Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2017 Jun;10(3):e001554.
doi: 10.1161/CIRCGENETICS.116.001554.

Prediction of Adulthood Obesity Using Genetic and Childhood Clinical Risk Factors in the Cardiovascular Risk in Young Finns Study

Fatemeh Seyednasrollah  1 , Johanna Mäkelä  2 , Niina Pitkänen  1 , Markus Juonala  1 , Nina Hutri-Kähönen  1 , Terho Lehtimäki  1 , Jorma Viikari  1 , Tanika Kelly  1 , Changwei Li  1 , Lydia Bazzano  1 , Laura L Elo  1 , Olli T Raitakari  1
Affiliations

Prediction of Adulthood Obesity Using Genetic and Childhood Clinical Risk Factors in the Cardiovascular Risk in Young Finns Study

Fatemeh Seyednasrollah et al. Circ Cardiovasc Genet. 2017 Jun.

Abstract

Background: Obesity 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 results: A 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, P<0.0001) and validation data (AUC=0.769 versus AUC=0.747, P=0.026). WGRS97 improved the accuracy in training (AUC=0.782 versus AUC=0.744, P<0.0001) but not in validation data (AUC=0.749 versus AUC=0.747, P=0.785). Higher WGRS19 associated with higher body mass index at 9 years and WGRS97 at 6 years. Replication in BHS confirmed our findings that WGRS19 and WGRS97 are associated with body mass index.

Conclusions: WGRS19 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.

Keywords: genetics; machine learning; obesity; risk factor; single-nucleotide polymorphism genetics; statistics.

PubMed Disclaimer

Figures

Figure 1.
Figure 1.
Receiver operating characteristic curves for all participants in the (A) training and (B) validation data representing prediction accuracy of WGRS19 and childhood clinical factors for obesity in adulthood.
Figure 2.
Figure 2.
The body mass index (BMI) trajectories with mean value and SEM bars in participants of the Cardiovascular Risk in YFS (Young Finns Study) with low or high genetic risk of obesity according to lowest and highest weighted genetic risk score quartile from 3 to 50 years according to (A) WGRS19 or (B) WGRS97 quartiles. Statistically significant differences (P<0.05) were seen between participants with high genetic risk compared with participants with low genetic risk starting from the age of 9 and 6 years, respectively.
Figure 3.
Figure 3.
The body mass index (BMI) trajectories with mean value and SEM bars in participants of the BHS (Bogalusa Heart Study) with low or high genetic risk of obesity according to lowest and highest weighted genetic risk score quartile from 6 to 45 years according to (A) WGRS19 and (B) WGRS97 quartiles.
See this image and copyright information in PMC

Comment in

References

    1. Van Gaal LF, Mertens IL, De Block CE. Mechanisms linking obesity with cardiovascular disease. Nature. 2006;444:875–880. doi: 10.1038/nature05487. - DOI - PubMed
    1. Kahn SE, Hull RL, Utzschneider KM. Mechanisms linking obesity to insulin resistance and type 2 diabetes. Nature. 2006;444:840–846. doi: 10.1038/nature05482. - DOI - PubMed
    1. Kotsis V, Stabouli S, Papakatsika S, Rizos Z, Parati G. Mechanisms of obesity-induced hypertension. Hypertens Res. 2010;33:386–393. doi: 10.1038/hr.2010.9. - DOI - PubMed
    1. Renehan AG, Zwahlen M, Egger M. Adiposity and cancer risk: new mechanistic insights from epidemiology. Nat Rev Cancer. 2015;15:484–498. doi: 10.1038/nrc3967. - DOI - PubMed
    1. Singh AS, Mulder C, Twisk JW, van Mechelen W, Chinapaw MJ. Tracking of childhood overweight into adulthood: a systematic review of the literature. Obes Rev. 2008;9:474–488. doi: 10.1111/j.1467-789X.2008.00475.x. - DOI - PubMed

MeSH terms