School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Pietermaritzburg, South Africa.
School of Nursing and Public Health, University of KwaZulu-Natal, Pietermaritzburg, South Africa.
Front Public Health. 2024 Jun 25;12:1393627. doi: 10.3389/fpubh.2024.1393627. eCollection 2024.
Understanding and identifying the immunological markers and clinical information linked with HIV acquisition is crucial for effectively implementing Pre-Exposure Prophylaxis (PrEP) to prevent HIV acquisition. Prior analysis on HIV incidence outcomes have predominantly employed proportional hazards (PH) models, adjusting solely for baseline covariates. Therefore, models that integrate cytokine biomarkers, particularly as time-varying covariates, are sorely needed.
We built a simple model using the Cox PH to investigate the impact of specific cytokine profiles in predicting the overall HIV incidence. Further, Kaplan-Meier curves were used to compare HIV incidence rates between the treatment and placebo groups while assessing the overall treatment effectiveness. Utilizing stepwise regression, we developed a series of Cox PH models to analyze 48 longitudinally measured cytokine profiles. We considered three kinds of effects in the cytokine profile measurements: average, difference, and time-dependent covariate. These effects were combined with baseline covariates to explore their influence on predictors of HIV incidence.
Comparing the predictive performance of the Cox PH models developed using the AIC metric, model 4 (Cox PH model with time-dependent cytokine) outperformed the others. The results indicated that the cytokines, interleukin (IL-2, IL-3, IL-5, IL-10, IL-16, IL-12P70, and IL-17 alpha), stem cell factor (SCF), beta nerve growth factor (B-NGF), tumor necrosis factor alpha (TNF-A), interferon (IFN) alpha-2, serum stem cell growth factor (SCG)-beta, platelet-derived growth factor (PDGF)-BB, granulocyte macrophage colony-stimulating factor (GM-CSF), tumor necrosis factor-related apoptosis-inducing ligand (TRAIL), and cutaneous T-cell-attracting chemokine (CTACK) were significantly associated with HIV incidence. Baseline predictors significantly associated with HIV incidence when considering cytokine effects included: age of oldest sex partner, age at enrollment, salary, years with a stable partner, sex partner having any other sex partner, husband's income, other income source, age at debut, years lived in Durban, and sex in the last 30 days.
Overall, the inclusion of cytokine effects enhanced the predictive performance of the models, and the PrEP group exhibited reduced HIV incidences compared to the placebo group.
了解和识别与 HIV 感染相关的免疫学标志物和临床信息对于有效实施暴露前预防（PrEP）以预防 HIV 感染至关重要。先前对 HIV 发病率结果的分析主要采用比例风险（PH）模型，仅调整基线协变量。因此，非常需要整合细胞因子生物标志物的模型，特别是作为时变协变量的模型。
我们使用 Cox PH 构建了一个简单的模型，以研究特定细胞因子谱对预测总体 HIV 发病率的影响。此外，我们使用 Kaplan-Meier 曲线比较了治疗组和安慰剂组之间的 HIV 发病率，同时评估了总体治疗效果。我们利用逐步回归，分析了 48 个纵向测量的细胞因子谱，建立了一系列 Cox PH 模型。我们考虑了细胞因子谱测量中的三种效应：平均值、差异和时变协变量。这些效应与基线协变量结合，以探讨它们对 HIV 发病率预测因子的影响。
根据 AIC 指标比较 Cox PH 模型的预测性能，模型 4（具有时变细胞因子的 Cox PH 模型）表现优于其他模型。结果表明，细胞因子白细胞介素（IL-2、IL-3、IL-5、IL-10、IL-16、IL-12P70 和 IL-17 alpha）、干细胞因子（SCF）、β神经生长因子（B-NGF）、肿瘤坏死因子 alpha（TNF-A）、干扰素（IFN）alpha-2、血清干细胞生长因子（SCG）-beta、血小板衍生生长因子（PDGF）-BB、粒细胞巨噬细胞集落刺激因子（GM-CSF）、肿瘤坏死因子相关凋亡诱导配体（TRAIL）和皮肤 T 细胞吸引趋化因子（CTACK）与 HIV 发病率显著相关。当考虑细胞因子效应时，与 HIV 发病率显著相关的基线预测因子包括：性伴侣中最年长的年龄、入组时的年龄、工资、与稳定伴侣的年数、性伴侣是否有其他性伴侣、丈夫的收入、其他收入来源、发病年龄、在德班居住的年数和过去 30 天的性别。
总体而言，纳入细胞因子效应增强了模型的预测性能，与安慰剂组相比，PrEP 组的 HIV 发病率降低。
