Nayak Saurav, Singh Arvind, Mangaraj Manaswini, Saharia Gautom Kumar
Department of Biochemistry, All India Institute of Medical Sciences (AIIMS), Bhubaneswar, Bhubaneswar, India.
Department of Community Medicine and Family Medicine, All India Institute of Medical Sciences (AIIMS), Bhubaneswar, Bhubaneswar, India.
Front Nutr. 2024 Oct 16;11:1443076. doi: 10.3389/fnut.2024.1443076. eCollection 2024.
Micronutrients have significant functional implications for the human immune response, and the quality of food is a major factor affecting the severity and mortality caused by HIV in individuals undergoing antiretroviral therapy. A decrease in CD4 lymphocyte count and an increase in CD8 lymphocyte count are the hallmarks of HIV infection, which causes the CD4/CD8 ratio to invert from a normal value of >1.6 to <1.0. In this study, we tried to analyze whether the nutritional status of HIV-positive patients has an impact on the CD4/CD8 ratio inversion by utilizing a machine learning (ML) algorithm.
In this study, 55 confirmed HIV-positive patients who had not started their anti-retroviral therapy were included after obtaining their informed, written consent. Moreover, 55 age-and sex-matched relatives and caregivers of the patients who tested negative in the screening were enrolled as controls. All individual patient data points were analyzed for model development with an 80-20 train-test split. Four trace elements, zinc (Zn), phosphate (P), magnesium (Mg), and calcium (Ca), were utilized by implementing a random forest classifier. The target of the study was the inverted CD4/CD8 ratio.
The data of 110 participants were included in the analysis. The algorithm thus generated had a sensitivity of 80% and a specificity of 83%, with a likelihood ratio (LR+) of 4.8 and LR-of 0.24. The utilization of the ML algorithm adds to the limited evidence that currently exists regarding the role of micronutrients, especially trace elements, in the causation of immune risk. Our inherent strength lies in the fact that this study is one of the first studies to utilize an ML-based decision tree algorithm to classify immune risk in HIV patients.
Our study uniquely corroborated the nutritional data to the immune risk in treatment-naïve HIV patients through the utilization of a decision tree ML algorithm. This could subsequently be an important classification and prognostic tool in the hands of clinicians.
微量营养素对人体免疫反应具有重要的功能影响，食物质量是影响接受抗逆转录病毒治疗的个体中由艾滋病毒引起的疾病严重程度和死亡率的主要因素。CD4淋巴细胞计数减少和CD8淋巴细胞计数增加是艾滋病毒感染的标志，这会导致CD4/CD8比值从正常的>1.6倒置为<1.0。在本研究中，我们试图通过使用机器学习（ML）算法分析艾滋病毒阳性患者的营养状况是否会对CD4/CD8比值倒置产生影响。
在本研究中，55名确诊的未开始抗逆转录病毒治疗的艾滋病毒阳性患者在获得其知情书面同意后被纳入研究。此外，55名在筛查中检测为阴性的患者的年龄和性别匹配的亲属及护理人员被招募为对照。所有个体患者数据点都以80-20的训练-测试分割用于模型开发。通过实施随机森林分类器来利用四种微量元素，即锌（Zn）、磷（P）、镁（Mg）和钙（Ca）。研究的目标是倒置的CD4/CD8比值。
110名参与者的数据被纳入分析。由此生成的算法灵敏度为80%，特异性为8
3%，阳性似然比（LR+）为4.8，阴性似然比（LR-）为0.24。ML算法的使用增加了目前关于微量营养素，特别是微量元素在免疫风险成因中作用的有限证据。我们的内在优势在于，本研究是首批利用基于ML的决策树算法对艾滋病毒患者的免疫风险进行分类的研究之一。
我们的研究通过使用决策树ML算法，独特地证实了初治艾滋病毒患者的营养数据与免疫风险之间的关系。这随后可能成为临床医生手中一种重要的分类和预后工具。
