HIV treatment interruptions are associated with heightened biomarkers of inflammation, coagulopathy and T-cell activation despite viral suppression
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- Peregrine Francis
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1 HIV treatment interruptions are associated with heightened biomarkers of inflammation, coagulopathy and T-cell activation despite viral suppression Nicholas Musinguzi on behalf of Jose Castillo-Mancila, Mary Marrow, Yap Boum, Conrad Muzoora, Bosco M. Bwana, Mark J. Siedner, Jeffrey N. Martin, Peter W. Hunt, David R. Bangsberg, Jessica E. Haberer
2 None Disclosures
3 Background Immune activation/inflammation predicts non-aids morbidity/mortality in treated HIV Among virologically suppressed, average adherence to antiretroviral therapy (ART) is inversely associated with biomarkers of immune activation/inflammation (Castillo- Mancilla, JAIDS 207) However, association with sustained treatment interruptions is unknown
4 Research questions Are sustained treatment interruptions also associated with heightened levels of biomarkers activation/inflammation? Does this relationship remain after controlling for percentage adherence?
5 Uganda AIDS Rural Treatment Outcomes Study (UARTO) Longitudinal observational cohort study among adults living with HIV and initiating ART 772 participants enrolled between Baseline and quarterly follow-up Socio-demographic data ART regimen data Electronic ART adherence (MEMS) Blood drawn for plasma and cell isolation
6 Biomarkers Inflammation Interleukin-6 (IL-6) Kynurenine/tryptophan (K/T) ratio Soluble (s) CD4 Soluble (s) CD63 T-cell activation HLA-DR+/CD38+ Coagulopahty D-dimer
7 Analysis Primary: For each biomarker, we fit a multivariable linear regression assessing effect of treatment interruptions on the logtransformed level of the biomarker Secondary: Primary model adjusted for percentage adherence
8 Eligibility criteria for analysis Restricted to first 6 months of follow-up after ART initiation Biomarker levels available at baseline and after 6 (+/- ) months on ART Virologically suppressed (VL<400 copies/ml) at the 6-month visit ART adherence data available for 3+ months in the 6-month period
9 Main predictor: Treatment interruptions Potential approaches to computing treatment interruptions ) Frequency of interruptions lasting X or more days Assumes that interruptions are equal in their relationship with treatment outcomes 2) Proportion of days when running average adherence was less than 0% or 20% etc
10 But are interruptions really equal in their relationship with treatment outcomes? id A Adherence B C C D D D2 E E E2 day Eg. Is the relationship between D2 and viral suppression similar to that between E2 and viral suppression?
11 But are interruptions really equal in their relationship with treatment outcomes? Haberer, JAIDS 205
12 Main predictor: Treatment interruptions We chose running average approach since it considers more information about the interruptions We computed treatment interruptions as the proportion of days when the running average (+/- 4 days) adherence was less than 0%
13 Other predictor variables Baseline of respective biomarker Age Gender Baseline viral load (log) Alcohol (AUDIT-C) Depression Percentage adherence Included in only secondary model (Total MEMS bottle openings*00)/total prescribed doses
14 Results: Participant characteristics Of 282 eligible participants, Female: 70% Median age: 35 years(iqr: 29, 39) Median pre-art CD4 count: 35 cells/mm 3 Median pre-art log viral load: 5.
15 Results: Multivariable regression Primary models: Treatment Interruption Effect Secondary models: Treatment Interruption Adjusted for Average Adherence Biomarker Effect (95% CI) P Effect (95% CI) P IL-6 2.4% (3.0, 22.7) % (-8.5, 8.9) 0.52 K/T ratio 4.3% ( 0.6, 8.0) % (0.4, 3.6) scd4 3.2% (.5, 4.9%) p< % (.5, 8.) scd63 4.7% (.8, 7.6) % (3.4, 3.2) 0.00 HLA-DR+/CD8+ 2.6% ( 0.4, 4.9) % (-2.5, 5.4) 0.50 D-dimer 9.8% (.5, 8.7) % (-0.5, 8.6) 0.67
16 Results: Multivariable regression Primary models: Treatment Interruption Effect Secondary models: Treatment Interruption Adjusted for Average Adherence Biomarker Effect (95% CI) P Effect (95% CI) P IL-6 2.4% (3.0, 22.7) % (-8.5, 8.9) 0.52 K/T ratio 4.3% ( 0.6, 8.0) % (0.4, 3.6) scd4 3.2% (.5, 4.9%) p< % (.5, 8.) scd63 4.7% (.8, 7.6) % (3.4, 3.2) 0.00 HLA-DR+/CD8+ 2.6% ( 0.4, 4.9) % (-2.5, 5.4) 0.50 D-dimer 9.8% (.5, 8.7) % (-0.5, 8.6) 0.67
17 Results: Effect of percentage adherence Percentage adherence no longer significantly associated with biomarkers after adjusting for treatment interruptions
18 Strengths and Limitations Strengths Objectively measured adherence Running average reflects impact of adherence interruptions better than count data Limitations Running average not intuitive Potential error in adherence measurement Results applicable to first 6 months as dynamics of adherence beyond 6 months may differ
19 Conclusions Within first 6 months of ART initiation, sustained treatment interruptions are associated with increased levels of biomarkers of immune activation/inflammation Relationship persists for K/T ratio, scd63 and scd4 after controlling for percentage adherence No evidence seen for an association between percentage adherence and levels of biomarkers after controlling for treatment interruptions
20 Acknowledgements Jessica E. Haberer Jose Castillo-Mancila, Peter W. Hunt, Jeffrey N. Martin, Mark J. Siedner Study participants Study staff Funding: R0MH054907
21 Questions, Comments?
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The study listed may include approved and non-approved uses, formulations or treatment regimens. The results reported in any single study may not reflect the overall results obtained on studies of a product.