BACKGROUND:

Research shows that the development of cardiometabolic disease can begin early in life with risk factors accumulating over time, but less is known about protective pathways to positive health. In this study, we use prospective data to test whether childhood assets predict a greater likelihood of being in optimal cardiometabolic health by age 17.

METHODS:

Data are from 3074 participants in the Avon Longitudinal Study of Parents and Children (mean age = 17.8). Four childhood assets were prospectively assessed via cognitive tests and parent report when children were between ages 8 and 10: strong executive functioning skills, prosocial behaviors, and low levels of internalizing and externalizing problems. Cardiometabolic health was assessed at ages 9 and 17 by using a composite dysregulation score derived from multiple biological parameters, including cholesterol, blood pressure, C-reactive protein, insulin resistance, and BMI. Associations between assets and optimal health at age 17 (ie, a dysregulation score of ≤1) were evaluated with Poisson regression models with robust error variances.

RESULTS:

After controlling for covariates (including sociodemographics, correlates of cardiometabolic health, and dysregulation scores at age 9), participants with multiple assets were 1.08 to 1.27 times more likely to be in optimal cardiometabolic health at age 17 compared with those with 0 or 1 asset. Each additional asset conferred a 6% greater likelihood of optimal health over time (relative risk = 1.06 [95% confidence interval: 1.01 to 1.11]).

CONCLUSIONS:

Childhood assets predicted cardiometabolic health with seemingly cumulative impacts. Identifying early assets may provide novel targets for prevention and elucidate pathways to positive adult health.

What’s Known on This Subject:

Previous work has found associations between positive psychosocial factors in childhood and cardiovascular health in adulthood, but few studies have examined similar relationships earlier in the life course to assess when potential protective effects may begin to emerge.

What This Study Adds:

In this study, we found that having more versus fewer childhood assets predicted a greater likelihood of optimal cardiometabolic health by late adolescence. Identifying early protective factors may offer novel insights into future targets for cardiovascular disease prevention.

Cardiovascular disease (CVD) is the leading cause of mortality worldwide, accounting for >30% of global deaths.1 Although most children are born with optimal cardiovascular health, less than a quarter of individuals possess it by adulthood.2 Research on the childhood origins of CVD has revealed that early life factors influence cardiovascular risk over the life course.3 To date, work in this area has focused primarily on the negative impact of early adversity,4 while the potential protective effects of childhood assets, such as interpersonal resources (eg, parental warmth) and intrapersonal competencies (eg, effective emotion regulation) have received less attention. Assets reflecting positive cognitive and psychological functioning have been shown to predict academic achievement and thriving in adolescence5,6 but remain understudied with respect to the establishment of early trajectories of physical health.

Previous research indicates that childhood assets, such as attention regulation and cognitive ability, are associated with favorable cardiovascular health at midlife,7,10 but it is unclear whether protective effects are evident before adulthood. Research on early life adversity has found that it is associated with poorer cardiometabolic profiles in childhood and adolescence,4,11,12 suggesting that psychosocial-related biological alterations are observable in the first decades of life. Because risk factors in childhood contribute to early health deteriorative processes, it is plausible that assets may serve a health-promoting function as youth transition to adulthood. Therefore, our goal for this study was to test whether positive childhood assets predict a greater likelihood of being in optimal cardiometabolic health by late adolescence.

Data are from the Avon Longitudinal Study of Parents and Children (ALSPAC) in England.13,15 Between April 1991 and December 1992, 14 541 women who were pregnant were enrolled, and the health and development of their children was assessed prospectively through age 17.16 Additional participants were enrolled in the study when children were 7 years old, resulting in 15 458 total participants. The eligible sample for the current study was a total of 14 181 singleton live-born children who lived past 12 months and did not have an acute infection at ages 9 or 17 (C-reactive protein [CRP] levels of >10 mg/L). The final analytic sample included 3074 participants with available data on at least 4 cardiometabolic measures at age 17. The sample composition over the study period is depicted in the flowchart provided in Fig 1.

FIGURE 1

Flowchart of study sample composition.

FIGURE 1

Flowchart of study sample composition.

Close modal

Data were collected through questionnaires administered periodically to mothers and children starting during pregnancy and continuing through age 17. Biological measures on child participants were obtained through clinical assessments conducted every 2 years from ages 9 to 17. Detailed information on all data can be accessed on the study Web site.17 All participants’ parents provided written, informed consent for their child to take part in the study, and children also assented to data collection starting at age 9.16 Research protocols were approved by the ALSPAC Law and Ethics Committee and Local Research Ethics Committee.

Childhood Assets

Four assets reflecting positive cognitive and psychological functioning were considered: (1) strong executive functioning (EF) skills, (2) prosocial behaviors, (3) low levels of internalizing problems (eg, being withdrawn or anxious), and (4) low levels of externalizing problems (eg, being aggressive or hyperactive). EF skills were directly measured by using tasks in which children’s cognitive functions were assessed, whereas prosocial behavior and internalizing and externalizing problems were assessed through maternal report at age 9 by using the validated Strengths and Difficulties Questionnaire (SDQ).18,19 

EF Skills

Participants completed computer-based tasks in which 5 discrete EF skills were assessed at 2 points in childhood. At age 8, measures of selective attention, dual attention, and attentional control were obtained by using tasks from the validated Test of Everyday Attention for Children assessment tool.20 At age 10, participants’ working memory was assessed by using the Counting Span Task,21,22 and inhibitory control was assessed by using the Stop Signal Task.22,24 More information on EF tasks is provided in the Supplemental Information.

Because EF comprises several related yet distinct skills,25,26 a composite EF asset measure was constructed by combining data on participants’ performance on each task following a procedure similar to that used in previous work on childhood self-regulation.27 First, to identify participants with a high level of proficiency in each domain, EF measures were dichotomized at the quintile of the sample distribution representing the highest level of task performance. The number of domains in which children performed well were summed to create an EF score, with higher scores indicating skill in multiple EF domains. Children who were top performers on ≥3 domains were considered to have strong EF skills.

Prosocial Behaviors

At age 9, participants’ mothers completed the 5-item prosocial subscale of the SDQ,18,19 endorsing statements about their child’s positive behaviors in the previous 6 months on a 3-point scale from 0 (not true) to 2 (certainly true). Items were summed to create a summary score, with higher values reflecting a stronger prosocial tendency (α = .68). Participants scoring within the average range defined in the SDQ manual (≥6) were defined as prosocial.

Low Internalizing or Externalizing Problems

Following previous work, internalizing behaviors were assessed by summing the emotional problems and peer problems subscales on the SDQ (α = .73).28 Following SDQ manual criteria, we defined low levels of internalizing problems as having scores below the top quintile of the sample distribution (≤4). Externalizing behaviors were measured as the sum of conduct problems and hyperactivity subscale scores (α = .77). We defined low levels of externalizing problems as having scores below the top quintile of the sample distribution (≤6).

Total Childhood Assets

Individual binary assets were summed into a categorical measure of total assets (0–1, 2, 3, or 4 assets). This served as the primary predictor in all analyses.

Cardiometabolic Health at Age 17 Years

The American Academy of Pediatrics recently recommended that clinicians assess youth health profiles on the basis of elevated levels of multiple biological risk markers.29 Therefore, cardiometabolic health was defined by the absence of multiple dysregulated cardiometabolic parameters. Data for each parameter was collected on-site following standard study protocols16,30 (see Supplemental Information). A composite measure of cardiometabolic health was created by using continuous data on fasting high-density lipoprotein cholesterol (HDL-C) (millimoles per liter), fasting non–high-density lipoprotein cholesterol (nHDL-C) (total cholesterol − HDL-C; millimoles per liter), systolic blood pressure (SBP) (millimeters of mercury), diastolic blood pressure (DBP) (millimeters of mercury), the homeostatic model assessment of insulin resistance (HOMA-IR) ([fasting glucose × fasting insulin]/22.5),31 CRP (milligrams per liter), and BMI (kilograms per meter squared). First, continuous measures were dichotomized to indicate dysregulation on the basis of the unhealthiest quintile of the sample distribution for each parameter (eg, ≥80th percentile for nHDL-C, SBP, DBP, HOMA-IR, CRP, and BMI; ≤20th percentile for HDL-C). Scores used to define the unhealthiest quintiles in ALSPAC were largely consistent with thresholds identified in pediatric populations, when available (see Supplemental Table 5). Therefore, for each parameter, dysregulation captured both clinical and subclinical risk. The number of dysregulated parameters was then summed to create an overall dysregulation score (0–7), with higher scores indicating poorer health. Participants were considered to be in optimal cardiometabolic health if they had dysregulated levels on 0 or only 1 parameter (ie, no evidence of clustered dysregulation). More information on the derivation of dysregulation scores is provided in the Supplemental Information.

Cardiometabolic Health at Age 9 Years

During clinical visits at age 9, nonfasting blood samples were collected, precluding the assessment of insulin resistance. Therefore, the parameters used to construct a measure of cardiometabolic health at age 9 included nonfasting nHDL-C (millimoles per liter), nonfasting HDL-C (millimoles per liter), SBP (millimeters of mercury), DBP (millimeters of mercury), CRP (milligrams per liter), and BMI (kilograms per meter squared). By using the same procedures described previously, cardiometabolic measures at age 9 were dichotomized to reflect dysregulation on the basis of the unhealthiest quintile of the distribution for each parameter, then summed to create a dysregulation score ranging from 0 to 6.

Covariates

Child- and family-level covariates were assessed by maternal-completed questionnaires. Child confounders included sex, precise chronological age at the age 9 clinical visit, and experiencing puberty by age 10 (Tanner stage 2 or higher, determined by pubic hair growth). Family-level confounders included maternal educational attainment (below ordinary level, ordinary level, advanced level, or university or higher), parental manual labor occupation based on the highest social class reported by either parent (categories III–V of the 1991 British Office of Population and Census Statistics classification), and living in poverty during the participant’s childhood (weekly family income of <£200 at age 3, 4, 7, or 8 years). Known correlates of future cardiometabolic health included the child’s birth weight (grams), presence of chronic conditions by age 10 (diabetes, asthma, or epilepsy), family history of cardiometabolic disease (ie, diabetes, coronary heart disease, or hypertension), and maternal prepregnancy BMI (kilograms per meter squared). Adolescent health behaviors that may serve as pathway variables included youth-reported, past–30-day cigarette use and weekly alcohol consumption at age 17.

We compared the distribution of study covariates among participants in the final analytic sample with that among participants lost to follow-up by using χ2 tests. Missing data due to participant attrition was accounted for by using a combination of multiple imputation (MI) and inverse probability weighting (IPW) (see Supplemental Information for more information).32 

We then examined bivariate associations between total assets and study covariates by using χ2 tests. Because most participants were healthy at age 17, multivariable associations were assessed by using Poisson regression models with robust error variances to minimize bias in the estimation of risk ratios.33 All results from Poisson regression analyses were exponentiated for interpretation as relative risk (RR) (ie, likelihood) of being in optimal cardiometabolic health at age 17. Associations between assets and health were tested by using 4 sequentially adjusted regression models, accounting for confounders, correlates of future cardiometabolic health (including cardiometabolic dysregulation scores at age 9), and adolescent health behaviors that may serve as pathway variables. Potential sex differences were evaluated by introducing an interaction term and stratification.

Sensitivity analyses were used to examine whether specific cardiometabolic parameters or specific assets accounted for most of the observed relationships. Separate Poisson regression analyses were used to evaluate associations between total assets and having healthy levels of each cardiometabolic parameter, controlling for confounders and cardiometabolic correlates. Separate adjusted analyses were also used to evaluate associations between individual assets and total cardiometabolic health. Finally, to assess the robustness of findings, associations between total assets and dysregulation scores were examined by using linear regression. All analyses were conducted by using Stata MP version 15.0 (Stata Corp, College Station, TX).

The average age of the final analytic sample was 17.8 years. Roughly half of participants were girls, and >95% were white. When considering childhood cardiometabolic-related factors, 11.6% of children had a chronic condition before age 10, and nearly half had a family history of cardiometabolic disease. With respect to attrition, participants who remained in the sample were more likely to be socially advantaged and in optimal cardiometabolic health at age 9 compared with those who were lost to follow-up (see Table 1).

TABLE 1

Distribution of Study Variables Used to Compare Participants Included in the Final Analytic Sample (n = 3074) With Those Who Were Lost to Follow-up (n = 11 107)

ParticipantsPa
Final Sample, n (%)Lost to Follow-up, n (%)
Total sample 3074 (21.7) 11 107 (78.3) — 
Child characteristics    
 Girls 1585 (51.6) 5315 (47.9) <.001 
 White race 2891 (95.1) 9993 (94.1) .006 
 Birth wt <2500 g 116 (4.0) 468 (4.5) .2 
 Childhood chronic condition 298 (11.6) 633 (12.8) .1 
 Cardiometabolic dysregulation score of ≥2 at age 9 y 681 (32.8) 1042 (38.5) <.001 
 Experienced puberty by 10 y 376 (16.7) 776 (18.5) .08 
Family characteristics    
 Parent history of cardiometabolic disease 1247 (43.8) 3595 (39.2) <.001 
 Mother with overweight or obesity prepregnancy 501 (19.0) 1785 (21.3) .009 
 Maternal education   <.001 
  Below ordinary level 502 (17.6) 3102 (34.0)  
  Ordinary level 919 (32.3) 3223 (35.3)  
  Advanced level 849 (29.8) 1860 (20.4)  
  University or higher 579 (20.3) 952 (10.4)  
 Parental manual labor job 917 (38.5) 3513 (51.8) <.001 
 Experienced poverty by clinical visit at 9 y 691 (33.7) 2688 (55.9) <.001 
ParticipantsPa
Final Sample, n (%)Lost to Follow-up, n (%)
Total sample 3074 (21.7) 11 107 (78.3) — 
Child characteristics    
 Girls 1585 (51.6) 5315 (47.9) <.001 
 White race 2891 (95.1) 9993 (94.1) .006 
 Birth wt <2500 g 116 (4.0) 468 (4.5) .2 
 Childhood chronic condition 298 (11.6) 633 (12.8) .1 
 Cardiometabolic dysregulation score of ≥2 at age 9 y 681 (32.8) 1042 (38.5) <.001 
 Experienced puberty by 10 y 376 (16.7) 776 (18.5) .08 
Family characteristics    
 Parent history of cardiometabolic disease 1247 (43.8) 3595 (39.2) <.001 
 Mother with overweight or obesity prepregnancy 501 (19.0) 1785 (21.3) .009 
 Maternal education   <.001 
  Below ordinary level 502 (17.6) 3102 (34.0)  
  Ordinary level 919 (32.3) 3223 (35.3)  
  Advanced level 849 (29.8) 1860 (20.4)  
  University or higher 579 (20.3) 952 (10.4)  
 Parental manual labor job 917 (38.5) 3513 (51.8) <.001 
 Experienced poverty by clinical visit at 9 y 691 (33.7) 2688 (55.9) <.001 

Sample sizes are based on observed values and may vary because of missing data. —, not applicable.

a

Calculated by using the χ2 test.

The characteristics of the sample according to participants’ total childhood assets are shown in Table 2. Assets were common, with 71.8% of participants possessing ≥3. No appreciable differences were observed by correlates of cardiometabolic health; however, assets were socially patterned. Having 1 or no assets was more common among boys (χ2 = 11.9; P = .005), participants who had parents in a manual labor job (χ2 = 8.0; P = .05), and those who experienced child poverty (χ2 = 20.4; P < .001). Additionally, participants who reported smoking in the past 30 days had fewer assets (χ2 = 18.7; P < .001).

TABLE 2

Descriptive Statistics of Study Sample by Total Childhood Assets

0–1 Asset, n (%)2 Assets, n (%)3 Assets, n (%)4 Assets, n (%)Pa
Total sample 153 (8.4) 359 (19.8) 1144 (63.0) 159 (8.8) — 
Child characteristics      
 Sex     .008 
  Girls 63 (6.7) 184 (19.5) 602 (63.7) 96 (10.2)  
  Boys 90 (10.3) 175 (20.1) 542 (62.3) 63 (7.2)  
 Birth wt, g     .9 
  <2500 7 (10.6) 14 (21.2) 40 (60.6) 5 (7.6)  
  ≥2500 140 (8.4) 327 (19.7) 1045 (62.9) 149 (9.0)  
 Childhood chronic condition     .8 
  Yes 20 (9.3) 40 (18.6) 139 (64.7) 16 (7.4)  
  No 125 (8.3) 293 (19.5) 947 (63.1) 137 (9.1)  
 Cardiometabolic dysregulation at age 9 y     .7 
  Dysregulation score of 0 or 1 70 (7.5) 185 (19.8) 598 (64.0) 82 (8.8)  
  Dysregulation score of ≥2 39 (9.2) 78 (18.4) 269 (63.4) 38 (9.0)  
 Experienced puberty by age 10 y     .4 
  Yes 29 (11.4) 46 (18.0) 156 (61.2) 24 (9.4)  
  No 105 (8.1) 257 (19.9) 817 (63.1) 115 (8.9)  
Family characteristics      
 Parent history of cardiometabolic disease     .9 
  Yes 62 (8.0) 152 (19.6) 491 (63.4) 69 (8.9)  
  No 84 (8.8) 190 (19.9) 600 (62.9) 80 (8.4)  
 Maternal prepregnancy BMI     .8 
  Overweight or obesity 112 (8.5) 260 (19.7) 832 (63.1) 114 (8.7)  
  <25 21 (6.9) 57 (18.8) 198 (65.1) 28 (9.2)  
 Maternal education     .2 
  Below ordinary level 28 (11.3) 53 (21.5) 150 (60.7) 16 (6.5)  
  Ordinary level 49 (9.0) 116 (21.3) 341 (62.5) 40 (7.3)  
  Advanced level 41 (7.5) 108 (19.7) 346 (63.0) 54 (9.8)  
  University or higher 25 (6.5) 65 (17.0) 251 (65.7) 41 (10.7)  
 Parental occupation     .05 
  Manual labor 48 (9.2) 118 (22.6) 317 (60.7) 39 (7.5)  
  Non–manual labor 70 (7.3) 172 (18.0) 621 (65.0) 93 (9.7)  
 Ever experienced poverty by age 9 y     <.001 
  Yes 48 (12.7) 90 (23.9) 213 (56.5) 26 (6.9)  
  No 71 (7.4) 174 (18.1) 624 (64.9) 93 (9.7)  
Health behaviors in adolescence      
 Smoked in past 30 d at age 17 y     .04 
  Yes 44 (10.8) 93 (22.8) 238 (58.3) 33 (8.1)  
  No 27 (7.5) 60 (16.6) 242 (66.9) 33 (9.1)  
 Consumed alcohol weekly at age 17 y     .7 
  Yes 29 (8.4) 60 (17.3) 224 (64.6) 34 (9.8)  
  No 93 (8.2) 227 (19.9) 721 (63.2) 100 (8.8)  
0–1 Asset, n (%)2 Assets, n (%)3 Assets, n (%)4 Assets, n (%)Pa
Total sample 153 (8.4) 359 (19.8) 1144 (63.0) 159 (8.8) — 
Child characteristics      
 Sex     .008 
  Girls 63 (6.7) 184 (19.5) 602 (63.7) 96 (10.2)  
  Boys 90 (10.3) 175 (20.1) 542 (62.3) 63 (7.2)  
 Birth wt, g     .9 
  <2500 7 (10.6) 14 (21.2) 40 (60.6) 5 (7.6)  
  ≥2500 140 (8.4) 327 (19.7) 1045 (62.9) 149 (9.0)  
 Childhood chronic condition     .8 
  Yes 20 (9.3) 40 (18.6) 139 (64.7) 16 (7.4)  
  No 125 (8.3) 293 (19.5) 947 (63.1) 137 (9.1)  
 Cardiometabolic dysregulation at age 9 y     .7 
  Dysregulation score of 0 or 1 70 (7.5) 185 (19.8) 598 (64.0) 82 (8.8)  
  Dysregulation score of ≥2 39 (9.2) 78 (18.4) 269 (63.4) 38 (9.0)  
 Experienced puberty by age 10 y     .4 
  Yes 29 (11.4) 46 (18.0) 156 (61.2) 24 (9.4)  
  No 105 (8.1) 257 (19.9) 817 (63.1) 115 (8.9)  
Family characteristics      
 Parent history of cardiometabolic disease     .9 
  Yes 62 (8.0) 152 (19.6) 491 (63.4) 69 (8.9)  
  No 84 (8.8) 190 (19.9) 600 (62.9) 80 (8.4)  
 Maternal prepregnancy BMI     .8 
  Overweight or obesity 112 (8.5) 260 (19.7) 832 (63.1) 114 (8.7)  
  <25 21 (6.9) 57 (18.8) 198 (65.1) 28 (9.2)  
 Maternal education     .2 
  Below ordinary level 28 (11.3) 53 (21.5) 150 (60.7) 16 (6.5)  
  Ordinary level 49 (9.0) 116 (21.3) 341 (62.5) 40 (7.3)  
  Advanced level 41 (7.5) 108 (19.7) 346 (63.0) 54 (9.8)  
  University or higher 25 (6.5) 65 (17.0) 251 (65.7) 41 (10.7)  
 Parental occupation     .05 
  Manual labor 48 (9.2) 118 (22.6) 317 (60.7) 39 (7.5)  
  Non–manual labor 70 (7.3) 172 (18.0) 621 (65.0) 93 (9.7)  
 Ever experienced poverty by age 9 y     <.001 
  Yes 48 (12.7) 90 (23.9) 213 (56.5) 26 (6.9)  
  No 71 (7.4) 174 (18.1) 624 (64.9) 93 (9.7)  
Health behaviors in adolescence      
 Smoked in past 30 d at age 17 y     .04 
  Yes 44 (10.8) 93 (22.8) 238 (58.3) 33 (8.1)  
  No 27 (7.5) 60 (16.6) 242 (66.9) 33 (9.1)  
 Consumed alcohol weekly at age 17 y     .7 
  Yes 29 (8.4) 60 (17.3) 224 (64.6) 34 (9.8)  
  No 93 (8.2) 227 (19.9) 721 (63.2) 100 (8.8)  

Sample sizes are based on observed values and may vary because of missing data. —, not applicable.

a

Calculated by using the χ2 test.

With respect to cardiometabolic health, 67.2% of participants were in optimal health at age 9, compared with 62.0% at age 17. Half of participants (49.2%) maintained good health from childhood to adolescence, and 15.2% were in poor health at age 9 but optimal health by age 17. Controlling for sex, optimal childhood cardiometabolic health was associated with a 1.67 times greater likelihood of optimal health in adolescence (95% confidence interval [CI]: 1.53 to 1.82; P < .001).

A positive association between childhood assets and cardiometabolic health at age 17 was observed across all models, with estimates attenuating slightly with increasing levels of adjustment (see Table 3). As assets accumulated, participants were increasingly more likely to be in optimal health at age 17, even after controlling for all study covariates. Tests for a linear trend revealed that each additional asset conferred a 6% greater likelihood of optimal cardiometabolic health over time (95% CI: 1.01 to 1.11; P = .01). Associations were robust to further adjustment for adolescent health behaviors.

TABLE 3

Associations Between Total Childhood Assets and Cardiometabolic Health at Age 17 Years (n = 3074)

Total Childhood AssetsCardiometabolic Health at Clinical Visit at Age 17 y
Model 1aModel 2bModel 3cModel 4d
RR (95% CI)PRR (95% CI)PRR (95% CI)PRR (95% CI)P
0–1 asset Reference — Reference — Reference — Reference — 
2 assets 1.09 (0.93 to 1.28) .3 1.06 (0.91 to 1.25) .4 1.08 (0.94 to 1.26) .3 1.11 (0.88 to 1.40) .4 
3 assets 1.16 (1.01 to 1.33) .04 1.12 (0.97 to 1.29) .1 1.11 (0.98 to 1.27) .1 1.16 (0.95 to 1.43) .1 
4 assets 1.36 (1.16 to 1.60) <.001 1.29 (1.09 to 1.52) .002 1.27 (1.09 to 1.48) .002 1.23 (0.96 to 1.57) .1 
Linear trend 1.08 (1.03 to 1.13) .001 1.06 (1.01 to 1.11) .01 1.06 (1.01 to 1.11) .01 1.06 (0.99 to 1.14) .08 
Total Childhood AssetsCardiometabolic Health at Clinical Visit at Age 17 y
Model 1aModel 2bModel 3cModel 4d
RR (95% CI)PRR (95% CI)PRR (95% CI)PRR (95% CI)P
0–1 asset Reference — Reference — Reference — Reference — 
2 assets 1.09 (0.93 to 1.28) .3 1.06 (0.91 to 1.25) .4 1.08 (0.94 to 1.26) .3 1.11 (0.88 to 1.40) .4 
3 assets 1.16 (1.01 to 1.33) .04 1.12 (0.97 to 1.29) .1 1.11 (0.98 to 1.27) .1 1.16 (0.95 to 1.43) .1 
4 assets 1.36 (1.16 to 1.60) <.001 1.29 (1.09 to 1.52) .002 1.27 (1.09 to 1.48) .002 1.23 (0.96 to 1.57) .1 
Linear trend 1.08 (1.03 to 1.13) .001 1.06 (1.01 to 1.11) .01 1.06 (1.01 to 1.11) .01 1.06 (0.99 to 1.14) .08 

Exponentiated RRs were estimated by using Poisson regression models with robust error variances, and P values were calculated by using F-tests. —, not applicable.

a

Unadjusted.

b

Adjusted for confounders, including sex, age at baseline, pubertal status at age 10 y, maternal education, experiencing poverty during childhood, and parental manual labor occupation.

c

Adjusted for confounders in Model 2 as well as correlates of future cardiometabolic health, including childhood chronic conditions (diabetes, asthma, or epilepsy), birth wt, family history of coronary heart disease or diabetes, maternal prepregnancy BMI, and number of dysregulated cardiometabolic parameters at the clinical visit at age 9 y.

d

Adjusted for covariates in Models 2 and 3 as well as adolescent health behaviors, including smoking status and weekly alcohol consumption at age 17 y.

Formal tests for interaction by sex were null. However, sex-stratified analyses appeared to yield larger estimates for boys (see Supplemental Fig 2).

Associations between total childhood assets and individual cardiometabolic parameters were in expected directions, with more assets associated with an equal or greater likelihood of being within a healthy range for each cardiometabolic parameter (see Table 4). The largest associations were evident among participants who possessed all 4 assets. Although estimates appeared to be comparable across parameters, the strongest protective associations were observed in relation to SBP and BMI. Mean levels of cardiometabolic parameters by total assets are provided in the Supplemental Information. For all other parameters (except nHDL-C), associations were less pronounced, but having 4 assets was protective. Analyses by individual asset revealed that EF skills (RR = 1.15; 95% CI: 1.05 to 1.26) had the strongest association with cardiometabolic health (see Supplemental Table 7).

TABLE 4

Likelihood of Being Below the Unhealthiest Quintile for Individual Cardiometabolic Parameters at Age 17 Years, Adjusted for Confounders and Correlates of Cardiometabolic Health (n = 3074)

Total Childhood AssetsHealthy Range of Cardiometabolic Health Parameters at Clinical Visit at Age 17 y
nHDL-C, RR (95% CI)HDL-C, RR (95% CI)SBP, RR (95% CI)DBP, RR (95% CI)CRP, RR (95% CI)HOMA-IR, RR (95% CI)BMI, RR (95% CI)
0–1 asset Reference Reference Reference Reference Reference Reference Reference 
2 assets 0.99 (0.91 to 1.08) 1.03 (0.93 to 1.13) 1.05 (0.96 to 1.15) 1.02 (0.93 to 1.12) 1.03 (0.94 to 1.12) 1.03 (0.93 to 1.14) 1.04 (0.95 to 1.15) 
3 assets 1.00 (0.93 to 1.08) 1.03 (0.95 to 1.12) 1.08a (1.00 to 1.17) 1.04 (0.96 to 1.14) 1.01 (0.94 to 1.09) 1.05 (0.96 to 1.15) 1.09a (1.00 to 1.18) 
4 assets 0.99 (0.89 to 1.10) 1.09 (0.98 to 1.21) 1.10b (1.00 to 1.21) 1.09 (0.98 to 1.21) 1.08 (0.98 to 1.19) 1.09 (0.97 to 1.21) 1.13b (1.02 to 1.26) 
Linear trend 1.00 (0.97 to 1.03) 1.02 (0.99 to 1.05) 1.03a (1.00 to 1.06) 1.03b (1.01 to 1.06) 1.01 (0.98 to 1.04) 1.02 (0.99 to 1.05) 1.04b (1.01 to 1.07) 
Total Childhood AssetsHealthy Range of Cardiometabolic Health Parameters at Clinical Visit at Age 17 y
nHDL-C, RR (95% CI)HDL-C, RR (95% CI)SBP, RR (95% CI)DBP, RR (95% CI)CRP, RR (95% CI)HOMA-IR, RR (95% CI)BMI, RR (95% CI)
0–1 asset Reference Reference Reference Reference Reference Reference Reference 
2 assets 0.99 (0.91 to 1.08) 1.03 (0.93 to 1.13) 1.05 (0.96 to 1.15) 1.02 (0.93 to 1.12) 1.03 (0.94 to 1.12) 1.03 (0.93 to 1.14) 1.04 (0.95 to 1.15) 
3 assets 1.00 (0.93 to 1.08) 1.03 (0.95 to 1.12) 1.08a (1.00 to 1.17) 1.04 (0.96 to 1.14) 1.01 (0.94 to 1.09) 1.05 (0.96 to 1.15) 1.09a (1.00 to 1.18) 
4 assets 0.99 (0.89 to 1.10) 1.09 (0.98 to 1.21) 1.10b (1.00 to 1.21) 1.09 (0.98 to 1.21) 1.08 (0.98 to 1.19) 1.09 (0.97 to 1.21) 1.13b (1.02 to 1.26) 
Linear trend 1.00 (0.97 to 1.03) 1.02 (0.99 to 1.05) 1.03a (1.00 to 1.06) 1.03b (1.01 to 1.06) 1.01 (0.98 to 1.04) 1.02 (0.99 to 1.05) 1.04b (1.01 to 1.07) 

Exponentiated RRs were estimated by using Poisson regression models with robust error variances, and P values were calculated by using F-tests.

a

P ≤ .10.

b

P ≤ .05.

Associations between assets and health defined by continuous dysregulation scores supported our initial findings. Participants with more assets had less cardiometabolic dysregulation at age 17 (fully adjusted linear trend β = −.11; P = .05; full results provided in Supplemental Table 5).

Our goal for this study was to test whether childhood assets predict positive cardiometabolic health in adolescence. Considering 4 psychological and behavioral assets measured in childhood, children with multiple assets were more likely to have optimal cardiometabolic health at age 17 compared with those with ≤1. Associations persisted after accounting for children’s cardiometabolic health at study baseline and controlling for relevant covariates. Our results are consistent with previous research on childhood assets and adult cardiovascular health,7,8 but extend that work by demonstrating that protective effects may be observed earlier in the life course than previously appreciated. Also consistent with previous findings,3 the accumulation of assets appears to drive positive health additively from childhood to adolescence, much like the accumulation of risk factors contributes to health deterioration over time. Sensitivity analyses used to evaluate associations with individual cardiometabolic parameters revealed comparable positive associations, suggesting childhood assets may have protective effects in multiple physiologic systems.

This study has some limitations. Given that this is observational research, causality cannot be determined conclusively. However, we included numerous covariates through a series of nested adjustment models, and all models yielded consistent estimates. Selective attrition is also a potential concern. Study retention was socially patterned because children who were disadvantaged were less likely to remain in the study than youth who were more advantaged. Although previous work suggests that estimates are only slightly affected by attrition from the ALSPAC,34 we adopted a rigorous approach to account for missing data, combining MI and IPW techniques. Lastly, assets were only assessed between ages 8 and 10. Because repeated measurements were not collected, we were unable to examine the stability of assets or whether acquiring assets during adolescence was associated with better cardiometabolic health over time.

This study also has numerous strengths. Data were from a large community-based sample of parents and children and collected at various time points beginning in fetal life and continuing through adolescence. Cardiometabolic measures were obtained during clinical visits starting in childhood, with data available at the time assets were assessed. Additionally, a rich array of information on health, social factors, and behaviors over the follow-up period was available, making it possible to adjust for many potential confounders and correlates of cardiometabolic health.

If replicated, our results could have important policy implications. Of the assets examined, EF skills appeared to have the strongest impact on participants’ future health. Effective interventions targeting this asset have been developed, and previous work has shown that EF skills can be improved in childhood with targeted enrichment.35 Programs that successfully improve EF skills include computer-based training, martial arts, and the Tools of the Mind and Montessori preschool curricula.35 These programs are touted for improving school readiness and reducing social disparities in education,36 but our findings suggest that they may also contribute to reducing cardiometabolic health disparities. Increasing investments in early initiatives to support the development of childhood assets may be a worthwhile future direction for the primordial prevention of CVD.

Our results also have important implications for epidemiological research. The childhood assets included in this study are foundational for developing self-regulation, decision-making skills, and emotional well-being, and are considered critical to lifelong health.37 Although there is considerable evidence of associations between early psychosocial risk factors and poor cardiometabolic outcomes,4 no cohort studies have prospectively collected substantive data on childhood assets to identify protective pathways to positive cardiometabolic health. Few studies measure positive functioning, such as children’s emotion regulation abilities or positive emotional states. Because of data limitations, we defined 2 assets by the absence of behavior problems, which may not fully capture the unique contributions of positive psychological functioning to health. Considered in conjunction with emerging research on the health-promoting (and potentially restorative) impact of positive psychological well-being among adults,38,39 our findings indicate a greater need for large-scale studies to monitor positive factors starting in childhood. A more comprehensive understanding of the distribution of childhood assets in the population and the pathways by which they influence heath and disease over the life course will provide a stronger evidence base with which to inform clinical practice.

With this study, we contribute to the growing field of positive cardiovascular health38 and primordial prevention.40 Although work in this area has historically been focused on adults,38 we examined pathways earlier in the life course and with respect to a broader array of developmentally informed psychological and behavioral assets. Our findings suggest that early investment in assets may help youth maintain health and combat the accumulation of health-damaging behavioral and biological risk factors over time. Identifying novel targets for early prevention and developing systems to monitor assets in the population are important future directions for pediatric epidemiology.

Ms Qureshi conceptualized and designed the study, conducted the analyses, drafted the manuscript, and revised the manuscript on the basis of coauthor and editor feedback; Dr Koenen, Dr Tiemeier, and Dr Williams provided substantive feedback on the study design, analyses, and interpretation of results and reviewed the manuscript at various points in its development; Ms Misra performed a quality control check on the data analysis code, provided substantive feedback on the manuscript, and proofread the final submission for spelling and grammar; Dr Kubzansky mentored the first author through the study’s conceptualization and design, provided substantive feedback on analyses and the interpretation of results, and reviewed the manuscript at various points in its development; and all authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work.

FUNDING: The UK Medical Research Council, the Wellcome Trust (102215/2/13/2), and the University of Bristol provided core support for the Avon Longitudinal Study of Parents and Children. This publication is the work of the authors and was funded by the cardiovascular epidemiology training grant at the Harvard T.H. Chan School of Public Health (National Heart, Lung, and Blood Institute at the National Institutes of Health grant number T32 098048), by the Julius B. Richmond Fellowship at the Center on the Developing Child at Harvard University, and by a dissertation award from the Lee Kum Sheung Center for Health and Happiness at the Harvard T.H. Chan School of Public Health. Funded by the National Institutes of Health (NIH).

COMPANION PAPER: A companion to this article can be found online at www.pediatrics.org/cgi/doi/10.1542/peds.2018-4004.

We are extremely grateful to all the families who took part in this study, the midwives for their help in recruiting them, and the whole ALSPAC team, including interviewers, technicians, clerical workers, research scientists, volunteers, managers, receptionists, and nurses. We also thank Simo Goshev, PhD, at the Institute for Quantitative Social Science at Harvard University for his statistical advice.

     
  • ALSPAC

    Avon Longitudinal Study of Parents and Children

  •  
  • CI

    confidence interval

  •  
  • CRP

    C-reactive protein

  •  
  • CVD

    cardiovascular disease

  •  
  • DBP

    diastolic blood pressure

  •  
  • EF

    executive functioning

  •  
  • HDL-C

    high-density lipoprotein cholesterol

  •  
  • HOMA-IR

    homeostatic model assessment of insulin resistance

  •  
  • IPW

    inverse probability weighting

  •  
  • MI

    multiple imputation

  •  
  • nHDL-C

    non–high-density lipoprotein cholesterol

  •  
  • RR

    relative risk

  •  
  • SBP

    systolic blood pressure

  •  
  • SDQ

    Strengths and Difficulties Questionnaire

1
World Health Organization
. Cardiovascular diseases (CVDs). Available at: www.who.int/mediacentre/factsheets/fs317/en/. Accessed May 25, 2018
2
Ford
ES
,
Greenlund
KJ
,
Hong
Y
.
Ideal cardiovascular health and mortality from all causes and diseases of the circulatory system among adults in the United States.
Circulation
.
2012
;
125
(
8
):
987
995
[PubMed]
3
Halfon
N
,
Verhoef
PA
,
Kuo
AA
.
Childhood antecedents to adult cardiovascular disease.
Pediatr Rev
.
2012
;
33
(
2
):
51
60; quiz 61
[PubMed]
4
Suglia
SF
,
Koenen
KC
,
Boynton-Jarrett
R
, et al;
American Heart Association Council on Epidemiology and Prevention
;
Council on Cardiovascular Disease in the Young
;
Council on Functional Genomics and Translational Biology
;
Council on Cardiovascular and Stroke Nursing
;
Council on Quality of Care and Outcomes Research
.
Childhood and adolescent adversity and cardiometabolic outcomes: a scientific statement from the American Heart Association.
Circulation
.
2018
;
137
(
5
):
e15
e28
[PubMed]
5
Scales
PC
,
Benson
PL
,
Roehlkepartain
EC
,
Sesma
A
 Jr
,
van Dulmen
M
.
The role of developmental assets in predicting academic achievement: a longitudinal study.
J Adolesc
.
2006
;
29
(
5
):
691
708
[PubMed]
6
Scales
PC
,
Benson
PL
,
Leffert
N
,
Blyth
DA
.
Contribution of developmental assets to the prediction of thriving among adolescents.
Appl Dev Sci
.
2000
;
4
(
1
):
27
46
7
Appleton
AA
,
Buka
SL
,
Loucks
EB
,
Rimm
EB
,
Martin
LT
,
Kubzansky
LD
.
A prospective study of positive early-life psychosocial factors and favorable cardiovascular risk in adulthood.
Circulation
.
2013
;
127
(
8
):
905
912
[PubMed]
8
Pulkki-Råback
L
,
Elovainio
M
,
Hakulinen
C
, et al
.
Cumulative effect of psychosocial factors in youth on ideal cardiovascular health in adulthood: the Cardiovascular Risk in Young Finns Study [published correction appears in Circulation. 2015;131(14):e403].
Circulation
.
2015
;
131
(
3
):
245
253
[PubMed]
9
Juonala
M
,
Pulkki-Råback
L
,
Elovainio
M
, et al
.
Childhood psychosocial factors and coronary artery calcification in adulthood: the Cardiovascular Risk in Young Finns Study.
JAMA Pediatr
.
2016
;
170
(
5
):
466
472
[PubMed]
10
Hakulinen
C
,
Pulkki-Råback
L
,
Elovainio
M
, et al
.
Childhood psychosocial cumulative risks and carotid intima-media thickness in adulthood: the Cardiovascular Risk in Young Finns Study.
Psychosom Med
.
2016
;
78
(
2
):
171
181
[PubMed]
11
Slopen
N
,
Kubzansky
LD
,
Koenen
KC
.
Internalizing and externalizing behaviors predict elevated inflammatory markers in childhood.
Psychoneuroendocrinology
.
2013
;
38
(
12
):
2854
2862
[PubMed]
12
Slopen
N
,
Goodman
E
,
Koenen
KC
,
Kubzansky
LD
.
Socioeconomic and other social stressors and biomarkers of cardiometabolic risk in youth: a systematic review of less studied risk factors.
PLoS One
.
2013
;
8
(
5
):
e64418
13
Golding
J
,
Pembrey
M
,
Jones
R
;
ALSPAC Study Team
.
ALSPAC–the Avon Longitudinal Study of Parents and Children. I. Study methodology.
Paediatr Perinat Epidemiol
.
2001
;
15
(
1
):
74
87
[PubMed]
14
Golding
J
;
ALSPAC Study Team
.
The Avon Longitudinal Study of Parents and Children (ALSPAC)–study design and collaborative opportunities.
Eur J Endocrinol
.
2004
;
151
(
suppl 3
):
U119
U123
[PubMed]
15
Fraser
A
,
Macdonald-Wallis
C
,
Tilling
K
, et al
.
Cohort profile: the Avon Longitudinal Study of Parents and Children: ALSPAC mothers cohort.
Int J Epidemiol
.
2013
;
42
(
1
):
97
110
[PubMed]
16
Boyd
A
,
Golding
J
,
Macleod
J
, et al
.
Cohort profile: the ‘children of the 90s’–the index offspring of the Avon Longitudinal Study of Parents and Children.
Int J Epidemiol
.
2013
;
42
(
1
):
111
127
[PubMed]
17
University of Bristol
. Access data and samples. Available at: http://www.bris.ac.uk/alspac/researchers/data-access/data-dictionary. Accessed January 4, 2019
18
Goodman
R
.
Psychometric properties of the Strengths and Difficulties Questionnaire.
J Am Acad Child Adolesc Psychiatry
.
2001
;
40
(
11
):
1337
1345
[PubMed]
19
Goodman
R
,
Meltzer
H
,
Bailey
V
.
The Strengths and Difficulties Questionnaire: a pilot study on the validity of the self-report version.
Eur Child Adolesc Psychiatry
.
1998
;
7
(
3
):
125
130
[PubMed]
20
Manly
T
,
Anderson
V
,
Nimmo-Smith
I
,
Turner
A
,
Watson
P
,
Robertson
IH
.
The differential assessment of children’s attention: the Test of Everyday Attention for Children (TEA-Ch), normative sample and ADHD performance.
J Child Psychol Psychiatry
.
2001
;
42
(
8
):
1065
1081
[PubMed]
21
Conway
AR
,
Kane
MJ
,
Bunting
MF
,
Hambrick
DZ
,
Wilhelm
O
,
Engle
RW
.
Working memory span tasks: a methodological review and user’s guide.
Psychon Bull Rev
.
2005
;
12
(
5
):
769
786
[PubMed]
22
Soreni
N
,
Crosbie
J
,
Ickowicz
A
,
Schachar
R
.
Stop signal and Conners’ continuous performance tasks: test–retest reliability of two inhibition measures in ADHD children.
J Atten Disord
.
2009
;
13
(
2
):
137
143
[PubMed]
23
Verbruggen
F
,
Logan
GD
.
Response inhibition in the stop-signal paradigm.
Trends Cogn Sci
.
2008
;
12
(
11
):
418
424
[PubMed]
24
Alderson
RM
,
Rapport
MD
,
Kofler
MJ
.
Attention-deficit/hyperactivity disorder and behavioral inhibition: a meta-analytic review of the stop-signal paradigm.
J Abnorm Child Psychol
.
2007
;
35
(
5
):
745
758
[PubMed]
25
Best
JR
,
Miller
PH
.
A developmental perspective on executive function.
Child Dev
.
2010
;
81
(
6
):
1641
1660
[PubMed]
26
Miyake
A
,
Friedman
NP
,
Emerson
MJ
,
Witzki
AH
,
Howerter
A
,
Wager
TD
.
The unity and diversity of executive functions and their contributions to complex “Frontal Lobe” tasks: a latent variable analysis.
Cogn Psychol
.
2000
;
41
(
1
):
49
100
[PubMed]
27
deBlois
ME
,
Kubzansky
LD
.
Childhood self-regulatory skills predict adolescent smoking behavior.
Psychol Health Med
.
2016
;
21
(
2
):
138
151
[PubMed]
28
Goodman
A
,
Lamping
DL
,
Ploubidis
GB
.
When to use broader internalising and externalising subscales instead of the hypothesised five subscales on the Strengths and Difficulties Questionnaire (SDQ): data from British parents, teachers and children.
J Abnorm Child Psychol
.
2010
;
38
(
8
):
1179
1191
[PubMed]
29
Magge
SN
,
Goodman
E
,
Armstrong
SC
;
Committee on Nutrition
;
Section on Endocrinology
;
Section on Obesity
.
The metabolic syndrome in children and adolescents: shifting the focus to cardiometabolic risk factor clustering.
Pediatrics
.
2017
;
140
(
2
):
e20171603
[PubMed]
30
Lawlor
DA
,
Macdonald-Wallis
C
,
Fraser
A
, et al
.
Cardiovascular biomarkers and vascular function during childhood in the offspring of mothers with hypertensive disorders of pregnancy: findings from the Avon Longitudinal Study of Parents and Children.
Eur Heart J
.
2012
;
33
(
3
):
335
345
[PubMed]
31
Gungor
N
,
Saad
R
,
Janosky
J
,
Arslanian
S
.
Validation of surrogate estimates of insulin sensitivity and insulin secretion in children and adolescents.
J Pediatr
.
2004
;
144
(
1
):
47
55
[PubMed]
32
Seaman
SR
,
White
IR
,
Copas
AJ
,
Li
L
.
Combining multiple imputation and inverse-probability weighting.
Biometrics
.
2012
;
68
(
1
):
129
137
[PubMed]
33
Zou
G
.
A modified Poisson regression approach to prospective studies with binary data.
Am J Epidemiol
.
2004
;
159
(
7
):
702
706
[PubMed]
34
Wolke
D
,
Waylen
A
,
Samara
M
, et al
.
Selective drop-out in longitudinal studies and non-biased prediction of behaviour disorders.
Br J Psychiatry
.
2009
;
195
(
3
):
249
256
[PubMed]
35
Diamond
A
.
Activities and programs that improve children’s executive functions.
Curr Dir Psychol Sci
.
2012
;
21
(
5
):
335
341
[PubMed]
36
Diamond
A
,
Lee
K
.
Interventions shown to aid executive function development in children 4 to 12 years old.
Science
.
2011
;
333
(
6045
):
959
964
[PubMed]
37
Shonkoff
JP
,
Garner
AS
;
Committee on Psychosocial Aspects of Child and Family Health
;
Committee on Early Childhood, Adoption, and Dependent Care
;
Section on Developmental and Behavioral Pediatrics
.
The lifelong effects of early childhood adversity and toxic stress.
Pediatrics
.
2012
;
129
(
1
). Available at: www.pediatrics.org/cgi/content/full/129/1/e232
[PubMed]
38
Boehm
JK
,
Kubzansky
LD
.
The heart’s content: the association between positive psychological well-being and cardiovascular health.
Psychol Bull
.
2012
;
138
(
4
):
655
691
[PubMed]
39
Labarthe
DR
,
Kubzansky
LD
,
Boehm
JK
,
Lloyd-Jones
DM
,
Berry
JD
,
Seligman
ME
.
Positive cardiovascular health: a timely convergence.
J Am Coll Cardiol
.
2016
;
68
(
8
):
860
867
[PubMed]
40
Gillman
MW
.
Primordial prevention of cardiovascular disease.
Circulation
.
2015
;
131
(
7
):
599
601
[PubMed]

Competing Interests

POTENTIAL CONFLICT OF INTEREST: The authors have indicated they have no potential conflicts of interest to disclose.

FINANCIAL DISCLOSURE: The authors have indicated they have no financial relationships relevant to this article to disclose.

Supplementary data