The Association of Cardiometabolic Risk Factors in Parent–Child Dyads in Guam: Pacific Islands Cohort on Cardiometabolic Health Study

TL;DR

This study examines how cardiometabolic risk factors in parents are linked to similar risks in their children in Guam, highlighting the need for family-based interventions.

Contribution

The study is the first to assess the association of cardiometabolic risk factors in parent–child dyads in the Guam population using the PICCAH cohort.

Findings

Child–parent risk for metabolic syndrome (MetS) was directly correlated in the Guam population.

Adults in Guam have a high prevalence of MetS, indicating a critical need for family-focused interventions.

Lifestyle factors like physical activity and screen time were examined for their influence on MetS risk in parent–child dyads.

Abstract

The Western Pacific region, including Guam, has the highest prevalence of prediabetes and type 2 diabetes mellitus, which are associated with metabolic syndrome (MetS)—a cluster of preventable risk factors. Children with parents with MetS are likely to develop MetS in the future. MetS prevalence in Guam and the impact of MetS on children are unknown. Data from the Pacific Islands Cohort on Cardiometabolic Health (PICCAH) study in Guam were analyzed to determine MetS in adults and MetS risk in children using the International Diabetes Federation criteria and sex- and age-specific waist circumference values for abdominal obesity, respectively. MetS Z-scores were calculated. MetS or MetS risk indicators, including MetS Z-scores, were examined by lifestyle risk factors (parent and child: physical activity and sleep; parent only: sedentary behavior and stress; child only: screen time). The relationship between adult MetS Z-scores and child MetS Z-scores was evaluated using linear-regression analyses. Child–parent risk for MetS was directly correlated in this population. The high prevalence of adult MetS in Guam demonstrates a critical need for interventions involving both parents and children. Expanding the analysis to assess the relationships between other lifestyle factors, like diet, in parent–child dyads is necessary to refine such intervention programs.