Abstract P3024: Phenotypic vs. Genetic Mismatch of Body Mass Index and Its Risk Factors: Evidence from the China Kadoorie Biobank and the UK Biobank

Background: The mismatch between phenotypic and genotypic BMI (BMI-PGM) contributes to the sky-rocketing obesity prevalence in both developed and developing countries. However, little is known about BMI-PGM and its risk factors. Methods: We included 86,205 participants who were genotyped and with valid BMI measurements from the China Kadoorie Biobank (CKB). BMI-PGM was calculated for each participant as the difference between the percentile for his/her adjusted BMI and the percentile for his/her adjusted polygenetic risk score of BMI (BMI-PRS), ranging from -100 to 100. A higher BMI-PGM means one’s measured BMI was much greater than his/her genetically-determined BMI. We then categorized participants into three groups according to BMI-PGM quartiles: discordantly low (bottom quartile), concordant (2nd and 3rd quartiles), and discordantly high (top quartile). Potential risk factors of BMI-PGM, including socio-demographic characteristics and lifestyle factors, were tested by multivariate linear regression. External replication analyses were performed among 102,514 UK Biobank (UKB) participants. Results: In both CKB and UKB, BMI-PGM exhibited a symmetric and normal distribution. Across all four BMI categories (underweight, normal weight, overweight, and obese), the concordant BMI-PGM group accounted for approximately 50% of participants. Of interest, over 50% of obese and underweight adults were categorized into the discordantly high BMI-PGM group and the discordantly low BMI-PGM group, respectively. In both cohorts, participants with higher BMI-PGM were more likely to be younger, current smokers, physically inactive, and meat lovers. However, the associations of BMI-PGM with sex, urban-rural status, education level, household income, alcohol consumption, fruit intake, and sleep duration varied between the two cohorts. Conclusions: A new metric was developed to quantify the mismatch between phenotypic and genetic BMI in both East Asian and European adults. The obesogenic environment affects obesity in different populations with both commonalities and distinct differences.

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Journal
Circulation
Published
2025-03-11
DOI
https://doi.org/10.1161/cir.151.suppl_1.p3024
Primary Topic
Nutrition, Genetics, and Disease
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article
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article

Abstract P3024: Phenotypic vs. Genetic Mismatch of Body Mass Index and Its Risk Factors: Evidence from the China Kadoorie Biobank and the UK Biobank

Shuo Gong, Ling Yang, Dianjianyi Sun, Canqing Yu et al.
Circulation
Nutrition, Genetics, and Disease
article

Abstract P3024: Phenotypic vs. Genetic Mismatch of Body Mass Index and Its Risk Factors: Evidence from the China Kadoorie Biobank and the UK Biobank

Shuo Gong, Ling Yang, Dianjianyi Sun, Canqing Yu, Iona Y. Millwood, Robin G. Walters, Jun Lv, Huaidong Du, Aolin Li, Xiaoming Yang, Yiping Chen, Zhengming Chen, Pei Pei, Junshi Chen, Liming Li
article en

Abstract

Background: The mismatch between phenotypic and genotypic BMI (BMI-PGM) contributes to the sky-rocketing obesity prevalence in both developed and developing countries. However, little is known about BMI-PGM and its risk factors. Methods: We included 86,205 participants who were genotyped and with valid BMI measurements from the China Kadoorie Biobank (CKB). BMI-PGM was calculated for each participant as the difference between the percentile for his/her adjusted BMI and the percentile for his/her adjusted polygenetic risk score of BMI (BMI-PRS), ranging from -100 to 100. A higher BMI-PGM means one’s measured BMI was much greater than his/her genetically-determined BMI. We then categorized participants into three groups according to BMI-PGM quartiles: discordantly low (bottom quartile), concordant (2nd and 3rd quartiles), and discordantly high (top quartile). Potential risk factors of BMI-PGM, including socio-demographic characteristics and lifestyle factors, were tested by multivariate linear regression. External replication analyses were performed among 102,514 UK Biobank (UKB) participants. Results: In both CKB and UKB, BMI-PGM exhibited a symmetric and normal distribution. Across all four BMI categories (underweight, normal weight, overweight, and obese), the concordant BMI-PGM group accounted for approximately 50% of participants. Of interest, over 50% of obese and underweight adults were categorized into the discordantly high BMI-PGM group and the discordantly low BMI-PGM group, respectively. In both cohorts, participants with higher BMI-PGM were more likely to be younger, current smokers, physically inactive, and meat lovers. However, the associations of BMI-PGM with sex, urban-rural status, education level, household income, alcohol consumption, fruit intake, and sleep duration varied between the two cohorts. Conclusions: A new metric was developed to quantify the mismatch between phenotypic and genetic BMI in both East Asian and European adults. The obesogenic environment affects obesity in different populations with both commonalities and distinct differences.

CirculationVol. 151(Suppl_1)
Peking University (CN), University of Oxford (GB), China National Center for Food Safety Risk Assessment (CN)
Openalex Percentile: Top 91%
Nutrition, Genetics, and Disease
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