Abstract
Objectives:
Peripheral artery disease (PAD) is associated with decreased physical function and increased mortality in the general population. We previously found that PAD is common in middle-aged women with and without HIV infection, but its association with functional decline is unclear. We examine the contribution of PAD to functional decline in the Women’s Interagency HIV Study, controlling for traditional cardiovascular risk factors and HIV-related factors.
Methods:
Analysis included 1,839 participants (72% with HIV) with measured ankle-brachial index (ABI) and 4-meter gait speed. ABI values categorized PAD severity. Linear models with repeated measures estimated the association of PAD severity with log-transformed gait speed after controlling for demographic, behavioral, and metabolic risk factors, and HIV/HCV status.
Results:
Median age was 50 years and >70% were Black. Compared to normal ABI, there was a dose-response relationship between increasing PAD severity and slower gait speed in univariable analyses: 6% slower gait speed for low-normal ABI (95% confidence interval[CI]:4%-9%), 10% for borderline PAD (95%CI:6%-13%), 14% for mild PAD (95%CI:9%-18%), and 16% for moderate-severe PAD (95%CI:5%-25%). PAD severity remained associated with slower gait speed in multivariable analyses. HIV/HCV coinfection was independently associated with 9%(95%CI:4%-14%) slower gait speed compared to those with neither infection. Among women with HIV, neither CD4 count nor HIV-RNA level was associated with gait speed.
Conclusions:
In middle-aged women with and without HIV infection, greater PAD severity is associated with progressively slower gait speed. Early detection of subclinical PAD may decrease the risk of lower extremity functional impairment and its long-term health consequences.
Keywords: peripheral artery disease, gait speed, women’s health, HIV, functional status
Introduction
Peripheral artery disease (PAD) is common in persons living with HIV (PLWH). We previously reported a PAD prevalence of 7.7% in middle-aged women with and without HIV infection in the U.S., on par with prevalence rates in general population studies of women a decade older.[1] These findings are of great clinical concern because PAD is associated with functional decline, decreased quality of life, and greater risk of disability and death.[2-14] Women with PAD may experience faster functional decline and higher prevalence of leg pain than men with PAD.[15, 16] Therefore, women with and without HIV infection may experience adverse health consequences of PAD at earlier ages.
Declines in physical function associated with PAD may be explained in part by symptoms of claudication that limit mobility. However, classic claudication is found in only 11% of patients with PAD in the general population.[17, 18] Studies using ankle brachial index (ABI) to assess subclinical PAD or PAD in the absence of symptoms have reported an association with physical function decline, especially limitations in lower extremity function.[8, 19] Most studies have been conducted in older populations and few have examined the association of ABI with physical function decline in middle-aged persons with and without HIV infection.
Gait speed (or walking speed at “usual” pace) is a well-established marker of lower extremity function and predicts disability and death in older adults without HIV infection.[20-22] The Multicenter AIDS Cohort Study (MACS) found that men living with HIV had earlier and faster declines in gait speed than HIV seronegative men.[23] Another study of mostly men who used injection drugs in Baltimore found an increased risk of poor functional performance including slower gait speed in PLWH compared to seronegative persons.[24] Studies to date have not examined the contribution of PAD to these functional declines among PLWH.
We aimed to examine the association of PAD severity, measured using ABI, with gait speed in a large nationally representative cohort of middle-aged women with and without HIV infection. Women with HIV are an understudied population that may be affected by PAD and functional decline, in specific ways that could impact public health and clinical care.
Methods
Setting and participants
The Women’s Interagency HIV Study (WIHS) (now part of the MACS-WIHS Combined Cohort Study [MWCCS]) is a multi-center prospective cohort study that enrolled a total of 4,982 women (3,678 with HIV infection and 1,304 without HIV infection). Enrollment occurred during four recruitment waves: 1994-1995, 2001-2002, 2011-2012, and 2013-2015 from 10 U.S. cities (Bronx and Brooklyn, NY; Chicago, IL; San Francisco, CA; Los Angeles, CA; Washington D.C.; Atlanta, GA; Chapel Hill, NC; Miami, FL; Jackson, MS; and Birmingham, AL). Full details of recruitment, retention, and demographics have been published previously.[25] Baseline sociodemographic characteristics and HIV risk factors were similar between HIV-seropositive and HIV-seronegative women. Each WIHS site’s institutional review board approved the study protocol and consent form, and each participant gave written informed consent. At semi-annual research visits, participants completed a brief physical examination, provided biological specimens, and completed an interviewer-administered questionnaire.
From October 2013 through October 2019, 2,010 WIHS participants aged older than 40 years underwent ABI measurements. Among the 2,010 participants, 1,845 also had measured 4-meter timed gait speed. Five participants were excluded due to unknown hepatitis C virus (HCV) status and one participant was excluded due to non-compressible vessels (ABI > 1.4). Among the 1,839 remaining participants, all had ABI measurements at one visit, 1,590 had ABI measurements at 2 visits, and 637 had ABI measurements at three visits.
PAD determination
The WIHS ABI protocol has been previously described.[1] Briefly, single measures of systolic blood pressure were measured utilizing a hand-held Doppler instrument with a 5mm Hz probe and an aneroid sphygmomanometer at the brachial artery on both arms and the dorsalis pedis and posterior tibial arteries on both legs. All technicians were trained and certified by the same central trainer after practicing on an average of 15 volunteers.
ABI was calculated for the left and the right limbs by dividing the higher pressure of the lower extremity arterial measurements for each side by the higher pressure of either the left or right brachial artery. Lower values correlate with arterial disease in the lower extremities. The lower of the two ABI values (right versus left) was used for analysis. This method is consistent with the American Heart Association (AHA)/American College of Cardiology (ACC) Guidelines for measurement of ABI.[26] Categories of PAD severity were based on prior studies.[7, 27]
Primary outcome: gait speed
Our primary physical performance outcome was 4-meter walking speed, which has been found to correlate with 6-minute walk test performance.[28] A walking course of 4 meters was created at participating sites as part of the WIHS physical performance assessments protocol. Women were asked to walk at their usual speed and a stopwatch was used for timing. Two attempts were completed and the time for each attempt was recorded in seconds. The faster of the two attempts was used in our analysis.
Predictors and covariates
The primary predictor was PAD severity, which was categorized into five groups based on ABI measurements as per prior studies:[7, 27] normal (ABI 1.1-1.4), low-normal (ABI 1.0-<1.1), borderline PAD (ABI 0.9-<1.0), mild PAD (ABI 0.7-<0.9), or moderate-severe PAD (ABI <0.7).
Several covariates were considered. Infectious disease covariates included HIV serostatus (yes/no) and HCV serostatus (defined by detectable HCV RNA following a positive anti-HCV antibody result and if treated, by detectable HCV RNA at least six months after HCV treatment completion). Sociodemographic covariates included age at the time of ABI measurement, gender, and race/ethnicity categorized as Hispanic, Non-Hispanic White, Non-Hispanic Black, and Non-Hispanic Other). Behavioral covariates included self-reported alcohol use, categorized as none, light (1-15 gm/day), moderate (15-30 gm/day), and heavy (>30 gm/day), self-reported smoking history (none, current, past), and duration of smoking (pack-years). Anthropometric measures included waist circumference and body mass index (BMI) in kg/m2. Metabolic variables included diabetes mellitus (DM) (defined by: 1. self-report of DM medication use, 2. elevated fasting glucose (FG) ≥126mg/dL confirmed by a subsequent FG≥126mg/dL, report of a DM medication, or a confirmed hemoglobin A1C (A1C) value ≥6.5%; or 3. self-report of DM confirmed by a subsequent report of DM medication use or two FG measurements ≥126mg/dL, or FG≥126mg/dL concurrent with A1C≥6.5%), systolic and diastolic blood pressure, hypertension (systolic blood pressure ≥ 140, diastolic blood pressure ≥ 90, self-reported hypertension, or use of anti-hypertensive medications), statin use, blood lipid measurements, and estimated glomerular filtration rate. Physical activity score was based on a standardized questionnaire[29, 30]. Peripheral neuropathy was defined by a combination of self-report of symptoms, deep tendon reflexes, and vibration sense, and categorized as mild, moderate, and severe. In HIV-seropositive participants, HIV-related covariates included current CD4 cell count, CD4 cell count nadir, current HIV RNA level, history of clinical AIDS and use of antiretroviral therapy (ART).
Statistical analysis
We first compared baseline sociodemographic and clinical characteristics across the five categories of PAD: normal ABI, low-normal ABI, borderline PAD, mild PAD, moderate-severe PAD. For continuous variables, we compared characteristics using ANOVA for normally distributed variables and the Kruskal-Wallis test for non-normally distributed variables. We used the chi-squared test or Fisher exact test for categorical variables.
Linear modeling with repeated measures was used to examine both unadjusted and adjusted associations of PAD category with gait speed. ABI was a time-dependent predictor and the outcome of gait speed was measured multiple times and analyzed with repeated measures. All models were fitted using auto correlation structure of order 1 among visits within patients. Nested models were then used to adjust sequentially for: 1) HIV, HCV, and the interactive effects of HIV and HCV; 2) demographic factors; 3) behavioral factors; and 4) metabolic factors. Sequential models included all the variables in the previous models, and therefore are nested. Among women with HIV, we additionally examined the impact of HIV-related factors as the last step. Gait speed was log transformed to approximate normal distribution. The coefficients were then exponentiated to reflect percentage differences. All analyses were performed using SAS system, version 9.4 (SAS Institute Inc., Cary, NC).
Results
The 1,839 women included in this study were median age 50 years, the majority were Black, and 51% were obese (BMI ≥30 kg/m2) (Table 1). Nearly half reported current smoking, approximately 25% had diabetes, and 50% had hypertension. Among the women with HIV, the majority had well-controlled HIV, with mean CD4>500 cells/mm3 and undetectable HIV viral loads. Table 1 shows clinical characteristics by PAD severity. Over one-third had ABI values in the normal range (41%), about one-third had low-normal ABI values (36%), 15% had borderline PAD, 6% had mild PAD, and 1.3% had moderate-severe PAD. Smoking, physical inactivity, higher triglyceride levels, lower median HDL cholesterol and LDL cholesterol levels, statin use, diabetes, hypertension, peripheral neuropathy, and report of symptoms consistent with claudication were more common in women with moderate-severe PAD than those in the other ABI categories.
Table 1.
Demographic and clinical characteristics of 1,839 WIHS women by ABI.
| Normal | Low-normal ABI | Borderline PAD | Mild PAD | Moderate-Severe PAD |
|
|---|---|---|---|---|---|
| Characteristics | ABI 1.10-1.40 | ABI 1.00-1.09 | ABI 0.90-0.99 | ABI 0.70-0.89 | ABI ≤0.69 |
| Median (IQR) or % (N) | (n=759) | (n=663) | (n=284) | (n=110) | (n=23) |
| Demographics | |||||
| Age (yrs) | 50 (45, 54) | 50 (45, 56) | 51 (46, 56) | 51 (47, 57) | 56 (46, 61) |
| Race/Ethnicity | |||||
| Black | 67% (510) | 75% (497) | 74% (210) | 85% (93) | 65% (15) |
| White | 13% (101) | 8% (51) | 8% (24) | 5% (6) | 17% (4) |
| Hispanic | 16% (118) | 13% (86) | 13% (36) | 9% (10) | 17% (4) |
| Other¶ | 4% (30) | 4% (29) | 5% (14) | 1% (1) | 0% (0) |
| Behavioral | |||||
| Current smoker | 35% (266) | 44% (295) | 53% (150) | 47% (52) | 52% (12) |
| Pack-years of smoking | 1.3 (0, 10.5) | 4.0 (0, 12.6) | 6.9 (0, 16.1) | 7.8 (0, 14.1) | 8.3 (0.9, 24.5) |
| Alcohol consumption | |||||
| None | 54% (407) | 51% (335) | 49% (138) | 55% (60) | 74% (17) |
| Light (<15gm/day) | 33% (250) | 32% (213) | 36% (103) | 36% (40) | 26% (6) |
| Moderate-Heavy (≥15 gm/day) | 13% (101) | 17% (115) | 15% (43) | 9% (10) | 0% (0) |
| Physical Activity total score | 90 (0, 213) | 65 (0, 204) | 48 (0, 144) | 48 (0, 144) | 18 (0, 192) |
| Metabolic | |||||
| BMI (kg/m2) | 30.3 (25.8, 35.4) | 29.9 (25.0, 35.9) | 29.2 (24.9, 36.2) | 31.1 (25.4, 37.0) | 29.3 (25.5, 36.7) |
| Waist circumference (cm) | 100 (89, 111) | 98 (87, 111) | 97 (87, 110) | 101 (90, 114) | 102 (95, 112) |
| Diabetes | 23% (174) | 20% (133) | 19% (55) | 25% (27) | 35% (8) |
| Triglycerides (mg/dL) | 101 (78, 121) | 102 (72, 142) | 102 (78, 147) | 108 (81, 151) | 124 (90, 161) |
| HDL (mg/dL) | 52 (43, 65) | 54 (44, 69) | 54 (44, 64) | 53 (42, 61) | 48 (42, 52) |
| LDL (mg/dL) | 100 (78, 121) | 100 (77, 123) | 101 (78, 122) | 101 (81, 136) | 97 (85, 134) |
| Statin use | |||||
| Current | 22% (157) | 27% (177) | 20% (58) | 27% (30) | 39% (9) |
| Ever | 32% (230) | 36% (241) | 31% (89) | 40% (44) | 57% (13) |
| Hypertension | 50% (357) | 53% (348) | 53% (150) | 68% (75) | 70% (16) |
| SBP (mmHg) | 123 (111, 135) | 122 (112, 135) | 122 (113, 136) | 127 (114, 143) | 124 (117, 142) |
| DBP (mmHg) | 76 (70, 83) | 76 (70, 83) | 75 (69, 82) | 78 (71, 85) | 71 (67, 79) |
| Peripheral Neuropathy | |||||
| Normal | 50% (381) | 48% (317) | 47% (134) | 47% (52) | 30% (7) |
| Mild | 27% (206) | 30% (201) | 29% (81) | 24% (26) | 35% (8) |
| Moderate-Severe | 12% (93) | 9% (62) | 12% (33) | 20% (22) | 26% (6) |
| Claudication (left or right) | 1% (9) | 2% (13) | 1% (3) | 2% (2) | 9% (2) |
| HIV and HCV related | |||||
| HIV monoinfection | 66% (498) | 63% (420) | 58% (166) | 65% (72) | 43% (10) |
| HIV/HCV coinfection | 6% (49) | 10% (67) | 12% (34) | 7% (8) | 35% (8) |
| HCV monoinfection | 3% (21) | 3% (19) | 4% (10) | 3% (3) | 0% (0) |
| HIV/HCV uninfected | 25% (191) | 24% (157) | 26% (74) | 25% (27) | 22% (5) |
| CD4 count (cells/mm3) | |||||
| Current* | 582 (370, 788) | 591 (423, 815) | 586 (400, 853) | 677 (362, 861) | 669 (432, 947) |
| Nadir | 314 (155, 628) | 308 (139, 566) | 287 (134, 644) | 319 (163, 674) | 271 (167, 693) |
| History of AIDS * | 31% (172) | 30% (144) | 35% (70) | 26% (21) | 39% (7) |
| ARV use | |||||
| Current | 63% (476) | 65% (434) | 64% (183) | 63% (69) | 74% (17) |
| Ever | 68% (516) | 70% (467) | 68% (193) | 70% (77) | 74% (17) |
| Undetectable HIV RNA (≤20)* | 67% (348) | 70% (339) | 71% (142) | 64% (51) | 61% (11) |
Current CD4, Undetectable HIV RNA≤20, and History of AIDS are available for HIV+ patients only
In unadjusted analyses, compared to normal ABI (absence of PAD), each category of increasing PAD severity was associated with progressively slower gait speed (Figure 1). The median gait speed was fastest in women with normal ABI at 0.25m/s (interquartile range [IQR]:0.21,0.29), followed by women with low-normal ABI and women with borderline PAD [0.23m/s (IQR:0.19,0.28) and 0.23m/s (IQR:0.18,0.27), respectively] and slowest in women with mild and moderate-severe PAD [0.20m/s (IQR:0.13,0.26) and 0.22m/s (IQR:0.14,0.26), respectively]. Overall, for each 0.1 unit decrease in ABI, there was a 3.5% decrease in gait speed (p<0.0001).
Figure 1:
PAD is associated with slower gait speed in women with and without HIV infection. Linear modeling with repeated measures was used to examine the unadjusted associations of PAD category with 4-meter gait speed. The coefficients were then exponentiated to reflect percentage differences. There was a dose-response relationship between PAD severity and slower gait speed. HIV: human immunodeficiency virus, PAD: peripheral artery disease.
Table 2 shows the association of increasing PAD severity with gait speed compared to women with normal ABI scores, after sequential adjustment for HIV and HCV status, followed by additional adjustment for demographic and behavioral factors, and finally, additional adjustment for metabolic factors. After adjustment for HIV and HCV status, each category of increasing PAD severity was associated with slower gait speed: 6% slower for low-normal ABI (95% confidence interval [CI]:4%, 9%), 9% slower for borderline PAD (95%CI:6%, 12%), 13% slower for mild PAD (95%CI: 9%, 17%), and 15% slower for moderate-severe PAD (95%CI: 4%, 24%). With additional adjustment for demographic and behavioral factors, each category of increasing PAD severity remained significantly associated with slower gait speed. In fully-adjusted models, each PAD category remained associated with slower gait speed: 6% slower for low-normal ABI (95%CI:4%, 9%), 10% for borderline PAD (95%CI:7%, 14%), 12% for mild PAD (95%CI:8%, 17%), and 11% for moderate-severe PAD (95%CI:0%, 22%), but the association of moderate-severe PAD with gait speed did not reach statistical significance.
Table 2:
Association of PAD category with 4-meter gait speed after controlling for HIV and HCV status, demographic, behavioral, and metabolic risk factors.
| % Difference (95% CI)* | p-value | |
|---|---|---|
| Adjusted for HIV & HCV 1 | ||
| Low-normal ABI | −6% (−9%, −4%) | <.001 |
| Borderline PAD | −9% (−12%, −6%) | <.001 |
| Mild PAD | −13% (−17%, −9%) | <.001 |
| Moderate-severe PAD | −15% (−24%, −4%) | 0.011 |
| Adjusted for HIV & HCV, and demographics 2 | ||
| Low-normal ABI | −6% (−9%, −4%) | <.001 |
| Borderline PAD | −9% (−12%, −6%) | <.001 |
| Mild PAD | −13% (−17%, −8%) | <.001 |
| Moderate-severe PAD | −14% (−24%, −3%) | 0.015 |
| Adjusted for HIV & HCV, demographics, and behavioral factors 3 | ||
| Low-normal ABI | −6% (−8%, −3%) | <.001 |
| Borderline PAD | −9% (−12%, −5%) | <.001 |
| Mild PAD | −12% (−16%, −7%) | <.001 |
| Moderate-severe PAD | −13% (−23%, −2%) | 0.022 |
| Adjusted for HIV & HCV, demographics, behavioral, and metabolic factors 4 | ||
| Low-normal ABI | −6% (−9%, −4%) | <.001 |
| Borderline PAD | −10% (−14%, −7%) | <.001 |
| Mild PAD | −12% (−17, −8%) | <.001 |
| Moderate-severe PAD | −11% (−22%, 0%) | 0.060 |
% difference from normal ABI reference group
HIV & HCV: HIV-monoinfection, HCV-monoinfection, and HIV/HCV-coinfection
demographics: age and race/ethnicity
behavioral factors: pack-years of smoking, alcohol consumption, and physical activity score
metabolic factors: diabetes, hypertension, waist circumference, statin use, and estimated glomerular filtration rate (eGFR)
When we examined the association of HIV and HCV infection status with gait speed in multivariable analysis, we found that HIV monoinfection was associated with only 1% slower gait speed, HCV monoinfection with 7% slower gait speed, and HIV/HCV coinfection 9% slower gait speed compared to women with neither infection; only the latter association was statistically significant (Table 3). Other individual factors associated with slower gait speed in multivariable analysis included older age, Hispanic ethnicity, Black race, more pack-years of smoking, larger waist circumference, and a history of statin use. Being in the highest tertile of physical activity was associated with faster gait speeds compared to women reporting no physical activity. There was no association of diabetes or hypertension with gait speed.
Table 3.
Factors associated with 4-meter gait speed in the entire cohort, controlling for HIV and HCV status, demographics, behavioral, and metabolic factors.
| PAD | % Difference (95% CI)*. | p-value |
|---|---|---|
| Low-normal ABI | −6% (−9%, −4%) | <.001 |
| Borderline PAD | −10% (−14%, −7%) | <.001 |
| Mild PAD | −12% (−17%, −8%) | <.001 |
| Moderate-severe PAD | −11% (−22%, 0%) | 0.060 |
| Infection Status | ||
| HIV monoinfection | −1% (−4%, 3%) | 0.667 |
| HCV monoinfection | −7% (−15%, 2%) | 0.134 |
| HIV/HCV coinfection | −9% (−14%, −4%) | 0.002 |
| Demographic factors | ||
| Age | −0.3% (−0.5%, −0.1%) | 0.017 |
| Race/Ethnicity (ref=White) | ||
| Black | −8% (−13%, −4%) | 0.001 |
| Hispanic | −15% (−20%, −9%) | <.001 |
| Other race | 0% (−8%, 9%) | 0.987 |
| Behavioral factors | ||
| Physical Activity (ref=no activity) | ||
| Lowest tertile | 3% (−1%, 6%) | 0.130 |
| Middle tertile | 1% (−2%, 5%) | 0.555 |
| Highest tertile | 8% (4%, 12%) | <.001 |
| Pack years of smoking | −0.2% (−0.3%, −0.1%) | 0.008 |
| Metabolic factors | ||
| Waist circumference (per 10 cm) | −3% (−4%, −2%) | <.001 |
| Hypertension | −1% (−4%, 2%) | 0.454 |
| Diabetes | −3% (−7%, 0%) | 0.088 |
| Statin use (ever) | −6% (−9%, −3%) | <.001 |
% difference from normal ABI reference group
Among women with HIV infection, associations of PAD category with gait speed were similar to those observed in the full sample of women. In multivariable analyses, PAD category was associated with slower gait speed: 6% slower for low-normal ABI (95%CI:2%, 9%), 9% slower for borderline PAD (95%CI:5%, 13%), 12% slower for mild PAD (95%CI:6%, 17%), and 11% slower for moderate-severe PAD (95%CI:−4%, 24%), although the association in the latter group did not reach significance. Reporting a history of antiretroviral therapy was associated with a 13% faster gait speed (95%CI:2%, 24%). There was little association of CD4 cell nadir and HIV viral load with gait speed.
Discussion
In this large cohort of middle-aged U.S. women with and without HIV, we found that increasing PAD severity was associated with progressively slower gait speed, even after adjustment for HIV and HCV status, demographic, behavioral, and metabolic risk factors. We found a 0.05m/s difference in median gait speed between women with normal ABI compared to women with mild PAD. A gait speed change of 0.05m/s has been shown to be a meaningful difference in the general population of older adults.[31] Of clinical concern is that ABI values previously defined as being low-normal and as borderline PAD were associated with progressively slower gait speed, suggesting that lower extremity functional declines occur in women with subclinical and asymptomatic PAD. Given our previous report of a high PAD prevalence that was similar to seronegative women a decade older, our findings could have important clinical implications regarding earlier onset of disability and potentially mortality, linked to lower extremity functional declines.[1]
Our findings are notable for several reasons. First, we demonstrated that increasing PAD severity was associated with increasing lower extremity functional impairment in women who were relatively young in age. Second, this relationship was observed in those with early, subclinical PAD as measured by low ABI values. While one study also showed that low-normal values of ABI and borderline, mild, moderate and severe PAD were associated with decreasing rates of mobility, the mean age of adults in that study was older than 70 years.[7] In other population studies among older adults, borderline ABI values have been associated with subclinical atherosclerosis and endothelial dysfunction.[7, 27] When we adjusted for metabolic risk factors, low-normal ABI values and borderline PAD remained independently associated with slower gait speed, suggesting that other unmeasured factors besides traditional cardiovascular risk factors may contribute to the association of subclinical PAD with lower extremity functional declines. PAD has been associated with skeletal muscle damage from ischemia-perfusion injury in microvascular beds, leading to lower calf muscle area and increased calf muscle fat.[32] Future investigation will examine whether factors such as lower extremity muscle volume might explain the association of subclinical PAD with lower extremity functional declines.
Another study of men and women without HIV infection found that increased physical activity improved ABI values in those with borderline ABI values who underwent a cardiovascular intervention.[33] Future study will evaluate the impact of ABI improvements on physical function. Whether ABI monitoring can be used as a tool for early detection and prevention of lower extremity functional declines in women at high risk for PAD needs further study.
As expected, we found that several vascular risk factors other than PAD were independently associated with lower extremity functional declines including smoking, waist circumference, and history of statin use, which could be a marker of dyslipidemia. These factors may contribute to reduced lower extremity vascular perfusion resulting from atherosclerosis. Being Black and of Hispanic ethnicity have also been associated with slower gait speed in men with and at risk for HIV, and with physical function declines in a general aging population cohort.[23, 34, 35] These studies have postulated that socioeconomic disparities may be a reason.
There was little association of HIV monoinfection with slower gait speed, although we found that HIV/HCV coinfection was associated with slower gait speed even after adjustment for demographic, behavioral, and clinical factors. This suggests that the effect of HIV/HCV coinfection on gait speed may be primarily driven by HCV rather than HIV. We initially hypothesized that peripheral neuropathy could be a cause of slower gait speed in women with HIV or HCV, but the study was not powered to make these conclusions. While peripheral neuropathy is common in HIV and HCV infection[36-38], HIV/HCV coinfection has been associated with distal sensory polyneuropathy compared with women with HIV alone.[39] Among women with HIV infection, we found that antiretroviral drug use was associated with faster gait speed, possibly due to improved health. However, we did not observe a significant association of CD4 count or HIV viral load with gait speed. Our findings may warrant additional investigation of the potential role of systemic inflammation associated with chronic viral infection on functional impairment.
While our study has a number of strengths including the large sample with repeated ABI measures and detailed covariate measurements, there are some limitations. First, our findings examining the relationship of PAD severity with gait speed may not be generalizable to men. Second, our study examined gait speed, which is only one measure of physical function, but also allowed examination of lower extremity PAD with lower extremity functional declines. Finally, there are likely unmeasured factors associated with gait speed for which we were not able to account.
Conclusions
In this cohort of middle-aged women with and without HIV infection, worsening PAD severity was associated with progressively slower gait speed, independent of HIV and HCV status, demographic, behavioral, and metabolic risk factors. Of clinical concern is that lower extremity functional declines were noted even in those with early and subclinical PAD. Early detection of subclinical PAD using ABI measurements in women at high risk for PAD could have major implications for decreasing the risk of lower extremity functional impairment and its long-term health consequences.
Sources of Funding:
Data in this manuscript were collected by the Women’s Interagency HIV Study (WIHS), now the MACS/WIHS Combined Cohort Study (MWCCS). The contents of this publication are solely the responsibility of the authors and do not represent the official views of the National Institutes of Health (NIH). MWCCS (Principal Investigators): Atlanta CRS (Ighovwerha Ofotokun, Anandi Sheth, and Gina Wingood), U01-HL146241; Baltimore CRS (Todd Brown and Joseph Margolick), U01-HL146201; Bronx CRS (Kathryn Anastos and Anjali Sharma), U01-HL146204; Brooklyn CRS (Deborah Gustafson and Tracey Wilson), U01-HL146202; Data Analysis and Coordination Center (Gypsyamber D’Souza, Stephen Gange and Elizabeth Golub), U01-HL146193; Chicago-Cook County CRS (Mardge Cohen and Audrey French), U01-HL146245; Chicago-Northwestern CRS (Steven Wolinsky), U01-HL146240; Northern California CRS (Bradley Aouizerat, Jennifer Price, and Phyllis Tien), U01-HL146242; Los Angeles CRS (Roger Detels and Matthew Mimiaga), U01-HL146333; Metropolitan Washington CRS (Seble Kassaye and Daniel Merenstein), U01-HL146205; Miami CRS (Maria Alcaide, Margaret Fischl, and Deborah Jones), U01-HL146203; Pittsburgh CRS (Jeremy Martinson and Charles Rinaldo), U01-HL146208; UAB-MS CRS (Mirjam-Colette Kempf, Jodie Dionne-Odom, and Deborah Konkle-Parker), U01-HL146192; UNC CRS (Adaora Adimora), U01-HL146194. The MWCCS is funded primarily by the National Heart, Lung, and Blood Institute (NHLBI), with additional co-funding from the Eunice Kennedy Shriver National Institute Of Child Health & Human Development (NICHD), National Institute On Aging (NIA), National Institute Of Dental & Craniofacial Research (NIDCR), National Institute Of Allergy And Infectious Diseases (NIAID), National Institute Of Neurological Disorders And Stroke (NINDS), National Institute Of Mental Health (NIMH), National Institute On Drug Abuse (NIDA), National Institute Of Nursing Research (NINR), National Cancer Institute (NCI), National Institute on Alcohol Abuse and Alcoholism (NIAAA), National Institute on Deafness and Other Communication Disorders (NIDCD), National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), National Institute on Minority Health and Health Disparities (NIMHD), and in coordination and alignment with the research priorities of the National Institutes of Health, Office of AIDS Research (OAR). MWCCS data collection is also supported by UL1-TR000004 (UCSF CTSA), UL1-TR003098 (JHU ICTR), UL1-TR001881 (UCLA CTSI), P30-AI-050409 (Atlanta CFAR), P30-AI-073961 (Miami CFAR), P30-AI-050410 (UNC CFAR), P30-AI-027767 (UAB CFAR), and P30-MH-116867 (Miami CHARM). The study was also supported by the National Institute of Allergy and Infectious Diseases [K24 AI 108516 (PCT)
Footnotes
Disclosures:
Emily R. Cedarbaum: none
Yifei Ma: none
Adaora A. Adimora: Merck (receipt of personal funds for consulting), Gilead (institution received funds for research)
Marcas Bamman: none
Mardge H. Cohen: none
Margaret A. Fischl: none
Deborah Gustafson: none
Kunihiro Matsushita: received personal fee from Fukuda Denshi outside of the submitted work
Igho Ofotokun: none
Michael Plankey: none
Eric C. Seaberg: none
Anjali Sharma: grant funding from Gilead Sciences
Phyllis C. Tien: grant funding for research from Merck
References
- 1.Cedarbaum E, Ma Y, Scherzer R, Price JC, Adimora AA, Bamman M, et al. Contributions of HIV, hepatitis C virus, and traditional vascular risk factors to peripheral artery disease in women. AIDS 2019; 33(13):2025–2033. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Qu B, Liu Q, Li J. Systematic Review of Association Between Low Ankle-Brachial Index and All-Cause Cardiovascular, or Non-cardiovascular Mortality. Cell Biochem Biophys 2015; 73(2):571–575. [DOI] [PubMed] [Google Scholar]
- 3.Ankle Brachial Index C, Fowkes FG, Murray GD, Butcher I, Heald CL, Lee RJ, et al. Ankle brachial index combined with Framingham Risk Score to predict cardiovascular events and mortality: a meta-analysis. JAMA 2008; 300(2):197–208. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Fan H, Hu X, Yu W, Cao H, Wang J, Li J, et al. Low ankle-brachial index and risk of stroke. Atherosclerosis 2013; 229(2):317–323. [DOI] [PubMed] [Google Scholar]
- 5.Gronewold J, Hermann DM, Lehmann N, Kroger K, Lauterbach K, Berger K, et al. Ankle-brachial index predicts stroke in the general population in addition to classical risk factors. Atherosclerosis 2014; 233(2):545–550. [DOI] [PubMed] [Google Scholar]
- 6.Matsushita K, Ballew SH, Sang Y, Kalbaugh C, Loehr LR, Hirsch AT, et al. Ankle-brachial index and physical function in older individuals: The Atherosclerosis Risk in Communities (ARIC) study. Atherosclerosis 2017; 257:208–215. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.McDermott MM, Guralnik JM, Tian L, Liu K, Ferrucci L, Liao Y, et al. Associations of borderline and low normal ankle-brachial index values with functional decline at 5-year follow-up: the WALCS (Walking and Leg Circulation Study). J Am Coll Cardiol 2009; 53(12):1056–1062. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.McDermott MM, Guralnik JM, Ferrucci L, Tian L, Liu K, Liao Y, et al. Asymptomatic peripheral arterial disease is associated with more adverse lower extremity characteristics than intermittent claudication. Circulation 2008; 117(19):2484–2491. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Wu A, Coresh J, Selvin E, Tanaka H, Heiss G, Hirsch AT, et al. Lower Extremity Peripheral Artery Disease and Quality of Life Among Older Individuals in the Community. J Am Heart Assoc 2017; 6(1). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Morris DR, Rodriguez AJ, Moxon JV, Cunningham MA, McDermott MM, Myers J, et al. Association of lower extremity performance with cardiovascular and all-cause mortality in patients with peripheral artery disease: a systematic review and meta-analysis. J Am Heart Assoc 2014; 3(4). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Jain A, Liu K, Ferrucci L, Criqui MH, Tian L, Guralnik JM, et al. Declining walking impairment questionnaire scores are associated with subsequent increased mortality in peripheral artery disease. J Am Coll Cardiol 2013; 61(17):1820–1829. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Gardner AW, Montgomery PS, Wang M, Xu C. Predictors of health-related quality of life in patients with symptomatic peripheral artery disease. J Vasc Surg 2018; 68(4):1126–1134. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Jain A, Liu K, Ferrucci L, Criqui MH, Tian L, Guralnik JM, et al. The Walking Impairment Questionnaire stair-climbing score predicts mortality in men and women with peripheral arterial disease. J Vasc Surg 2012; 55(6):1662–1673 e1662. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.McDermott MM, Tian L, Liu K, Guralnik JM, Ferrucci L, Tan J, et al. Prognostic value of functional performance for mortality in patients with peripheral artery disease. J Am Coll Cardiol 2008; 51(15):1482–1489. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.McDermott MM, Ferrucci L, Liu K, Guralnik JM, Tian L, Kibbe M, et al. Women with peripheral arterial disease experience faster functional decline than men with peripheral arterial disease. J Am Coll Cardiol 2011; 57(6):707–714. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.McDermott MM, Greenland P, Liu K, Criqui MH, Guralnik JM, Celic L, et al. Sex differences in peripheral arterial disease: leg symptoms and physical functioning. J Am Geriatr Soc 2003; 51(2):222–228. [DOI] [PubMed] [Google Scholar]
- 17.Hirsch AT, Criqui MH, Treat-Jacobson D, Regensteiner JG, Creager MA, Olin JW, et al. Peripheral arterial disease detection, awareness, and treatment in primary care. JAMA 2001; 286(11):1317–1324. [DOI] [PubMed] [Google Scholar]
- 18.Regensteiner JG, Hiatt WR, Coll JR, Criqui MH, Treat-Jacobson D, McDermott MM, et al. The impact of peripheral arterial disease on health-related quality of life in the Peripheral Arterial Disease Awareness, Risk, and Treatment: New Resources for Survival (PARTNERS) Program. Vasc Med 2008; 13(1):15–24. [DOI] [PubMed] [Google Scholar]
- 19.McDermott MM, Tian L, Ferrucci L, Liu K, Guralnik JM, Liao Y, et al. Associations between lower extremity ischemia, upper and lower extremity strength, and functional impairment with peripheral arterial disease. J Am Geriatr Soc 2008; 56(4):724–729. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Cummings SR, Studenski S, Ferrucci L. A diagnosis of dismobility--giving mobility clinical visibility: a Mobility Working Group recommendation. JAMA 2014; 311(20):2061–2062. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Guralnik JM, Ferrucci L, Pieper CF, Leveille SG, Markides KS, Ostir GV, et al. Lower extremity function and subsequent disability: consistency across studies, predictive models, and value of gait speed alone compared with the short physical performance battery. J Gerontol A Biol Sci Med Sci 2000; 55(4):M221–231. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Studenski S, Perera S, Patel K, Rosano C, Faulkner K, Inzitari M, et al. Gait speed and survival in older adults. JAMA 2011; 305(1):50–58. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Schrack JA, Althoff KN, Jacobson LP, Erlandson KM, Jamieson BD, Koletar SL, et al. Accelerated Longitudinal Gait Speed Decline in HIV-Infected Older Men. J Acquir Immune Defic Syndr 2015; 70(4):370–376. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Greene M, Covinsky K, Astemborski J, Piggott DA, Brown T, Leng S, et al. The relationship of physical performance with HIV disease and mortality. AIDS 2014; 28(18):2711–2719. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Adimora AA, Ramirez C, Benning L, Greenblatt RM, Kempf MC, Tien PC, et al. Cohort Profile: The Women's Interagency HIV Study (WIHS). Int J Epidemiol 2018; 47(2):393–394i. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Aboyans V, Criqui MH, Abraham P, Allison MA, Creager MA, Diehm C, et al. Measurement and interpretation of the ankle-brachial index: a scientific statement from the American Heart Association. Circulation 2012; 126(24):2890–2909. [DOI] [PubMed] [Google Scholar]
- 27.McDermott MM, Liu K, Criqui MH, Ruth K, Goff D, Saad MF, et al. Ankle-brachial index and subclinical cardiac and carotid disease: the multi-ethnic study of atherosclerosis. Am J Epidemiol 2005; 162(1):33–41. [DOI] [PubMed] [Google Scholar]
- 28.Chen X, Stoner JA, Montgomery PS, Casanegra AI, Silva-Palacios F, Chen S, et al. Prediction of 6-minute walk performance in patients with peripheral artery disease. J Vasc Surg 2017; 66(4):1202–1209. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Sidney S, Jacobs DR Jr.,, Haskell WL, Armstrong MA, Dimicco A, Oberman A, et al. Comparison of two methods of assessing physical activity in the Coronary Artery Risk Development in Young Adults (CARDIA) Study. Am J Epidemiol 1991; 133(12):1231–1245. [DOI] [PubMed] [Google Scholar]
- 30.Hoegerman GS, Lewis CE, Flack J, Raczynski JM, Caveny J, Gardin JM. Lack of association of recreational cocaine and alcohol use with left ventricular mass in young adults. The Coronary Artery Risk Development in Young Adults (CARDIA) study. J Am Coll Cardiol 1995; 25(4):895–900. [DOI] [PubMed] [Google Scholar]
- 31.Perera S, Mody SH, Woodman RC, Studenski SA. Meaningful change and responsiveness in common physical performance measures in older adults. J Am Geriatr Soc 2006; 54(5):743–749. [DOI] [PubMed] [Google Scholar]
- 32.McDermott MM. Lower extremity manifestations of peripheral artery disease: the pathophysiologic and functional implications of leg ischemia. Circ Res 2015; 116(9):1540–1550. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Heikkila A, Venermo M, Kautiainen H, Aarnio P, Korhonen P. Physical Activity Improves Borderline Ankle-Brachial Index Values in a Cardiovascular Risk Population. Ann Vasc Surg 2016; 32:50–56. [DOI] [PubMed] [Google Scholar]
- 34.Masters MC, Perez J, Tassiopoulos K, Andrade A, Ellis R, Yang J, et al. Gait Speed Decline Is Associated with Hemoglobin A1C, Neurocognitive Impairment, and Black Race in Persons with HIV. AIDS Res Hum Retroviruses 2019; 35(11-12):1065–1073. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Thorpe RJ Jr., Koster A, Kritchevsky SB, Newman AB, Harris T, Ayonayon HN, et al. Race, socioeconomic resources, and late-life mobility and decline: findings from the Health, Aging, and Body Composition study. J Gerontol A Biol Sci Med Sci 2011; 66(10):1114–1123. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Mathew S, Faheem M, Ibrahim SM, Iqbal W, Rauff B, Fatima K, et al. Hepatitis C virus and neurological damage. World J Hepatol 2016; 8(12):545–556. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Stavros K, Simpson DM. Understanding the etiology and management of HIV-associated peripheral neuropathy. Curr HIV/AIDS Rep 2014; 11(3):195–201. [DOI] [PubMed] [Google Scholar]
- 38.Ellis RJ, Rosario D, Clifford DB, McArthur JC, Simpson D, Alexander T, et al. Continued high prevalence and adverse clinical impact of human immunodeficiency virus-associated sensory neuropathy in the era of combination antiretroviral therapy: the CHARTER Study. Arch Neurol 2010; 67(5):552–558. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Anziska Y, Helzner EP, Crystal H, Glesby MJ, Plankey M, Weber K, et al. The relationship between race and HIV-distal sensory polyneuropathy in a large cohort of US women. J Neurol Sci 2012; 315(1-2):129–132. [DOI] [PMC free article] [PubMed] [Google Scholar]