De Francesco Davide, Verboeket Sebastiaan O, Underwood Jonathan, Bagkeris Emmanouil, Wit Ferdinand W, Mallon Patrick W G, Winston Alan, Reiss Peter, Sabin Caroline A
Institute for Global Health, University College London, London, UK.
Department of Global Health, Amsterdam University Medical Centers, University of Amsterdam and Amsterdam Institute for Global Health and Development, Amsterdam, the Netherlands.
Open Forum Infect Dis. 2018 Oct 24;5(11):ofy272. doi: 10.1093/ofid/ofy272. eCollection 2018 Nov.
The aims of this study were to identify common patterns of comorbidities observed in people living with HIV (PLWH), using a data-driven approach, and evaluate associations between patterns identified.
A wide range of comorbidities were assessed in PLWH participating in 2 independent cohorts (POPPY: UK/Ireland; AGEIV: Netherlands). The presence/absence of each comorbidity was determined using a mix of self-reported medical history, concomitant medications, health care resource use, and laboratory parameters. Principal component analysis (PCA) based on Somers' statistic was applied to identify patterns of comorbidities.
PCA identified 6 patterns among the 1073 POPPY PLWH (85.2% male; median age [interquartile range {IQR}], 52 [47-59] years): cardiovascular diseases (CVDs), sexually transmitted diseases (STDs), mental health problems, cancers, metabolic disorders, chest/other infections. The CVDs pattern was positively associated with cancer ( = .32), metabolic disorder ( = .38), mental health ( = .16), and chest/other infection ( = .17) patterns (all < .001). The mental health pattern was correlated with all the other patterns (in particular cancers: = .20; chest/other infections: = .27; both < .001). In the 598 AGEIV PLWH (87.6% male; median age [IQR], 53 [48-59] years), 6 patterns were identified: CVDs, chest/liver, HIV/AIDS events, mental health/neurological problems, STDs, and general health. The general health pattern was correlated with all the other patterns (in particular CVDs: = .14; chest/liver: = .15; HIV/AIDS events: = .31; all < .001), except STDs ( = -.02; = .64).
Comorbidities in PLWH tend to occur in nonrandom patterns, reflecting known pathological mechanisms and shared risk factors, but also suggesting potential previously unknown mechanisms. Their identification may assist in adequately addressing the pathophysiology of increasingly prevalent multimorbidity in PLWH.
本研究的目的是采用数据驱动的方法,确定在艾滋病毒感染者(PLWH)中观察到的共病常见模式,并评估所确定模式之间的关联。
在参与2个独立队列研究(罂粟花研究:英国/爱尔兰;荷兰艾滋病与衰老研究)的PLWH中评估了多种共病情况。使用自我报告的病史、伴随用药情况、医疗资源使用情况和实验室参数相结合的方法来确定每种共病的存在与否。基于萨默斯统计量的主成分分析(PCA)被用于识别共病模式。
PCA在1073名罂粟花研究的PLWH中(85.2%为男性;中位年龄[四分位间距{IQR}],52[47 - 59]岁)识别出6种模式:心血管疾病(CVDs)、性传播疾病(STDs)、心理健康问题、癌症、代谢紊乱、胸部/其他感染。心血管疾病模式与癌症( = 0.32)、代谢紊乱( = 0.38)、心理健康( = 0.16)和胸部/其他感染( = 0.17)模式呈正相关(所有 < 0.001)。心理健康模式与所有其他模式相关(特别是癌症: = 0.20;胸部/其他感染: = 0.27;两者 < 0.001)。在598名荷兰艾滋病与衰老研究的PLWH中(87.6%为男性;中位年龄[IQR],53[48 - 59]岁),识别出6种模式:心血管疾病、胸部/肝脏疾病、艾滋病毒/艾滋病事件、心理健康/神经问题、性传播疾病和总体健康状况。总体健康状况模式与所有其他模式相关(特别是心血管疾病: = 0.14;胸部/肝脏疾病: = 0.15;艾滋病毒/艾滋病事件: = 0.31;所有 < 0.001),但与性传播疾病无关( = -0.02; = 0.64)。
PLWH中的共病往往以非随机模式发生,反映了已知的病理机制和共同的风险因素,但也暗示了潜在的此前未知的机制。对它们的识别可能有助于充分应对PLWH中日益普遍的多重共病的病理生理学问题。