Adolescent Psychological Assets and Cardiometabolic Health Maintenance in Adulthood: Implications for Health Equity
Bibliographic record
Abstract
Background Positive cardiometabolic health (CMH) is defined as meeting recommended levels of multiple cardiometabolic risk factors in the absence of manifest disease. Prior work finds that few individuals—particularly members of minoritized racial and ethnic groups—meet these criteria. This study investigated whether psychological assets help adolescents sustain CMH in adulthood and explored interactions by race and ethnicity. Methods and Results Participants were 3478 individuals in the National Longitudinal Study of Adolescent Health (49% female; 67% White, 15% Black, 11% Latinx, 6% other [Native American, Asian, or not specified]). In Wave 1 (1994–1995; mean age=16 years), data on 5 psychological assets (optimism, happiness, self‐esteem, belongingness, and feeling loved) were used to create a composite asset index (range=0–5). In Waves 4 (2008; mean age=28 years) and 5 (2016–2018; mean age=38 years), CMH was defined using 7 clinically assessed biomarkers. Participants with healthy levels of ≥6 biomarkers at Waves 4 and 5 were classified as maintaining CMH over time. The prevalence of CMH maintenance was 12%. Having more psychological assets was associated with better health in adulthood (odds ratio [OR] linear trend , 1.12 [95% CI, 1.01–1.25]). Subgroup analyses found substantive associations only among Black participants (OR, 1.35 [95% CI, 1.00–1.82]). Additionally, there was some evidence that racial and ethnic disparities in CMH maintenance may be less pronounced among participants with more assets. Conclusions Youth with more psychological assets were more likely to experience favorable CMH patterns 2 decades later. The strongest associations were observed among Black individuals. Fostering psychological assets in adolescence may help prevent cardiovascular disease and play an underappreciated role in shaping health inequities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".