日本大阪大学Yukinori Okada研究团队取得一项新突破。他们在跨生物库分析中发现体重指数（BMI）分类可优化2型糖尿病（T2D）的多基因预测。相关论文发表在2024年6月11日出版的《自然-遗传学》杂志上。

研究人员根据体重指数进行了分类，以优化与体重指数相关疾病的预测。研究利用日本生物库（BBJ）和英国生物库中超过195,000人（nT2D=55,284人）的数据获得了BMI分类数据集。低体重指数组的T2D遗传率高于高体重指数组。针对低体重指数组的T2D多基因预测的假R²值比体重指数未分层组的预测高出22%以上。从低体重指数组获得的多基因风险评分（PRS）优于从高体重指数获得的多基因风险评分，而从体重指数未分层组获得的多基因风险评分表现最佳。

特异性通路风险评分凸显了致病通路的生物学作用。低体重指数T2D病人的神经病变和视网膜病变发生率较高。体重指数分层和跨人群效应整合相结合的方法，使T2D预测结果比未分层的匹配人群提高了37%以上。该研究结果表明，基于现有性状的目标分层可以改善异质性疾病的多基因预测。

附：英文原文

Title: Body mass index stratification optimizes polygenic prediction of type 2 diabetes in cross-biobank analyses

Author: Ojima, Takafumi, Namba, Shinichi, Suzuki, Ken, Yamamoto, Kenichi, Sonehara, Kyuto, Narita, Akira, Kamatani, Yoichiro, Tamiya, Gen, Yamamoto, Masayuki, Yamauchi, Toshimasa, Kadowaki, Takashi, Okada, Yukinori

Issue&Volume: 2024-06-11

Abstract: Type 2 diabetes (T2D) shows heterogeneous body mass index (BMI) sensitivity. Here, we performed stratification based on BMI to optimize predictions for BMI-related diseases. We obtained BMI-stratified datasets using data from more than 195,000 individuals (nT2D=55,284) from BioBank Japan (BBJ) and UK Biobank. T2D heritability in the low-BMI group was greater than that in the high-BMI group. Polygenic predictions of T2D toward low-BMI targets had pseudo-R2 values that were more than 22% higher than BMI-unstratified targets. Polygenic risk scores (PRSs) from low-BMI discovery outperformed PRSs from high BMI, while PRSs from BMI-unstratified discovery performed best. Pathway-specific PRSs demonstrated the biological contributions of pathogenic pathways. Low-BMI T2D cases showed higher rates of neuropathy and retinopathy. Combining BMI stratification and a method integrating cross-population effects, T2D predictions showed greater than 37% improvements over unstratified-matched-population prediction. We replicated findings in the Tohoku Medical Megabank (n=26,000) and the second BBJ cohort (n=33,096). Our findings suggest that target stratification based on existing traits can improve the polygenic prediction of heterogeneous diseases.

DOI: 10.1038/s41588-024-01782-y

Source: https://www.nature.com/articles/s41588-024-01782-y