aging women with hivregist2.virology-education.com/presentations/2020/hiv... · 2020-03-12 ·...

Aging Women with HIVFit for Purpose & Fit for Community

Deborah R Gustafson MS PhD

Professor, Department of Neurology, Section for NeuroEpidemiologyState University of New York Downstate Health Sciences University, Brooklyn, New York, USA

Guest Professor, Department of Health and Education, University of Skövde, SwedenGuest Professor/Docent, Institute of Neuroscience & Physiology, Sahlgrenska Academy; EPINEP, AGECAP,

University of Gothenburg, Sweden

Cross-Pollinate Aging Epidemiology to

Aging HIV Epidemiology

Fit for Purpose and Fit for Aging HIV Communities

Gustafson DR. Low-cost cognitive assessment among low SES and diverse populations.

Executive Summary: Cost Effective Early Detection of Cognitive Impairment.

National Institute of Aging/National Institutes of Health. Oct 2017:19.

https://www.nia.nih.gov/sites/default/files/2018-01/final-cognitive-decline-summary.pdf.

1. Improve definitions and descriptions of terminology such as low socioeconomic status, diversity & disparities on regional, national and global levels

2. Advance identification & characterization of populations at risk and

actively recognize & use these characteristics in study design, data interpretation and designing interventions

3. Broaden measurement & detection environments through active & integrated partnerships comprised of industry, healthcare, public health, government & academic entities

What are some ‘translational’ needs across diverse,

aging HIV communities?

MACS/WIHS Combined Cohort Study Sites

US Map from AIDSVu with shading reflecting 2016 HIV Prevalence per 100K

Miami, FL

San Francisco, CA

Los Angeles, CA

Chicago, IL

Birmingham, AL/Jackson, MS

Atlanta, GA

Baltimore, MD

Washington, DC

Chapel Hill, NC

Bronx, NY

Brooklyn, NYPittsburgh, PA

Columbus, OH

• Johns Hopkins serves as the MWCCS Data Analysisand Coordination Center (DACC)

• 57-Member Executive Committee

• >30 Scientific & Operational Working Groups

Age Distribution of Active* MWCCS Participants

* Active = All participants who contributed data at Visit 48 or 49 for WIHS; 69 or 70 for MACS

Pe

rce

nt

of C

oh

ort

0

5

10

15

20

25

30

35

40

45

50

<30 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65+

WIHS MACS

HIV- PLWH HIV- PLWH

Median Age 50 52 63 58 (IQR) (43, 57) (46, 58) (57, 70) (50, 64)

Age > 60 18% 20% 64%42%

WIHS DemographicsWWH

N=1,516

HIV-

N=651

Median Age (IQR) 52 (46, 58) 50 (43, 57)

Race/Ethnicity (%):

Black/African American 73% 72%

Hispanic/Latina, any race 15% 14%

White 7% 11%

Multiple Races/Other 5% 3%

Median (mean) Follow-up Time, years 16.3 (13.3) 16.5 (13.7)

WIHS Chronic Disease IndicatorsWWH

N=1,516

HIV-

N=651

Smoking (%)

Current

Former

34%

32%

43%

30%

BMI Median (IQR)

% BMI >30 [>40]

31 (26, 37)

54% [18%]

32 (27, 38)

58% [18%]

Hypertension (SBP>140, DBP>90, meds) 53% 53%

Diabetes (HbA1C>6.5%, FG>126, meds) 25% 25%

Dyslipidemia (LDL>130, HDL<40) 36% 32%

Chronic Kidney Disease

CKD-Epi eGFR <60 [<30] 15% [2%] 7% [2%]

% with Confirmed Cancers (% AIDS Defining Event) 6% (1%) 4%

Not only are causes of death with higher ages important….

… but …Geriatric syndromes are more prevalent

Geriatric Syndromes

Fried Frailty Phenotype (FFP)

• Three or more of five characteristics: • impaired mobility

• reduced grip strength

• physical exhaustion

• unintentional weight loss

• low physical activity

Prevalence of frailty (FFP >3)

• At average age 39 years• in HIV+, 17%

• in HIV-, 10%

Terzian et al 2009; Gustafson et al 2016

6 9 9 14 1217

32 2831 30

7759 63 55 59

0

20

40

60

80

100

40-44 45-49 50-54 55-59 60+

Par

tici

pan

ts, %

HIV+

Frail Prefrail Robust(n=1 (n=2 (n=2 (n=2 (n=1

9 1221

62713

18

31

25

23

78 71

4870

50

0

20

40

60

80

100

40-44 45-49 50-54 55-59 60+

Par

tici

pan

ts, %

HIV-

Frail Prefrail Robust(n=5 (n=9 (n=8 (n=6 (n=7

WIHS Prevalence of the Frailty Phenotype WIHS by

HIV Status and Age in2015-2017

(n=1,404)

Fatukasi TV et al., AIDS. 2019.

Overall Frailty Prevalence using

Cardiovascular Health Study (CHS) criteria:

HIV+ 10%; HIV- 15%

Gustafson DR, et al. J Frailty Aging, 2016;5:43-8.

WIHS participants with VACS, FFP, and CES-D measured in 2005 and follow-up

over approximately 8 years

1627 HIV+ women eligible for FFI measurement

1570 not ART-naïve

1395 completed FFI 1525 completed CES-D 1525 had a VACS Index score

1385 had all 3 measures

Total Deaths 0-3 years, N=73AIDS deaths, N=39

Non-AIDS deaths, N=32Unknown cause of death, N=2

Censored, N=28Total alive and followed after 3 years, N=1284

Total Deaths >3-8 years, N=111AIDS deaths, N=35

Non-AIDS deaths, N=45Unknown cause of death, N=31

Censored, >3-7 years, N=181Total alive and followed after 7 years, N=992 = 1284-(111 + 181)

Gustafson DR, et al.

BMJ Open, 2017.

Veterans Aging Cohort Study (VACS) Index

Justice AC, Tate AP. Aids Res Hum Retroviruses, 2019.

Justice AC et al., J Acquir Immune Defic Syndr, 2013.

Justice AC, et al., HIV Med. 2010.

Variable Crude HR

(95% CI)

Chi2

(C2)

P-value

Univariate Analyses of Indices

VACS Score (0-164), per 20 units 2.21

(1.99, 2.46)

209.7 < 0.0001

FFP 3-5 vs 0-2 3.92

(2.92, 5.26)

83.2 < 0.0001

CESD (< 16 vs > 16) 2.01

(1.50, 2.68)

22.2 <0.0001

Proportional Hazards models

Time to all deaths by FFP, VACS, and CESD

among women with HIV infection

Gustafson DR, et al. BMJ Open, 2017.

All deaths AIDS deaths Non-AIDS deathsIndex C2 HR

(95% CI)P-

value

C2 HR

(95% CI)P-

value

C2 HR

(95% CI)P-value

FFP 26.2 2.36

(1.79, 3.28)0.00 9.76 2.34

(1.37, 3.98)0.00 20.8 3.20

(1.94, 5.26)0.00

VACS 97.1 1.85

(1,64, 2.09)0.00 100.1 2.54

(2.12, 3.05)0.00 7.01 1.33

(1.08, 1.65)0.01

CES-D 1.8 1.24

(0.91, 1.70)0.19 4.29 1.75

(1.03, 2.96)0.04 0.02 0.96

(0.59, 1.57)0.89

Age per

decade

10.2 1.37

(1.13, 1.66)0.00 0.00 0.99

(0.72, 1.36)0.96 11.1 1.63

(1.22, 2.17)0.00

Multivariable proportional hazards models of time to

all, AIDS and non-AIDS deaths over ~8 years follow-up

among women with HIV infection

Gustafson DR, et al. BMJ Open.

All Deaths Within 3 Years After FFI

Visit

All Deaths Later Than 3 Years After

FFI Visit

Variable C2 Multivariate-

Adjusted HR

(95% CI)

P-value C2 Multivariate-

Adjusted HR

(95% CI)

P-Value

VACS Index per

20 points

(max 164)

72.50 2.21 (1.84, 2.65) 0.0000 26.75 1.59 (1.33, 1.89) <0.0001

FFP

(3-5 vs 0-2)

7.46 2.11 (1.23, 3.59) 0.006 16.85 2.44 (1.59, 3.73) <0.0001

CES-Dc

(< 16 vs >=16)

1.74 1.42 (0.84, 2.40) 0.187 0.35 1.13 (0.75, 1.69) 0.550

Age per Decade 3.23 1.33 (0.97, 1.83) 0.072 7.89 1.42 (1.11, 1.82) 0.005

Multivariable proportional hazards models of time to

all deaths within 3 years vs >3 years later

Gustafson DR, et al. BMJ Open.

Diabetes

ADA and FINRISC Diabetes Risk Scores

• Risk scores include:• Age

• BMI (weight [kg]/height2 [meters])

• waist circumference

• history of hypertension (systolic blood pressure ≥140 mmHg and diastolic blood pressure ≥90 mmHg or self-reported hypertension or as self-reported anti-hypertension medication use prior to or at index visit)

• history of hyperglycemia (FBG measure of 100–125 mg/dL at index visit)

• family history of diabetes.

• Physical activity was excluded from the ADA model

• FINRISC concise model, excludes physical activity and fruit and vegetable consumption

Galaviz et al., AIDS, 2019

ADA and FINRISC diabetes risk scoresROC curves

Galaviz et al., AIDS, 2019

Use of Diabetes Risk Scores in the WIHS:ADA and FINRISC

• 1111 HIV+ (median age 41) and 454 HIV- women (median age 38)

• ADA sensitivity did not differ: HIV+ (77%), HIV- women (81%)• Specificity better in HIV- women (42% vs. 49%, p=0.006).

• Overall ADA discrimination suboptimal in both HIV+ (AUROC=0.64 [95% CI: 0.58, 0.70]) and HIV- women (AUROC=0.67 [95% CI: 0.57, 0.77]).

• FINRISC sensitivity and specificity did not differ: HIV+ (72% and 49%, respectively), HIV- women (86% and 52%, respectively). • Overall FINRISC discrimination was suboptimal in HIV+ (AUROC=0.68 [95%

CI: 0.62, 0.75]) and HIV- women (AUROC=0.78 [95% CI: 0.66, 0.90]).

• Greater misclassification was generally observed among HIV+ women.

Galaviz et al., AIDS, 2019

HIV–Diabetes Care Continuum Over 15 Years

• HIV-specific risk factors known to contribute to diabetes risk

should be explored in these models.

• Gains in life expectancy through optimal control of HIV

infection with antiretroviral therapy (ART) may be threatened

if other comorbidities, such as diabetes, are not optimally

managed.

Colasanti et al., Open Forum Inf Diseases, 2018

• Proportions of HIV+ and HIV- women with diabetes who were

engaged in care and achieved treatment goals, ABCs:

• Hemoglobin A1c [A1c] <7.0%

• Blood pressure [BP] <140/90 mmHg

• LDL cholesterol <100 mg/dL

• No smoking

• 486 HIV-positive and 258 HIV-negative women with diabetes

• Cross-sectional WIHS data from 2001, 2006, and 2015.

HIV–Diabetes Care Continuum Over 15 Years

Colasanti et al., Open Forum Inf Diseases, 2018

HIV–Diabetes Care Continuum Over 15 Years

• In 2001, 91.8% visited a health care provider

• 60.7% achieved the HbA1c target

• 70.5% achieved the BP target

• 38.5% achieved the LDL cholesterol target

• 49.2% were nonsmokers

• 23.3% achieved combined ABC targets (A1c, BP, and cholesterol)

• 10.9% met combined ABC targets and did not smoke

• No differences by HIV status

• Patterns similar in 2006 and 2015.

• Viral suppression increased from 41% in 2001 to 87% in 2015 in WWH

• Viral suppression not associated with achievement of diabetes care goals

Colasanti et al., Open Forum Inf Diseases, 2018

HIV–Diabetes Care Continuum Over 15 Years

• Successful management of HIV is outpacing that of diabetes.

• Future studies are needed to identify factors associated with gaps in the HIV–diabetes care continuum and design interventions to better integrate effective diabetes management into HIV care.

• Findings reinforce the importance of considering HIV a chronic infectious disease, for which the aggressive management of comorbid cardio-renal risk factors, such as diabetes, is important.

Colasanti et al., Open Forum Inf Diseases, 2018

Obesity & ART

Justman JE, et al. J Acquir Immune Defic Syndr. 2008.

Palella FJ, Jr., Phair JP. Current opinion in HIV and AIDS. 2011.

Gustafson DR et al. J Neurovirol. 2013.

Kerchberger AM, et al. Clin Infect Dis. 2019.

• Adherence to several ART increases total and central obesity, CVD and potentially cerebrovascular disease.

• Integrase strand-transfer inhibitor (INSTI)-based ART, for example, is associated with severe body weight gain.

• WWH who switched to an INSTI-based ART or added it to their ongoing regimen experienced, within 2 years, an average increase of • 2.1 kg in body weight

• 0.8 kg/m² in BMI

• 1.4% in percent body fat

• 2.0, 1.9, 0.6, and 1.0cm in waist, hip, arm, and thigh circumferences, respectively (all p <0.05).

• There were no differences in magnitude of these changes by INSTI type.

Cross-Sectional Adiposity Conclusions

• Adipokines measures may provide mechanistic insights of fat-brain or gut-brain associations in middle-aged HIV+ and at risk HIV- women.

Gustafson et al., J Neurovirol, 2013; Gustafson et al., J Gerontol Geriatr Res, 2015;

McFarlane… Gustafson, Neurology & Neurophysiology, 2017, E-pub Feb;8(1);Macaluso, Dellinger…. Gustafson, in preparation.

Higher BMI

Higher waist circumference and WHR

Lower leptin

Higher gut hormones, esp ghrelin

Higher BMI

Higher waist circumference and WHR

Higher leptin

Age 40 years 50 years

Better cognition associated with

Longitudinal Adiposity ConclusionsBody mass index and leptin are associated with executive function over 10

years in women with and without HIV infection

• 301 HIV+ and 113 HIV- WIHS participants

• Over 10 years

• HIV+ and HIV- women transitioned from overweight (29.1+/-7.9 kg/m2) to obese (30.4+/-7.9 kg/m2).

• Leptin increased (11.4+/-26.4 ng/ml).

• Higher baseline BMI and leptin predicted lower EF 10 years later in HIV- women (BMI b=-0.368,

SE=0.099, p<0.001; leptin b = -0.134 SE=0.038, p=0.001).

• Among HIV+ women, 10y increases in BMI and leptin predicted lower EF score (BMI b= -0.383, SE=0.16,

p=0.019; leptin b= -0.071, SE=0.025, p=0.006) adjusted for HIV viral load and CD4+.

Age 40 years 50 years

Leptin BMI

Outcome:

Executive function T-scoreBaseline

Leptin

BMI

Macaluso F, Weber K, Dellinger E, Holman S, Minkoff H, Keating S, Merlin L, Gustafson D. American Academy of Neurology, 2020, Toronto.

Bone Health in a DXA substudy

• Among WWH (average obese and adherent to ART), older age, White (vs. Black) race, prior fracture, history of cocaine use, and history of injection drug use were significant predictors of incident fracture, a marker of low bone mineral density, over 10 years follow-up.

• Evaluation over 5.4 years during premenopause only, there was no association of HIV status with fracture risk• Traditional risk factors such as White (vs. Black) race, hepatitis C virus infection,

and higher serum creatinine were associated with fracture

Sharma A, et al. Bone. 2015.

Yin MT, et al. J Acquir Immune Defic Syndr. 2010.

• Cross-sectionally, when compared to uninfected women, after adjusting for age, race, menopause status, and BMI, WWH have:• degraded skeletal microarchitecture (27% vs. 9%, P=0.001)

• lower mean lumbar spine (LS) & trabecular bone score (TBS) • 1.3 ± 0.1 vs. 1.4 ± 0.1, P< 0.001

• No differences between WWH and uninfected women in:• correlations between annual changes in

• TBS and BMD

• LS and BMD

• mean % annual change in TBS • -1.0%/yr ± 2.9% for WWH

• -0.8%/yr ± 1.7% for uninfected women

Bone Health

Sharma A, et al. J Acquir Immune Defic Syndr. 2018.

Sharma A, et al. Antivir Ther. 2016.

Sharma A, et al. Antivir Ther. 2019.

Falls and Frailty

• Falls associated with the Fried Frailty Phenotype (FFP) in WWH

• FFP predicts almost 2-fold higher odds of recurrent falls (not single falls) among middle-aged WWH.

• Unintentional weight loss and exhaustion most informative.

• Multi-faceted bone health and frailty risk profile in, on average, obese WWH.

Self-Reported Successful Aging

Rubtsova A, et al. J Acquir Immune Defic Syndr. 2019.

Better SRSA

Score

No differences in SRSA prevalence by HIV status:

• 84% of older (50+) WWH and 83% of HIV- women report an SRSA ≥7 (range = 2-10)

• SRSA ≥7 associated with higher levels of positive psychosocial characteristics • e.g., resilience and optimism, in older WWH and HIV- women.

Aging in Women

Gustafson, Lancet Neurology, 2006; Gustafson, Eur J Clin Pharm, 2008; Gustafson, et al. J Alz Dis, 2012;

Gustafson, et al. Neurology, 2009; Gustafson et al., J Frailty Aging 2016;5:43-8; Gustafson..Crystal, et al., J Gerontol Geriatr Res, 2015;4(5):240; Kiliaan et

al., Lancet Neurology, 2016; Thurn & Gustafson, Current HIV/AIDS Reports, 2017;

McFarlane Gustafson et al., Neurol & Neurophysiol, 2017; Gustafson et al., BMJ Open, 2017.

Metabolic alterations

Obesity

Bone

FrailtyMental Health

Cognition

Vascular

HIV Infection

Aging in Women

Gustafson, Lancet Neurology, 2006; Gustafson, Eur J Clin Pharm, 2008; Gustafson, et al. J Alz Dis, 2012;

Gustafson, et al. Neurology, 2009; Gustafson et al., J Frailty Aging 2016;5:43-8; Gustafson..Crystal, et al., J Gerontol Geriatr Res, 2015;4(5):240; Kiliaan et

al., Lancet Neurology, 2016; Thurn & Gustafson, Current HIV/AIDS Reports, 2017;

McFarlane Gustafson et al., Neurol & Neurophysiol, 2017; Gustafson et al., BMJ Open, 2017.

Metabolic alterations

Obesity

Bone

FrailtyMental Health

Cognition

Vascular

HIV Infection

Health disparities

Socioeconomic status

Social network

Built environment

Food insecurity

Ethnoracial background

Acces to healthcare

Control of infection

ART adherence & type

Control of multi-morbidity

Polypharmacy

50-59 60-69 70-79 80-89 90+ yearsMid- to late-life longitudinal risk

trajectories, e.g., blood pressure, BMI,

type 2 diabetes markers, lipids;

stratify by type & number of

exposures

Later-life outcomes by increasing age

decade baselines

Mid-life exposures Later-life outcomes

50-59 60-69 70-79 80-89 90+ yearsLater-life and within later life

longitudinal risk trajectories, e.g.,

blood pressure, BMI, type 2 diabetes

markers, lipids; stratify by type &

number of exposures

Later-life outcomes by increasing age

decade baselines

Later life exposures Later-life outcomes

Gustafson, Neurology & Neuromed, 2018.

Outcomes assessment

Outcomes assessment

Better identification & characterization of aging HIV populations at risk to identify intervention windows

Multiple Targets &

Interventions

Targets

• Clinical

• Diagnoses – symptoms, disorders

• Individual or clusters

• Change

• Peripheral & central fluid biomarkers

• Organ-specific events

Interventions

• Pharmaceutical

• Fit for purpose

• Repurposed

• Lifestyle

• Diet

• Exercise

• Intellectual activities

• Reducing stigma

• Reducing disparity

Cognitive decline

Neuropathological changes

Cerebrovascular Disease

Type 2 Diabetes Mellitus

Metabolic syndromes

Hypoglycemic episodes

Cardiovascular Disease

Adiposity & Obesity

Higher blood glucose, HbA1c, lipids

Hypertension, Obesity

Higher Framingham Stroke Risk

Pre-Type 2 Diabetes Mellitus

Genetic vascular risk – APOE, ACE, Clusterin, FTO

Cognitive Impairments

& Dementias

HIV Infection

In conclusion…

HIV AgingPrevention and Treatment Strategies

need to beFit for Purpose and Fit for Community

Cross-Pollinate Usual Aging Epidemiology to

Aging HIV Epidemiology

Aging in Women

Metabolic alterations

Obesity

Bone

FrailtyMental Health

Cognition

Vascular

HIV Infection

Health disparities

Socioeconomic status

Social network

Built environment

Food insecurity

Ethnoracial background

Acces to healthcare

Control of infection

ART adherence & type

Control of multi-morbidity

Polypharmacy

Deborah.Gustafson@downstate.edu

THANK YOU