# Presentation: 533970

## Women’s Interagency HIV Study (WIHS) 

- Prepared in part by WIHS Data Management and Analysis Center (WDMAC)
- Phone: 410-614-1340 (-7125 FAX)
- email: wdmac@jhsph.edu
- http://statepiaps.jhsph.edu/wihs

*Range of Data, specimens and analyses available to researchers investigating drug use and its ramifications*
*Kathy Anastos MD*

## Women’s Interagency HIV Study(WIHS)

**San **
**Francisco**

**Bronx, NY**

**Chicago**

**Los**** ****Angeles**

**Washington **
**DC**

**Brooklyn, NY**

**Baltimore, MD **
**(Data Center)**

## WIHS Sites and Principal Investigators

- Consortia:
- Bronx, NY (K. Anastos)
- Brooklyn, NY (H. Minkoff)
- Chicago, IL (M. Cohen)
- Los Angeles, CA (A. Levine)
- Northern California (R. Greenblatt)
- Washington, DC (M. Young)
- Data Coordinating Center (WDMAC):
- Johns Hopkins University, Baltimore, MD(S. Gange)

## WIHS Sponsoring Institutions (Project Officers)

- National Institute of Allergy and Infectious Diseases
- 	(G. Sharp; C. Williams – Epidemiology Branch Chief)
- National Cancer Institute (G. Dominguez)
- National Institute of Child Health and Human Development (H. Watts)
- National Institute on Drug Abuse (K. Davenny)

## Areas of Focused Scientific Research in the WIHS

- Neurocognition
- Cancer
- Cardiovascular Disease
- Drug Use & Behavior
- Genetics
- Pharmacology

- Gynecology/Menopause
- HPV
- Immunology/Pathogenesis
- Metabolic
- Hepatitis / Liver Disease
- Epidemiology

## Cardiovascular Disease: Assessment of aspects of cardiovascular disease among HIV+ and HIV- women, including cardiovascular risk factors, carotid artery intima-media thickness (IMT), and clinical cardiovascular events

Genetics and Disease Progression: Interaction between genetic factors, substance abuse, mood, cognitive impairment, and progression of HIV disease

Neurocognition: Interaction between aging and HIV/AIDS on neurocognitive functioning among HIV+ and HIV at-risk women

Physical Functioning: Cross-sectional assessment to identify and characterize physical impairment and frailty among HIV+ and HIV at-risk women

**Genetics and Disease Progression****:** Interaction between genetic factors, substance abuse, mood, cognitive impairment, and progression of HIV disease
**Neurocognition:** Interaction between aging and HIV/AIDS on neurocognitive functioning among HIV+ and HIV at-risk women
**Physical Functioning:** Cross-sectional assessment to identify and characterize physical impairment and frailty among HIV+ and HIV at-risk women

**WIHS Major Active Substudies**

## WIHS Major Active Substudies cont’d. 

**Metabolic ****:** Cross-sectional and longitudinal assessment of metabolic parameters including fat distribution, lipoprotein levels, bone density and glucose tolerance determined via DXA scan, glucose tolerance testing (GTT), and MRI
**Intensive Pharmacokinetics (PK)****:** Bioavailability and clearance rates for five target antiretroviral medications
**Sex Steroid****:** Effect of HIV infection on age of onset of diminished ovarian reserve

## Semi-Annual Visit

**Interview Administered Questionnaires**:
- Medical and OB/GYN History
- Health Services
**Physical and Gynecological Examination**
**Body Habitus Exam** (anthropometrics, BIA)
**Medication use** (ART, OI prophylaxis, hepatitis, etc.)
**Participant Samples**:
- Blood (virologic, immunologic, fasting lipodystrophy markers, liver/renal function, etc.)
- Other (CVL, saliva, hair)
- Local and National Repositories

-   Demographics/Psychosocial
-   Behavior

## Continuous Outcome Ascertainment

- Seroconversion
- Clinical Outcomes:
- AIDS Diagnoses
- Malignancies
- Mortality
- Tuberculosis
- Cardiovascular Diagnoses
- Liver biopsies
- Hysterectomies

- Sources:
- Medical Record Abstractions
- Registries (Cancer, AIDS, TB)
- National Death Index

## WIHS Cohort (as of 3/06) *

**WIHS Cohort ****(as of 3/06)**** *******

**3766**

**Seroprevalent: 2791 (74%)**

**Seronegative: 975 (26%)**

**AIDS**

**(34%)**

**AIDS**

**(47%)**

**1378**

**(66%)**

**AIDS baseline**

**715 (26%)**

**AIDS-free baseline**

**2076 (74%)**

**Seroconverter**

**17 ****(****** **1) ***** (2%)**

**Seronegative**

**958 (98%)**

**Dead**

**(39%)**

**Dead**

**375 ****(****** **16)**

**(52%)**

**Dead**

**(9%)**

**Dead**

**43 ****(******** 3) **

**(4%)**

**Dead**

**9**

**(53%)**

**Dead**

**1 ****(****** **1) ********

**(25%)**

**AIDS-free**

**AIDS-free**

**273 ****(****** **20)**

**122 ****(****** **7)**

**698 ****(****** **18)**

***HIV and AIDS status as of the of visit 23 (3/31/06). Deaths reflect those ascertained through 12/31/04 using the National Death Index and through 4/7/06 using other reports.**

**(11%)**

**2 ****(****** **1)**

****One seroconverter found at death.**

**8 ****(****** **1)**

**Notes:**
Lorie
Update

## Baseline Characteristics (1)(Barkan, Melnick, . . . , Feldman, Epidemiology 1998; 9:117-125)**

- 			94/95 Cohort		01/02 Cohort
- 		_HIV+__	__HIV-__	__HIV+__	__HIV-_
- Median age
- 		36		34		33	29
- Race/ethnicity:
- 	African-American		56%	54%		60%	61%
- 	Latina
- 		23%	28%		32%	28%
- Exposure Category:
- 	Intravenous drug use		34%	28%		10%	13%
- 	Heterosexual risk		42%	26%		41%	24%
- 	Transfusion risk		4%	3%		--*	--*
- 	No identified risk		20%	43%		48%	63%

***  Transfusion risk not assessed in 01/02 cohort           ** 01/02 cohort data added**

## 			94/95 Cohort		01/02 Cohort
				HIV+	HIV-	HIV+	HIV-
Median per capita 
	household income		$4,500	  $5,000		$4,500	   $4,500

No health insurance		18%	41%		15%	39%
History of physical/sexual abuse		66%	67%		9%	16%

Median CD4+ count (cells/mm3)		330	1028		493	984
Median viral load 
	(copies/ml, NASBA assay)		22,000	---		610	---

- 				_HIV+__	__HIV-__	__HIV+__	__HIV-_
- Median per capita
- 	household income		$4,500	  $5,000		$4,500	   $4,500
- No health insurance		18%	41%		15%	39%
- History of physical/sexual abuse		66%	67%		9%	16%
- Median CD4+ count (cells/mm3)		330	1028		493	984
- Median viral load
- 	(copies/ml, NASBA assay)		22,000	---		610	---

**Baseline Characteristics (2)****(Barkan, Melnick, . . . , Feldman,  *****Epidemiology *****1998; 9:117-125)***

*** 01/02 cohort data added**

## Baseline Characteristics (3)(Bacon, Von Wyl, . . . , Young, CDLI 2005; 12:1013-1019)

- Parameter or condition	      94/95 Cohort       01/02 Cohort
- HBV core antibodies			43		19
- HBV surface antigen			2.6		1.4
- HCV antibodies				39		13
- SIL on Pap test*				13		8
- Diabetes					4.4		4.6
- Hypertension				17		12
- Obesity					53		65
- Smoking					58		42

- *Low-grade SIL, high-grade SIL, or carcinomas were detected in situ on initial Pap smear.
- HBV, hepatitis B virus; HCV, hepatitis C virus; SIL, squamous intraepithelial lesion.

**Percent with comorbidity**

## WIHS Database (as of 3/06)

- Total number of PARTICIPANTS enrolled	2807	HIV+
- 		959	HIV-
- Total number of PERSON-YEARS	17,659	HIV+
- 		5,627	HIV-
- Median FOLLOW-UP TIME in years	10.10	94/95
- 		3.59	01/02
- Total number of PERSON-VISITS	34,233	HIV+
- 		11,010	HIV-
- Total number of CD4 MEASUREMENTS	32,436	HIV+
- 		6,978	HIV-
- Total number of VIRAL LOAD MEASUREMENTS	32,368
- Total number of REPOSITORY ALIQUOTS* 	**1,842,688**
-   * Available as of 11/14/2006

**Notes:**
Johanna – update number of repository aliquots
Lorie – update everything else

## WIHS Self-Reported AIDS Incidence, 

**Year**

**Incidence per 100 person years**

**HAART Use Among HIV+ (%)**

**HAART**

**WIHS Self-Reported AIDS Incidence, **
**Mortality and Use of HAART**
**(Gange, Barrón, . . . , Muñoz, *****J Epi Comm Health *****2002; 56:153-159) (as of 3/06)**

**AIDS**

**Death**

## Antiretroviral Therapy Use Among WIHS HIV+(Cook, Cohen, . . . , Young, AJPH 2001, 92:82-87) (as of 3/06)

**Antiretroviral Therapy Use Among WIHS HIV+****(Cook, Cohen, . . . , Young, *****AJPH *****2001, 92:82-87) (as of 3/06)**

## Antiretroviral Use Among WIHS HIV+(Kirstein, Greenblatt, ..., Gange, JAIDS 2002, 29:495) (as of 3/06)

## Effect of Substance Use on HIV disease progression

## Association of Race and Gender with HIV-1 RNA

**(Anastos, Gange, . . . , Greenblatt, *****JAIDS *****2000; 24:218-226)**

**200 ******** CD4 < 350**

**CD4 < 200**

**Relative Difference of HIV-1 RNA**
**in WIHS Women relative to MACS Men**** **

**350 ******** CD4 < 500**

**500 ******** CD4**

**Relative Difference of **
**HIV-1 RNA in Whites **
**relative to Non-whites **

******p < 0.05   ******** p < 0.005**

- 

- 

- 

- 

- 

- 

- 

## Association of HIV-1 RNA with history of Injection Drug Use with

**0**

**20,000**

**40,000**

**60,000**

**80,000**

**100,000**

**120,000**

**140,000**

**160,000**

**180,000**

**Non-IDU**

**IDU**

**(267)**

**(136)**

**(345)**

**(133)**

**(516)**

**(162)**

**(986)**

**(314)**

**CD4****200-350**

**CD4****<200**

**CD4****350-500**

**CD4****>500**

**(*****n*****)**

**Median HIV RNA**

- Anastos.  *JAIDS* 2000;24:218.

**Rates ********21% lower (*****P*****<.001)**

**Association of HIV-1 RNA with history of Injection Drug Use with**

## Factors Associated with Response to HAART (1)(Anastos, Schneider, . . . , Cohen, JAIDS 2005; 39,5:537-544)

**Proportion Alive**

- 0

- 1

- 2

- 3

- 4

- 5

- 6

- 7

- 8

- 0.0

- 0.2

- 0.4

- 0.6

- 0.8

- 1.0

-                                                 **No.        No. of              RH          ( 95% CI )      Adjusted      ( 95% CI ) ****                                                             Deaths                                                       RH***

**African-American   573          113                  1                 --                    1                    --**

**White                       184            25                0.63      ( 0.41, 0.98 )         1.11        ( 0.64, 1.93 )**

**Hispanic                  204            31                0.68      ( 0.46, 1.01 )         0.81        ( 0.47, 1.39 )**

**Factors Associated with Response to HAART (1)****(Anastos, Schneider, . . . , Cohen, *****JAIDS *****2005; 39,5:537-544)**

*** Adjusted for ART use prior to HAART initiation, age at last pre-HAART visit, pre-HAART AIDS status, pre-HAART nadir CD4****+**** cell count, pre-HAART peak HIV-1 RNA, self-reported baseline HIV-1 exposure category, depression, current drug use, cigarette smoking, income, ART used following HAART initiation.**

## Factors Associated with Response to HAART (2)(Anastos, Schneider, . . . , Cohen, JAIDS 2005; 39,5:537-544)

| Exposure | Virologic Response | Virologic Rebound | Immunologic Response | Immunologic Failure | Incident ADI | Death | AIDS Death |
| --- | --- | --- | --- | --- | --- | --- | --- |
| ART naïve prior to HAART | 1.77 | 0.82 | 0.96 | 0.72 | 1.29 | 0.94 | 1.41 |
| Age at last pre-HAART visit (per 10 years) | 1.19 | 0.91 | 1.12 | 1.00 | 0.98 | 1.36 | 1.18 |
| Pre-HAART AIDS | 0.98 | 1.04 | 0.93 | 0.92 | 2.19 | 1.35 | 1.62 |
| Pre-HAART nadir CD4+ count (per 100 cells) | 1.08 | 1.02 | 1.09 | 1.27 | 0.86 | 0.61 | 0.30 |
| Pre-HAART peak HIV-1 RNA (per log10) | 0.62 | 1.48 | 1.02 | 0.89 | 1.38 | 1.61 | 1.63 |
| Therapy used after HAART |  |  |  |  |  |  |  |
|    No therapy | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
|    Mono/combo | 8.55 | 0.30 | 4.07 | 0.59 | 0.72 | 0.53 | 0.71 |
|    HAART | 16.14 | 0.26 | 7.04 | 0.31 | 0.74 | 0.46 | 0.33 |
| Depression (CES-D > 16) | 0.81 | 1.22 | 0.96 | 1.98 | 1.62 | 1.65 | 1.06 |
| Current drug use | 0.89 | 1.23 | 0.85 | 1.11 | 1.49 | 1.04 | 2.35 |
| Currently smoke cigarettes | 0.72 | 1.30 | 1.04 | 0.93 | 1.18 | 1.38 | 1.05 |
| Income <$12,000 | 0.91 | 1.25 | 0.98 | 1.45 | 1.19 | 1.64 | 1.39 |
| Adherence | 2.19 | 0.37 | 1.47 | 0.66 | 0.65 | 0.67 | 0.53 |

- Note: Adjusted for HIV-1 exposure and race.  Significant values are in bold and yellow.

## Time to AIDS Among Non-injecting Drug Users (NIDU) in the HAART Era

**Time to AIDS Among Non-injecting Drug Users (NIDU) in the HAART Era**
**(Kapadia, Cook, . . . , Vlahov, *****Addiction *****2005; 100:990-1002)**

- LRT=0.001

- Never User

- Former NIDU

- Inconsistent NIDU

- Consistent NIDU

**Years to AIDS**

**Survival Probability**

**Kaplan-Meier curves of cumulative probabilities of survival for AIDS by pattern and type of NIDU among HAART users, WIHS (n=1046). **

**Notes:**
KM curves for AIDS among women reporting use of HAART.  Time to AIDS differed significantly by both pattern and type of NIDU with consistent users and stimulant-only users showing greater likelihood of progessing to AIDS compared with other categories in their respective groups.

## Time to Death Among Non-injecting Drug Users (NIDU) in the HAART Era

**Time to Death Among Non-injecting Drug Users (NIDU) in the HAART Era**
**(Kapadia, Cook, . . . , Vlahov, *****Addiction *****2005; 100:990-1002)**

**Kaplan-Meier curves of cumulative probabilities of survival for death by pattern and type of NIDU among HAART users, WIHS (n=1046). **

**Survival Probability**

**Years to Death**

**Notes:**
KM curves for all-cause death among women reporting the use of HAART.  Survival probability for all-cause death among individuals who reported initiating HAART was significantly different by pattern of NIDU indicating a greater likelihood of death among former users compared to other patterns of NIDU.  Time to all-cause death did not differ significantly by type of NIDU.  Additionally, survival probabilities did  not differe significantly for AIDS-related deaths by either pattern or type of NIDU among those not reporting HAART initiation during follow-up (not shown).

## Association of Smoking with CD4 response to HAART

- Feldman J, Schneider M, Gange S, Cohen M, Watts H, Gandhi M, Young M, Mocharnuk R, and **Anastos K**.  Impact of
- Cigarette Smoking on HIV Prognosis Among Women in the HAART Era- A Report from the WIHS.  *Am J Public Health*  2006

## Association of Smoking with HIV-1 RNA response to HAART

- Feldman J, Schneider M, Gange S, Cohen M, Watts H, Gandhi M, Young M, Mocharnuk R, and **Anastos K**.  Impact of
- Cigarette Smoking on HIV Prognosis Among Women in the HAART Era- A Report from the WIHS.  *Am J Public Health*  2006

## Progression to AIDS-defining condition by smoking

- Feldman J, Schneider M, Gange S, Cohen M, Watts H, Gandhi M, Young M, Mocharnuk R, and **Anastos K**.  Impact of
- Cigarette Smoking on HIV Prognosis Among Women in the HAART Era- A Report from the WIHS.  *Am J Public Health*  2006

## All-cause mortality by smoking

- Feldman J, Schneider M, Gange S, Cohen M, Watts H, Gandhi M, Young M, Mocharnuk R, and **Anastos K**.  Impact of
- Cigarette Smoking on HIV Prognosis Among Women in the HAART Era- A Report from the WIHS.  *Am J Public Health*  2006

## Association of Substance Use with Other Outcomes

## Relative Hazard for New HPV Infection in HIV+ Women(Minkoff, Feldman, . . . , Anastos, JID 2004; 189:1821-1828)

**	*****RH (95% CI)******	******P***
**Monotherapy vs. none****	****0.81 (0.49 – 1.32)****	****.40**
**Combination therapy vs. none****	****1.06 (0.64 – 1.75)****	****.83**
**HAART vs. none****	****1.08 (0.66 – 1.77)****	****.76**
**Oral contraceptive use (yes vs. no)****	****1.09 (0.69 – 1.72)****	****.72**
**Log CD4+ cell count****	****0.90 (0.83 – 0.97)****	****.008**
**Log viral load****	****1.06 (1.01 – 1.11)****	****.01**
**Smoking status (yes vs. no)****	****1.33 (1.10 – 1.60)****	****.003**
**Parity****	****1.02 (0.98 – 1.06)****	****.34**
**No. sex partners, recent or lifetime****	****0.99 (0.97 – 1.03)****	****.80**
**Age at baseline****	****0.988 (0.97 – 1.00)****	****.04**
**Age at first intercourse****	****1.00 (0.97 – 1.03)****	****.79**

- Parity and age at first intercourse are fixed variables; smoking, number of sex partners, oral contraceptive use, CD4+ cell count, and viral load are time-dependent variables.
- 1797 women; 501 events.

## Accidental Death in WIHS and MACS

-   IDU and smoking each independently associated with higher likelihood of accidental death:
- IDU:  		OR=2.6, p=0.0007
- Smoking:	OR=1.63, p=0.027

- Hessol N et al,  CID 2007

## Predictors of Menopause and Amenorrhea

- Opiate use predicted amenorrhea, but not menopause
- Menopause also predicted by age, BMI
- Menopause *not *predicted by HIV status, or any HIV related parameter (CD4, Viral load)

- Cejtin et al, unpublished data

## Association of Substance Use with Behaviors

- Including Adherence

## Correlates of ART Adherence in the

- 0.1

- 1

- 10

**Correlates of ART Adherence in the**
**Women's Interagency HIV Study**** **
**(Wilson, Barrón, . . . , Young, *****Clin Infect Dis *****2002; 34:529-534)**

**Odds Ratio**

**Crack, Cocaine or Heroin Use**

***< 200***

***200 - 500***

**CD4 Cell Count **
**(*********** 500***** reference)**

**Undetectable**
** ****HIV RNA**

**Age**
**(10 year     ) **

**Health Perception Score   **
**(10 pts     )**

## Drug Use and Sexual Behavior

- Women using crack, cocaine or heroin less likely to use condoms: OR=0.63, p<0.0011
- Women who undergoing treatment for drug use were less likely to be sexually active: OR=0.83, p=0.00012

- 1Wilson et al, AIDS 1999; 13: 591

- 2Latka et al. J Subs Abuse Treat 2005; 29:329

## Domestic Violence and Childhood Sexual Abuse in WIHS Women(Cohen, Deamant, . . . , Melnick, Am J Public Health 2000; 90:560-565)

**Multivariate Behavioral Correlates of Childhood Sexual Abuse**
**Drug use, ever **
**	****	****(OR* = 4.25, p < .001)**
**Male partner w/HIV risk **
**	****	****(OR* = 2.07, p < .001)**
**Lifetime male sex partners (>10)**
**	****	****(OR* = 2.29, p < .001)**
**Sex for drugs, money or shelter**
**	****	****(OR* = 2.62, p < .001)**
***OR adjusted for HIV serostatus, age, race/ethnicity and annual **
**      ****household income**

**NS**

**p < .01**

**NS**

**Baseline Prevalence**

## Hepatitis C

## Association between Renal Disease and Outcomesin HIV-Infected Women Receiving HAART(Szczech, Hoover, . . . , Anastos, Clin Infect Dis 2004; 39:1199–1206)

## Predictors of HCV viremia in adjusted models

- Age (>35 years)		   OR= 1.6,   p=0.005
- African Ancestry	   OR= 1.6,   p=0.04
- Crack/cocaine use	   OR= 1.7,   p=0.02
- HBV viremia		   OR= 0.3    p=0.006
- Current smoking	   OR= 1.7,   p=0.009

- Operkalski et al unpublished data

## Opiate Use, HCV and incident Diabetes

- Howard et al, unpublished data 2007

## Opiate Use, HCV and incident Diabetes, cont’d.

- Howard et al, unpublished data 2007

## Translational Studies

## Impact of Drug Use on CD4 Cell Activation(Landay, Benning, . . . , Kovacs, JID 2003; 188:209-218)

**CD4 cell count per mm****3**

**HIV-1 RNA (copies/mL)**

**1**

**10**

**200**

**500**

**2,000**

**100**

**1**

**10**

**100**

**200**

**500**

**2,000**

**80**

**400**

**4,000**

**40,000**

**400,000**

** ****% HLADR+/DR38+/CD4+**

** ****% HLADR+/DR38+/CD4+**

**HIV-negative**

**HIV-positive**

**2**

**3**

**4**

**5**

**6**

**7**

**8**

**9**

**10**

**20**

**100**

**90**

**80**

**70**

**60**

**50**

**40**

**30**

**100**

**90**

**80**

**70**

**60**

**50**

**40**

**30**

**20**

**10**

**9**

**8**

**7**

**6**

**5**

**4**

**3**

**2**

**A**

**B**

**C**

**Impact of Drug Use on CD4 Cell Activation****(Landay, Benning, . . . , Kovacs, *****JID *****2003; 188:209-218)**

**Drug Users**

**Non-drug Users**

## HCV quasispecies

- Women with active IDU more likely to experience shift in quasispecies of HCV over time:  OR=5.12, p=0.004

- Laskus.....Kovacs, unpublished data

## Slide 46

## Cohort Composition

- 935 participants
- 715 HIV infected women
- 221 HIV negative women
- ~50% of each group are women who survived genocidal rape
- At 1.5 years follow-up 410 HIV+ women have initiated ART
- No tobacco or drug use
- 20% use alcohol

## Slide 49

## Slide 50