Identification of novel hypertension biomarkers using explainable AI and metabolomics.

IF 3.5 3区 医学 Q2 ENDOCRINOLOGY & METABOLISM Metabolomics Pub Date : 2024-11-03 DOI:10.1007/s11306-024-02182-3
Karthik Sekaran, Hatem Zayed
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Abstract

Background: The global incidence of hypertension, a condition of elevated blood pressure, is rising alarmingly. According to the World Health Organization's Qatar Hypertension Profile for 2023, around 33% of adults are affected by hypertension. This is a significant public health concern that can lead to serious health complications if left untreated. Metabolic dysfunction is a primary cause of hypertension. By studying key biomarkers, we can discover new treatments to improve the lives of those with high blood pressure.

Aims: This study aims to use explainable artificial intelligence (XAI) to interpret novel metabolite biosignatures linked to hypertension in Qatari Population.

Methods: The study utilized liquid chromatography-mass spectrometry (LC/MS) method to profile metabolites from biosamples of Qatari nationals diagnosed with stage 1 hypertension (n = 224) and controls (n = 554). Metabolon platform was used for the annotation of raw metabolite data generated during the process. A comprehensive series of analytical procedures, including data trimming, imputation, undersampling, feature selection, and biomarker discovery through explainable AI (XAI) models, were meticulously executed to ensure the accuracy and reliability of the results.

Results: Elevated Vanillylmandelic acid (VMA) levels are markedly associated with stage 1 hypertension compared to controls. Glycerophosphorylcholine (GPC), N-Stearoylsphingosine (d18:1/18:0)*, and glycine are critical metabolites for accurate hypertension prediction. The light gradient boosting model yielded superior results, underscoring the potential of our research in enhancing hypertension diagnosis and treatment. The model's classification metrics: accuracy (78.13%), precision (78.13%), recall (78.13%), F1-score (78.13%), and AUROC (83.88%) affirm its efficacy. SHapley Additive exPlanations (SHAP) further elucidate the metabolite markers, providing a deeper understanding of the disease's pathology.

Conclusion: This study identified novel metabolite biomarkers for precise hypertension diagnosis using XAI, enhancing early detection and intervention in the Qatari population.

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利用可解释人工智能和代谢组学鉴定新型高血压生物标记物。
背景:高血压是一种血压升高的病症,其全球发病率正在以惊人的速度上升。根据世界卫生组织的《2023 年卡塔尔高血压概况》,约有 33% 的成年人受到高血压的影响。这是一个重大的公共卫生问题,如果不及时治疗,会导致严重的健康并发症。代谢功能障碍是导致高血压的主要原因。通过研究关键的生物标志物,我们可以发现新的治疗方法,改善高血压患者的生活。研究目的:本研究旨在使用可解释人工智能(XAI)解释卡塔尔人口中与高血压有关的新型代谢物生物特征:该研究采用液相色谱-质谱法(LC/MS)对确诊为高血压 1 期的卡塔尔人(n = 224)和对照组(n = 554)的生物样本中的代谢物进行分析。Metabolon 平台用于注释过程中产生的原始代谢物数据。为确保结果的准确性和可靠性,研究人员精心执行了一系列综合分析程序,包括数据修剪、归因、低采样、特征选择以及通过可解释人工智能(XAI)模型发现生物标记物:结果:与对照组相比,香草酸(VMA)水平升高与一期高血压明显相关。甘油磷酸胆碱(GPC)、N-硬脂酰鞘氨醇(d18:1/18:0)*和甘氨酸是准确预测高血压的关键代谢物。光梯度提升模型取得了优异的结果,凸显了我们的研究在加强高血压诊断和治疗方面的潜力。该模型的分类指标:准确率(78.13%)、精确率(78.13%)、召回率(78.13%)、F1-分数(78.13%)和 AUROC(83.88%)证实了其有效性。SHapley Additive exPlanations(SHAP)进一步阐明了代谢物标志物,使人们对疾病的病理有了更深入的了解:本研究发现了新的代谢物生物标志物,可用于使用 XAI 对高血压进行精确诊断,从而加强对卡塔尔人群的早期检测和干预。
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来源期刊
Metabolomics
Metabolomics 医学-内分泌学与代谢
CiteScore
6.60
自引率
2.80%
发文量
84
审稿时长
2 months
期刊介绍: Metabolomics publishes current research regarding the development of technology platforms for metabolomics. This includes, but is not limited to: metabolomic applications within man, including pre-clinical and clinical pharmacometabolomics for precision medicine metabolic profiling and fingerprinting metabolite target analysis metabolomic applications within animals, plants and microbes transcriptomics and proteomics in systems biology Metabolomics is an indispensable platform for researchers using new post-genomics approaches, to discover networks and interactions between metabolites, pharmaceuticals, SNPs, proteins and more. Its articles go beyond the genome and metabolome, by including original clinical study material together with big data from new emerging technologies.
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