HIV-infected individuals are living longer on antiretroviral therapy, but many patients display signs that in some ways resemble premature aging. To investigate and quantify the impact of chronic HIV infection on aging, we report a global analysis of the whole-blood DNA methylomes of 137 HIV+ individuals under sustained therapy along with 44 matched HIV− individuals. First, we develop and validate epigenetic models of aging that are independent of blood cell composition. Using these models, we find that both chronic and recent HIV infection lead to an average aging advancement of 4.9 years, increasing expected mortality risk by 19%. In addition, sustained infection results in global deregulation of the methylome across >80,000 CpGs and specific hypomethylation of the region encoding the human leukocyte antigen locus (HLA). We find that decreased HLA methylation is predictive of lower CD4 / CD8 T cell ratio, linking molecular aging, epigenetic regulation, and disease progression.

Here we begin to address these questions by analyzing the methylomes of HIV-infected, cART-treated subjects, in which we observe a strong shared phenotype of HIV and age. To understand this signal, we develop models of biological age that allow us to establish a clear quantitative link between HIV infection and aging as observed in the general population. We identify both global and targeted epigenomic effects of HIV, including specific hypomethylation of the HLA locus. Together, these results shed light on the epigenetic consequences and/or gerontological aspects of chronic HIV infection.

In parallel with such epidemiological observations, a number of studies report age effects using blood-based biomarkers. Analysis of cell surface markers in T cells has shown HIV+ subjects to show phenotypes of older cells (). Other studies have observed shortened telomeres in certain cell populations () as well as whole blood (), indirectly linking HIV to aging via the well-studied connection between telomere length and age (). Furthermore, a recent analysis of untreated HIV+ individuals found DNA methylation sites that are associated with both HIV infection and age (). Together, these results raise the possibility that HIV infection results in an increase in biological age. Many questions remain, however: Are the epigenetic changes associated with HIV the same as those previously identified () in normal individuals as markers of “biological age,” and how complete is the correspondence between these two responses? What is the quantitative effect on aging in years, and is it fixed age advancement or continuous acceleration? What is the impact on aging of chronic HIV infection and sustained cART treatment? Are there other impacts of HIV on the methylome that are unrelated to aging?

CIHR Emerging Team Grant on HIV Therapy and Aging: CARMA Association between short leukocyte telomere length and HIV infection in a cohort study: No evidence of a relationship with antiretroviral therapy.

Biological aging has become of particular interest in treatment of HIV, in which the development of combination Anti-Retroviral Therapy (cART) now enables infected individuals to live many decades (). Several studies have suggested links between chronic HIV infection and early onset of neurodegeneration (), liver or kidney failure (), cancer (), cardiovascular disease (), and telomere shortening (), leading to the hypothesis that HIV+ patients might experience advanced or accelerated aging (). While these studies report rough estimates of HIV-mediated age advancement in the range of 0–20 years, it has been difficult to accurately quantify this number due to sampling effects, co-morbidities, and relatively low incidence rates of any single age-associated disease. To this effect, the existence, extent, and molecular basis of a bona-fide increase in aging have been unclear (), in part due to lack of an objective biological clock or aging biomarker.

No difference in the rate of change in telomere length or telomerase activity in HIV-infected patients after three years of darunavir/ritonavir with and without nucleoside analogues in the MONET trial.

Antiretroviral drug-related liver mortality among HIV-positive persons in the absence of hepatitis B or C virus coinfection: the data collection on adverse events of anti-HIV drugs study.

It is an open question why some people show early or delayed onset of aging-associated disorders (). Recent studies have found that aging is associated with epigenetic changes (), and based on this work we () and others () have built models capable of predicting a person’s age using DNA methylation patterns across a large number of CpG sites. Although these models are fairly accurate, errors of prediction—differences between the chronological and predicted age—serve as a quantitative readout of the relative advancement or retardation of the “biological age” of an individual. Biological age advancement has been correlated with factors such as gender, genetic polymorphisms, and diseases including cancer and diabetes, and it may influence the onset of other age-associated disorders (). A recent longitudinal study validated the clinical utility of these models by demonstrating a link between biological age advancement and increased mortality rates ().

Differential DNA methylation with age displays both common and dynamic features across human tissues that are influenced by CpG landscape.

Aging of blood can be tracked by DNA methylation changes at just three CpG sites.

Differential DNA methylation with age displays both common and dynamic features across human tissues that are influenced by CpG landscape.

Thus far, we had observed multiple effects of HIV on the methylome, including changes in cellular composition, age advancement, and a general increase in methylome disorder. We next sought to determine whether there are specific genomic regions for which the methylation state is particularly associated with HIV infection, independent of aging or other factors ( Experimental Procedures ). Toward this aim, we conducted an analysis of HIV-associated CpG markers independent of disorder or age, controlling for the effects of cellular composition. Analysis of the whole-blood data identified a single genomic region that was enriched in CpG markers associated with HIV; this region, consisting of 10 Mb on chromosome 6 including histone gene cluster 1 and the entire HLA locus, had particularly reduced methylation levels in HIV+ cases as compared to HIV− controls (p < 10 Figure 5 A; Experimental Procedures ). HLA genes encode the Major Histocompatibility Complexes (MHCs), the key antigen-presenting molecules that govern the acquired immune response and impact innate immunity ( Figure 5 B) (). We found that the differentially methylated markers surround the rs2395029 variant, for which common genetic variation has been repeatedly implicated in HIV host control ( Figures 5 C and 5D) (). Examination of this locus in the validation samples of purified neutrophils and CD4T cells identified the HCP5 gene body as particularly differentially methylated in neutrophils ( Figures 5 E, 5F, and S6 ). As further evidence that the observed changes are functional, we found that the amount of methylation at this gene was correlated with a patient’s CD4/CD8T cell ratio ( Figure S6 ). Taken together, these results indicate that the HLA locus is likely differentially methylated across blood cell types and also changes within individual cell types in response to HIV. An intriguing interpretation of our results is that some of the previously reported changes in HLA expression and corresponding HIV control () are attributable to methylation dynamics.

(E and F) Validation screen of HIV-downregulated markers in purified populations of neutrophils (E) and CD4T cells (F). See also Table S2 and Figure S6

(B and C) p values of genome-wide association of SNPs with host control of HIV, reproduced from

(A) Epigenome-wide association of CpG methylation (mCpG) with HIV status (presence or absence). Each point represents the P-value of enrichment for differentially methylated CpG within a bin of ± 100 consecutive markers along the genome.

We have previously reported that age-associated markers in older subjects tend away from a fully methylated or unmethylated state and instead move toward disorder (with a methylation fraction of 50% representing complete disorder) (). We found that HIV-infected patients displayed a similar trait: among markers associated with HIV, 66% tended toward disorder, compared with 70% of age-associated markers ( Experimental Procedures Figure S5 ). Furthermore, whereas age-associated markers tended to have a low methylation fraction that increased with age, HIV-associated markers were more equally balanced between low and high methylation states ( Figure 4 C).

Having identified a large effect of HIV infection on age associated methylation signals, we then sought to better understand the wider changes instigated by HIV. We identified 81,361 CpG markers associated with HIV infection (Benjamini-Hochberg corrected p < 0.01; likelihood ratio test using a multivariate linear model, Table S2 ). Of these, 2,569 upregulated markers and 1,769 downregulated markers were also associated with aging, a 3.2 and 1.4-fold enrichment over random expectation, respectively ( Figure 4 A, Fisher’s Exact Test p < 10 Table S6 ). We found that markers associated with both HIV and aging were enriched in DNase hypersensitivity sites and CpG islands, suggesting methylation changes in DNA regions under active regulation. These CpG markers were also enriched in binding sites for polycomb repressive complex (PRC2) ( Figure 4 B), a switch that tightly regulates genes required for differentiation and renewal, and in Drosophilia is linked to longevity (). These findings reinforce previous reports that PRC2 targets are irreversibly repressed by methylation during the aging process (). Interestingly, markers associated with HIV but not aging had a very different functional enrichment profile ( Figure 4 B), indicating an additional mechanism(s) for epigenetic alteration associated with HIV.

(C) Distribution of methylation states for the CpG marker sets defined in (A). See also Tables S2 and S6

(B) Odds ratios of enrichment for a panel of genomic features, evaluated in sets of markers associated with age, HIV, or both. PRC2, polycomb repressive complex 2 binding sites; DHS, DNase hypersensitivity sites; TSS, transcription start sites.

(A) Overlap table comparing the set of CpG markers associated with HIV and the set of validated age-associated markers (see Figure 1 A). Numbers indicate probe counts in each overlap, colors correspond to odds ratio of overlap compared to background.

As in whole blood, unsupervised analysis showed a clear effect of HIV in age-associated methylation markers ( Figures 3 A and 3B ). Application of epigenetic models of aging in these pure-cell datasets showed good concordance of predicted age with chronological age in both cell types ( Figures 3 C–3F). In neutrophils, the Hannum model predicted a 2.5 year increase in age due to HIV infection (p < 0.03, 95% CI 0.6–5.0 years, Figure 3 E) whereas the Horvath model showed a smaller effect of 0.4 year (p > 0.05). In contrast, CD4T cells had a much stronger and more consistent HIV response in both models, with the consensus aging model showing an increase of 5.7 years in the HIV+ subjects (p < 10, 95% CI 3.4–7.9 years, Figure 3 F). These data indicate that the effect of epigenetic age advancement is not merely an artifact of changing blood composition, but likely reflects true aging signals. The stronger effect size within CD4T cells ( Figures 3 G and 3H) suggests that these cells may be exposed to more age-like stress than neutrophils, although further work is needed to understand how disease may affect aging rates across different cell types and tissues.

(G and H) Violin plots showing age advancement in the two sorted cell datasets. For (B), in initial analysis the first PC heavily reflected an outlier point, which was removed after which the PC was recalculated. See also Figure S2

(C–F) Control ([C] and [D]) and HIV+ ([E] and [F]) subjects for sorted cell validation datasets comparing chronological age to the Hannum et al. epigenetic aging model in neutrophils ([C] and [E]) and consensus aging model in CD4 + T cells ([D] and [F]).

(A and B) Unsupervised principal component (PC) analysis of methylation patterns in purified blood cell types, in which the first PC is positively associated with both age (x axis) and disease status (HIV+, green; HIV−, blue).

We also sought to experimentally assess if the observed age advancement due to HIV infection was observed in purified cell populations. Using standard calculations of statistical power, we estimated that a sample of 48 patients, balanced approximately between cases and controls, would have 81% power to detect the same aging advancement effect as our primary screen at p < 0.01. Accordingly, this number of subjects was prospectively recruited from the University of Nebraska Medical Center under an approved IRB protocol (501-15-EP), and blood obtained following informed consent ( Experimental Procedures Table S5 ). Whole blood was separated immunomagnetically to isolate pure populations of neutrophils and CD4T cells.

While the direct effects of cell type composition on the whole-blood methylome were corrected by the adjustment described above (also see Experimental Procedures ), we considered that it was still possible that changes in cell type composition could lead to downstream, indirect changes in the epigenomes of all blood cells. If this were the case, cell-type-associated changes could be responsible for the observed increase in biological age in the HIV+ cases. To assess this possibility, we constructed a multivariate linear model in which cell type composition variables and HIV status were used to predict biological age as measured by the methylome ( Table 1 ). In this model, the presence of HIV was associated with an age advancement of 3.8 ± 1.1 years, while the presence of natural killer cells accounted for additional increases in biological age. In an even more conservative test, we modeled age advancement with cell type composition variables alone and found that the unexplained variation in this model still had a significant association with HIV infection (p = 0.02, Likelihood Ratio Test, Table 1 ). Thus, even in a very conservative analysis, HIV infection has association with advanced aging that is independent of cell composition.

In Model 3, residuals from model 2 are carried over to a second regression against HIV status.

Notably, patients more recently infected with HIV (<5 years) had no significant difference in age advancement from those patients with chronic (>12 years) infection (p > 0.5, Mann-Whitney U Test; Figure 2 F). Similar findings emerged from a regression analysis of the chronological versus biological time since infection: the slope did not differ from one (0.98 ± 0.06, SE) whereas the y-intersect was significantly positive (5.2 ± 0.9; Figures 2 E and 2F). These findings lend support to the theory that age advancement occurs early in the course of disease as a consequence of acute infection or reaction to drug treatment (). The lack of an increase of age advancement with disease duration seems to contradict alternative views that HIV-mediated aging occurs through cumulative effects of latent virus () or chronic therapeutic intervention (). We did however observe less variation in age advancement within the chronically infected HIV+ individuals ( Figure 2 F, p < 0.002, Bartlett’s test relative to recently infected group), perhaps reflecting the comparative stability of infection and immune response on long-term cART therapy ().

We next used this consensus aging model to calculate the “biological age” of each individual in our cohort ( Table S4 ). For uninfected controls, the calculated biological age had a very high concordance with chronological age ( Figure 2 D, Pearson’s r = 0.94). In contrast, the HIV+ patients had a biological age advancement of 4.9 years on average (p < 10by Student’s t test, 95% confidence interval 3.4–7.1 years, Figures 2 E and 2F). These results were consistent with our previous unsupervised analysis ( Figures 1 B and 1C) in suggesting that HIV infection leads to advanced aging. Furthermore, we found that the age advancement of HIV+ individuals was negatively correlated with the ratio of CD4/ CD8T lymphocytes (Spearman’s rho = −0.2, p < 0.02). CD4T cells are a major indicator of immune integrity () and are inversely associated with morbidity and mortality, including from non-AIDS defining diseases (); similarly, the CD4/CD8 ratio predicts non-AIDS morbidity (). This finding links biological aging of HIV-infected individuals to a clinical measure of disease progression, and it raises the possibility that patients with stable immune responses may be less affected by the advanced aging phenotype. Taking into account a recently estimated 4.2% increase in mortality risk per year of biological age advancement using the Hannum model (), the changes observed in HIV+ patients result in an expected total mortality risk increase of 19%.

CANOC Collaboration Predictors of CD4:CD8 ratio normalization and its effect on health outcomes in the era of combination antiretroviral therapy.

CANOC Collaboration Predictors of CD4:CD8 ratio normalization and its effect on health outcomes in the era of combination antiretroviral therapy.

While we therefore expect the contribution of cell composition to be minimal, we nonetheless developed an algorithm to individually normalize each methylation profile using methylation-derived cell type information. In brief, we used a previously reported method () to reliably predict blood composition ( Figure S3 ) and adjust out the expected contribution of cell-type-specific effects. This procedure greatly limited the effects of age- and HIV-induced blood composition changes in downstream analyses ( Experimental Procedures Figure S4 ).

A potential issue with these models arises in the fact that methylation profiles from whole blood are influenced by cell composition, and different cell types have different methylation states (). These differences might be particularly pronounced in HIV-infected patients, some of whom have low CD4T cell counts (). To understand the sensitivity of epigenetic aging models to cell type composition, we downloaded two datasets profiling sorted cells across shared sets of individuals (, GEO: GSE59250 , GEO: GSE56046 ). Among these sorted cell datasets, we saw good concordance of epigenetic age predictions with chronological age ( Figures S2 A–S2F). Epigenetic age was reproducible across different cell types profiled from the same patients, with high agreement of age estimates (r >0.77–0.88) and moderate but very significant agreement of age advancement (Pearson’s r > 0.45–0.68; p < 0.0001 for all associations, Figures S2 G–S2J).

Given the shared effects of HIV and aging, we sought to determine whether HIV causes the same biological aging signature as previously found in cohorts of uninfected individuals (). We tested aging models from both our group () and Horvath () in independent datasets derived from whole blood samples ( Table S4 ). Although the Hannum and Horvath modeling efforts were based on different methodologies and training data, we found they made very similar predictions (r = 0.9, Pearson’s correlation, Figure 2 A) and furthermore that a consensus of the two models outperformed either model individually ( Figures 2 B and 2C; Table S4 ). For this reason, we used this consensus model for all remaining analyses.

(F) Violin plots showing the distribution of residuals from regression of biological versus chronological age. Three groups are shown: HIV− controls, short-term HIV+ infected individuals, and long-term HIV+ infected individuals. Note that the red circle indicates an outlier, which is not used to fit the violin profile, but is used in all statistical assessments.

(B and C) Accuracy of the consensus model (y axis) to predict true chronological age (x axis) in datasets from Hannum et al. (n = 497, [B]) or EPIC (n = 637, [C]).

(A) Scatter plot comparing the ages predicted using the Hannum et al. and Horvath models on healthy controls (n = 1,242 from HIV−, Hannum et al. and EPIC datasets). Red points indicate patients that were discarded due to disagreement between the two aging models (n = 68).

Among these validated age-associated sites, we found a striking association with methylation in the HIV+ patients relative to healthy controls (p < 10 Figure 1 B). Further analysis of these sites found a positive association of the first principal component with both age and HIV status ( Figure 1 C; Table S3 , association by multivariate linear model p < 10). These findings support a link between HIV infection and aging (), as quantitatively measured by epigenomic profiling ( Figure 1 D).

As a preliminary exploration of this dataset, we ran an unsupervised analysis to identify age-associated methylation sites and their relation to HIV infection. Analysis of a previous methylome-wide screen of 538 healthy subjects () identified as many as 61,592 methylation sites associated with age at a 1% false-discovery rate (FDR) (likelihood ratio test in multivariate regression model with Benjamini-Hochberg correction). Validation of these sites in whole blood from a second control cohort from the European Prospective Investigation into Cancer and Nutrition () (EPIC, N = 662) confirmed 26,927 of these sites as strongly associated with age ( Figure 1 A; Table S2 ).

(D) Potential relationships among HIV infection, epigenetics, disease, and aging. Black: known; Dashed gray: potential; Green: connections explored in this study. See also Tables S2 and S3

(C) Principal component (PC) analysis of the validated age-associated markers, in which the first PC (y axis) is positively associated with both age (x axis) and disease status (HIV+, green; HIV−, blue).

(B) Distribution of t-statistics measuring association of each methylation marker with HIV status. Colors indicate groups of markers identified in (A): Gray, all markers; yellow, age-associated markers from discovery phase; violet, subset of age-associated markers confirmed in validation.

To determine whether HIV is associated with signs of aberrant biological aging, samples of whole-blood DNA were obtained from 137 HIV-infected, cART-treated but otherwise healthy non-Hispanic white males (no hepatitis C co-infection, no diabetes, and high adherence to therapy) and 44 healthy non-Hispanic white male controls ( Table S1 Figure S1 ). Genome-wide methylation profiles of each sample were determined using the Illumina Infinium HumanMethylation450 BeadChip array. Data were normalized and controlled for quality using standard techniques, resulting in removal of two control patients due to poor signal ( Experimental Procedures ).

Discussion

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et al. DNA methylation age of blood predicts all-cause mortality in later life. We have shown that methylome-wide changes previously ascribed to aging are also induced by HIV ( Figures 1 and 3 ). By using highly accurate, externally trained and validated models of biological aging, our study provides a robust estimate of a 5-year age advancement in HIV/cART individuals ( Figure 2 ). These results, in combination with the link between molecular age advancement and increased mortality risk (), support the idea that chronic HIV infection is accompanied by a tangible gerontological phenotype. In addition to an aggregate estimate of HIV age advancement, the methylation aging model allows for patient-by-patient estimates. Patients deemed more likely to suffer from HIV-mediated aging effects might be placed on alterative schedules for preventative care, including early screening and further testing if warranted.

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van Spek H. Premature and accelerated aging: HIV or HAART?. While epidemiological studies have attempted to measure age acceleration and increased mortality rates in HIV+ individuals (), such measurements are made difficult by the myriad co-factors associated with HIV infection. For instance, metabolic disorders such as diabetes, HCV infection, and medication adherence are important factors of HIV infection that are also suspected to significantly affect mortality rates. Most previous studies have not attempted to control for these factors; in contrast, our study has focused specifically on well-characterized subjects. Nonetheless, our estimate of HIV age advancement of 4.9 years, calculated from a quantitative analysis of the methylome, falls within the range of the previous epidemiological studies. Further work will be needed to understand if the observed epigenetic age advancement is generalizable to broader slices of the HIV+ population (i.e., patients with complex co-morbidities such as drug use or additional viral infections).

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Glauber R.R. Multicollinearity in Regression Analysis: The Problem Revisited. Our finding of a 5-year age advancement in cART-treated subjects ( Figure 2 E) is similar to one recent report () but contrasts with another study in untreated patients, in which shared effects of age and HIV on the methylome were used to report an age advancement of 14 years (). Although this discrepancy could be due to a beneficial effect of cART, we believe it is more likely due to differing statistical approaches. The previous number is based on comparison of the effects of HIV and age in a single cohort, rather than an epigenetic model of aging built for normal individuals, as performed here. Moreover, the authors derive their estimate from the ratio of linear coefficients for HIV and age, which are themselves highly correlated; such co-linearity is a well-known cause of instability in such estimates ().

In summary, we have shown that an extrinsic perturbation to a human population, driven by HIV infection and cART, is capable of inducing changes in the epigenomic state of affected individuals. This perturbation may influence regulation of HLA gene expression and also encompasses signatures of aging. Our findings help address a long-standing debate regarding the effects of HIV infection on biological aging in cART-treated individuals, in a manner that can be assessed numerically using an epigenome-based readout. Taken together, our findings show that the epigenome adds a quantitative means of assessing the interaction of HIV with normal and pathogenic processes associated with aging, and they shed light on the underlying mechanisms by which acute and chronic viral infection impact the host.