Epidemiology of coronary artery disease among people with HIV in rural sub-Saharan Africa

摘要 已投资超过500亿美元，以扩大艾滋病患者获得抗逆转录病毒治疗(ART)的机会 撒哈拉以南非洲的艾滋病毒(PLWH)。然而，地区性艾滋病毒项目在很大程度上忽视了非艾滋病发病率的原因， 从而有可能破坏已取得的巨大成果。在美国，冠心病(CAD)是 在PLWH中明显比未感染艾滋病毒的人更常见。然而，CAD的决定因素 在美国，由于独特的环境，煽动性， 和社会风险因素。例如，在一项由NHLBI R21资助的试验中，我们招募了一群PLWH接受ART治疗 乌干达农村和以人口为基础的艾滋病毒阴性对照，并表明PLWH有更高的 动脉僵硬和心电图异常，尽管血压较低，低密度较低 脂蛋白和较低的主动吸烟率。我们还确定了乌干达的炎症途径，例如 犬尿氨酸/色氨酸比率因性别不同而不同，与动脉粥样硬化和 乌干达的死亡率高于美国。我们现在建议通过以下方式加强我们现有的研究基础设施 冠状动脉对比增强计算机断层扫描，以直接和非侵入性测量 艾滋病毒对乌干达农村地区冠心病的影响，并通过以下目标确定冠心病的区域决定因素： 目的1：确定冠心病在PLWH中是否比未感染HIV的人更普遍和更严重 乌干达农村地区的参照者。我们将完成600名参与者的冠状动脉CT血管成像，并比较 PLWH和HIV非感染对照人群的冠心病患病率和严重程度。假设：PLWH In 与未感染艾滋病毒的对照国家相比，乌干达的冠心病患病率和严重程度更高。 目标2：确定艾滋病毒感染和冠心病之间的关联程度 根据性别和地区进行修饰。在我们的队列中，我们将测试性别对效果的影响(目标2a)。然后，通过 将我们的数据与参加Repreve研究的PLWH的美国数据汇集在一起，我们将测试效果修改 按原籍国分列(目标2b)。假设：HIV感染与冠心病之间的关联性在 妇女(与男性)和乌干达的妇女解放妇女联盟(PLWH)(在美国)。 目标3：确定冠心病的区域相关性，并为存在的存在制定风险预测评分 在撒哈拉以南非洲的农村地区的公共卫生部门中开展非传染性疾病的研究。我们将收集有关传统(如年龄、吸烟、 糖尿病)、艾滋病毒特异性因素(如巨噬细胞激活、CD4计数、抗逆转录病毒治疗病史)和特定地区因素(如 生物量暴露、肺结核病史、K：T比率)。在目标3a中，我们将包括传统的、针对艾滋病毒的和 模型中的区域性风险因素识别冠心病的相关因素。在目标3b中，我们将提出一个简化的风险评分，以 用CAD确定PLWH。假设：非传统风险因素为 估计美国和欧洲风险预测模型中通常使用的传统因素之外的CAD风险。 好了！

ABSTRACT More than $50 billion has been invested to expand access to antiretroviral therapy (ART) for people living with HIV (PLWH) in sub-Saharan Africa. Yet, regional HIV programs largely overlook causes of non-AIDS morbidity, thereby threatening to undermine the tremendous gains achieved. In the US, coronary artery disease (CAD) is significantly more common among PLWH than HIV-uninfected individuals. However, determinants of CAD among PLWH in the US are unlikely to generalize to rural Africa, due to unique environmental, inflammatory, and social risk factors. For example, in an NHLBI R21-funded pilot, we enrolled a cohort of PLWH on ART in rural Uganda and population-based, HIV-negative controls, and demonstrated that PLWH have greater rates of arterial stiffness and electrocardiographic abnormalities despite having lower blood pressure, lower low density lipoprotein and lower rates of active smoking. We also identified inflammatory pathways in Uganda, such as the kynurenine:tryptophan ratio, which differ by sex, and correlate more strongly with atherosclerosis and mortality in Uganda than the US. We now propose to augment our established research infrastructure with contrast enhanced computed tomography of the coronary arteries, to directly and non-invasively measure the impact of HIV on CAD in rural Uganda, and identify regional determinants of CAD through the following aims: Aim 1: Determine whether CAD is more prevalent and severe among PLWH than HIV-uninfected comparators in rural Uganda. We will complete coronary CT angiography in 600 participants, and compare the prevalence and severity of CAD between PLWH and HIV uninfected comparators. Hypothesis: PLWH in Uganda have a higher prevalence and severity of CAD compared to HIV-uninfected comparators. Aim 2: Determine the extent to which the association between HIV infection and CAD is modified by sex and region. Within our cohort, we will test for effect modification by sex (Aim 2a). Then, by pooling our data with US data from PLWH enrolled in the REPREIVE study, we will test for effect modification by country of origin (Aim 2b). Hypothesis: The association between HIV infection and CAD is greater among women (vs. men) and among PLWH in Uganda (vs. PLWH in the US). Aim 3: Identify regional correlates of CAD, and develop a risk prediction score for the presence of CAD among PLWH in rural sub-Saharan Africa. We will collect data on traditional (e.g. age, smoking, diabetes), HIV-specific (e.g. macrophage activation, CD4 count, ART history), and region-specific factors (e.g. biomass exposure, tuberculosis history, K:T ratio). In Aim 3a we will include traditional, HIV-specific and regional risk factors in models to identify correlates of CAD. In Aim 3b, we will propose a simplified risk score to identify PLWH with CAD. Hypothesis: Non-traditional risk factors provide substantive explanatory power for estimating CAD risk beyond traditional factors typically used in risk prediction models in the US and Europe. !