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Advanced Statistical Approaches to Dietary Intervention in Pediatric Cardiovascular Health

Abstract Number:

3007 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Poster 

Participants:

Anahita Saeedi (1), Bruce Barton (2), Raji Balasubramanian (1)

Institutions:

(1) University of Massachusetts, Amherst, Amherst, MA, USA, (2) University of Massachusetts Medical School, Worcester, MA, USA

Co-Author(s):

Bruce Barton  
University of Massachusetts Medical School
Raji Balasubramanian  
University of Massachusetts, Amherst

First Author:

Anahita Saeedi  
University of Massachusetts, Amherst

Presenting Author:

Anahita Saeedi  
N/A

Abstract Text:

Background: The Dietary Intervention Study in Children (DISC) clinical trial was designed to shed light on pediatric cardiovascular health's response to interventions over three years.We re-analyzed the original data using advanced statistical methods.Objectives: We aimed to reassess DISC data with linear mixed-effects models and splines, examine treatment effects across subgroup and analyze compliance effect on dietary outcomes.Results: Employing B-splines, effectively minimized the AIC for LDL analysis.The intervention lowered the average LDL by -2.32 (p-value: 0.027).Increased attendance at intervention sessions was significantly associated with a reduction in fat intake (p<0.0001).This effect varied by gender, being less pronounced in girls(p : 0.003).Furthermore, children from families with higher parental education and marital stability were more likely to attend sessions, subsequently influencing dietary outcomes(p-values:0.044, 0.017).Conclusions: Our reanalysis highlights the importance of adherence in pediatric dietary interventions for cardiovascular health. It reveals gender differences and sociodemographic impacts, suggesting tailored dietary strategies are needed.

Keywords:

Dietary Intervention
|Pediatric Cardiovascular Health
|Linear Mixed Effects Models|Spline Models|Compliance Analysis|

Sponsors:

Section on Statistics in Epidemiology

Tracks:

Disease Prediction

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