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Judith Schouten on behalf of the AGE h IV study group

Comorbidity and ageing with HIV A prospective comparative cohort study. XIX International AIDS Conference July 26 th 2012, Washington DC. Judith Schouten on behalf of the AGE h IV study group. Background & Rationale.

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Judith Schouten on behalf of the AGE h IV study group

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  1. Comorbidity and ageing with HIVA prospective comparative cohort study XIX International AIDS Conference July 26th 2012, Washington DC Judith Schouten on behalf of the AGEhIV study group

  2. Background & Rationale • Combination antiretroviral therapy (cART): decline in AIDS-associated morbidity and mortality • Life-expectancy: still shorter than expected, particularly when cART started late1 • Large proportion of HIV-patients: broad range of comorbidities2 • 1 Bhaskaran K, Hamouda O, Sannes M, et al. JAMA 2008 Jul 2;300(1):51-9. • 2 Hasse B, Ledergerber B, Furrer H, et al. Clin Infect Dis 2011 Dec;53(11):1130-9. • Are age-related comorbidities more prevalent and/or occurring at a younger age in HIV-infected individuals as compared to in HIV-uninfected individuals?

  3. Design& Study population • Prospective comparative cohort study (started October 2010) • Prevalence (and incidence) of age-associated non-communicable comorbidities (AANCC) and their risk factors in persons ≥45 yrs Participants: • HIV-1-infected: from the HIV outpatient clinic at the Academic Medical Center (Amsterdam) • HIV-1-uninfected: from the Amsterdam Municipal Health Service sexual health clinic, and the ongoing Amsterdam Cohort Studies on HIV/AIDS

  4. Statistical analysis Preliminary comparison of prevalence of AANCC using currently available baseline data Multivariable ordinal logistic regression (proportional odds model) to assess the contribution of HIV and traditional risk factors towards AANCC Outcome measure: Number of AANCC per participant Covariates explored: age, gender, ethnicity, packyears of smoking, alcohol abuse, substance abuse (XTC, cocaine, cannabis), BMI, sexual orientation (MSM), HIV-status

  5. Comorbidities analyzed • Data on the following AANCC were available for analysis • Most of the AANCC were identified as self-reported by participants using a standardized questionnaire (validation in progress) • In addition, objective assessment of: • Hypertension: 3 measurements with a 1-minute interval;RR ≥140 and/or ≥90 in all 3 measurements • Chronic Obstructive Pulmonary Disease: 3 forced expiratory measurements; FEV1/FVC < 0.7 in all 3 measurements • Diabetes mellitus:HbA1c (IFCC) ≥ 48 mmol/mol and/or Glucose (non-fasting) ≥ 11.1 mmol/L and/orGlucose (fasting) ≥ 7.0 mmol/L • Reduced renal function:eGFR (CKD-EPI) < 60 mL/min • Osteoporosis:DXA T-score < -2.5 SD

  6. Demographic and HIV characteristics Data presented as median (IQR) or percentage as appropriate

  7. Comorbidity risk factors Data presented as median (IQR) or percentage as appropriate

  8. Comorbidity prevalence

  9. Comorbidity in relation to age

  10. Comorbidity in relation to age

  11. Comorbidity in relation to age

  12. Comorbidity distribution *

  13. Risk factors for comorbidity • Multivariable ordinal logistic regression model • BMI, use of XTC/cocaine/cannabis/alcohol, ethnicity and sexual orientation (MSM) were not found to be independent risk factors

  14. Risk factors for comorbidity • Multivariable ordinal logistic regression model • BMI, use of XTC/cocaine/cannabis/alcohol, ethnicity and sexual orientation (MSM) were not found to be independent risk factors OR 2.1

  15. Risk factors for comorbidity • Multivariable ordinal logistic regression model • Adding estimated duration of HIV infection to the model

  16. Risk factors for comorbidity • Multivariable ordinal logistic regression model • Adding estimated duration of HIV infection to the model OR 1.16

  17. Risk factors for comorbidity • Multivariable ordinal logistic regression model • Adding duration of ART exposure to the model

  18. Risk factors for comorbidity • Multivariable ordinal logistic regression model • Adding duration of ART exposure to the model OR 1.35

  19. The role of AGE accumulation • AGEs = Advanced Glycation Endproducts • Non-enzymatic glycation of proteins/lipids/DNA • AGE accumulation is influenced by age, smoking, inflammation, renal function, diabetes • Level of AGEs increases with age (ageing biomarker?) • AGEs in the skin measured with skin autofluorescence (AGE-reader) • Measured AGE-values were compared to existing reference values, according to age, gender and smoking1 • 1 Koetsier M, Lutgers HL, de Jonge C, et al. Diabetes Technol Ther 2010 May; 12(5); 399-403.

  20. The role of AGE accumulation

  21. The role of AGE accumulation

  22. The role of AGE accumulation

  23. The role of AGE accumulation • Adding excess AGE accumulation to the model (per 10% above the • reference AGE value)

  24. The role of AGE accumulation • Adding excess AGE accumulation to the model (per 10% above the • reference AGE value) OR 1.08

  25. Conclusions • AANCC were significantly more prevalent amongst HIV-positives compared to uninfected controls of similar age • (Longer duration of) being HIV infected / exposure to cART were associated with a higher prevalence of AANCC(in a cohort almost exclusively on cART) • HIV-positive participants consistently had excess accumulation of AGEs • Excess accumulation of AGEs was independently associated with a higher prevalence of AANCC (causality cannot be established in a cross-sectional analysis)

  26. AgehIV Study Team Academic Medical Center P. Reiss (PI) F.W. Wit M. van der Valk J. Schouten K. Kooij B.C. Elsenga A. Henderiks Public Health Service Amsterdam M. Prins (co-PI) I.G. Stolte M. Martens J. Berkel S. Moll A. van Roosmalen G.R. Visser HIV Monitoring Foundation F. de Wolf S. Zaheri Y.M. Ruijs L. Gras A. Kesselring Amsterdam Institute of Global Health and Development M. Heidenrijk R. Meester F. Janssen Financial support: The Netherlands Organisation for Health Research and Development (ZonMW) grant nr. 300020007 & Stichting AIDS Fonds grant nr. 2009063 Additional unconditional grants from: Gilead SciencesBoehringer Ingelheim ViiV HealthcareAbbott Janssen Pharmaceuticals Merck & Co Bristol Myers Squibb

  27. AgehIV Study Team: additional collaborators AMC Dept. of Infectious DiseasesS.E. Geerlings, J.M. Prins, J.T. van der Meer, F.J. Nellen, M.H. Godfried, T. van der Poll, M. van Vugt, W.J. Wiersinga, H.E. Nobel, J. van Eden, M. Mutschelknauss-Rikken, A. van Hes, A. Westerman, F. Pijnappel Experimental Immunology & Lab Viral ImmunopathogenesisN. Kootstra, J. Hamann, E. van Leeuwen, M. Joerink, A.B. van ‘t Wout, H. Schuitemaker, P. Baars, R. Lutter Cardiology J. de Jong Vascular medicine B.J. van den Born, E. Stroes, B. van den Bogaard, E. de Groot Endocrinology M. Serlie, P. Bisschop, P. Lips (VUMC) Oncology D. Richel Geriatric medicine S. de Rooij Nuclear medicine B. van Eck-Smit, M. de Jong Pulmonary medicineR. van Steenwijk, E. Dijkers NefrologyJ. Willemsen, R. Krediet Gastro-enterologyE. Dekker PsychologyP. Nieuwkerk SexuologyR. van Lunsen, M. Nievaard NeurologyP. Portegies NeuropsychologyB. Schmand NeuroradiologyC. Majoie, M. Caan, T. Su, F. Vos OphthalmologyF. Verbraak, N. Demirkaya PsychiatryE. Ruhé, I. Visser HIV Vereniging NederlandM. Mulder And of course many thanks to all study participants!

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