Kui Deng, Deepak K Gupta, Xiao-Ou Shu, Loren Lipworth, Wei Zheng, Hui Cai, Qiuyin Cai, Danxia Yu
{"title":"不同种族和地域人群的循环代谢物谱与冠心病风险。","authors":"Kui Deng, Deepak K Gupta, Xiao-Ou Shu, Loren Lipworth, Wei Zheng, Hui Cai, Qiuyin Cai, Danxia Yu","doi":"10.1161/CIRCGEN.123.004437","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Metabolomics may reveal novel biomarkers for coronary heart disease (CHD). We aimed to identify circulating metabolites and construct a metabolite risk score (MRS) associated with incident CHD among racially and geographically diverse populations.</p><p><strong>Methods: </strong>Untargeted metabolomics was conducted using baseline plasma samples from 900 incident CHD cases and 900 age-/sex-/race-matched controls (300 pairs of Black Americans, White Americans, and Chinese adults, respectively), which detected 927 metabolites with known identities among ≥80% of samples. After quality control, 896 case-control pairs remained and were randomly divided into discovery (70%) and validation (30%) sets within each race. In the discovery set, conditional logistic regression and least absolute shrinkage and selection operator over 100 subsamples were applied to identify metabolites robustly associated with CHD risk and construct the MRS. The MRS-CHD association was evaluated using conditional logistic regression and the C-index. Mediation analysis was performed to examine if MRS mediated associations between conventional risk factors and incident CHD. The results from the validation set were presented as the main findings.</p><p><strong>Results: </strong>Twenty-four metabolites selected in ≥90% of subsamples comprised the MRS, which was significantly associated with incident CHD (odds ratio per 1 SD, 2.21 [95% CI, 1.62-3.00] after adjusting for sociodemographics, lifestyles, family history, and metabolic health status). MRS could distinguish incident CHD cases from matched controls (C-index, 0.69 [95% CI, 0.63-0.74]) and improve CHD risk prediction when adding to conventional risk factors (C-index, 0.71 [95% CI, 0.65-0.76] versus 0.67 [95% CI, 0.61-0.73]; <i>P</i><0.001). The odds ratios and C-index were similar across subgroups defined by race, sex, socioeconomic status, lifestyles, metabolic health, family history, and follow-up duration. The MRS mediated large portions (46.0%-74.2%) of the associations for body mass index, smoking, diabetes, hypertension, and dyslipidemia with incident CHD.</p><p><strong>Conclusions: </strong>In a diverse study sample, we identified 24 circulating metabolites that, when combined into an MRS, were robustly associated with incident CHD and modestly improved CHD risk prediction beyond conventional risk factors.</p>","PeriodicalId":10326,"journal":{"name":"Circulation: Genomic and Precision Medicine","volume":null,"pages":null},"PeriodicalIF":6.0000,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11335450/pdf/","citationCount":"0","resultStr":"{\"title\":\"Circulating Metabolite Profiles and Risk of Coronary Heart Disease Among Racially and Geographically Diverse Populations.\",\"authors\":\"Kui Deng, Deepak K Gupta, Xiao-Ou Shu, Loren Lipworth, Wei Zheng, Hui Cai, Qiuyin Cai, Danxia Yu\",\"doi\":\"10.1161/CIRCGEN.123.004437\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Metabolomics may reveal novel biomarkers for coronary heart disease (CHD). We aimed to identify circulating metabolites and construct a metabolite risk score (MRS) associated with incident CHD among racially and geographically diverse populations.</p><p><strong>Methods: </strong>Untargeted metabolomics was conducted using baseline plasma samples from 900 incident CHD cases and 900 age-/sex-/race-matched controls (300 pairs of Black Americans, White Americans, and Chinese adults, respectively), which detected 927 metabolites with known identities among ≥80% of samples. After quality control, 896 case-control pairs remained and were randomly divided into discovery (70%) and validation (30%) sets within each race. In the discovery set, conditional logistic regression and least absolute shrinkage and selection operator over 100 subsamples were applied to identify metabolites robustly associated with CHD risk and construct the MRS. The MRS-CHD association was evaluated using conditional logistic regression and the C-index. Mediation analysis was performed to examine if MRS mediated associations between conventional risk factors and incident CHD. The results from the validation set were presented as the main findings.</p><p><strong>Results: </strong>Twenty-four metabolites selected in ≥90% of subsamples comprised the MRS, which was significantly associated with incident CHD (odds ratio per 1 SD, 2.21 [95% CI, 1.62-3.00] after adjusting for sociodemographics, lifestyles, family history, and metabolic health status). MRS could distinguish incident CHD cases from matched controls (C-index, 0.69 [95% CI, 0.63-0.74]) and improve CHD risk prediction when adding to conventional risk factors (C-index, 0.71 [95% CI, 0.65-0.76] versus 0.67 [95% CI, 0.61-0.73]; <i>P</i><0.001). The odds ratios and C-index were similar across subgroups defined by race, sex, socioeconomic status, lifestyles, metabolic health, family history, and follow-up duration. 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Circulating Metabolite Profiles and Risk of Coronary Heart Disease Among Racially and Geographically Diverse Populations.
Background: Metabolomics may reveal novel biomarkers for coronary heart disease (CHD). We aimed to identify circulating metabolites and construct a metabolite risk score (MRS) associated with incident CHD among racially and geographically diverse populations.
Methods: Untargeted metabolomics was conducted using baseline plasma samples from 900 incident CHD cases and 900 age-/sex-/race-matched controls (300 pairs of Black Americans, White Americans, and Chinese adults, respectively), which detected 927 metabolites with known identities among ≥80% of samples. After quality control, 896 case-control pairs remained and were randomly divided into discovery (70%) and validation (30%) sets within each race. In the discovery set, conditional logistic regression and least absolute shrinkage and selection operator over 100 subsamples were applied to identify metabolites robustly associated with CHD risk and construct the MRS. The MRS-CHD association was evaluated using conditional logistic regression and the C-index. Mediation analysis was performed to examine if MRS mediated associations between conventional risk factors and incident CHD. The results from the validation set were presented as the main findings.
Results: Twenty-four metabolites selected in ≥90% of subsamples comprised the MRS, which was significantly associated with incident CHD (odds ratio per 1 SD, 2.21 [95% CI, 1.62-3.00] after adjusting for sociodemographics, lifestyles, family history, and metabolic health status). MRS could distinguish incident CHD cases from matched controls (C-index, 0.69 [95% CI, 0.63-0.74]) and improve CHD risk prediction when adding to conventional risk factors (C-index, 0.71 [95% CI, 0.65-0.76] versus 0.67 [95% CI, 0.61-0.73]; P<0.001). The odds ratios and C-index were similar across subgroups defined by race, sex, socioeconomic status, lifestyles, metabolic health, family history, and follow-up duration. The MRS mediated large portions (46.0%-74.2%) of the associations for body mass index, smoking, diabetes, hypertension, and dyslipidemia with incident CHD.
Conclusions: In a diverse study sample, we identified 24 circulating metabolites that, when combined into an MRS, were robustly associated with incident CHD and modestly improved CHD risk prediction beyond conventional risk factors.
期刊介绍:
Circulation: Genomic and Precision Medicine is a distinguished journal dedicated to advancing the frontiers of cardiovascular genomics and precision medicine. It publishes a diverse array of original research articles that delve into the genetic and molecular underpinnings of cardiovascular diseases. The journal's scope is broad, encompassing studies from human subjects to laboratory models, and from in vitro experiments to computational simulations.
Circulation: Genomic and Precision Medicine is committed to publishing studies that have direct relevance to human cardiovascular biology and disease, with the ultimate goal of improving patient care and outcomes. The journal serves as a platform for researchers to share their groundbreaking work, fostering collaboration and innovation in the field of cardiovascular genomics and precision medicine.