A new study reveals that in people living with HIV, comorbidities occur in non-random patterns and appear to be correlated to one another, highlighting the complexity of multimorbidity patterns.
For a physician treating patients with several comorbidities can be increasingly challenging, and for individuals living with HIV, comorbidities can have a significant impact on the overall health outcomes of these patients.
In a new study published in Open Forum Infectious Diseases, a team of international investigators found that comorbidities in people living with HIV tend to cluster in specific patterns that can be consistently identified, which may lead to a greater understanding of their potential impact on health and treatment outcomes.
“The presence of multiple co-occurring comorbidities is becoming increasingly common in people living with HIV, with negative consequences on health outcomes, daily functioning, working status, and health care costs,” Davide DeFrancesco, a research statistician and PhD candidate at the Institute for Global Health, part of University College London, and an author of the study told Contagion®. “The findings highlighted in our paper could have important implications for both clinical and research purposes.”
For the study, investigators used a data-driven approach to identify patterns of comorbidities in people living with HIV and evaluated associations between the patterns.
The data analyzed in the study were collected from 2 European cohorts focusing on people living with HIV: the POPPY study which was conducted in the United Kingdom and Ireland, and the AGEhIV study which was conducted in the Netherlands. Both studies focused on the impact of a wide variety of comorbidities on people living with HIV.
According to the study investigators, the presence and/or absence of each comorbidity was determined using a mix of self-reported medical history, concomitant medications, health care resource use, and laboratory parameters. Additionally, patterns were identified through principal component analysis (PCA) based on Somers’ D statistic.
The PCA identified 6 patterns among the participants in the POPPY study, which included 1073 participants [85.2% male, median (IQR) age 52 (age 47-59) years]. Patterns were found in cardiovascular diseases, sexually transmitted diseases, mental health problems, cancers, metabolic disorders, and chest/other infections.
Furthermore, the cardiovascular disease pattern was found to have a positive association with the cancers (r = .32), metabolic disorders (r = .38), mental health (r = .16) and chest/other infections (r = .17) patterns (all P values <.001). Additionally, the mental health pattern was correlated with all the other patterns, with the strongest correlations in cancers (r = .20) and chest/other infections (r = .27), (both P values <.001).
Six patterns were also observed in the AGEhIV study, which included 598 participants [97.6% male, media IQR age 53 (age 48-59) years]. These patterns were found in cardiovascular diseases, chest/liver, HIV/AIDS events, mental health/neurological problems, sexually transmitted diseases, and general health.
The general health pattern was correlated with all other patterns with the strongest correlations in cardiovascular diseases (r = .14), chest/liver: (r = .15) and HIV/AIDS events: (r = .31), (all P values <.001 with the exception of sexually transmitted diseases STDs (r = .02, P = .64).
“Knowledge about comorbidity clusters could inform decisions about how to optimize prevention,” DeFrancesco explained to Contagion®. “For example, an individual experiencing some of the comorbidities in a cluster can be considered more likely to further develop other comorbidities in the same cluster. This information can be used to improve the monitoring and screening for these conditions in order to achieve timely diagnosis.”
DeFrancesco also stressed that the findings support the need for patient-centered treatment strategies and guidelines to address important areas of need in people living with HIV who are experiencing several comorbidities.
The authors are hopeful that data-driven research may reveal new pathophysiological mechanisms that may lead to future research focusing on elucidating these pathophysiological pathways and their potential impact on health and treatment outcomes.