日本語フィールド
著者：*Nakamura S, Fang X, Saito Y, Narimatsu H, Ota A, Ikezaki H, Shimanoe C, Tanaka K, Kubo Y, Tsukamoto M, Tamura T, Hishida A, Oze I, Koyanagi YN, Nakamura Y, Kusakabe M, Takezaki T, Nishimoto D, Suzuki S, Otani T, Kuriyama N, Matsui D, Kuriki K, Kadota A, Nakamura Y, Arisawa K, Katsuura-Kamano S, Nakatochi M, Momozawa Y, Kubo M, Takeuchi K, Wakai K題名：Effects of gene–lifestyle interactions on obesity based on a multi-locus risk score: A cross-sectional analysis発表情報：PLoS ONE 巻: 18 号: 2 ページ: e0279169キーワード：概要：Background The relationship between lifestyle and obesity is a major focus of research. Personalized nutrition, which utilizes evidence from nutrigenomics, such as gene–environment interactions, has been attracting attention in recent years. However, evidence for gene–environment interactions that can inform treatment strategies is lacking, despite some reported interactions involving dietary intake or physical activity. Utilizing gene–lifestyle interactions in practice could aid in optimizing interventions according to genetic risk. Methods This study aimed to elucidate the effects of gene–lifestyle interactions on body mass index (BMI). Cross-sectional data from the Japan Multi-Institutional Collaborative Cohort Study were used. Interactions between a multi-locus genetic risk score (GRS), calculated from 76 ancestry-specific single nucleotide polymorphisms, and nutritional intake or physical activity were assessed using a linear mixed-effect model. Results The mean (standard deviation) BMI and GRS for all participants (n = 12,918) were 22.9 (3.0) kg/m2 and -0.07 (0.16), respectively. The correlation between GRS and BMI was r (12,916) = 0.13 (95% confidence interval [CI] 0.11–0.15, P < 0.001). An interaction between GRS and saturated fatty acid intake was observed (β = -0.11, 95% CI -0.21 to -0.02). An interaction between GRS and n-3 polyunsaturated fatty acids was also observed in the females with normal-weight subgroup (β = -0.12, 95% CI -0.22 to -0.03). Conclusion Our results provide evidence of an interaction effect between GRS and nutritional intake and physical activity. This gene–lifestyle interaction provides a basis for developing prevention or treatment interventions for obesity according to individual genetic predisposition.抄録：英語フィールド
Author：*Nakamura S, Fang X, Saito Y, Narimatsu H, Ota A, Ikezaki H, Shimanoe C, Tanaka K, Kubo Y, Tsukamoto M, Tamura T, Hishida A, Oze I, Koyanagi YN, Nakamura Y, Kusakabe M, Takezaki T, Nishimoto D, Suzuki S, Otani T, Kuriyama N, Matsui D, Kuriki K, Kadota A, Nakamura Y, Arisawa K, Katsuura-Kamano S, Nakatochi M, Momozawa Y, Kubo M, Takeuchi K, Wakai KTitle：Effects of gene–lifestyle interactions on obesity based on a multi-locus risk score: A cross-sectional analysisAnnouncement information:PLoS ONE Vol: 18 Issue: 2 Page: e0279169An abstract：Background The relationship between lifestyle and obesity is a major focus of research. Personalized nutrition, which utilizes evidence from nutrigenomics, such as gene–environment interactions, has been attracting attention in recent years. However, evidence for gene–environment interactions that can inform treatment strategies is lacking, despite some reported interactions involving dietary intake or physical activity. Utilizing gene–lifestyle interactions in practice could aid in optimizing interventions according to genetic risk. Methods This study aimed to elucidate the effects of gene–lifestyle interactions on body mass index (BMI). Cross-sectional data from the Japan Multi-Institutional Collaborative Cohort Study were used. Interactions between a multi-locus genetic risk score (GRS), calculated from 76 ancestry-specific single nucleotide polymorphisms, and nutritional intake or physical activity were assessed using a linear mixed-effect model. Results The mean (standard deviation) BMI and GRS for all participants (n = 12,918) were 22.9 (3.0) kg/m2 and -0.07 (0.16), respectively. The correlation between GRS and BMI was r (12,916) = 0.13 (95% confidence interval [CI] 0.11–0.15, P < 0.001). An interaction between GRS and saturated fatty acid intake was observed (β = -0.11, 95% CI -0.21 to -0.02). An interaction between GRS and n-3 polyunsaturated fatty acids was also observed in the females with normal-weight subgroup (β = -0.12, 95% CI -0.22 to -0.03). Conclusion Our results provide evidence of an interaction effect between GRS and nutritional intake and physical activity. This gene–lifestyle interaction provides a basis for developing prevention or treatment interventions for obesity according to individual genetic predisposition.