TY - JOUR
T1 - Metabolic characterization of hypertrophic cardiomyopathy in human heart
AU - Wang, Wenmin
AU - Wang, Jizheng
AU - Yao, Ke
AU - Wang, Shuiyun
AU - Nie, Meng
AU - Zhao, Yizi
AU - Wang, Bohong
AU - Pang, Huanhuan
AU - Xu, Jingjing
AU - Wu, Guixin
AU - Lu, Minjie
AU - Tang, Nan
AU - Qi, Chunmei
AU - Pei, Hengzhi
AU - Luo, Xufang
AU - Li, Dongsheng
AU - Yang, Tianshu
AU - Sun, Qing
AU - Wei, Xiang
AU - Li, Yan
AU - Jiang, Dingsheng
AU - Li, Peng
AU - Song, Lei
AU - Hu, Zeping
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive licence to Springer Nature Limited.
PY - 2022/5
Y1 - 2022/5
N2 - Hypertrophic cardiomyopathy (HCM) is a common inherited cardiovascular disease with heterogeneous clinical presentations, governed by multiple molecular mechanisms. Metabolic perturbations underlie most cardiovascular diseases; however, the metabolic alterations and their function in HCM are unknown. Here, we describe the metabolome and lipidome of heart and plasma samples from individuals with and without HCM. Correlation analyses showed strong association between metabolic alterations and cardiac function and prognosis of patients with HCM. Using machine learning we identified metabolite panels as potential HCM diagnostic markers or predictors of survival. Clustering based on metabolome and lipidome of heart enabled stratification of patients with HCM into three subgroups with distinct cardiac function and survival. Integration of metabolomics and proteomics data identified metabolic pathways significantly altered in patients with HCM, with the pentose phosphate pathway and oxidative stress being particularly upregulated. Thus, targeting the pentose phosphate pathway and oxidative stress may serve as potential therapeutic strategies for HCM.
AB - Hypertrophic cardiomyopathy (HCM) is a common inherited cardiovascular disease with heterogeneous clinical presentations, governed by multiple molecular mechanisms. Metabolic perturbations underlie most cardiovascular diseases; however, the metabolic alterations and their function in HCM are unknown. Here, we describe the metabolome and lipidome of heart and plasma samples from individuals with and without HCM. Correlation analyses showed strong association between metabolic alterations and cardiac function and prognosis of patients with HCM. Using machine learning we identified metabolite panels as potential HCM diagnostic markers or predictors of survival. Clustering based on metabolome and lipidome of heart enabled stratification of patients with HCM into three subgroups with distinct cardiac function and survival. Integration of metabolomics and proteomics data identified metabolic pathways significantly altered in patients with HCM, with the pentose phosphate pathway and oxidative stress being particularly upregulated. Thus, targeting the pentose phosphate pathway and oxidative stress may serve as potential therapeutic strategies for HCM.
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U2 - 10.1038/s44161-022-00057-1
DO - 10.1038/s44161-022-00057-1
M3 - Article
AN - SCOPUS:85149863192
SN - 2731-0590
VL - 1
SP - 445
EP - 461
JO - Nature Cardiovascular Research
JF - Nature Cardiovascular Research
IS - 5
ER -