Not all clusters are equal: dynamics of molecular HIV-1 clusters in a statewide Rhode Island epidemic. (1st March 2023)

• Record Type: Journal Article
• Authors: Novitsky, Vlad; Steingrimsson, Jon; Howison, Mark; Dunn, Casey W.; Gillani, Fizza S.; Fulton, John; Bertrand, Thomas; Howe, Katharine; Bhattarai, Lila; Ronquillo, Guillermo; MacAskill, Meghan; Bandy, Utpala; Hogan, Joseph; Kantor, Rami
• Is Part Of: AIDS. Volume 37:Number 3(2023)
• Page Start: 389
• Page End: 399
• Publication Date: 2023-03-01
• Subjects: abnormal clusters -- cluster dynamics -- molecular HIV clusters -- normal clusters -- types of HIV clusters; AIDS (Disease) -- Periodicals; Acquired Immunodeficiency Syndrome; AIDS (Disease); Periodicals; Periodicals; 616.9792005
• DOI: 10.1097/QAD.0000000000003426
• Languages: English
• ISSNs: 0269-9370

Abstract

Abstract : Objectives: Molecular epidemiology is a powerful tool to characterize HIV epidemics and prioritize public health interventions. Typically, HIV clusters are assumed to have uniform patterns over time. We hypothesized that assessment of cluster evolution would reveal distinct cluster behavior, possibly improving molecular epidemic characterization, towards disrupting HIV transmission. Design: Retrospective cohort. Methods: Annual phylogenies were inferred by cumulative aggregation of all available HIV-1 pol sequences of individuals with HIV-1 in Rhode Island (RI) between 1990 and 2020, representing a statewide epidemic. Molecular clusters were detected in annual phylogenies by strict and relaxed cluster definition criteria, and the impact of annual newly-diagnosed HIV-1 cases to the structure of individual clusters was examined over time. Results: Of 2153 individuals, 31% (strict criteria) – 47% (relaxed criteria) clustered. Longitudinal tracking of individual clusters identified three cluster types: normal, semi-normal and abnormal . Normal clusters (83–87% of all identified clusters) showed predicted growing/plateauing dynamics, with approximately three-fold higher growth rates in large (15–18%) vs. small (∼5%) clusters. Semi-normal clusters (1–2% of all clusters) temporarily fluctuated in size and composition. Abnormal clusters (11–16% of all clusters) demonstrated collapses and re-arrangements over time. Borderline values of cluster-defining parameters explained dynamics of non-normal clusters. Conclusions: Comprehensive tracing of molecular HIV clusters over time in a statewide epidemic identified distinct cluster types, likely missed in cross-sectional analyses, demonstrating that not all clusters are equal. This knowledge challenges current perceptions of consistent cluster behavior over time and could improve molecular surveillance of local HIV epidemics to better inform public health strategies.