Biomarker Profiles and Comorbidity Burden in HIV: A Latent Profile Analysis
The study explores and compares the biomarker profiles and comorbidity burden in people with and without HIV. Using Latent Profile Analysis, the AGEhIV Cohort Study sheds light on crucial insights for better understanding HIV-related health complexities.
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Exploring and comparing biomarker profiles and comorbidity burden in people with and without HIV A Latent Profile Analysis of the AGEhIV Cohort Study Manon C. Vanbellinghen, Anders Boyd, Neeltje Kootstra, Maarten F. Schim van der Loeff, Marc van der Valk, Peter Reiss On behalf of the
Disclosures No conflicts of interest to declare
Background People with HIV (PWH) experience a higher burden of ageing- associated comorbidities1 Partly associated with ongoing inflammation and immune dysfunction2 The specific pathways by which these immunological changes drive comorbidities are not well defined Clustering can be used on multiple biomarkers to identify profiles in PWH3 The group of Paddy Mallon has previously described inflammatory profiles in PWH, cross-sectionally associated with different CVD risk scores4 1 Verheij E. et al., Lancet HIV. 2023;10(3):e164-e74 2Gabuzda D. et al., Pathog Immun. 2020;5(1):143-74. 3McGettrickP. et al., CROI. 2021. 4Sukumaran L. et al., Aids. 2023;37(4):595-603.
Aims Identify biomarker profiles in PWH and controls from the AGEhIV Cohort Study Explore the association between biomarker profiles and ageing- associated comorbidities over time
Methods Biomarker data from 94 PWH and 95 age-matched controls enrolled in AGEhIV between November 2010 and December 2011 Clinical outcomes from baseline and 4 follow-up visits, from November 2010 to December 2020 Immunological pathway Biomarker Comorbidity Coagulation D-dimer Diabetes Type 2 Innate immune activation sCD14, sCD163 non-AIDS malignancy Systemic inflammation IL-6, hsCRP Osteoporosis Microbial translocation i-FABP Frailty Adaptive Immunity T-cell subsets Chronic kidney disease sjTREC content* Cardiovascular disease** Thymic function Immune cell turnover Telomere length *Signal-Joint T-cell Receptor Excision circle ** Myocardial infarction, angina pectoris, peripheral artery disease, ischemic stroke, hemorrhagic stroke and heart failure
Statistical analysis Latent profile analysis to identify biomarker profiles Factors associated with profile membership: multivariable logistic regression Association profile membership and comorbidity burden: Poisson mixed-effects model
Results Baseline characteristics controls (n=95) PWH (n=94) p value 55 (49 60) 0.46 83 (88) 0.54 Age, y Male sex at birth Ethnicity White Asian Black MSM Current smoker BMI,kg/m2 CMV IgG positive CMV antibody titer Hepatitis C coinfection Hepatitis B coinfection CD4/CD8 ratio Time since HIV diagnosis, yr Taking ART at enrollment Cumulative exposure to ART, yr Plasma HIV RNA <40 copies/mL CD4 nadir, cells/ L Prior clinical AIDS 55 (49 63) 81 (85) 0.08 92 (97) 1 (1) 2 (2) 72 (76) 19 (20) 85 (90) 1 (1) 8 (9) 62 (66) 0.34 24 (26) 0.21 24 (22 27) 0.29 84 (89) 0.52 56 (28 80) <0.0001 5 (5) 0.12 5 (5) 0.12 0.81 (0.62 1.14) 13 (8 19) 94 (100) 10 (6 15) 94 (100) 170 (63 240) 32 (34) 24 (23 26) 82 (86) 29 (17 50) 1 (1) 1 (1) 1.98 (1.43 2.61) <0 0001 ... Data are n (%) or median (IQR)
Results: biomarker profiles Innate immune activation Systemic inflammation
Results: biomarker profiles Innate immune activation Systemic inflammation
Results: prevalence of biomarker profiles in PWH and controls
Results: Factors associated with profile membership multivariable logistic regression Covariates: HIV, CMV and CMV antibody titer, sex at birth, active smoking, obesity, and age
Results: association between profile membership and ageing-associated comorbidities PWH
Results: association between profile membership and ageing-associated comorbidities PWH
Results: association between profile membership and ageing-associated comorbidities PWH Controls
Conclusions Three biomarker profiles were identified, the prevalence of which differed in PWH and controls The High thymic output/Low inflammation profile was associated with having HIV, and with a lower comorbidity burden over time in PWH, but not in controls These findings suggest that greater thymic function may help mitigate chronic inflammation in a subset of PWH on ART as they age, thereby reducing their risk of ageing-associated comorbidities
AGEhIV Cohort Study Group Participating HIV physicians and nurses Participating HIV physicians and nurses: S.E. Geerlings, A. Goorhuis, J.W.R. Hovius, F.J.B. Nellen, J.M. Prins, T. van der Poll, M. van der Valk, W.J. Wiersinga, M. van Vugt, G. de Bree, B. A. Lemkes, V. Spoorenberg, F.W.N.M. Wit; J. van Eden, F.J.J. Pijnappel, A. Weijsenfeld, S. Smalhout, I.J. Hylkema - van den Bout, C. Bruins, M.E. Spelbrink (Amsterdam UMC, Division of Infectious Diseases). Scientific oversight and coordination: Scientific oversight and coordination: P. Reiss (principal investigator), F.W.N.M. Wit, M. van der Valk, A. Boyd, M.L. Verburgh, I.A.J. van der Wulp, MC. Vanbellinghen, C.J. van Eeden (Amsterdam University Medical Centers (Amsterdam UMC), University of Amsterdam, Department of Global Health and Amsterdam Institute for Global Health and Development (AIGHD)). Central laboratory support Central laboratory support: N.A. Kootstra, A.M. Harskamp-Holwerda, I. Maurer, M.M. Mangas Ruiz, B.D.N. Boeser-Nunnink, O.S. Starozhitskaya (Amsterdam UMC, Laboratory for Viral Immune Pathogenesis and Department of Experimental Immunology). L. van der Hoek, M. Bakker, M.J. van Gils (Amsterdam UMC, Department of Medical Microbiology and Infection Prevention, Laboratory of Experimental Virology). Other collaborators Other collaborators: P.G. Postema (Amsterdam UMC, Department of Cardiology); P.H.L.T. Bisschop (Amsterdam UMC, Division of Endocrinology and Metabolism); E. Dekker (Amsterdam UMC, Department of Gastroenterology); N. van der Velde, R. Franssen (Amsterdam UMC, Division of Geriatric Medicine); J.M.R. Willemsen, L. Vogt (Amsterdam UMC, Division of Nephrology); P. Portegies, G.J. Geurtsen (Amsterdam UMC, Department of Neurology); I. Visser, A. Schad (Amsterdam UMC, Department of Psychiatry); P.T. Nieuwkerk (Amsterdam UMC, Department of Medical Psychology); R.P. van Steenwijk, R.E. Jonkers (Amsterdam UMC, Department of Pulmonary medicine); C.B.L.M. Majoie, M.W.A. Caan (Amsterdam UMC, Department of Radiology); B.J.H. van den Born, E.S.G. Stroes, (Amsterdam UMC, Division of Vascular Medicine); S. van Oorspronk (HIV Vereniging Nederland). M.F. Schim van der Loeff (co-principal investigator), J.C.D. Koole, L. del Grande, I. Agard (Public Health Service of Amsterdam, Department of Infectious Diseases). Financial Financial support: The Netherlands Organisation for Health Research and Development (ZonMW) [grant nr. 300020007] Stichting AIDS Fonds [grant nr. 2009063] support: Statistical Statistical support Monitoring Foundation). support: : A. Boyd, F.W.N.M. Wit (HIV Data management Data management: S. Zaheri, M.M.J. Hillebregt, Y.M.C. Ruijs, D.P. Benschop, A. el Berkaoui (HIV Monitoring Foundation). Additional unrestricted scientific grants for parent study from: Gilead Sciences; ViiV Healthcare; Merck Sharp & Dohme Corp; Janssen Pharmaceuticals N.V. Project management and administrative support Project management and administrative support: L. Dol, G. Rongen (AIGHD). And many thanks to all study participants! And many thanks to all study participants!
