Atherosclerosis · 2026
Plasma metabolomic profiles associated with cardiovascular disease in type 2 diabetes from the Trans-Omics for Precision Medicine (TOPMed) program.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Zhang Yixin, Nguyen Ngoc Quynh H, Hanson Paul A, Sevilla-Gonzalez Magdalena, Haessler Jeffrey, Sarnowski Chloé, Yao Jie, Yu Bing, Jamshidi Afshin, Boerwinkle Eric, Brown Michael R, Chen Zsu-Zsu, Ida Chen Yii-Der, Clish Clary, DiCorpo Daniel, Durda Peter, Gerszten Robert, Goodarzi Mark O, Guo Xiuqing, Heard-Costa Nancy L, Johnson W Craig, Kooperberg Charles, Pankow James S, Post Wendy S, Reiner Alexander P, Rich Stephen S, Rotter Jerome I, de Vries Paul S, Wood Alexis C, Taylor Kent D, Manning Alisa K, Dupuis Josée, Meigs James B, Liu Ching-Ti
Аннотация
BACKGROUND AND AIMS: People with Type 2 diabetes (T2D) are twice as likely to develop cardiovascular disease (CVD), though not all excess risk has been fully elucidated. Plasma metabolomics profiles shared between these conditions may uncover molecular mechanisms linking T2D to CVD.
METHODS: We conducted a cross-sectional case-control analysis, comparing T2D individuals who had prevalent CVD to those without CVD at the time of metabolite measurement. Using untargeted liquid chromatography-mass spectrometry (LC-MS), we collected 522 metabolite abundances measured in 1374 participants with T2D (224 CVD cases) from the Trans-Omics for Precision Medicine (TOPMed) program. We used a mixed effects linear model to assess the association of CVD events with each metabolite abundance, adjusting for key covariates. Metabolites meeting a suggestive significance threshold were examined using metabolite set enrichment analysis and evaluated for replication in an independent cohort Atherosclerosis Risk in Communities (ARIC) (n = 1891; 214 CVD cases). We performed meta-analysis to combine both the discovery and replication associations, and assessed overall significance using an experiment-wide Bonferroni-corrected threshold.
RESULTS: Metabolites meeting a suggestive threshold were enriched in metabolite sets linked to obesity and kidney disease. Meta-analysis identified eight metabolites reaching experiment-wide significance, confirming previously established associations of asymmetric dimethylarginine, phosphatidylcholines, and gluconic acid, while additionally identifying specific phosphatidylethanolamine species, N-acetyl-L-methionine, and allantoin associated with prevalent CVD among individuals with T2D.
CONCLUSIONS: Our results established and replicated metabolite associations with prevalent CVD in people with T2D. These metabolites may help characterize metabolic alterations underlying cardiovascular complications that arise in T2D.
METHODS: We conducted a cross-sectional case-control analysis, comparing T2D individuals who had prevalent CVD to those without CVD at the time of metabolite measurement. Using untargeted liquid chromatography-mass spectrometry (LC-MS), we collected 522 metabolite abundances measured in 1374 participants with T2D (224 CVD cases) from the Trans-Omics for Precision Medicine (TOPMed) program. We used a mixed effects linear model to assess the association of CVD events with each metabolite abundance, adjusting for key covariates. Metabolites meeting a suggestive significance threshold were examined using metabolite set enrichment analysis and evaluated for replication in an independent cohort Atherosclerosis Risk in Communities (ARIC) (n = 1891; 214 CVD cases). We performed meta-analysis to combine both the discovery and replication associations, and assessed overall significance using an experiment-wide Bonferroni-corrected threshold.
RESULTS: Metabolites meeting a suggestive threshold were enriched in metabolite sets linked to obesity and kidney disease. Meta-analysis identified eight metabolites reaching experiment-wide significance, confirming previously established associations of asymmetric dimethylarginine, phosphatidylcholines, and gluconic acid, while additionally identifying specific phosphatidylethanolamine species, N-acetyl-L-methionine, and allantoin associated with prevalent CVD among individuals with T2D.
CONCLUSIONS: Our results established and replicated metabolite associations with prevalent CVD in people with T2D. These metabolites may help characterize metabolic alterations underlying cardiovascular complications that arise in T2D.