精神分裂症和双相情感障碍经常面临诊断的严重延误，导致早期漏诊或误诊。这两种疾病也与特征和状态免疫异常有关。最近基于机器学习的研究在预测模型中使用诊断生物标记物显示出令人鼓舞的结果，但很少有人关注基于免疫的标记物。我们的主要目标是开发监督机器学习模型，仅使用一组外周犬尿氨酸代谢物和细胞因子来预测精神分裂症和双相情感障碍的诊断和疾病状态。横断面 I-GIVE 队列包括住院的急性双相情感障碍患者 (n=205) ，稳定双相情感障碍门诊患者（n = 116），住院急性精神分裂症患者（n = 111），稳定精神分裂症门诊患者（n = 75）和健康对照（n = 185）。使用液相色谱对血清犬尿氨酸代谢物，即色氨酸（TRP）、犬尿氨酸（KYN）、犬尿酸（KA）、喹哪酸（QUINA）、黄嘌呤酸（XA）、喹啉酸（QUINO）和吡啶甲酸（PICO）进行定量。串联质谱 (LC-MS/MS)，同时使用 V-plex 人类细胞因子测定法测量细胞因子（白细胞介素 6 (IL-6)、IL-8、IL-17、IL-12/IL23-P40、肿瘤坏死因子-α (TNF-ɑ)、干扰素-γ (IFN-γ))。使用 JMP Pro 17.0.0 执行监督机器学习模型。我们将使用嵌套交叉验证的主要分析与作为敏感性分析的分割集进行比较。事后，我们仅使用显着特征重新运行模型以获得关键标记。模型产生了良好的曲线下面积 (AUC)（0.804，正预测值 (PPV)=86.95；负预测值 (NPV) )=54.61) 用于区分所有患者与对照组。这意味着阳性检测在识别患者方面非常准确，但阴性检测则不确定。精神分裂症患者和双相情感障碍患者都可以以良好的准确性与对照组分开（SCZ AUC 0.824；BD AUC 0.802）。总体而言，IL-6、TNF-ɑ 和 PICO 水平升高以及 IFN-γ 和 QUINO 水平降低可预测个体被归类为患者。急性与稳定患者的分类达到了 0.713 的公平 AUC。区分精神分裂症和双相情感障碍的 AUC 为 0.627。这项研究强调了使用基于免疫的措施建立精神分裂症和双相情感障碍预测分类模型的潜力，其中包括 IL-6、TNF-ɑ、IFN-γ、QUINO和 PICO 为主要候选人。虽然机器学习模型成功地将精神分裂症和双相情感障碍与对照组区分开来，但区分精神分裂症患者和双相情感障碍患者的挑战可能反映了这两种疾病的共同免疫途径以及更大的状态特异性效应的混淆。需要更大规模的多中心研究和多领域模型来提高可靠性并转化为临床。版权所有 © 2024 作者。由爱思唯尔公司出版。保留所有权利。

Schizophrenia and bipolar disorder frequently face significant delay in diagnosis, leading to being missed or misdiagnosed in early stages. Both disorders have also been associated with trait and state immune abnormalities. Recent machine learning-based studies have shown encouraging results using diagnostic biomarkers in predictive models, but few have focused on immune-based markers. Our main objective was to develop supervised machine learning models to predict diagnosis and illness state in schizophrenia and bipolar disorder using only a panel of peripheral kynurenine metabolites and cytokines.The cross-sectional I-GIVE cohort included hospitalized acute bipolar patients (n = 205), stable bipolar outpatients (n = 116), hospitalized acute schizophrenia patients (n = 111), stable schizophrenia outpatients (n = 75) and healthy controls (n = 185). Serum kynurenine metabolites, namely tryptophan (TRP), kynurenine (KYN), kynurenic acid (KA), quinaldic acid (QUINA), xanthurenic acid (XA), quinolinic acid (QUINO) and picolinic acid (PICO) were quantified using liquid chromatography-tandem mass spectrometry (LC-MS/MS), while V-plex Human Cytokine Assays were used to measure cytokines (interleukin-6 (IL-6), IL-8, IL-17, IL-12/IL23-P40, tumor necrosis factor-alpha (TNF-ɑ), interferon-gamma (IFN-γ)). Supervised machine learning models were performed using JMP Pro 17.0.0. We compared a primary analysis using nested cross-validation to a split set as sensitivity analysis. Post-hoc, we re-ran the models using only the significant features to obtain the key markers.The models yielded a good Area Under the Curve (AUC) (0.804, Positive Prediction Value (PPV) = 86.95; Negative Prediction Value (NPV) = 54.61) for distinguishing all patients from controls. This implies that a positive test is highly accurate in identifying the patients, but a negative test is inconclusive. Both schizophrenia patients and bipolar patients could each be separated from controls with a good accuracy (SCZ AUC 0.824; BD AUC 0.802). Overall, increased levels of IL-6, TNF-ɑ and PICO and decreased levels of IFN-γ and QUINO were predictive for an individual being classified as a patient. Classification of acute versus stable patients reached a fair AUC of 0.713. The differentiation between schizophrenia and bipolar disorder yielded a poor AUC of 0.627.This study highlights the potential of using immune-based measures to build predictive classification models in schizophrenia and bipolar disorder, with IL-6, TNF-ɑ, IFN-γ, QUINO and PICO as key candidates. While machine learning models successfully distinguished schizophrenia and bipolar disorder from controls, the challenges in differentiating schizophrenic from bipolar patients likely reflect shared immunological pathways by the both disorders and confounding by a larger state-specific effect. Larger multi-centric studies and multi-domain models are needed to enhance reliability and translation into clinic.Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.