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Metabolomic profiles predict individual multidisease outcomes
Nature Medicine ( IF 82.9 ) Pub Date : 2022-09-22 , DOI: 10.1038/s41591-022-01980-3
Thore Buergel 1 , Jakob Steinfeldt 2 , Greg Ruyoga 1 , Maik Pietzner 3, 4 , Daniele Bizzarri 5, 6 , Dina Vojinovic 7, 8 , Julius Upmeier Zu Belzen 1 , Lukas Loock 1 , Paul Kittner 1 , Lara Christmann 1 , Noah Hollmann 1 , Henrik Strangalies 1 , Jana M Braunger 1 , Benjamin Wild 1 , Scott T Chiesa 9 , Joachim Spranger 10, 11 , Fabian Klostermann 12, 13 , Erik B van den Akker 5, 6, 14 , Stella Trompet 15, 16 , Simon P Mooijaart 15 , Naveed Sattar 17 , J Wouter Jukema 16, 18 , Birgit Lavrijssen 7, 19 , Maryam Kavousi 7 , Mohsen Ghanbari 7 , Mohammad A Ikram 7 , Eline Slagboom 5, 20 , Mika Kivimaki 21, 22 , Claudia Langenberg 3, 4 , John Deanfield 9 , Roland Eils 1, 23 , Ulf Landmesser 2
Affiliation  

Risk stratification is critical for the early identification of high-risk individuals and disease prevention. Here we explored the potential of nuclear magnetic resonance (NMR) spectroscopy-derived metabolomic profiles to inform on multidisease risk beyond conventional clinical predictors for the onset of 24 common conditions, including metabolic, vascular, respiratory, musculoskeletal and neurological diseases and cancers. Specifically, we trained a neural network to learn disease-specific metabolomic states from 168 circulating metabolic markers measured in 117,981 participants with ~1.4 million person-years of follow-up from the UK Biobank and validated the model in four independent cohorts. We found metabolomic states to be associated with incident event rates in all the investigated conditions, except breast cancer. For 10-year outcome prediction for 15 endpoints, with and without established metabolic contribution, a combination of age and sex and the metabolomic state equaled or outperformed established predictors. Moreover, metabolomic state added predictive information over comprehensive clinical variables for eight common diseases, including type 2 diabetes, dementia and heart failure. Decision curve analyses showed that predictive improvements translated into clinical utility for a wide range of potential decision thresholds. Taken together, our study demonstrates both the potential and limitations of NMR-derived metabolomic profiles as a multidisease assay to inform on the risk of many common diseases simultaneously.



中文翻译:

代谢组学概况预测个体多病结局

风险分层对于早期识别高危人群和疾病预防至关重要。在这里,我们探索了核磁共振 (NMR) 光谱衍生的代谢组学概况的潜力,以告知超过常规临床预测因素的多病风险,以预测 24 种常见病症的发生,包括代谢、血管、呼吸、肌肉骨骼和神经系统疾病和癌症。具体而言,我们训练了一个神经网络,以从 117,981 名参与者中测量的 168 种循环代谢标记物中学习疾病特异性代谢组学状态,英国生物银行进行了约 140 万人年的随访,并在四个独立队列中验证了该模型。我们发现代谢组学状态与所有研究条件下的事件发生率相关,但乳腺癌除外。对于 15 个终点的 10 年结果预测,有或没有确定的代谢贡献,年龄和性别以及代谢组学状态的组合等于或优于已确定的预测因子。此外,代谢组学状态增加了八种常见疾病综合临床变量的预测信息,包括 2 型糖尿病、痴呆和心力衰竭。决策曲线分析表明,预测改进转化为广泛的潜在决策阈值的临床效用。综上所述,我们的研究证明了核磁共振衍生的代谢组学概况作为一种多疾病检测同时告知许多常见疾病风险的潜力和局限性。年龄和性别以及代谢组学状态的组合等于或优于已建立的预测因子。此外,代谢组学状态增加了八种常见疾病综合临床变量的预测信息,包括 2 型糖尿病、痴呆和心力衰竭。决策曲线分析表明,预测改进转化为广泛的潜在决策阈值的临床效用。综上所述,我们的研究证明了核磁共振衍生的代谢组学概况作为一种多疾病检测同时告知许多常见疾病风险的潜力和局限性。年龄和性别以及代谢组学状态的组合等于或优于已建立的预测因子。此外,代谢组学状态增加了八种常见疾病综合临床变量的预测信息,包括 2 型糖尿病、痴呆和心力衰竭。决策曲线分析表明,预测改进转化为广泛的潜在决策阈值的临床效用。综上所述,我们的研究证明了核磁共振衍生的代谢组学概况作为一种多疾病检测同时告知许多常见疾病风险的潜力和局限性。决策曲线分析表明,预测改进转化为广泛的潜在决策阈值的临床效用。综上所述,我们的研究证明了核磁共振衍生的代谢组学概况作为一种多疾病检测同时告知许多常见疾病风险的潜力和局限性。决策曲线分析表明,预测改进转化为广泛的潜在决策阈值的临床效用。总而言之,我们的研究证明了核磁共振衍生的代谢组学概况作为一种多疾病检测同时告知许多常见疾病风险的潜力和局限性。

更新日期:2022-09-23
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