Abstract
Background
Aging-related comorbidities are more common in people with human immunodeficiency virus (HIV) compared to people without HIV. The gut microbiome may play a role in healthy aging; however, this relationship remains unexplored in the context of HIV.
Methods
16S rRNA gene sequencing was conducted on stool from 1409 women (69% with HIV; 2304 samples) and 990 men (54% with HIV; 1008 samples) in the MACS/WIHS Combined Cohort Study. Associations of age with gut microbiome diversity, uniqueness, and genus-level abundance were examined in women and men separately, followed by examining relationships of aging-related genera with frailty (Fried frailty phenotype) and mortality risk (Veterans Aging Cohort Study [VACS] index).
Results
Older age was associated with greater microbiome diversity and uniqueness, greater abundance of Akkermansia and Streptococcus, and lower abundance of Prevotella and Faecalibacterium, among others; findings were generally consistent by sex and HIV status. An aging-related microbiome score, generated via combination of 18 age-related genera, significantly increased with age in both women and men independently of demographic, behavioral, and cardiometabolic factors. In general, age was more strongly related to microbiome features (eg, diversity, microbiome score) in men without compared to with HIV, but age-microbiome associations were similar in women with and without HIV. Some age-related genera associated with healthy/unhealthy aging, such as Faecalibacterium (related to reduced frailty) and Streptococcus (related to higher VACS index).
Conclusions
Age is associated with consistent changes in the gut microbiome in both women and men with or without HIV. Some aging-related microbiota are associated with aging-related declines in health.
Keywords: age, gut microbiome, healthy aging, HIV, frailty
Older age is associated with consistent changes in the gut microbiome in women and men with or without HIV, including higher diversity and uniqueness, and lower abundance of Prevotella and Faecalibacterium. Aging-related microbiota may contribute to aging-related declines in health.
Life expectancy for people with human immunodeficiency virus (PWH) has improved due to modern antiretroviral therapy (ART) [1]. Consequently, healthy aging, defined as a delay in age-related declines of physical, metabolic, and cognitive function, has increasingly become a focus of HIV clinical care and research. Aging-related comorbidities are more common in PWH compared to people without HIV (PWOH), thus PWH may face unique risks associated with aging [2–4].
The gut microbiome may play a role in healthy aging. Gut microbiota perform many tasks, including metabolism of dietary and endogenous compounds, and influencing immunity and inflammation. An individual's gut microbiome composition shifts over their lifetime, including in adulthood, when microbiome diversity increases and composition transitions towards uniqueness with advancing age [5–8]. Specific groups of gut microbial taxa have been shown to consistently relate to healthy or unhealthy aging [9].
Gut microbiome composition is altered in PWH, potentially contributing to non-AIDS comorbidities [10]. However, a comprehensive analysis of aging and the gut microbiome has not been conducted in PWH. In the Multicenter AIDS Cohort Study (MACS)/Women's Interagency HIV Study (WIHS) Combined Cohort Study (MWCCS), stool samples have been collected over time, providing the unique opportunity to study the relationship of aging with the gut microbiome in women and men with and without HIV.
METHODS
Study Population
The WIHS and MACS were 2 US longitudinal cohort studies of PWH and otherwise similar PWOH [11, 12], which merged in 2019 to create the MWCCS [13]. Participants attended semiannual (pre-2019) or annual (2019 to present) core visits in which they underwent a physical examination, provided specimens, and completed questionnaires.
From 2015 to 2019, WIHS participants from the Bronx, Brooklyn, and Chicago sites collected stool samples at home using study-provided kits. Beginning in 2020, stool collection has continued at all MWCCS sites. For this analysis, we included participants with ≥1 stool sample for which 16S rRNA sequencing data are available. We excluded persons identifying as transgender (to facilitate sex-stratified analysis), person-visits with current pregnancy, and samples with <2000 sequence reads after bioinformatic processing. After exclusions, 1409 women (2304 person-visits) and 990 men (1008 person-visits) remained (Supplementary Table 1 and Supplementary Figure 1).
Microbiome Measurement and Bioinformatics
Stool DNA underwent amplification of the 16S rRNA V4 region and sequencing on an Illumina platform. Reads were processed using QIIME2 and Deblur to obtain amplicon sequence variants (ASVs), which were assigned taxonomy (Supplementary Methods). Richness (number of ASVs) and the Shannon diversity index were assessed on rarefied data. Uniqueness, based on the Jensen Shannon divergence, was determined by sex and HIV serostatus separately, as the minimum distance of each sample to other samples in the dataset (excluding samples from the same participants). The higher the uniqueness, the more distinct the microbiome from others.
Covariate Data
Data for analysis were taken from the core visit closest to stool sample receipt in the laboratory. Covariates are shown in Table 1. Missing data for covariates were imputed based on the immediately prior study visit with available data.
Table 1.
Characteristics of Women and Men With or Without HIV in the MWCCS at the Time of First Stool Sample
| Characteristics | Women | Men | ||||||
|---|---|---|---|---|---|---|---|---|
| Overall (n = 1409) |
WWH (n = 972) |
WWOH (n = 437) | P Valueg | Overall (n = 990) |
MWH (n = 533) |
MWOH (n = 457) |
P Valueg | |
| Age, y, median (IQR) | 54 (48–59) | 54 (48–59) | 53 (45–59) | .02 | 60 (52–67) | 58 (49–64) | 63 (57–69) | <.001 |
| Living with HIV, n (%) | 972 (69.0) | … | … | … | 533 (53.8) | … | … | … |
| Race/ethnicity, n (%) | .22 | <.001 | ||||||
| Black non-Hispanic | 1016 (72.1) | 688 (70.8) | 328 (75.1) | 333 (33.6) | 219 (41.1) | 114 (24.9) | ||
| White non-Hispanic or other | 167 (11.9) | 123 (12.7) | 44 (10.1) | 520 (52.5) | 226 (42.4) | 294 (64.3) | ||
| Hispanic | 226 (16.0) | 161 (16.6) | 65 (14.9) | 137 (13.8) | 88 (16.5) | 49 (10.7) | ||
| Annual income, n (%) | .50 | <.001 | ||||||
| $12 000 or less | 674 (47.8) | 456 (46.9) | 218 (49.9) | 178 (18.0) | 118 (22.1) | 60 (13.1) | ||
| $12001–$24 000 | 317 (22.5) | 226 (23.3) | 91 (20.8) | 158 (16.0) | 103 (19.3) | 55 (12.0) | ||
| $24 001 or more | 418 (29.7) | 290 (29.8) | 128 (29.3) | 654 (66.1) | 312 (58.5) | 342 (74.8) | ||
| Educational attainment, n (%) | .80 | <.001 | ||||||
| Less than high school | 386 (27.4) | 269 (27.7) | 117 (26.8) | 75 (7.6) | 45 (8.4) | 30 (6.6) | ||
| Completed high school | 355 (25.2) | 248 (25.5) | 107 (24.5) | 171 (17.3) | 115 (21.6) | 56 (12.3) | ||
| Any college | 668 (47.4) | 455 (46.8) | 213 (48.7) | 744 (75.2) | 373 (70.0) | 371 (81.2) | ||
| Employed, n (%) | 487 (34.6) | 328 (33.7) | 159 (36.4) | .37 | 463 (46.8) | 253 (47.5) | 210 (46.0) | .70 |
| Smoking status, n (%) | <.001 | .08 | ||||||
| Never smoker | 426 (30.2) | 316 (32.5) | 110 (25.2) | 364 (36.8) | 190 (35.6) | 174 (38.1) | ||
| Current smoker | 483 (34.3) | 300 (30.9) | 183 (41.9) | 186 (18.8) | 114 (21.4) | 72 (15.8) | ||
| Former smoker | 500 (35.5) | 356 (36.6) | 144 (33.0) | 440 (44.4) | 229 (43.0) | 211 (46.2) | ||
| Alcohol use, n (%) | .01 | .07 | ||||||
| Abstainer | 613 (43.5) | 444 (45.7) | 169 (38.7) | 215 (21.7) | 130 (24.4) | 85 (18.6) | ||
| >0–7 drinks/wk | 666 (47.3) | 452 (46.5) | 214 (49.0) | 595 (60.1) | 314 (58.9) | 281 (61.5) | ||
| >7 drinks/wk | 130 (9.2) | 76 (7.8) | 54 (12.4) | 180 (18.2) | 89 (16.7) | 91 (19.9) | ||
| Drug use,a n (%) | 407 (28.9) | 250 (25.7) | 157 (35.9) | <.001 | 465 (47.0) | 259 (48.6) | 206 (45.1) | .30 |
| HCV seropositive, n (%) | 261 (18.5) | 192 (19.8) | 69 (15.8) | .09 | 102 (10.3) | 69 (12.9) | 33 (7.2) | .004 |
| MSM status, n (%) | … | … | … | … | 875 (88.4) | 481 (90.2) | 394 (86.2) | .06 |
| BMI, kg/m2, median (IQR) | 31.5 (26.6–37.4) | 31.3 (26.6–37.3) | 32.1 (26.6–37.7) | .42 | 27.2 (24.3–30.8) | 27.0 (24.2–30.6) | 27.4 (24.4–31.2) | .51 |
| Systolic blood pressure, mmHg, median (IQR) | 127 (115–141) | 126 (115–141) | 128 (113–143) | .39 | 126 (116–137) | 126 (116–136) | 127 (117–139) | .03 |
| Diastolic blood pressure, mmHg, median (IQR) | 76 (69–83) | 76 (69–83) | 75 (69–83) | .99 | 77 (70–84) | 77 (70–85) | 76 (70–83) | .09 |
| Diabetes,b n (%) | 347 (24.6) | 239 (24.6) | 108 (24.7) | .99 | 184 (18.6) | 103 (19.3) | 81 (17.7) | .57 |
| Antidiabetic medication, n (%) | 241 (17.1) | 169 (17.4) | 72 (16.5) | .73 | 126 (12.7) | 72 (13.5) | 54 (11.8) | .48 |
| Lipid-lowering medication, n (%) | 337 (23.9) | 248 (25.5) | 89 (20.4) | .04 | 400 (40.4) | 226 (42.4) | 174 (38.1) | .19 |
| Antihypertensive medication, n (%) | 623 (44.2) | 441 (45.4) | 182 (41.6) | .21 | 410 (41.4) | 223 (41.8) | 187 (40.9) | .82 |
| Estimated GFR,c mL/min/1.73 m2, median (IQR) | 82 (68–98) | 79 (65–96) | 89 (74–101) | <.001 | 84 (71–96) | 81 (67–95) | 88 (76–98) | <.001 |
| Detectable HIV viral load, n (%) | … | 245 (25.2) | … | … | … | 137 (25.7) | … | … |
| CD4+ T-cell count, cells/mm3, median (IQR) | … | 715.00 (511.25–973.25) | … | … | … | 662.00 (472.00–883.00) | … | … |
| Antiretroviral therapy regimen,d n (%) | ||||||||
| None | … | 31 (3.2) | … | … | … | 0 | … | … |
| PI-based | … | 86 (8.8) | … | … | … | 35 (6.6) | … | … |
| II based | … | 517 (53.2) | … | … | … | 314 (58.9) | … | … |
| NNRTI based | … | 128 (13.2) | … | … | … | 63 (11.8) | … | … |
| NRTI based or other | … | 210 (21.6) | … | … | … | 121 (22.7) | … | … |
| Fried frailty phenotype,e % of visits, mean (SD) | 13.1 (25.3) | 11.9 (23.6) | 16.1 (29.0) | .14 | 7.2 (18.7) | 8.4 (20.2) | 5.8 (16.7) | .05 |
| VACS Index 2.0,f median (IQR) | 55.98 (46.27–68.94) | 58.11 (47.64–72.55) | 52.31 (44.00–60.90) | <.001 | 52.67 (41.66–65.41) | 53.60 (42.37–66.94) | 51.53 (41.07–63.87) | .02 |
Abbreviations: BMI, body mass index; GFR, glomerular filtration rate; HCV, hepatitis C virus; HIV, human immunodeficiency virus; II, integrase inhibitor; IQR, interquartile range; MSM, men who have sex with men; MWCCS, Multicenter AIDS Cohort Study/Women's Interagency HIV Study Combined Cohort Study; MWH, men with HIV; MWOH, men without HIV; NNRTI, nonnucleoside reverse transcriptase inhibitor; NRTI, nucleoside reverse transcriptase inhibitor; PI, protease inhibitor; VACS, Veterans Aging Cohort Study; WWH, women with HIV; WWOH, women without HIV.
aDefined as current injected or noninjected recreational drug use including marijuana.
bDefined as ever any fasting glucose ≥ 126 mg/dL, hemoglobin A1C ≥ 6.5%, self-reported diabetes, or diabetes medication.
cBased on the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) 2021 race-free creatinine equation.
dPI based, at least 1 PI and 1 NRTI; II based, at least 1 II and 1 NRTI; NNRTI based, at least 1 NNRTI and 1 NRTI; NRTI based, 3 or more NRTIs.
eDefined as percent of visits with frailty out of 2–5 prior/current visits with available frailty data; calculated at time of last stool sample; includes 987 women (707/280 with/without HIV) and 751 men (397/354 with/without HIV) with ≥2 visits with available frailty data.
fIncludes 1401 women (965/436 with/without HIV) and 978 men (523/455 with/without HIV) with available data to calculate VACS Index 2.0.
g P value for difference by HIV serostatus determined by Kruskal-Wallis test or χ2 test for continuous or categorical variables, respectively.
Indicators of Unhealthy Aging
Fried frailty phenotype was defined using well-described criteria [14]. Person-visits were classified as frail if exhibiting ≥3 of 5 characteristics: (1) impaired mobility, (2) reduced grip strength, (3) physical exhaustion, (4) unintentional weight loss, and (5) low physical activity. Cumulative visits with frailty were determined over 2–5 visits prior to/inclusive of the last visit with a stool sample (Supplementary Methods).
Veterans Aging Cohort Study (VACS) index 2.0, a mortality risk index, includes age, body mass index (BMI), CD4+ T-cell count, HIV RNA, hemoglobin, platelets, alanine aminotransferase, aspartate aminotransferase, creatinine, white blood cell count, albumin, and hepatitis C virus (HCV) serostatus [15]. The VACS index is generalizable to people without HIV by assuming HIV RNA is undetectable and CD4+ T-cell counts are normal [16].
Statistical Analysis
General Principles
All analyses (using R version 4.3.3) were conducted separately for women and men (due to unique characteristics of the WIHS and MACS), first combining people with and without HIV, and then stratified by HIV serostatus. In all models with repeated measures, most covariates were time-varying, excepting time-fixed variables (eg, race/ethnicity).
Covariate Adjustments for Age-Gut Microbiome Associations
In statistical models for age and the microbiome, we first considered unadjusted associations given that age has no true causes. Then, to control for selection bias that may result from associations of age with other factors due to cohort criteria and self-selection, we used inverse probability weighting (IPW) [17, 18]. Specifically, using R package “ipw,” we generated weights based on multivariable linear regression models for age (outcome) with race/ethnicity, study site, income, educational attainment, employment, smoking status, alcohol use, drug use, HCV serostatus, and HIV serostatus (and men who have sex with men status), separately in women and men, using the first visit per participant (same weight applied to all repeated visits per participant). Weights were specified in models with age as the main exposure. We then examined these models with adjustment for aging-related cardiometabolic health indicators (BMI, diastolic blood pressure, systolic blood pressure, diabetes, use of antidiabetic mediation, use of lipid-lowering medication, use of antihypertensive medication, and estimated glomerular filtration rate), as they are potential mediators of the association of age with the microbiome. Among people with HIV, additional adjustment for detectable viral load, CD4+ T-cell count, and ART regimen was also performed. In sensitivity analyses, we adjusted age-microbiome associations for anal receptive intercourse since last visit, stool frequency, Bristol stool scale, and antibiotics use in the last 6 months. These factors were not included in the main analysis due to high levels of missingness (% person-visits missing any in women and men, 49% and 36%).
Associations of Age With Diversity and Uniqueness
Linear mixed-effects models with a random intercept for repeated measures were used to evaluate associations of age (continuous, predictor) with number of ASVs, Shannon index, and uniqueness (outcomes).
Associations of Age With Genus Abundance
Analysis of compositions of microbiomes with bias correction (ANCOM-BC) [19] was used to assess unadjusted associations of age with genera; models included a random intercept. We tested 169 genera with ≥20% presence in both women and men. An aging-related microbiome score was derived by adding (or subtracting) centered log ratio (CLR)-transformed standardized abundance for genera positively (or inversely) associated with age (false discovery rate-adjusted P < .10 in both women and men), following by standardization of the score. Associations of age with CLR-transformed aging-related genera and the aging-related microbiome score were then tested in linear mixed-effects models with random intercepts, with accounting for IPW and adjustment for potential mediators as described above. Associations of the aging-related microbiome score with HIV serostatus, detectable viral load, and CD4+ T-cell count were also determined using linear mixed-effects models with random intercepts.
Aging-Related Microbiome and Unhealthy Aging
We used the last visit with microbiome data per participant to perform the frailty analysis. Binomial logistic regression was used to assess the association of microbiome with frailty, with outcome being the number of visits with frailty out of 2–5 available visits. For the VACS index, linear mixed-effects models with random intercept were used. We tested main effects, and interactions with age, for the aging-related microbiome score and individual aging-related genera with the frailty and VACS index outcomes. For visualization, we determined sex- and HIV-specific medians of the aging-related microbiome score, and plotted associations of age with the frailty and VACS index outcomes among participants with low and high (ie, below/above median) microbiome scores.
RESULTS
Aging and the Gut Microbiome
At time of first stool sample, women and men were a median of 54 and 60 years old, respectively, which differed by HIV serostatus and study site (Table 1, Supplementary Figure 2, and Supplementary Table 2). Older age was associated with higher microbiome richness and diversity in women and men, even after IPW of age and adjustment for cardiometabolic factors (Figure 1A and1B). The associations of age with richness and diversity were similar in WWH and WWOH (P interaction age × HIV ≥ .58), but attenuated in MWH compared to MWOH (P interaction < .001) (Supplementary Table 3). Older age was associated with higher microbiome uniqueness in women and men in unadjusted models; however, the association was fully abolished in women after adjustment for cardiometabolic factors, while retaining statistical significance in men (Figure 1C), with no interaction by HIV serostatus (Supplementary Table 4). Further adjustment for HIV-related factors did not alter associations of age with richness, diversity, or uniqueness in WWH and MWH (Supplementary Tables 3 and 4).
Figure 1.
Associations of age with gut microbiome diversity and uniqueness in women and men with and without HIV. Linear mixed-effects models with random intercept were used to assess the association of age with (A) number of amplicon sequence variants, (B) the Shannon diversity index, and (C) uniqueness based on the Jensen Shannon divergence, among 1409 women (2304 person-visits) and 990 men (1008 person-visits). Model 1 includes weights from inverse probability weighting of age based on race/ethnicity, study site, income, educational attainment, employment, smoking status, alcohol use, drug use, hepatitis C virus serostatus, and HIV serostatus (and men who have sex with men status in men only). Model 2 includes aforementioned weights and is adjusted for body mass index, diastolic blood pressure, systolic blood pressure, diabetes, antidiabetic medication, lipid-lowering medication, antihypertensive medication, and estimated glomerular filtration rate. Abbreviations: CI, confidence interval; PWH, people with HIV; PWOH, people without HIV.
Associations of age with gut microbiome genera in unadjusted ANCOM-BC models were largely consistent between women and men (Spearman r = 0.58, P < .0001), with 18 genera associated with older age (q < 0.10) in both women and men (11 genera enriched [eg, Akkermansia, Streptococcus] and 7 depleted [eg, Prevotella, Faecalibacterium] with older age) (Figure 2A and Supplementary Table 5). Inverse-probability weighting of age improved consistency of age-genus associations between women and men in linear mixed-effects models (Spearman r = 0.64, P < .0001). For the 18 genera, most of their associations with age remained significant after IPW, except for associations of age with Enterocloster and Oribacterium in men, which were attenuated (Figure 2B and Supplementary Table 6). Additionally, many age-genus associations were similar by HIV serostatus, although some were not significant in MWH (eg, Akkermansia, Ruthenibacterium) or WWOH (eg, Eubacterium J, Ruthenibacterium) (Figure 2B and Supplementary Figure 3). An aging-related microbiome score, generated via combination of the 18 aging-related genera, was significantly enriched with older age in women and men with and without HIV (Figure 2C), even with IPW of age and additional adjustment for cardiometabolic- and HIV-related factors (Figure 2D and Supplementary Table 7). The relationship of age with aging-related microbiome score was similar for WWH and WWOH (P interaction age × HIV = .44), but tended to be stronger in MWOH compared to MWH (P interaction age × HIV = .06) (Figure 2C and 2D, and Supplementary Table 7).
Figure 2.
Age and gut microbiome genera in women and men with and without HIV. A, LFC in genus abundance per year of age for women (x-axis) and men (y-axis), from unadjusted analysis of compositions of microbiomes with bias correction (ANCOM-BC) models among 1409 women (2304 person-visits) and 990 men (1008 person-visits): 169 tested genera are plotted; 18 genera shown in color represent those significantly associated with age in both women and men at false discovery rate-adjusted P < .10. B, Association of age with CLR-transformed abundance of 18 genera in linear mixed-effects models, stratified by sex and HIV serostatus. Models include weights from inverse probability weighting of age based on race/ethnicity, study site, income, educational attainment, employment, smoking status, alcohol use, drug use, hepatitis C virus serostatus, and HIV serostatus (and men who have sex with men status in men only). C, Scatter plot of age (x-axis) and age-related microbiome score (y-axis), derived for each sample by adding (or subtracting) CLR-transformed abundance for 18 genera positively (or inversely) associated with age. Linear regression lines are displayed stratified by sex and HIV serostatus. D, Association of age with age-related microbiome score in linear mixed-effects models. Model 1 includes weights from inverse probability weighting of age described in (B). Model 2 includes aforementioned weights and adjusts for body mass index, diastolic and systolic blood pressure, diabetes, antidiabetic, lipid-lowering, and antihypertensive medications, and estimated glomerular filtration rate. Abbreviations: CI, confidence interval; CLR, centered log ratio; LFC, log fold change; MWH, men with HIV; MWOH, men without HIV; PWH, people with HIV; PWOH, people without HIV; WWH, women with HIV; WWOH, women without HIV.
On average, the aging-related microbiome score was higher in WWH compared to WWOH while adjusting for age, but not after further adjustment for demographic and behavioral factors; in contrast, the aging-related microbiome score was lower in MWH compared to MWOH, which persisted after adjustment for age and demographic, behavioral, and cardiometabolic factors (Supplementary Table 8). Consistently, aging-related genera were mostly not associated with HIV serostatus in women, but many were in men (Supplementary Table 9). Among WWH and MWH, the aging-related microbiome score was not associated with HIV load or CD4+ T-cell count (Supplementary Table 8), and the same was generally true for aging-related genera (Supplementary Table 9).
Aging-related microbiome features, including richness, diversity, uniqueness, and genera, were correlated with each other (Figure 3A and 3B, and Supplementary Figure 4). In sensitivity analyses adjusting for anal receptive intercourse, stool frequency, Bristol stool scale, and antibiotic use while accounting for IPW of age, most associations of age with microbiome features remained statistically significant (Supplementary Table 10); in particular, the association of age with the aging-related microbiome score remained significant in both women and men with and without HIV (P < .003) despite the reduced sample size for this analysis.
Figure 3.
Correlations among aging-related gut microbiome features in women and men with and without HIV. Spearman correlations are shown for (A) women (n = 1409) and (B) men (n = 990). Only 1 sample per participant (ie, the first stool sample) was included in correlation analysis. Abbreviation: ASV, amplicon sequence variant. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.
Aging-Related Gut Microbiome and Frailty
The aging-related microbiome score was associated with lower odds of frailty in women, and not associated with frailty in men, after adjusting for age (Figure 4A) and demographic and behavioral factors (Supplementary Table 11). Accordingly, some genera depleted with aging (eg, Prevotella, Alloprevotella) were related to greater frailty in women (Figure 4A and Supplementary Table 12). Some aging-related genera were consistently associated with frailty in women and men, including Megasphaera A 38685, Enterocloster, and Ruthenibacterium (related to greater frailty), and Faecalibacterium (related to reduced frailty) (Figure 4A and Supplementary Table 12). Associations of the aging-related microbiome score with frailty did not differ by HIV serostatus (P interaction score × HIV = .14 [women] and .71 [men]) (Supplementary Table 11).
Figure 4.
Association of aging-related microbiome score and genera with indicators of unhealthy aging in women and men with and without HIV. Relationship of the aging-related microbiome score or centered log ratio-transformed genus abundance with (A) frailty using binomial logistic regression models, or (B) the VACS index using linear mixed-effects models. For the outcome of frailty, we calculated cumulative visits with frailty out of total visits with available data over the 2–5 visits prior to/inclusive of the last person-visit with a stool sample. All models were adjusted for age. Color indicates whether the genus was found to increase or decrease with age. Abbreviations: CI, confidence interval; OR, odds ratio; VACS, Veterans Aging Cohort Study.
For WWH and MWOH in the upper half of aging-related microbiome score, the association of age with frailty was greater as compared to those in the lower half (Figure 5A–D). When considering aging-related microbiome score as a continuous variable along with adjustment for demographic and behavioral factors, there remained a significant positive interaction of age and aging-related microbiome score on frailty in WWH only (Table 2 and Supplementary Table 11). Accordingly, some genera enriched with aging (eg, Eubacterium J, Anaerotruncus) showed a synergistic interaction with age on frailty in WWH (Supplementary Table 12).
Figure 5.
Interaction of age and aging-related microbiome score on indicators of unhealthy aging in women and men with and without HIV. Relationship of age to frailty (A–D) and the VACS index (E–H) among participants with low or high age-related microbiome score, defined as below or above the sex- and HIV-specific median of the age-related microbiome score. For the outcome of frailty, we calculated cumulative visits with frailty out of total visits with available data over the 2–5 visits prior to/inclusive of the last person-visit with a stool sample. P values displayed on plots are for the interaction of high versus low age-related microbiome score with age, in binomial logistic regression models for the frailty outcome, and linear mixed-effects models for the VACS index outcome. Abbreviations: Int, interactive; VACS, Veterans Aging Cohort Study.
Table 2.
Interaction of Age and Aging-Related Microbiome Score on Indicators of Unhealthy Aging in Women and Men With and Without HIV
| Participant group | Frailty | VACS Index | ||||
|---|---|---|---|---|---|---|
| n | Odds Ratioa (95% CI) | P Value | n (Person-Visits) | Betaa (95% CI) | P Value | |
| Women | 987 | 1.02 (1.00–1.03) | .04 | 1401 (2296) | 0.13 (.06–.21) | .001 |
| Women with HIV | 707 | 1.02 (1.00–1.04) | .03 | 965 (1592) | 0.13 (.03–.23) | .01 |
| Women without HIV | 280 | 0.99 (.97–1.02) | .49 | 436 (704) | 0.17 (.07–.26) | .001 |
| Men | 751 | 1.00 (.99–1.02) | .85 | 978 (996) | 0.09 (.01–.16) | .02 |
| Men with HIV | 397 | 0.99 (.97–1.01) | .22 | 523 (534) | 0.07 (−.06 to .20) | .30 |
| Men without HIV | 354 | 1.01 (.99–1.04) | .34 | 455 (462) | 0.11 (.03–.20) | .01 |
Abbreviations: CI, confidence interval; HIV, human immunodeficiency virus; VACS, Veterans Aging Cohort Study.
Coefficients represent the interaction of age and age-related microbiome score on outcome. For the outcome of frailty, cumulative visits with and without frailty were determined over the 2–5 visits prior to/inclusive of the last person-visit with a stool sample, and used as the outcome in binomial logistic regression. For the continuous outcome of VACS index, linear mixed-effects models were used. Models were adjusted for race/ethnicity, study site, income, educational attainment, employment, smoking status, alcohol use, drug use, and HCV serostatus (and HIV serostatus in combined analysis).
aEffect estimate for interaction term of age (per 1 year) × aging-related microbiome score (per 1 − SD).
Aging-Related Gut Microbiome and the VACS Index
The aging-related microbiome score was associated with higher VACS index in men, but not women, after adjusting for age (Figure 4B), and demographic and behavioral factors (Supplementary Table 13). Some aging-related genera were associated with the VACS index consistently in women and men, including Streptococcus, Ruthenibacterium, and Enterocloster (related to higher VACS), and Prevotella (related to lower VACS) (Figure 4B and Supplementary Table 14). Associations of the aging-related microbiome score with the VACS index did not differ by HIV serostatus (P interaction score × HIV = .45 [women] and .45 [men]) (Supplementary Table 13).
For WWH, WWOH, and MWOH in the upper half of aging-related microbiome score, the association of age with the VACS index was greater as compared to those in the lower half (Figure 5E–H); but this was not observed in MWH. When considering the aging-related microbiome score as a continuous variable along with adjustment for demographic and behavioral factors, there was a significant positive interaction of age and aging-related microbiome score on the VACS index in women and men overall, and in WWH, WWOH, and MWOH, but not MWH (Table 2 and Supplementary Table 13). Specific genera significantly interacting with age were generally not consistent between women and men; only Mediterraneibacter A 155507 (enriched with aging) had a positive interaction with age on the VACS index in women and men (Supplementary Table 14).
DISCUSSION
In this large study of PWH and PWOH, we observed consistent associations of age with gut microbiome diversity, uniqueness, and genus abundance, across women and men. In general, age was more strongly related to microbiome features in MWOH compared to MWH, but age-microbiome associations were similar in WWH and WWOH. Our findings suggest that the aging-related microbiome may augment declines in physical health (ie, frailty) in WWH, and risk of mortality (ie, VACS index) in women and men. Thus, the extent of gut microbiome aging may be important for healthy aging.
A wide number of studies reported alterations in the gut microbiome related to aging in the general population [9], but few have done so in populations with HIV. In general populations, older age was associated with greater gut microbiome diversity [5–7] and uniqueness [8], loss of dominant commensal taxa (eg, Prevotella, Faecalibacterium), and increases in pathobionts (eg, Streptococcus) and other commensals (eg, Akkermansia) [9], consistent with our observations. Inconsistent with other studies, we did not observe aging-related changes in Bacteroides and many other taxa [8, 9, 20]; however, cross-study differences in aging-related taxa may be common [20] and could relate to differences in health of the study populations, geographical location, or methods. Prior studies of aging and the gut microbiome in PWH were limited by small sample sizes (≤22 PWH), predominantly male participants, and/or lack of a comparison group of PWOH [21, 22], precluding the ability to produce rigorous results. Our study, featuring thousands of samples from women and men with and without HIV, represents a significant advance in understanding the effect of aging on the gut microbiome in the context of HIV.
Many environmental and personal factors that change with aging can impact the gut microbiome, including human contact, lifestyle, polypharmacy, and comorbidities [9]. These changes can impact gastrointestinal physiology, including thinning of the mucosal barrier and increasing inflammation, which, in turn, modify the microenvironment for microbes [9]. This may be especially relevant in HIV, where there is mucosal damage and “leaky gut” [23]. Thus, some aging-related microbiome changes may represent a consequence of aging-related changes in intestinal physiology [24]. However, it is also likely that some aging-related microbiome changes contribute to unhealthy aging based on their known functionality [9]. For example, Faecalibacterium (depleted during aging) produces butyrate, involved in reduction of inflammation and improvement of gut barrier function [25]. Conversely, Streptococcus (enriched during aging) produces hydrogen peroxide contributing to oxidative stress [26].
Given that HIV infection leads to alterations in gut physiology that resemble aging (eg, mucosal damage, inflammation), it may seem reasonable to expect a synergistic effect of HIV and age on the gut microbiome [27], where PWH experience enhanced aging of the gut microbiome. To the contrary, we found that the effect of age on the microbiome was similar for WWH and WWOH, and greater for MWOH than MWH. It is promising that in the era of modern ART, aging of the gut microbiome does not appear worse in PWH compared to PWOH. While it is not clear why age-microbiome associations were stronger in MWOH, the significantly older age of MWOH versus MWH in this study population (median 63 vs 58 years) may have contributed to this observation.
Hallmarks of healthy aging include maintenance of physical and metabolic health during older age. PWH face higher prevalence of frailty [28, 29] and chronic diseases [2] than PWOH of similar age, suggesting an acceleration of biological aging in PWH [30], possibly linked to persistent immune dysregulation and inflammation in PWH, even with viral suppression on ART [31, 32]. Indeed, recent studies have shown that HIV infection is associated with accelerated biological aging measured by epigenetic markers [33, 34]. There is accumulating evidence that the gut microbiome is also related to accelerated biological aging defined by epigenetic or metabolic markers [35, 36] and to declines in physical health [37, 38]. For example, higher gut microbiome diversity was related to accelerated epigenetic aging and decreased physical fitness [36], while higher Streptococcus abundance was associated with accelerated biological aging [35, 39]. Additionally, a number of observational studies noted lower abundance of Faecalibacterium in frail versus nonfrail adults [37, 38]. Our findings recapitulate some of this research, particularly our observations that Faecalibacterium was associated with reduced frailty, and Streptococcus with higher VACS index, in women and men.
There is relatively little information on the role of gut microbiota in healthy aging among PWH. A recent study (25 PWH, 23 PWOH) found that genera enriched in the colon of PWH, such as Prevotella and Alloprevotella, were associated with accelerated epigenetic aging [40]. Another study (24 PWH, 12 PWOH) found no difference in gut microbiome composition according to frailty status [41]. Among 14 MWH, greater abundance of Escherichia, Prevotella, and Megasphaera was associated with reduced muscle function [42]. Similar to these prior studies in PWH, we found that Prevotella and Alloprevotella were related to greater frailty in women, and Megasphaera A 38685 to greater frailty in women and men. In general, our results did not indicate that genera enriched with aging were exclusively detrimental to physical health (ie, frailty) or vice versa; for example, Megasphaera A 38685 was depleted with aging and related to greater frailty. Rather, specific genera may be associated with frailty regardless of their association with age. In contrast, genera enriched with aging tended to associate with higher VACS index and vice versa, particularly in men.
We also observed interactions of age with the aging-related microbiome score on frailty and the VACS index, suggesting that greater gut microbiome aging may enhance effects of age on health. In particular, in WWH, the effect of age on frailty was more pronounced in those with higher aging-related microbiome scores. Similarly, in WWH, WWOH, and MWOH, the effect of age on the VACS index was more pronounced in those with higher aging-related microbiome scores. Interestingly, the interaction of age and aging-related microbiome on the VACS index was not observed in MWH, although MWH did have a significant direct association of aging-related microbiome score with the VACS index. Few studies have examined whether aging-related microbiota may modify effects of age on health outcomes. Using a different analysis strategy, Wilmanski et al reported that health status modified the association of age with gut Bacteroides abundance [8]; however, we did not observe any associations of age with Bacteroides. Our novel results regarding interactions of age with the aging-related microbiome will require validation in future studies.
There are several study limitations. Because diet is an important determinant of the gut microbiome and may change with age, lack of dietary data in the MWCCS precludes examination of whether diet plays a role in aging-related gut microbiome associations. Furthermore, incomplete data on other important microbiome determinants that may change with age (stool characteristics, anal receptive intercourse, antibiotics use) prevented us from fully accounting for these factors in our main analysis. Finally, as this is an observational study, causal effects cannot be determined, and associations of age-related microbiota with health outcomes could result from unmeasured confounders (eg, immune factors).
In summary, in this largest study of aging and the gut microbiome in PWH and PWOH, aging of the microbiome was not enhanced in PWH compared to PWOH. Aging-related microbiome associations identified widely in the literature, including increases in diversity, uniqueness, Streptococcus and Akkermansia, and decreases in Prevotella and Faecalibacterium, were recapitulated here. Some aging-related microbiota may influence healthy or unhealthy aging, such as Faecalibacterium (associated with reduced frailty) and Streptococcus (associated with higher VACS index). Finally, the aging-related microbiome may augment some effects of aging on health. Given the high burden of aging-related comorbidities in PWH, the gut microbiome may be an important target for modification for healthy aging among PWH.
Supplementary Material
Contributor Information
Brandilyn A Peters, Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, New York, USA.
Xiaonan Xue, Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, New York, USA.
David B Hanna, Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, New York, USA.
Yi Wang, Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, New York, USA.
Zheng Wang, Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, New York, USA.
Anjali Sharma, Department of Medicine, Albert Einstein College of Medicine, Bronx, New York, USA.
Michelle Floris-Moore, Department of Medicine, University of North Carolina School of Medicine, Chapel Hill, North Carolina, USA.
Deborah Konkle-Parker, Schools of Nursing, Medicine, and Population Health Sciences, University of Mississippi Medical Center, Jackson, Mississippi, USA.
Maria L Alcaide, Department of Medicine, University of Miami Miller School of Medicine, Miami, Florida, USA.
Anandi N Sheth, Department of Medicine, Emory University School of Medicine, Atlanta, Georgia, USA; Ponce de Leon Center, Grady Health System, Atlanta, Georgia, USA.
Elizabeth F Topper, Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.
Kathleen M Weber, Hektoen Institute of Medicine, Chicago, Illinois, USA.
Phyllis C Tien, Medical Service, Department of Veterans Affairs Medical Center, San Francisco, California, USA; Department of Medicine, University of California San Francisco, San Francisco, California, USA.
Daniel Merenstein, Department of Family Medicine, Georgetown University, Washington, District of Columbia, USA.
Elizabeth Vásquez, Department of Epidemiology and Biostatistics, College of Integrated Health Science, State University of New York, Rensselaer, New York, USA.
Yue Chen, Department of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
Matthew J Mimiaga, Department of Epidemiology, University of California Los Angeles, Los Angeles, California, USA.
Valentina Stosor, Department of Medicine, Northwestern Feinberg School of Medicine, Chicago, Illinois, USA.
Todd T Brown, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
Kristine M Erlandson, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA.
Stephanie M Dillon, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA.
Noha S Elsayed, Department of Pediatrics, Albert Einstein College of Medicine, Bronx, New York, USA.
Mykhaylo Usyk, Department of Pediatrics, Albert Einstein College of Medicine, Bronx, New York, USA.
Christopher C Sollecito, Department of Pediatrics, Albert Einstein College of Medicine, Bronx, New York, USA.
Robert C Kaplan, Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, New York, USA; Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA.
Robert D Burk, Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, New York, USA; Department of Pediatrics, Albert Einstein College of Medicine, Bronx, New York, USA; Department of Microbiology and Immunology, Albert Einstein College of Medicine, Bronx, New York, USA; Obstetrics and Gynecology and Women's Health, Albert Einstein College of Medicine, Bronx, New York, USA.
Qibin Qi, Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, New York, USA.
Supplementary Data
Supplementary materials are available at The Journal of Infectious Diseases online (http://jid.oxfordjournals.org/). Supplementary materials consist of data provided by the author that are published to benefit the reader. The posted materials are not copyedited. The contents of all supplementary data are the sole responsibility of the authors. Questions or messages regarding errors should be addressed to the author.
Notes
Disclaimer. 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.
Financial support. This work was supported primarily by the National Heart, Lung, and Blood Institute (NHLBI) for data collection by the Multicenter AIDS Cohort Study/Women's Interagency HIV Study (MACS/WIHS) Combined Cohort Study (MWCCS) at the following MWCCS sites, Principal Investigators (grant number): Atlanta Clinical Research Site (CRS), Ighovwerha Ofotokun, Anandi Sheth, and Gina Wingood (U01-HL146241); Baltimore CRS, Todd Brown and Joseph Margolick (U01-HL146201); Bronx CRS, Kathryn Anastos, David Hanna, 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 Topper (U01-HL146193); Chicago-Cook County CRS, Mardge Cohen, Audrey French, and Ryan Ross (U01-HL146245); Chicago-Northwestern CRS, Steven Wolinsky, Frank Palella, and Valentina Stosor (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, James B. Brock, Emily Levitan, and Deborah Konkle-Parker (U01-HL146192); University of North Carolina CRS, M. Bradley Drummond and Michelle Floris-Moore (U01-HL146194). The MWCCS was supported with additional cofunding from the Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institute on Aging, National Institute of Dental and Craniofacial Research, National Institute of Allergy and Infectious Diseases, National Institute of Neurological Disorders and Stroke, National Institute of Mental Health, National Institute on Drug Abuse, National Institute of Nursing Research, National Cancer Institute, National Institute on Alcohol Abuse and Alcoholism, National Institute on Deafness and Other Communication Disorders, National Institute of Diabetes and Digestive and Kidney Diseases, National Institute on Minority Health and Health Disparities, and in coordination and alignment with the research priorities of the National Institutes of Health, Office of AIDS Research. MWCCS data collection was also supported by the National Center for Advancing Translational Sciences (grant numbers UL1-TR000004 to University of California San Francisco Center for Translational Science Awards [CTSA]; UL1-TR003098 to Johns Hopkins University Institute for Clinical and Translational Research; UL1-TR001881 to University of California Los Angeles Clinical and Translational Science Institute [CTSI]; UL1-TR001409 to DC CTSA; KL2-TR001432 to DC CTSA; and TL1-TR001431 to DC CTSA), the National Institute of Allergy and Infectious Diseases (grant numbers P30-AI-050409 to Atlanta Centers for AIDS Research [CFAR]; P30-AI-073961 to Miami CFAR; P30-AI-050410 to University of North Carolina CFAR; P30-AI-027767 to University of Alabama at Birmingham CFAR), and the National Institute of Mental Health (grant number P30-MH-116867 to Miami Center for HIV and Research in Mental Health). B. A. P. was supported by the NHLBI (grant number K01HL160146).
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