BACKGROUND Modern medicine is rapidly moving towards a data-driven paradigm based on comprehensive multimodal health assessments. Integrated analysis of data from different modalities has the potential of uncovering novel biomarkers and disease signatures. METHODS We collected 1385 data features from diverse modalities, including metabolome, microbiome, genetics, and advanced imaging, from 1253 individuals and from a longitudinal validation cohort of 1083 individuals. We utilized a combination of unsupervised machine learning methods to identify multimodal biomarker signatures of health and disease risk. RESULTS Our method identified a set of cardiometabolic biomarkers that goes beyond standard clinical biomarkers. Stratification of individuals based on the signatures of these biomarkers identified distinct subsets of individuals with similar health statuses. Subset membership was a better predictor for diabetes than established clinical biomarkers such as glucose, insulin resistance, and body mass index. The novel biomarkers in the diabetes signature included 1-stearoyl-2-dihomo-linolenoyl-GPC and 1-(1-enyl-palmitoyl)-2-oleoyl-GPC. Another metabolite, cinnamoylglycine, was identified as a potential biomarker for both gut microbiome health and lean mass percentage. We identified potential early signatures for hypertension and a poor metabolic health outcome. Additionally, we found novel associations between a uremic toxin, p-cresol sulfate, and the abundance of the microbiome genera Intestinimonas and an unclassified genus in the Erysipelotrichaceae family. CONCLUSIONS Our methodology and results demonstrate the potential of multimodal data integration, from the identification of novel biomarker signatures to a data-driven stratification of individuals into disease subtypes and stages-an essential step towards personalized, preventative health risk assessment.

中文翻译：

---

一种无监督学习方法，用于从多模式数据中识别健康和疾病的新特征。


背景技术现代医学正在迅速转向基于综合多模式健康评估的数据驱动范式。对不同模式数据的综合分析有可能发现新的生物标志物和疾病特征。方法 我们从 1253 名个体和 1083 名个体的纵向验证队列中收集了来自不同模式的 1385 个数据特征，包括代谢组、微生物组、遗传学和高级成像。我们结合使用无监督机器学习方法来识别健康和疾病风险的多模式生物标志物特征。结果我们的方法确定了一组超越标准临床生物标志物的心脏代谢生物标志物。根据这些生物标志物的特征对个体进行分层，识别出具有相似健康状况的个体的不同子集。与葡萄糖、胰岛素抵抗和体重指数等已建立的临床生物标志物相比，亚组成员资格是糖尿病更好的预测指标。糖尿病特征中的新型生物标志物包括 1-硬脂酰-2-二高亚麻酰-GPC 和 1-(1-烯基-棕榈酰)-2-油酰-GPC。另一种代谢物肉桂酰甘氨酸被确定为肠道微生物组健康和瘦肉质量百分比的潜在生物标志物。我们发现了高血压和代谢健康状况不佳的潜在早期特征。此外，我们发现尿毒症毒素对甲酚硫酸盐与肠单胞菌属和丹毒科中未分类的属的丰富度之间存在新的关联。 结论我们的方法和结果证明了多模式数据集成的潜力，从识别新的生物标志物特征到数据驱动的个体疾病亚型和阶段分层——这是迈向个性化、预防性健康风险评估的重要一步。