HIV and Aging: The Spectrum of Disease Changes Amy C. Justice, MD, PhD Associate Professor of Medicine and Public Health Yale University International AIDS Society–USA

Disclosure Information Dr Justice had no relevant financial affiliations to disclose. (02/24/09) International AIDS Society–USA

Are the increasing number of “non-AIDS” events primarily due to: The price of success: People are living long enough on HAART to die of something else HIV disease progression Chronic inflammation ARV toxicity No primary driver

“By 2015, an estimated 50% of people living with HIV/AIDS [in the US] will be over 50 years of age.” Aging Hearing: HIV over fifty, exploring the new threat. Senate Committee on Aging. Washington, DC. 2005.

People with HIV are Living Longer Denmark: Ann Intern Med 2007:146:87-95 New York IDU: CID 2005:41:864-72 Barcelona: HIV Medicine 2007;8:251-8

Older People are Becoming Infected: New US AIDS Cases Number of New Cases per Year Distribution of New Cases 80000 25% 70000 20% 60000 50000 15% 40000 10% 30000 20000 Since 1993, the number of new AIDS cases has dramatically decreased in younger patients but has remained relatively constnat in older patients. As a consequence, the proporiton of cases in older patients has incresed, now accounting for over 20% of all new cases of AIDS in the US as of 2007. 1981 – 1999 data from CDC Wonder data; cases diagnosed in the year shown. 2002 - 2006 & cumulative data from http://www.cdc.gov/hiv/topics/surveillance/resources/reports/2006report/table2.htm 200 – 2001 data from http://www.cdc.gov/hiv/topics/surveillance/resources/reports/2004report/table3.htm All surveillance reports available at http://www.cdc.gov/hiv/topics/surveillance/resources/reports/past.htm#surveillance 5% 10000 0% 1981 1985 1989 1993 1997 2001 2005 1981 1985 1989 1993 1997 2001 2005 Age <50 Age ≥50 Age  50 Age >=50 http://www.cdc.gov/hiv/topics/surveillance/resources/reports/2007report/default.htm

Older People Do Not Do As Well: Survival/AIDS by Seroconversion Age Median time to AIDS Age 15 – 24: 11 years Age  65: 5 years The mortality rate per 1000 person-years increased by a factor of 1·47 (1·41­1·53) for each 10-year increase in age at seroconversion, whereas for AIDS excluding Kaposi's sarcoma the corresponding factor was 1·32 (1·26­1·38). Data from 38 separate cohorts of seroconverters. Lancet 2000; 355:1131

Age-Related CD4 Response Viard, Data from EuroSIDA, JID 2001; 183:1292

Virologic Response to HIV Treatment Most studies suggest that older patients have as good or better virologic response to HAART Excellent response to VL <50 Possible relationship to better adherence

Life Expectancy is Not “Normal” At HAART Initiation CD4 Cell Count (mm3) <100 100-199 >200 A 20 yr old will live to 52 62 70 A 35 yr old will live to 65 72 Years lost/1000 PY 461 265 138 Adapted from ART-CC, Lancet 2008;372:293-99 by adding additional expected survival to age at treatment initiation.

“Non AIDS” Deaths More Common Source Non AIDS Leading Causes Ref NY Death Certificates 26% Alcohol/drug abuse (31%), CVD (24%), Cancer (21%) Ann Intern Med 2006;145:397-406 Barcelona Death 60% Liver ( 23%), Infection (14%), Cancer (11%), CVD (6%) HIV Med 2007:8;251-8 HOPS Chart Rev. 63% Liver (18%), CVD (18%), Pulmonary (16%), Renal (12%), GI (11%), Infection (10%) Cancer (8%) J Acquir Immune Defic Syndr 2006;43:27-34 Cascade Liver (20%), Infections (24%), Unintentional (33%), Cancer (10%), CVD (9%) AIDS 2006; 20;741-9

Frailty vs Age and Duration of HIV Infection Yrs of HIV infection Desquilbet L. J Gerontol Series A. 2007; 62:1279.

Spectrum Change More people over 50 with HIV More people over 50 seroconverting Higher prevalence of Non-AIDS disease -Comorbidity -Toxicity More people dying of Non-AIDS conditions

Is This The Price of Success? No surprise that older people have an increased risk of mortality. Are younger people simply living long enough to experience unrelated conditions and to die from them? Or, is something else happening?

Adapted from Goulet et al, AIDS 2005 (Suppl 3); S99-S105

Strategies for Management of ARV Therapy (SMART) RCT of interrupted ARV treatment based on immune reconstitution to minimize toxicity As expected, found that AIDS events (and deaths) were decreased among those on continuous ARVs Also found that non-AIDS events were higher in those randomized to interrupted therapy Liver, renal and cardiovascular events HR 1.7, 95% CI: 1.1-2.5 Strategies for Management of Antiretrivoral Therapy NEJM 2006;355:2283-96

More AIDS and “Non-AIDS” Events Among Rx. Sparing Arm (SMART) Intensive Total All Cause Death 55 30 85 Serious OI 13 2 15 Nonserious OI 63 18 81 Major CAD, Renal, or Liver Disease 65 39 104 Strategies for Management of Antiretrivoral Therapy NEJM 2006;355:2283-96

But, how do we determine which are and which aren’t? SMART concluded that some “non-AIDS” events may be caused by HIV But, how do we determine which are and which aren’t?

What Does It Matter? If a condition is more likely or progresses rapidly due to HIV infection Early HAART may be indicated for those with or at high risk for the condition If a condition is more likely or progresses rapidly due to a specific ARV or class Then other ARVs or classes might be selected If a condition is independent of HIV or its treatment Then conventional approaches to management can be adapted to those with HIV

Associations Offering Clues to Etiology Caveat: these studies are based upon observational data. In such analyses, causality can never be proven, only increasingly strong associations documented.

Associated with HIV Infection Increased among those with HIV infection (vs. without) Increased at lower CD4 count or higher HIV RNA Should improve with ARV therapy Warning: make sure uninfected controls are demographically and behaviorally similar to those with HIV

Association with Antiretroviral Therapy (ARV) Increased among particular drugs or drug classes Should increase with increased drug exposure Warning: -Difficult to differentiate from HIV except when in opposite directions (e.g. hyperlipidemia) -Consider bias by indication -Consider “return to health” (e.g. weight gain)

Comorbidity Associated with Warning: Race, gender, age Socioeconomic status Tobacco, alcohol, drugs Other lifestyle behaviors (obesity, inactivity) Warning: -May confound association with HIV or ARVs -Possibility of synergy—need to study populations at risk

Real Life Example: Liver Disease 52 y/o past IDU with HIV/HCV Same regimen of HAART for 8 years with good viral suppression Dies with a CD4 cell count of 250 and hepatocellular carcinoma

One Condition, Multiple Etiologies Substance use Drugs, ALCOHOL Cause of nonadherence Viral hepatitis Chronic Hepatitis C and B Medication toxicity Antiretrovirals (nevaripine, D drugs) Non-HIV medications HIV infection Chronic inflammation Immune compromise with deregulation Liver Disease

Hepatic Mortality Among HIV+ Risk Factors for Hepatic Mortality Mortality vs CD4 Count Relative Rate Risk per 2x  CD4 1.23 Risk per 1.0 log VL 1.27 Risk per 5 yr  age 1.32 IDU 2.01 Active HBV infection 3.73 HCV infection 6.66 0.01 0.1 1 10 100 <50 50-99 100-199 200-349 350-499 500 Latest CD4 Count Deaths/100 patient years AIDS mortality Hepatic mortality Adjusted Relative Rate is shown D:A:D study: Weber et al, Arch Intern Med 2006

Alcohol & Liver Disease Percent FIB-4 >3.25 Lim et al, under review, Hepatology

Characteristic IRR 95% CI IRR 95% CI VC Hepatocellular Carcinoma Standardized IRRs Comparing HIV Infected Patients With HIV Negative Controls (n=42,037) Model 1 Model 2 Characteristic IRR 95% CI IRR 95% CI HIV 1.7 1.0 to 2.8 0.96 0.56 to 1.6 HCV — — 12.5 6.5 to 24.3 Alcohol ab/depend — — 1.9 1.0 to 3.4 Age 1.1 1.0 to 1.1 1.1 1.0 to 1.1 Race Black 2.1 1.1 to 4.1 1.4 0.7 to 2.7 Hispanic 4.9 2.2 to 10.8 4.0 1.8 to 8.8 Unknown/other 1.3 0.6 to 3.0 1.7 0.75 to 4.0 McGinnis et al. Hepatocellular Carcinoma and Non-Hodgkin’s Lymphoma: The Role of HIV, Hepatitis C Infection, and Alcohol Abuse J Clin Oncol 2006 24:5005-5009.

2 More Real Life Examples

Intracranial Hemorrhage Black box warning for Tipranavir (TPV) Rate on TPV exceeded uninfected rates VA and California Medicaid data showed: Similar rate among those with HIV prior to TPV Risk of ICH for HIV infected: 2.5, 95% CI 1.5-4.0 Risk of ICH after AIDS: 2.1, 95% CI 1.8-2.6 VA and California Medicaid rates differed Need to treat 455-500 patients with TPV for a year before seeing a single excess ICH events AC Justice, DS Zingmond, KS Gordon, et al. CID 2008 47:1226-30

Bone Mineral Density in HIV+/- Brown TT & Qaqish RB. AIDS. 2006; 20:2165-2174. Overton T et al. CROI 2007. Abstract 836

Osteoporosis/Osteopenia Osteoporosis requires a fragility fracture, osteopenia is a risk factor for fracture Increased osteopenia (BMD of femoral neck and lumbar spine: Arnsten et al AIDS 21:617-623 ) has been demonstrated One age adjusted study (Triant et al J Clin Endo and Metabolism 2008 93:3499-504) documented increased fragility fractures among men and women with a RR of ~1.3-2

Snapshot of “NonAIDS” Associations

Non AIDS Conditions Increased Comparing HIV+/- Condition (confounder) Evidence HAART Anemia (zidovudine) Strong Improves Venous Thrombosis Moderate Unknown Intracranial Hemorrhage CAD (cocaine use, HCV) Increasing Conflicting Obstructive Lung Disease (tobacco) Early Non HIVAN Renal Disease (hypertension, diabetes) Osteoporosis (tob., alc., wasting) Chronic viral hepatitis (alcohol)

Non-AIDS Cancers Increased Comparing HIV+/- Cancer (confounder) Evidence IRR Anal Cancer (MSM) Strong 10-30 Hodgkin’s Lymphoma ~5 Lung Cancer (smoking) Moderate 1-3 Melanoma (sun exposure)

Non-AIDS Conditions Associated with Treatment (Comparing HAART+/-) Evidence Time on Drug Obesity HAART Strong Unknown Hyperlipidemia Hypertension Early (PIs) Diabetes/ Glu Intolerance Strong (PIs) Renal Disease Tenofovir Increases Liver Disease Nevaripine, D Drugs CAD PIs, Abacavir?

Implications for HIV Care HIV infection increases risk and progression of common infectious and noninfectious conditions Screening/treatment guidelines for non-AIDS condition need to be tailored for those with HIV Some non-AIDS conditions may justify earlier or more aggressive ARV treatment Selected ARV treatments likely cause/exacerbate some non-AIDS conditions, but effects are often less pronounced than those of HIV itself We need a more integrated index of relevant biomarkers with which to follow HIV as a complex chronic disease

Implications for Research: Cause of Death Until we know what is truly driving “NonAIDS” mortality we should not be so quick to dismiss it

Implications for Research: We Need A Clinical Index for Chronic HIV AIDS-defining conditions are rare and have variable associations with mortality CD4 count and HIV-RNA do not capture the full effect of ARV treatment Mortality events are too delayed to be primary outcome for most RCTs We need an index that integrates and prioritizes these events with respect to overall risk of mortality

West Haven/Yale VACS Project Team

National VACS Project Team

Veterans Aging Cohort Study PI and Co-PI: AC Justice, DA Fiellin Scientific Officer (NIAAA): K Bryant Participating VA Medical Centers: Atlanta (D. Rimland, C Jones-Taylor), Baltimore (KA Oursler, R Titanji), Bronx (S Brown, S Garrison), Houston (M Rodriguez-Barradas, N Masozera), Los Angeles (M Goetz, D Leaf), Manhattan-Brooklyn (M Simberkoff, D Blumenthal, J Leung), Pittsburgh (A Butt, E Hoffman), and Washington DC (C Gibert, R Peck) Core Faculty: K Mattocks (Deputy Director), S Braithwaite, C Brandt, K Bryant, R Cook, J Conigliaro, K Crothers, J Chang, S Crystal, N Day, J Erdos, M Freiberg, M Kozal, M Gaziano, M Gerschenson, B Good, A Gordon, J Goulet, M Hernan, K Kraemer, J Lim, S Maisto, P Miller, L Mole, P O’Connor, R Papas, H Paek, J Robins, C Rinaldo, M Roberts, J Samet, B Tierney, J Whittle Staff: D Cohen, A Consorte, K Gordon, F Kidwai, F Levin, K McGinnis, M Rambo, J Rogers, M Skanderson, F Whitsett Major Collaborators: Immunology Case Registry, Pharmacy Benefits Management, Framingham Heart Study, Women’s Interagency HIV Study, Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), Health Economics Research Center (HERC), Center for Health Equity Research and Promotion (CHERP), ART-CC, NA-ACCORD Funded by: National Institute on Alcohol Abuse and Alcoholism (2U10 AA 13566); National Institute on Aging (K23 G00826); Robert Wood Johnson Generalist Faculty Scholar Award; an Inter-Agency Agreement between National Institute on Aging, National Institute of Mental Health, and the Veterans Health Administration; the VHA Office of Research and Development; and, VHA Public Health Strategic Health Care Group.

Stay tuned.

Are the increasing number of “non-AIDS” events primarily due to: The price of success: People are living long enough on HAART to die of something else HIV disease progression Chronic inflammation ARV toxicity No primary driver