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Epidemiology and Population Health

Dissecting the clinical relevance of polygenic risk score for obesity—a cross-sectional, longitudinal analysis

International Journal of Obesity volume 46, pages 1686–1693 (2022)Cite this article

Subjects

Abstract

Background

Obesity is a global pandemic disease whose prevalence is increasing worldwide. The clinical relevance of a polygenic risk score (PRS) for obesity has not been fully elucidated in Asian populations.

Method

We utilized a comprehensive health check-up database from the Korean population in conjunction with genotyping to generate PRS for BMI (PRS-BMI). We conducted a phenome-wide association (PheWAS) analysis and observed the longitudinal association of BMI with PRS-BMI.

Results

PRS-BMI was generated by PRS-CS. Adding PRS-BMI to a model predicting ten-year BMI based on age, sex, and baseline BMI improved the model’s accuracy (p = 0.003). In a linear mixed model of longitudinal change in BMI with aging, higher deciles of PRS were directly associated with changes in BMI. In the PheWAS, significant associations were observed for metabolic syndrome, bone density, and fatty liver. In the lean body population, those having the top 20% PRS-BMI had higher BMI and body fat mass along with better metabolic trait profiles compared to the bottom 20%. A bottom-20% PRS-BMI was a risk factor for metabolically unhealthy lean body (odds ratio 3.092, 95% confidence interval 1.707–6.018, p < 0.001), with adjustment for age, sex and BMI.

Conclusions

Genetic predisposition to obesity as defined by PRS-BMI was significantly associated with obesity-related disease or trajectory of obesity. Low PRS-BMI might be a risk factor associated with a metabolically unhealthy lean body. Better understanding the mechanisms of these relationships may allow tailored intervention in obesity or early selection of populations at risk of metabolic disease.

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Fig. 1: Overview of study design.
Fig. 2: Results of the phenome-wide association study for PRS-BMI.
Fig. 3: Association analysis of extreme PRS-BMI (top/bottom 20%) with metabolic syndrome in the lean body population.
Fig. 4: Correlation plots for PRS-BMI and clinical variables in lean body population.

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Data availability

Complete raw data set are not publicly available due to restrictions (institutional policy to protect the privacy of research participants), but are available from the corresponding author on reasonable request. However, all other data are contained in the article and its supplementary information are available upon reasonable request.

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Acknowledgements

We acknowledge Jong-Eun Lee of DNA Link for the collaborative support to establish the GENIE Cohort. This work has been supported by the NIGMS R01 GM138597.

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Author notes

  1. These authors contributed equally: Eun Kyung Choe, Manu Shivakumar.

Authors and Affiliations

  1. Department of Biostatistics, Epidemiology & Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA

    Eun Kyung Choe, Manu Shivakumar & Dokyoon Kim

  2. Department of Surgery, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, 06236, South Korea

    Eun Kyung Choe

  3. Department of Obstetrics and Gynecology, Seoul National University College of Medicine, Seoul, 03080, South Korea

    Seung Mi Lee

  4. Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA

    Anurag Verma

  5. Department of Medicine, Division of Translational Medicine and Human Genetics, University of Pennsylvania, Philadelphia, PA, 19104, USA

    Anurag Verma

  6. Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, 19104, USA

    Dokyoon Kim

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Contributions

EKC, MS, SML, AV, and DK made contribution for conceptualization; EKC and MS curated the clinical and genomic data; EKC, MS, AV, and DK designed the study framework for methodology. EKC, MS, AV and DK analyzed and interpreted the results. EKC, MS were the major contributor in writing the manuscript. All authors read, provided critical feedback, helped shape the research, analysis, manuscript and approved the final manuscript.

Corresponding authors

Correspondence to Anurag Verma or Dokyoon Kim.

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Choe, E.K., Shivakumar, M., Lee, S.M. et al. Dissecting the clinical relevance of polygenic risk score for obesity—a cross-sectional, longitudinal analysis. Int J Obes 46, 1686–1693 (2022). https://doi.org/10.1038/s41366-022-01168-2

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