in young adulthood (N=3,464).a,b
| CMH Maintenance | |||||
|---|---|---|---|---|---|
|
| |||||
| Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | |
|
| |||||
| OR (95% CI) | OR (95% CI) | OR (95% CI) | OR (95% CI) | OR (95% CI) | |
|
| |||||
| Happiness, per 1-SD | 1.20 (1.04, 1.39)*** | 1.14 (0.98, 1.34) | 1.11 (0.95, 1.31) | 1.12 (0.93, 1.34) | 1.05 (0.89, 1.26) |
| Optimism, per 1-SD | 1.16 (0.99, 1.36) | 1.12 (0.95, 1.33) | 1.11 (0.93, 1.32) | 1.11 (0.92, 1.32) | 1.07 (0.89, 1.28) |
| Self-esteem, per 1-SD | 1.05 (0.91, 1.20) | 1.18 (1.02, 1.36)* | 1.13 (0.97, 1.31) | 1.13 (0.96, 1.32) | 1.02 (0.84, 1.24) |
| Belonging, per 1-SD | 1.19 (1.02, 1.38)* | 1.26 (1.07, 1.48)** | 1.22 (1.04, 1.45)* | 1.23 (1.03, 1.47)* | 1.22 (0.99, 1.50) |
| Feeling Loved, per 1-SD | 1.07 (0.93, 1.22) | 1.09 (0.95, 1.25) | 1.10 (0.96, 1.27) | 1.10 (0.94, 1.27) | 0.96 (0.81, 1.15) |
Results for individual dimensions of psychological well-being presented from separate models.
Model 1 is adjusted for age only. Model 2 added sex, race and ethnicity, family annual income, parental education, and household structure. Model 3 added parental obesity or diabetes, participants’ BMI in adolescence, and pubertal maturation at baseline. Model 4 added negative affect. Model 5 additionally included all psychological assets.
p≤0.05,
p≤0.01,
p≤0.001
When considering differences by social structural factors, protective associations with belonging were apparent across all subgroups. However, some differences were noted with other well-being dimensions (Figure 2; Supplemental Tables S2–S5). Stratified analyses hinted at less potent associations among females relative to males for all dimensions of well-being. Models stratified by race and ethnicity hinted at weak associations among white youth and stronger positive associations with multiple dimensions among youth of color but estimates among the latter were imprecise. The most sizeable disparities were observed with happiness. Fully adjusted associations were substantially stronger and more clearly protective among Black youth than white youth (ORBlack=2.10, 95% CI=1.29, 3.43; ORWhite=0.99, 95% CI=0.79, 1.22; pinteraction=0.0) and among males compared to females (ORMale=1.72, 95% CI=1.22, 2.44; ORFemale=0.92, 95% CI=0.76, 1.11; pinteraction<0.001).
Figure 2.
Sub-group differences in associations between psychological well-being dimensions and cardiometabolic health.
* p≤0.05, **p≤0.01, ***p≤0.001
DISCUSSION
To advance our understanding of adolescent mental health beyond its traditional focus on the role of psychological distress, we examined different dimensions of psychological well-being and their independent associations with CMH maintenance in adulthood and assessed whether these factors serve as interchangeable health assets. We also investigated differences in their distribution and impact on CMH maintenance by social structural factors to identify which youth were more likely to both have these assets and benefit from them. We found the strongest independent protective associations with belonging, and that health benefits were apparent across the whole sample, with no subgroup differences observed by any social structural factors. This finding suggests that the cumulative associations we previously documented with well-being in this cohort7 may not reflect additive processes but possibly synergistic relationships in which belonging plays a key role. While rarely studied in research on well-being, a sense of belonging is a basic psychological need24 that is critical in human development and especially salient in adolescence when young people’s social relationships gain importance.25 Furthermore, belonging elicits positive psychological responses – like feelings of happiness and self-esteem24 – that may make it a foundational health asset that potentially magnifies the beneficial effects of other related but distinct dimensions of well-being. Although we were unable to test this in our study, interrelationships between well-being dimensions should be a subject of greater inquiry moving forward.
When considering other dimensions of well-being, we found different CMH benefits by sex and race and ethnicity. Happiness, self-esteem, and feeling loved were more potently associated with a greater likelihood of CMH maintenance among boys than girls, despite evidence from older adults that found stronger associations among women.26–28 Racial and ethnic differences were also apparent with multiple factors. Consistent with our previous study in this cohort which found evidence of racial differences in cumulative associations between psychological well-being dimensions and CMH maintenance,7 protective associations were apparent primarily among Black youth, especially in relation to happiness. Given limited understanding of the role both gender and race and ethnicity play in this context, future research would benefit from more nuanced explorations of mechanisms that may underlie these differences.
When exploring differences in the distribution of individual psychological well-being dimensions, we did not observe substantial socioeconomic disparities despite prior work finding that socially advantaged adults generally report higher levels of optimism and life satisfaction than less advantaged adults.29 With respect to race and ethnicity, Latinx youth reported lower levels of happiness and optimism compared to white youth, but Black and white youth had comparable levels. Furthermore, Black youth reported the highest levels of self-esteem, belonging, and love. Notably, these findings are consistent with the comparatively low rates of mental disorders observed among Black individuals.30 There were no sex differences in optimism or love, but in line with prior research,31 we found that boys reported higher levels of self-esteem and belonging. Perhaps surprisingly, girls reported higher levels of happiness than boys despite the disproportionately higher rates of mental health problems they tend to experience in adolescence.32
Public Health Implications
The subgroup differences observed in this study have critical implications for future public health research on health assets. In addition to demonstrating that distinct dimensions of adolescent psychological well-being have distinct social correlates, our results highlight the need to interrogate how associations may differ by social structural factors, like sex and race and ethnicity. When we considered associations with well-being dimensions in the full Add Health sample, we found the strongest associations with belonging while factors like happiness and optimism appeared to be unrelated to CMH maintenance. However, when considering differences by subgroup, we found that happiness had beneficial associations with CMH maintenance among boys and Black youth, but not among girls or youth of other races or ethnicities. These disparate findings illustrate how only considering pooled associations may mask important differences between groups.
Although our findings still need to be replicated, they have ramifications for population-based interventions, particularly in schools.33 School-based mental health programs can improve well-being and reduce depressive symptoms in adolescence,34 and may be a valuable way of addressing the sex disparities we observed in happiness, especially since boys are often perceived as facing fewer mental health challenges than girls. When considering aspects of well-being for which girls experienced a deficit (i.e., self-esteem and belonging), substantial evidence finds that youth participation in organized team sports can help build feelings of connection and self-worth.35 Given documented sex differences in sports involvement,36 expanding school-level investments in girls’ sports programs may be a promising avenue for addressing sex disparities in well-being while also laying the foundation for lifelong CMH.
Our findings could also provide insight into ways public health professionals can engage with young people from minoritized racial and ethnic groups. To date, most work about Black youth adopts a deficits orientation, focusing on the challenges37,38 and the unique social risks39,40 they face. While it is critical to identify and reduce structural barriers to health equity, an exclusive focus on these factors may inadvertently obscure the inherent strengths Black youth possess. If replicated, it is possible that the higher levels of self-esteem, belonging, and love observed among Black youth can be leveraged in future efforts to mitigate CMH inequities through group-based interventions in community settings where young people feel empowered and connected.
Limitations and Strengths
This study has limitations. Since it was based on observational data, causal associations could not be definitively ascertained. Adequate information on baseline health behaviors (e.g., diet, physical activity) was not available, so it is possible our findings were influenced by unmeasured confounding by these (or other) factors. To address this concern, we used prior literature to identify a comprehensive set of confounders that were included in all fully adjusted models. Residual confounding may be another concern, particularly in relation to our psychological well-being measures, some of which were based on single-items and others derived from existing scales for other constructs (e.g., CES-D Scale, Rosenberg Self-Esteem Scale). Due to our focus on a subset of participants with available Wave 5 biomarker data, our analyses were limited by the scarcity of individuals from several minoritized groups, including lesbian, gay, bisexual, transgender, or queer (LGBTQ) youth. Since these young people face greater risks of poor mental health compared to their cisgender peers, they are a vulnerable group for which greater investments to foster psychological well-being may be particularly critical. With respect to race and ethnicity, Add Health has limited representation among Latinx, Native American, and Asian American youth, which likely explains the imprecision of some of our subgroup estimates. Finally, there was substantial attrition in the biomarker sample across the follow-up period. Although we used sample weights to address differential non-response, it is possible that weighting methods do not fully mitigate this concern.
This study also has numerous strengths. Using a large, nationally representative cohort of diverse adolescents, we characterized the social patterning of different dimensions of psychological well-being. Furthermore, we were able to examine longitudinal associations between these dimensions and CMH maintenance in adulthood using measures that were directly and prospectively obtained from study participants. Finally, we used objective clinical assessments of 7 cardiometabolic biomarkers in adulthood that were measured at 2 time points roughly 10 years apart to provide a thorough assessment of CMH patterns over time.
Conclusion
In prior work in Add Health, youth with high levels of multiple facets of psychological well-being were found to be more likely maintain CMH in adulthood,7 but it was unclear which factors mattered most and for whom. Among the well-being dimensions investigated, belonging was the most strongly associated with CMH in adulthood for all study participants, regardless of their social standing, suggesting that it serves as a health asset that warrants greater attention in future population-based interventions. Happiness and optimism were also important for boys and Black participants, pointing to the value of addressing these factors in prevention efforts moving forward. Creating accepting and inclusive social environments and cultivating joy in young people should be critical goals to promote youth mental health and prevent chronic disease in adulthood.
Supplementary Material
Implications and Contributions:
Teens who feel a greater sense of belonging were more likely to maintain optimal cardiometabolic health in young adulthood. Fostering belonging through supportive social environments may help set youth on positive health trajectories and prevent chronic disease across the lifespan.
Acknowledgements:
The authors express their gratitude for Jennifer Choi, MSPH student at Johns Hopkins Bloomberg School of Public Health, for her assistance conducting background research.
Funding Disclosures:
This research uses data from Add Health, funded by grant P01 HD31921 (Harris) from the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), with cooperative funding from 23 other federal agencies and foundations. Add Health is currently directed by Robert A. Hummer and funded by the National Institute on Aging cooperative agreements U01 AG071448 (Hummer) and U01AG071450 (Aiello and Hummer) at the University of North Carolina at Chapel Hill. Add Health was designed by J. Richard Udry, Peter S. Bearman, and Kathleen Mullan Harris at the University of North Carolina at Chapel Hill.
For this work, F. Qureshi was supported by NIH grant T32 CA 009001. A.J. Guimond was supported by the Canadian Institute of Health Research. S. Delaney was supported by NIH grant T32 MH 017119. A.J. Guimond received support from the Lee Kum Sheung Center for Health and Happiness at the Harvard T.H. Chan School of Public Health. The sponsors of this work had no involvement in the study design, analysis and interpretation of data, or authorship. The content of this manuscript is the sole responsibility of the authors.
Footnotes
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