Department of Internal Medicine (F.V., T.N.), University of Turku, Turku, Finland.
Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston (H.K., M.U.).
Circ Genom Precis Med. 2022 Aug;15(4):e003583. doi: 10.1161/CIRCGEN.121.003583. Epub 2022 May 23.
Hypertension comprises a heterogeneous range of phenotypes. We asked whether underlying genetic structure could explain a part of this heterogeneity.
Our study sample comprised N=198 148 FinnGen participants (56% women, mean age 58 years) and N=21 168 well-phenotyped FINRISK participants (53% women, mean age 50 years). First, we identified genetic hypertension components with an unsupervised Bayesian non-negative matrix factorization algorithm using public genome-wide association data for 144 genetic hypertension variants and 16 clinical traits. For these components, we computed their (1) cross-sectional associations with clinical traits in FINRISK using linear regression and (2) longitudinal associations with incident adverse outcomes in FinnGen using Cox regression.
We observed 4 genetic hypertension components corresponding to recognizable clinical phenotypes: obesity (high body mass index), dyslipidemia (low high-density lipoprotein cholesterol and high triglycerides), hypolipidemia (low low-density lipoprotein cholesterol and low total cholesterol), and short stature. In FINRISK, all hypertension components had robust associations with their respective clinical characteristics. In FinnGen, the Obesity component was associated with increased diabetes risk (hazard ratio per 1 SD increase 1.08 [Bonferroni corrected CI, 1.05-1.10]) and the Hypolipidemia component with increased autoimmune disease risk (hazard ratio per 1 SD increase 1.05 [Bonferroni corrected CI, 1.03-1.07]). In addition, all hypertension components were related to both hypertension and cardiovascular disease.
Our unsupervised analysis demonstrates that the genetic basis of hypertension can be understood as a mixture of 4 broad, clinically interpretable components capturing disease heterogeneity. These components could be used to stratify individuals into specific genetic subtypes and, therefore, to benefit personalized health care and pharmaceutical research.
高血压包含多种不同的表型。我们想知道潜在的遗传结构是否可以解释部分这种异质性。
我们的研究样本包括 N=198148 名 FinnGen 参与者（56%为女性，平均年龄 58 岁）和 N=21168 名特征良好的 FINRISK 参与者（53%为女性，平均年龄 50 岁）。首先，我们使用公共全基因组关联数据，使用无监督贝叶斯非负矩阵分解算法，确定了 144 个遗传高血压变异和 16 个临床特征的遗传高血压成分。对于这些成分，我们使用线性回归计算了它们在 FINRISK 中的（1）与临床特征的横断面关联，使用 Cox 回归计算了它们在 FinnGen 中的（2）与不良事件的纵向关联。
我们观察到 4 个与可识别的临床表型相对应的遗传高血压成分：肥胖（高体重指数）、血脂异常（低高密度脂蛋白胆固醇和高甘油三酯）、低血脂（低低密度脂蛋白胆固醇和低总胆固醇）和身材矮小。在 FINRISK 中，所有高血压成分与各自的临床特征均具有很强的关联。在 FinnGen 中，肥胖成分与糖尿病风险增加相关（每增加 1 个标准差的风险比为 1.08[Bonferroni 校正的置信区间，1.05-1.10]），低血脂成分与自身免疫性疾病风险增加相关（每增加 1 个标准差的风险比为 1.05[Bonferroni 校正的置信区间，1.03-1.07]）。此外，所有高血压成分都与高血压和心血管疾病有关。
我们的无监督分析表明，高血压的遗传基础可以理解为 4 个广泛的、具有临床可解释性的成分的混合物，这些成分可以捕捉疾病的异质性。这些成分可用于将个体分层为特定的遗传亚型，从而有助于个性化的医疗保健和药物研究。
