Tendency to overeat predicts an elevated body mass index trajectory across school-age years

Overeating is a complex appetite trait, cross-sectionally linked to an elevated weight in children. However, little is known about longitudinal associations. Therefore, we studied how a tendency towards overeating predicts weight development between 8 and 16 years of age. In this study among 4517 children from the Finnish Health in Teens cohort, parents reported their child's tendency to overeat when children were on average 11.2 (SD 0.8) years old. Children were then categorised as overeating, possibly overeating, or not overeating. Height and weight measurements from two data collection periods were combined with growth data from a national health register, and age- and sex-standardised body mass index z-scores (BMIz) were calculated using the International Obesity Task Force reference. Children also reported their lifestyle factors, including food consumption, physical activity, screen time, and sleep patterns. We examined the association between overeating and BMIz using a linear mixed model, adjusting for age, sex, specific food consumption frequencies, physical activity, screen time, and sleep duration. We further analysed whether associations differed by age, food consumption frequencies, or physical activity. The average BMIz in the overeating group was 1.18 (95% CI 1.10─1.26) units higher compared with those without overeating, but remained stable as age increased. Among those without overeating, BMIz increased 0.043 units per year of age (p < 0.001). Physical activity, but not food consumption, modified the association between overeating and BMIz (p for interaction = 0.038). In the lowest third of physical activity (≤ 5 h/week), BMIz was 1.28 units higher (95% CI 1.15─1.41) in the overeating compared with the no overeating group, while in the highest third (≥ 9 h/week) the effect size was 1.08 (95% CI 0.93─1.24). In conclusion, children with a parent-reported tendency to overeat exhibited an elevated, but stable mean BMIz across adolescence. Public health programmes tackling the obesity epidemic should consider the differences in appetite self-regulation among children.

Authors

Institutions

Publication Details

Journal
Scientific Reports
Published
2025-02-22
DOI
https://doi.org/10.1038/s41598-025-90786-7
Citations
1
Primary Topic
Obesity, Physical Activity, Diet
Type
article
Field-Weighted Citation Impact
1.88

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Tendency to overeat predicts an elevated body mass index trajectory across school-age years

Satu Männistö, Sohvi Lommi, Heli Viljakainen, Catharina Sarkkola et al.
1 citations
Scientific Reports
Obesity, Physical Activity, Diet
1.88
article

Tendency to overeat predicts an elevated body mass index trajectory across school-age years

Satu Männistö, Sohvi Lommi, Heli Viljakainen, Catharina Sarkkola, Eero Kajantie, Kris Elomaa
article en
1 citations

Abstract

Overeating is a complex appetite trait, cross-sectionally linked to an elevated weight in children. However, little is known about longitudinal associations. Therefore, we studied how a tendency towards overeating predicts weight development between 8 and 16 years of age. In this study among 4517 children from the Finnish Health in Teens cohort, parents reported their child's tendency to overeat when children were on average 11.2 (SD 0.8) years old. Children were then categorised as overeating, possibly overeating, or not overeating. Height and weight measurements from two data collection periods were combined with growth data from a national health register, and age- and sex-standardised body mass index z-scores (BMIz) were calculated using the International Obesity Task Force reference. Children also reported their lifestyle factors, including food consumption, physical activity, screen time, and sleep patterns. We examined the association between overeating and BMIz using a linear mixed model, adjusting for age, sex, specific food consumption frequencies, physical activity, screen time, and sleep duration. We further analysed whether associations differed by age, food consumption frequencies, or physical activity. The average BMIz in the overeating group was 1.18 (95% CI 1.10─1.26) units higher compared with those without overeating, but remained stable as age increased. Among those without overeating, BMIz increased 0.043 units per year of age (p < 0.001). Physical activity, but not food consumption, modified the association between overeating and BMIz (p for interaction = 0.038). In the lowest third of physical activity (≤ 5 h/week), BMIz was 1.28 units higher (95% CI 1.15─1.41) in the overeating compared with the no overeating group, while in the highest third (≥ 9 h/week) the effect size was 1.08 (95% CI 0.93─1.24). In conclusion, children with a parent-reported tendency to overeat exhibited an elevated, but stable mean BMIz across adolescence. Public health programmes tackling the obesity epidemic should consider the differences in appetite self-regulation among children.

Scientific ReportsVol. 15(1)
University of Helsinki (FI), Norwegian University of Science and Technology (NO), Oulu University Hospital (FI), Finnish Institute for Health and Welfare (FI), Folkhälsans Forskningscentrum (FI), University of Oulu (FI)
Helsingin Yliopisto, Folkhälsanin Tutkimussäätiö, Academy of Finland, Päivikki ja Sakari Sohlbergin Säätiö, Signe ja Ane Gyllenbergin Säätiö, Lastentautien Tutkimussäätiö, Sigrid Juséliuksen Säätiö, Novo Nordisk Fonden, Terveyden ja hyvinvoinnin laitos
Zero hunger
Openalex Percentile: Top 18%
Obesity, Physical Activity, Diet
1.88
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.