Objectively Measured Physical Activity in Asymptomatic Middle-Aged Men Is Associated With Routine Blood-Based Biomarkers

Abstract

Background

The use of circulating clinically routine biomarkers and volitional physical activity using wristband accelerometry in preclinical middle-aged adults may provide sensitive measures of physical function and predict sooner the onset of age- and HIV-related physical decline.

Methods

Nested cross-sectional cohort study of adult men 50–65 years old with HIV infection on potent antiretroviral therapy and uninfected control participants within the Boston metropolitan area. Gait speed derived from wristband accelerometry, gait speed derived from a standardized 6-minute walk test, cellular immune biomarker levels (CD4 T cell, CD8 T cell), and serum anabolic biomarker levels (total and free testosterone, and sex-hormone-binding globulin) were measured.

Results

Of the five measured biomarkers, four were significantly associated with volitional gait speed based on accelerometry, whereas only one was associated with gait speed based on the 6-minute walk test collected in a laboratory environment.

Conclusion

Levels of selected immune and anabolic biomarkers were associated with volitional physical activity in middle-aged individuals. Digital and circulating biomarkers may be useful in future studies designed to identify presymptomatic individuals at increased risk for age- and HIV-associated functional decline.

The trajectory of functional aging varies based on genetic and environmental factors that shape the life history of an individual, but can be generally characterized by a maintenance of functional capacity for decades that is followed by a steep decline in function occurring at variable times later in life (1,2). There is a growing interest in functional aging as an outcome of the balance between life stressors, both acute (eg, injury) and chronic conditions (eg, infection) and physiologic reserve, and capacity for compensation to delay stressor-driven functional decline. Identifying early biomarkers that reflect this dynamic balance and that precede clinical evidence for functional decline would be useful in preventative care designed to optimize healthy aging.

The onset and burden of age-related morbidities (eg, renal failure, diabetes, loss in bone density, hypertension, heart disease) that limit healthy aging all occur at earlier ages and with a higher prevalence in people aging with HIV infection (PAWH), even despite achieving undetectable viral load with combination antiretroviral therapy (3). We recently described a cohort of 170 asymptomatic middle-aged (50–65 years old) men and women with or without HIV infection, the Muscle and Aging Treated Chronic HIV (MATCH) cohort (4). Although overall the HIV-infected participants had subclinical deficits in physical function with persistent inflammation and immune activation (4), the men did not differ significantly in their gait speed, based on a 6-minute walk test (6-MWT) that was administered in a laboratory setting. Provocatively, in a follow-up substudy, volitional gait speed (VGS) using wristband accelerometry revealed that male PAWH were slower and less active compared to uninfected men, despite similar 6-MWT results (5). This difference detected by activity monitors raises the question of whether preclinical changes in activity patterns using accelerometry as a digital biomarker might be correlated to circulating age-related biomarkers.

In this study, middle-aged men with and without HIV (n = 46, 50% HIV+ men, age 50–65 years) without evidence for loss in functional performance were enrolled in a substudy to characterize physical activity profiles and gait speed under free-living conditions for three continuous weeks using wristband accelerometers. Circulating biomarkers for anabolic and immune status were measured in blood to identify associations with physical activity and gait speed.

The objective of the current study was to determine whether levels of routinely collected biomarkers (ie, CD4 and CD8 T-cell count and serum free and total testosterone, and sex-hormone-binding globulin) vary in association with PA measured as motion detected by the accelerometer, including fragmented activity, activity defined as walking (54–144 steps per minute), and activity defined as running (>144 steps per minute), using a formula developed by Withings (Eva Roitmann, Withings, E-mail communication; March 28, 2018) and expressed as VGS using data from wristband accelerometry and gait speed measured using a standardized laboratory-based 6-MWT. We hypothesized that circulating biomarkers would be associated with volitional PA, which in a prior study distinguished HIV+ from HIV− individuals that were asymptomatic and did not differ in their 6-MWT results. Identifying digital and blood-based biomarkers for functional aging may help to inform the care of aging persons at increased risk for frailty.

Methods

Study Population

Participants in this substudy consisted of 46 adults that included 23 men with HIV infection (HIV+) and 23 men without HIV infection (HIV−), all between the ages of 50–65 years and enrolled in the MATCH study (4) and who consented to this study. Eligible participants were required to have sufficient capacity to engage in activities of daily living (bathing, grooming) and to participate in functional assessment. Participants were excluded if they reported use of anabolic therapy in the last 6 months. Participants in this study were enrolled randomly from among subjects currently enrolled in the parental MATCH study. Subjects enrolled in MATCH were approached at their next MATCH study visit. Enrollment was balanced to achieve equivalent numbers of HIV+ men and controls. The MATCH cohort is a longitudinal observational study of middle-aged HIV+ individuals on effective antiretroviral therapy, along with aged-matched uninfected controls, all living in the Boston metropolitan area in Massachusetts (4). The parental study and this cross-sectional follow-up substudy sample have been recently described (4,5). All study procedures were approved by the Partners Human Research Committee Institutional Review Board.

Data Collection

Blood biomarkers that are routinely measured were chosen (CD4 T-cell count, CD8 T-cell count, free testosterone (FT), total testosterone (TT), and sex-hormone-binding globulin (SHBG) and were measured and reported as described (4,5). The accelerometer used in this study was the Nokia Pulse Ox, a consumer-grade triaxial accelerometer with published validity for step counts when compared to research-grade accelerometers (6,7). Data were collected in 1-minute intervals when any step activity was detected by the accelerometer, including low-intensity levels of activity that are not typically analyzed or accessible using commercial trackers. Mean gait speed in meters per second was based on the number of steps per minute and a formula using participant’s height in meters: ([steps × height × 0.414]/60) (8). Participants were instructed to wear the accelerometer on their nondominant wrist 24 hours per day, 7 days per week for 3 consecutive weeks, as described (5).

Statistical Analysis

Data distributions were inspected graphically and variables (VGS, 6-MWT, CD4 T cell, CD8 T cell, FT, TT, and SHBG) were log-transformed prior to analysis to remove the effect of skewness on the analysis. Crude association between biomarkers and volitional or laboratory-based gait speed was evaluated in univariate linear regression models. Furthermore, multivariate models were used to examine potential effect modification due to HIV serostatus and age. Slopes of the regressions with 95% CIs and R² were reported to examine magnitude of the association. Correlations between gait speed measured by tracker and the 6-MWT were evaluated using Pearson’s coefficient. The hypotheses were constructed with proof-of-concept approach and p values were not adjusted due to multiple testing. Analyses were conducted using two-sided alpha level of 0.05 and performed using SAS, v.9.4 (SAS Institute, Cary, NC) and Stata, v15.0 (StataCorp LLC, College Station, TX).

Results

Baseline characteristics of the study participants have been recently published (4,5). Briefly, the baseline values for outcome and predictive variables were as follows: VGS mean = 0.48 ± 0.15 meters/second (m/s); laboratory gait speed from 6-MWT mean = 1.52 ± 0.27 m/s; CD8 T-cell mean = 608.20 ± 388.82 cells/µL; CD4 T-cell mean = 813.04 ± 313.95 cells/µL; FT mean = 15.22 ± 8.62 ng/dL; TT mean = 18.61 ± 10.38 nmol/L, and SHBG mean = 58.42 ± 28.90 nmol/L. Significant differences based on HIV status were observed for VGS, CD8 T cell, and CD4 T cells. There were no significant differences based on HIV status in laboratory gait speed, SHBG, TT, or FT (4,5). Correlation in our sample between gait speed measured by tracker and 6-MWT was r = .35 (in the HIV− group was r = .49 and in the HIV+ group was r = .15). The HIV+ men differed in VGS but not in the 6-MWT, as previously reported (5).

Figure 1 illustrates the associations between levels of blood-based biomarkers (immune: CD8 T cell, CD4 T cell; anabolic: FT, TT, and SHBG) and gait speed based on wristband accelerometry (ie, VGS). Data for HIV positive and negative participants were combined as interaction between biomarkers and HIV status was nonsignificant (all ps > .2). CD8 T-cell levels declined in association with increasing VGS (R² = .157, p = .006) (Figure 1A) and CD4 T-cell levels declined in association with VGS (R² = .02, p = .327) (Figure 1B); however, this relationship was not statistically significant. Association between FT, TT, and SHBG and VGS was positive and had moderate strength (R² = .10, p = .042; R² = .11, p = .032; R² = .09, p = .041, respectively) (Figure 1C–E).

Figure 1.

Two-way scatterplots of log-transformed volitional gait speed with levels of log-transformed CD8 T cells (A), CD4 T cells (B), free testosterone (C), total testosterone (D), and SHBG (E). Linear fitted plots with 95% confidence intervals are shown for study participants (A–E). p values and R² are extracted from univariate linear regression models.

Results remained similar after adjusting for HIV serostatus and age with CD4 T cell showing statistically significant association with VGS in multivariate regression model (Table 1)

Table 1.

Linear Regression Model for Volitional Gait Speed Based on Blood Biomarkers

Volitional Gait Speed	Variable	Univariate		HIV-Adjusted		HIV- and Age-Adjusted
Domain		β (95% CI)	p value	β (95% CI)	p value	β (95% CI)	p value
Immune	CD8 T cell (n = 46)	−0.209 (−0.357, −0.062)	.006	−0.205 (−0.392, −0.017)	.033	−0.198 (−0.389, −0.007)	.042
	CD4 T cell (n = 46)	−0.127 (−0.385, 0.131)	.327	−0.275 (−0.479, −0.072)	.026	−0.313 (−0.605, −0.021)	.037
Anabolic	FT (n = 42)	0.190 (0.007, 0.374)	.042	0.185 (0.003, 0.367)	.046	0.183 (−0.023, 0.389)	.079
	TT (n = 42)	0.197 (0.018, 0.375)	.032	0.202 (0.026, 0.378)	.026	0.205 (0.006, 0.405)	.044
	SHBG (n = 46)	0.179 (0.007, 0.351)	.041	0.210 (0.043, 0.377)	.015	0.214 (0.031, 0.396)	.023

Notes: Slopes, 95% CIs, and p values are extracted from univariate and adjusted linear regression models for volitional gait speed. All variables were log-transformed. FT = Free testosterone; TT = Total testosterone; SHBG = Sex-hormone-binding globulin.

In contrast to VGS, analyses examining 6-MWT showed only CD4 T-cell levels being significantly associated with outcome in HIV-adjusted and HIV- and age-adjusted models (univariate β = −0.138, p = .060; HIV-adjusted β = −0.195, p = .023; HIV- and age-adjusted β = −0.212, p = .017). Associations between gait speed calculated from 6-MWT and other biomarkers (CD8 T cell, FT, TT, and SHBG) were statistically not significant in any of the analyzed regressions (Table 2, Figure 2).

Table 2.

Linear Regression Model for 6-Minute Walk Test (6-MWT)-Derived Gait Speed and Blood Biomarkers

6-MWT	Variable	Univariate		HIV-Adjusted		HIV- and Age-Adjusted
Domain		β (95% CI)	p value	β (95% CI)	p value	β (95% CI)	p value
Immune	CD8 T cell (n = 46)	−0.060 (−0.151, 0.031)	.191	−0.089 (−0.204, 0.026)	.125	−0.093 (−0.210, 0.024)	.115
	CD4 T cell (n = 46)	−0.138 (−0.283, 0.006)	.060	−0.195 (−0.363, −0.028)	.023	−0.212 (−0.385, −0.040)	.017
Anabolic	FT (n = 42)	0.049 (−0.067, 0.165)	.398	0.049 (−0.068, 0.167)	.402	0.065 (−0.067, 0.198)	.325
	TT (n = 42)	0.061 (−0.052, 0.174)	.281	0.061 (−0.054, 0.176)	.288	0.080 (−0.049, 0.209)	.217
	SHBG (n = 46)	0.058 (−0.045, 0.160)	.261	0.061 (−0.044, 0.166)	.251	0.078 (−0.036, 0.192)	.175

Notes: Slopes, 95% CLs, and p values are extracted from univariate and adjusted linear regression models for gait speed based on the 6-MWT. All variables were log-transformed. FT = Free testosterone; TT = Total testosterone; SHBG = Sex-hormone-binding globulin.

Figure 2.

Two-way scatterplots of log-transformed lab-based gait speed using the 6-MWT with levels of log-transformed CD8 T cells (A), CD4 T cells (B), free testosterone (C), total testosterone (D), and SHBG (E). Linear fitted plots with 95% confidence intervals are shown for study participants (A–E). p values and R² are extracted from univariate linear regression models.

Discussion

This study of asymptomatic middle-aged men living with and without HIV infection revealed that physical activity measured using wristband accelerometry detects differences that are not yet evident in laboratory-based assessment and that physical activity levels measured using wristband accelerometry were associated with levels of immune and anabolic biomarkers measured in blood.

Accelerometry-based gait speed, after adjusting for HIV serostatus, was associated with cell-surface immune biomarkers (CD8 and CD4 expressing T cells) and circulating anabolic biomarkers, that is, TT and FT and SHBG. When also adjusted for age, CD8 and CD4 expressing T cells, TT, and SHBG remained significant predictors. By contrast, gait speed based on the 6-MWT was only associated with CD4 T-cell levels, which remained significant when adjusted for HIV serostatus and age.

Interestingly, correlation between the 6-MWT and VGS was lower in the male PAWH compared to the uninfected. This may reflect a reduced exercise tolerance and fatigability in the HIV+, as VGS reflects longer-term activity, whereas the 6-MWT is a shorter-term assessment. Future studies will need to directly confirm whether PAWH have reduced exercise tolerance and increased fatigability compared to their uninfected counterparts.

Prior to the onset of a frail phenotype, multiple studies have observed a more rapid decline in gait speed and strength in PAWH compared to individuals of similar age that are not infected (9,10). Identifying convenient biomarkers that precede declines in gait speed and strength would provide an early window of intervention to delay (or prevent) impairment and frailty.

Age-related decline in physical function can reflect the accumulation of impairments across multiple biological systems that eventually overcome compensatory reserve mechanisms resulting in clinical presentation and eventually overt disability. A conceptual model for functional aging was proposed by Lopez-Otin (11) wherein underlying biological aging (eg, molecular damage, defective repair, energy exhaustion, and nutrient sensing) and phenotypic aging (eg, body composition, energetics, homeostatic mechanisms, and brain health) accumulate until a threshold is reached with subsequent overt presentation of functional impairment. Whether HIV infection accelerates or alters the deficit accumulation that occurs with functional aging remains unclear, but the conceptual model proposed by Lopez-Otin (11) and developed by Ferrucci and colleagues (1,2) does provide a framework for evaluating causative models in the context of chronic infection.

We and others have shown that biomarkers for inflammation are upregulated with aging (12–15). Circulating levels of inflammatory factors have also been associated with physical activity, both in the general population (16,17) and in PAWH, reviewed by d’Ettorre and colleagues (18). Notably, laboratory-based assessment of gait speed has been associated with levels of inflammation (19) and anabolic levels (20). Regulators of anabolic activity have been previously linked to gait speed (21,22), however, there is a paucity of data relating circulating cellular immune and anabolic factor levels with VGS based on accelerometry.

Our study builds off an existing cohort of aging participants with and without HIV infection, and for which comprehensive data are being collected including multiple metrics of physical function. Furthermore, the accelerometry data are based on 3 weeks of continuous monitoring providing a robust assessment of volitional physical activity.

Our study also has many limitations. First, our study population was relatively small and comprises only males; hence association between biomarkers and VGS should be validated in larger populations of men and women. Second, the study population is relatively young, 50–65 years old, therefore, reproducibility of our findings should be also assessed in different cohorts. Third, the number of biomarkers measured is limited and were evaluated individually, given the small sample size affecting statistical power to develop a more complex model. The role of body mass index in our study outcomes was not addressed directly, but was reported in a prior study (5). Biomarkers were chosen based on clinical convenience of sampling routine levels of immune and anabolic biomarkers, as well as data implicating these biomarkers in our prior studies (4,5) and their general role as different physiologic domains of healthy aging (15). Fourth, in this study, we did not evaluate different levels of activity detected by the tracker. Recent studies suggest that fragmented physical activity captured by trackers may reflect a key phenotype of higher fatigability (23,24) that should be tested directly in a future study. As our study is cross-sectional in nature, future research is necessary to examine longitudinal trends in functional decline and its early indication using a simple panel of biomarkers.

In summary, our findings suggest VGS measured with accelerometry correlated better with immune and anabolic biomarkers than did laboratory-based assessment using the 6-MWT. Blood-based and digital biomarkers may provide presymptomatic tools for identifying risk of functional decline.

Funding

This paper was published as part of a supplement sponsored and funded by AARP. The statements and opinions expressed herein by the authors are for information, debate, and discussion, and do not necessarily represent official policies of AARP.

Conflict of Interest

None reported.