Abstract P1047: Pre- and Perinatal Determinants of Cardiovascular Health Trajectories Across Childhood and Adolescence

Introduction: The American Heart Association put forth the Life’s Essential 8 construct to assess cardiovascular health (CVH) status based on 8 health and behavioral factors. However, few studies have identified early-life determinants of CVH trajectories from childhood to adolescence, important life stages during which health behaviors and disease precursors are established. We sought to address this research gap in Project Viva, a prospective pre-birth cohort in Boston. Methods: We included 1,338 children with data on ≥3 CVH metrics in early childhood (median age 3.2y; range 2.8–6.2y) or ≥4 metrics in mid-childhood (7.7y; 6.6–10.9y), early adolescence (13.0y; 11.9–16.6y), or late adolescence (17.5y; 15.4–20.1y). We derived CVH score (range 0-100 points) at each life stage and used segmented mixed-effect models to estimate three CVH trajectory parameters: timing of inflection point when CVH score declines; slope before and after the inflection. Linear regression models evaluated associations of pre- and perinatal factors with each trajectory parameter, adjusting for maternal sociodemographic characteristics at enrollment. Results: The mean (SD) CVH score was 82.7 (8.4) in early childhood, 84.3 (8.2) in mid-childhood, 82.2 (9.7) in early adolescence, and 74.2 (11.4) in late adolescence. Estimated age of inflection when CVH score declined was 10.2y (0.7) for males and 10.0y (0.6) for females. Prepregnancy overweight or obesity (vs. healthy or under-weight), smoking during pregnancy (vs. never), and being weaned or formula-fed (vs. breastfeeding) in the first 6 months were each associated with persistently lower CVH from childhood to adolescence. Prepregnancy obesity (vs. healthy- or under-weight) was associated with later inflection (β 0.1y; 95% CI 0.0, 0.2) and slower subsequent CVH score decline (0.2 points/y; 0.1, 0.4). Gestational hypertension or preeclampsia (vs. normal blood pressure) was associated with faster CVH score gain before inflection (0.3 points/y; 0.1, 0.5), earlier timing of inflection (-0.1y; -0.2, 0.0), and faster CVH score decline (-0.3 points/y; -0.5, -0.1), while smoking during pregnancy (vs. never) was associated with later inflection (0.2y, 0.1, 0.3). Conclusions: This study provides insights into potentially modifiable pre- and perinatal factors of CVH trajectory across childhood and adolescence. Future work should examine whether early-life interventions addressing these factors would be effective in optimizing CVH in children.

Authors

Institutions

Publication Details

Journal
Circulation
Published
2025-03-11
DOI
https://doi.org/10.1161/cir.151.suppl_1.p1047
Primary Topic
Cardiovascular Health and Risk Factors
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Abstract P1047: Pre- and Perinatal Determinants of Cardiovascular Health Trajectories Across Childhood and Adolescence

Izzuddin M. Aris, Sheryl L. Rifas‐Shiman, Wei Perng, Sarah D. de Ferranti et al.
Circulation
Cardiovascular Health and Risk Factors
article

Abstract P1047: Pre- and Perinatal Determinants of Cardiovascular Health Trajectories Across Childhood and Adolescence

Izzuddin M. Aris, Sheryl L. Rifas‐Shiman, Wei Perng, Sarah D. de Ferranti, Marie‐France Hivert
article en

Abstract

Introduction: The American Heart Association put forth the Life’s Essential 8 construct to assess cardiovascular health (CVH) status based on 8 health and behavioral factors. However, few studies have identified early-life determinants of CVH trajectories from childhood to adolescence, important life stages during which health behaviors and disease precursors are established. We sought to address this research gap in Project Viva, a prospective pre-birth cohort in Boston. Methods: We included 1,338 children with data on ≥3 CVH metrics in early childhood (median age 3.2y; range 2.8–6.2y) or ≥4 metrics in mid-childhood (7.7y; 6.6–10.9y), early adolescence (13.0y; 11.9–16.6y), or late adolescence (17.5y; 15.4–20.1y). We derived CVH score (range 0-100 points) at each life stage and used segmented mixed-effect models to estimate three CVH trajectory parameters: timing of inflection point when CVH score declines; slope before and after the inflection. Linear regression models evaluated associations of pre- and perinatal factors with each trajectory parameter, adjusting for maternal sociodemographic characteristics at enrollment. Results: The mean (SD) CVH score was 82.7 (8.4) in early childhood, 84.3 (8.2) in mid-childhood, 82.2 (9.7) in early adolescence, and 74.2 (11.4) in late adolescence. Estimated age of inflection when CVH score declined was 10.2y (0.7) for males and 10.0y (0.6) for females. Prepregnancy overweight or obesity (vs. healthy or under-weight), smoking during pregnancy (vs. never), and being weaned or formula-fed (vs. breastfeeding) in the first 6 months were each associated with persistently lower CVH from childhood to adolescence. Prepregnancy obesity (vs. healthy- or under-weight) was associated with later inflection (β 0.1y; 95% CI 0.0, 0.2) and slower subsequent CVH score decline (0.2 points/y; 0.1, 0.4). Gestational hypertension or preeclampsia (vs. normal blood pressure) was associated with faster CVH score gain before inflection (0.3 points/y; 0.1, 0.5), earlier timing of inflection (-0.1y; -0.2, 0.0), and faster CVH score decline (-0.3 points/y; -0.5, -0.1), while smoking during pregnancy (vs. never) was associated with later inflection (0.2y, 0.1, 0.3). Conclusions: This study provides insights into potentially modifiable pre- and perinatal factors of CVH trajectory across childhood and adolescence. Future work should examine whether early-life interventions addressing these factors would be effective in optimizing CVH in children.

CirculationVol. 151(Suppl_1)
Boston Children's Hospital (US), Harvard University (US), Colorado School of Public Health (US), Boston Children's Museum (US)
Openalex Percentile: Top 87%
Cardiovascular Health and Risk Factors
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.