ArticleCirculation. Cardiovascular genetics2017
Prediction of Adulthood Obesity Using Genetic and Childhood Clinical Risk Factors in the Cardiovascular Risk in Young Finns Study.
Article in Circulation. Cardiovascular genetics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT00005129 (The Bogalusa Heart Study), which is not on this map. Cited by 24 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
The Bogalusa Heart Study
Who cites it
24 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Socioeconomic position, genetic susceptibility, and epigenetic profiles in obesity across the life course: a systematic review.International journal of obesity (2005) · 2026Pooled it
- A Systematic Review on Applications of Artificial Intelligence for Obesity Prevention.Obesity reviews : an official journal of the International Association for the Study of Obesity · 2026Pooled it
- Clinical relevance and validity of tools to predict infant, childhood and adulthood obesity: a systematic review.Public health nutrition · 2018Pooled it
- The RASA2/RASA3 ortholog RasGAP1 modulates obesity-linked phenotypes and is associated with leptin-analog signaling inFrontiers in endocrinology · 2026Article
- Development of Machine Learning-Based Risk Prediction Models to Predict Rapid Weight Gain in Infants: Analysis of Seven Cohorts.JMIR public health and surveillance · 2025Article
- Canine genome-wide association study identifiesScience (New York, N.Y.) · 2025Article
- Harnessing Artificial Intelligence in Obesity Research and Management: A Comprehensive Review.Diagnostics (Basel, Switzerland) · 2025Review
- Covariate dependent Markov chains constructed with gradient boost modeling can effectively generate long-term predictions of obesity trends.BMC research notes · 2023Article
- Genomic Innovation in Early Life Cardiovascular Disease Prevention and Treatment.Circulation research · 2023Review
- Applications of Artificial Intelligence to Obesity Research: Scoping Review of Methodologies.Journal of medical Internet research · 2022Article
- The Risk of Atrial Fibrillation Increases with Earlier Onset of Obesity: A Mendelian Randomization Study.International journal of medical sciences · 2022Article
- Healthful plant-based dietary patterns, genetic risk of obesity, and cardiovascular risk in the UK biobank study.Clinical nutrition (Edinburgh, Scotland) · 2021Article
- Development of a Genetic Risk Score to predict the risk of overweight and obesity in European adolescents from the HELENA study.Scientific reports · 2021Article
- Prediction for the Risk of Multiple Chronic Conditions Among Working Population in the United States With Machine Learning Models.IEEE open journal of engineering in medicine and biology · 2021Article
- Article
- Longitudinal association of a body mass index (BMI) genetic risk score with growth and BMI changes across the life course: The Cardiovascular Risk in Young Finns Study.International journal of obesity (2005) · 2020Article
- Cardiometabolic-Based Chronic Disease, Adiposity and Dysglycemia Drivers: JACC State-of-the-Art Review.Journal of the American College of Cardiology · 2020Review
- Study protocol: The Dutch 20|30 Postmeningitis study: a cross-sectional follow-up of two historical childhood bacterial meningitis cohorts on long-term outcomes.BMC pediatrics · 2019Article
- Genetic risk score predicts risk for overweight and obesity in Finnish preadolescents.Clinical obesity · 2019Article
- Association between Transcription Factor AP-2B genotype, obesity, insulin resistance and dietary intake in a longitudinal birth cohort study.International journal of obesity (2005) · 2019Article
Corrections and comments
- Commented on by
Authors and funding
12 authors.
Funding
Abstract
backgroundObesity is a known risk factor for cardiovascular disease. Early prediction of obesity is essential for prevention. The aim of this study is to assess the use of childhood clinical factors and the genetic risk factors in predicting adulthood obesity using machine learning methods. METHODS AND
resultsA total of 2262 participants from the Cardiovascular Risk in YFS (Young Finns Study) were followed up from childhood (age 3-18 years) to adulthood for 31 years. The data were divided into training (n=1625) and validation (n=637) set. The effect of known genetic risk factors (97 single-nucleotide polymorphisms) was investigated as a weighted genetic risk score of all 97 single-nucleotide polymorphisms (WGRS97) or a subset of 19 most significant single-nucleotide polymorphisms (WGRS19) using boosting machine learning technique. WGRS97 and WGRS19 were validated using external data (n=369) from BHS (Bogalusa Heart Study). WGRS19 improved the accuracy of predicting adulthood obesity in training (area under the curve [AUC=0.787 versus AUC=0.744,
conclusionsWGRS19 improves prediction of adulthood obesity. Predictive accuracy is highest among young children (3-6 years), whereas among older children (9-18 years) the risk can be identified using childhood clinical factors. The model is helpful in screening children with high risk of developing obesity.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.