Opposite effects of chronic HIV infection and antiretroviral medication on organismal and organ-specific biological aging

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We assessed whether HIV infection accelerates biological aging in two independent cohorts of PWH using six organ-specific and three organism-wide aging clocks derived from plasma proteomics of healthy individuals. Proteomic age acceleration significantly correlated with DNA methylation age and was linked to comorbidities and mortality. HIV infection accelerated systemic biological aging, with Mendelian randomization demonstrating causality between organ aging and inflammatory or metabolic complications. Accelerated aging in PWH was further related to the total HIV reservoir, and specific antiretroviral drugs decreased age acceleration. These data reveal important causal effects between chronic HIV infection, antiretroviral medication, biological aging and age-associated diseases, highlighting targets for improving health span in PWH. Health sciences/Medical research/Translational research Biological sciences/Computational biology and bioinformatics Introduction Many studies in the last two decades focused on the physiological and molecular mechanisms underpinning biological aging, with the aim of identifying therapeutic targets to slow or even reverse this process. Several ‘hallmarks of aging’ have been proposed, including telomere attrition, epigenetic alterations, cellular senescence, stem cell exhaustion, and chronic inflammation ( 1 ). This has led to an increased interest in understanding the host and environmental factors that modulate the kinetics of aging, in order to try to slow these processes. Interestingly, an increasing body of evidence suggests that severe infections may impact the aging process. Indeed, after severe infections such as sepsis ( 2 ), tuberculosis ( 3 ), or COVID-19 ( 4 ), metabolic and epigenetic scars can lead to dysregulation of immune responses leading, on the one hand, to systemic inflammation (inflammaging), and on the other hand, to poor responses to microbial stimulation (immune paralysis). Both inflammaging and immune paralysis are associated with biological aging. These effects likely contribute to the increased susceptibility to infectious and cardiovascular complications after sepsis, tuberculosis, or post-viral syndromes ( 5 ). A comprehensive understanding of the effects and mechanisms through which chronic infections impact biological aging and age-dependent complications is however missing. Human immunodeficiency virus (HIV) is a lentivirus that causes chronic infection, leading to loss of CD4 T-lymphocytes and eventually to severe opportunistic complications. With the advent of combination antiretroviral therapy (cART), people with HIV (PWH) can now achieve long-term viral suppression, leading to substantially increased life expectancy, close to individuals without HIV. However, PWH using cART are at increased risk to develop chronic inflammatory non-AIDS comorbidities, such as cardiovascular diseases, liver steatosis and fibrosis, and cancer ( 6 , 7 ), a pattern suggestive of premature aging processes ( 8 – 10 ). Therefore, understanding which biological processes underly premature aging in well-treated PWH may provide important insights into age-related comorbidities, the effect of chronic inflammation on aging, and future therapies to slow the aging process. Progress in the methodologies used to characterize the aging process led to the development of molecular scores that mirror biological aging and longevity ( 11 ), with the epigenetic scores based on DNA methylation being the best known ( 12 ). More recently, plasma proteomics has emerged as a promising tool for studying accelerated aging, with initial efforts focusing on whole-body aging clocks ( 13 , 14 ). The latest advances extend this approach to the development of organ-specific aging clocks based on plasma proteomic signatures ( 15 ). In the present study we employed these novel molecular tools to investigate organismal and organ-specific aging in PWH treated with long-term cART. The study is based on the 2000HIV project (n = 1850), a prospective longitudinal cohort of virally suppressed PWH using cART, integrating in-depth multi-omics data and clinical measurements ( 16 ). We developed whole-body and organ-specific proteomic aging clocks using blood plasma proteomic data from a healthy cohort, we validated them in an independent PWH cohort (200HIV), and applied them thereafter to the entire 2000HIV cohort. We systematically analyzed the associations between proteomic age with DNA methylation age and with key clinical features, such as HIV disease stage, cART, comorbidities, and medication use. We used Mendelian randomization to identify causality relationship between aging scores and comorbidities. Finally, we assessed the effect of the HIV reservoir and cART on the biological aging of PWH. Methods Human cohorts Our study investigated two independent cohorts of PWH, the 200HIV and 2000HIV cohorts, as well as a population-based cohort of individuals without HIV from the general population (the 200FG cohort) recruited within the Human Functional Genomics Project (HFGP). Detailed information regarding these cohorts has been previously described ( 14 ). In this study, we utilized proteomic data from 98 volunteers from the 200FG cohort, 205 volunteers from the 200HIV cohort, and 1850 volunteers from the 2000HIV cohort, along with DNA methylation data, genetic data, and clinical information (Table 1). Proteomic profiling of circulating plasma proteins Plasma proteins were measured using a proximity extension assay coupled with next generation sequencing as a readout method by OLINK Proteomics AB (Uppsala Sweden) ( 17 ). Protein measurements are delivered as Normalized Protein expression (NPX) values, which is Olink’s relative protein quantification unit on log2 scale. Olink has developed a built-in quality control (QC) system using internal controls to control over technical performance of assays and samples. Plasma proteins from the 2000HIV and 200FG cohort were measured in three batches. The first batch (n = 692 samples) was measured using the library Olink® Explore 1536 consisting of 1472 proteins divided into four 384-plex panels focused on inflammation, oncology, cardiometabolic and neurology proteins (panels I). The second batch (n = 692 samples) was measured using the Olink® Explore Expansion 1536 consisting of 1472 proteins divided into four 384-plex panels focused on additional inflammation II, oncology II, cardiometabolic II and neurology II proteins (panels II). The third batch was measured using the full library (Olink® Explore 3072) consisting of 3500 proteins divided into eight 384-plex panels focused on inflammation, oncology, cardiometabolic and neurology proteins (panels I and II). Detailed panel information can be found in Table S1. For this study, we used the first and third batch (1500 proteins). Bridging normalization was performed to remove batch effect between panels by following the next steps for each protein: (1) we first calculated the median of the bridging samples for each protein in the two batches; (2) subsequently, we calculated the median difference keeping one batch as a reference; (3) finally, we subtracted the median difference from each protein in the non-reference batch. Limit of detection (LOD) values per protein were re-adjusted by the same adjustment factor as the respective protein measurements after bridging normalization. After removing batch effects using bridging normalization, standard QC per protein and sample was performed prior to statistical data analysis. In each of the four panels from the Olink® Explore 1536 platform, IL6, TNF, CXCL8 proteins were measured as technical duplicates for QC purposes. Strong correlations were observed between the technical duplicates among panels (Spearman rho correlation r > 0.9), and therefore, we selected the measurements from the inflammatory panel. Next, we excluded proteins with lower limit of detection (LOD) >= 25 of the samples, resulting in 1306 proteins in the 2000HIV and 200FG cohort for follow-up analysis. In addition, to detect outliers, we performed principal component analysis (PCA) using the NPX values. Outliers were defined as those samples falling above or below four standard deviations (SD) from the mean of principal component one (PC1) and/or two (PC2). After removing outliers, 1850 and 98 samples remained in the 2000HIV and 200FG cohort, respectively. Proteomic profiling of the 200HIV was performed using the library Olink Explore 1536 platform. QC per protein and sample was performed as described above. After excluding proteins with LOD >= 25 and samples based on PCA (Extended Data Fig.1a), 1254 protein measurements of 205 samples remained for follow-up analysis of the 200HIV cohort. Model benchmarking We compared the performance of four machine learning methods—LASSO, elastic net, ridge regression, and LightGBM—on the training set 200FG (n = 98) and age was predicted into two independent cohorts of PWH, the 200HIV (n = 205) and 2000HIV cohort (n = 1,850). For all models, the normalized expression levels of 1254 proteins measured using the Olink platform were used as input features to predict the chronological age of each sample. During model training, the training set was subjected to 500 rounds of bootstrap resampling, for each bootstrap iteration hyperparameter tuning was conducted using five-fold cross-validation implemented through the caret package (18) in R. The final predicted age values are derived by averaging the outputs from all 500 optimized models. The final model selection was based on the average R2 and average RMSE in both training set and validation cohorts. The robustness test of all bootstrap aggregated LASSO aging models was shown in Extended Data Fig.2 and Extended Data Fig.3. Given the relatively small size of our training dataset, we sought to enhance the credibility of our aging clock by benchmarking its performance against previously published proteomic aging clocks trained using LASSO regression (Lehallier et al., 2019 (39); Hamilton et al., 2023 (15)) (Extended Data Fig.4a). The predicted proteomic ages exhibited a strong Spearman correlation with those from the reference models (Extended Data Fig.4b, c), providing robust support for the reliability of our approach. Organ-specific protein selection We used normalized bulk tissue RNA-seq expression data from the GTEx database ( 19 ) to identify organ-specific proteins, as previously described ( 20 ). Since the GTEx dataset includes a large number of sub-organs, we consolidated these sub-organs into corresponding main organs followed by the same categories as previous research and defined the expression level of each gene in a main organ as the highest expression value observed among its sub-organs. Considering that the LASSO model inherently applies stringent feature selection, we aimed to retain as many input protein features as possible. To define organ-specific genes, we required the expression level of a gene in one organ to be at least twice as high as its expression in any other organ. These organ-specific genes were then mapped to our Olink proteomic data. As a result, we identified 513 organ-specific proteins that successfully mapped to our dataset, accounting for approximately 41% of the total proteins analyzed. Based on the distribution of organ-specific protein counts (Extended Data Fig.1f), we first selected the brain, lung, artery, liver, pancreas, and intestine (at least with over 15 features) as target organs. Organ-specific aging clock training and age gap calculation We applied the bootstrap LASSO model using the expression levels of organ-specific proteins identified for the artery, brain, intestine, lung, liver, and pancreas in the training set (200FG) as input features to predict their biological age. The predicted values for the training set were calculated as the mean of the outputs from 500 bootstrap iterations. As a supplementary analysis, we also trained three additional aging clock models for comparison: a conventional age clock with all proteomic expression, an all-organ age clock with all organ-specific protein, and an organismal age clock with all non-organ specific protein. After evaluating the average performance across 500 bootstrap-trained models (Extended Data Fig.2,3) and corresponding out-of-bag (OOB) (Table S2) estimates for each organ, we selected the brain, artery, liver, and intestine for downstream analyses based on their consistently robust predictive accuracy. These models were included to evaluate and contrast their predictive performance with the organ-specific aging clocks. The predicted age obtained from each model was designated as the organ-specific age. The trained models were then applied to the 200HIV and 2000HIV datasets to calculate the organ-specific age for PWH. The age gap for each sample in each model was defined as the difference between the predicted age and the chronological age: Age gap = Predicted age – Chronological age To account for baseline age gaps in healthy individuals and their variation across age groups, we adjusted the age gap in the 2000HIV cohort by subtracting the mean age gap of the corresponding age group in the 200FG dataset, as follows: Corrected age gap≤35=Age gap−E[(Predicted age−Chronological age) ∣ FG200≤35] Corrected age gap36−60=Age gap−E[(Predicted age−Chronological age) ∣ FG200 36−60] Corrected age gap>60=Age gap−E[(Predicted age−Chronological age) ∣ FG200>60] where E[ ⋅ ]represents the expected value (mean) within each respective age group in the 200FG dataset (≤35 years, 36–60 years, and >60 years). This adjusted 2000HIV age gap was used in all subsequent analyses. DNA methylation profiling DNA methylation profiling was performed on 1914 samples as previously described ( 16 , 21 ). DNA was extracted from EDTA whole blood by the Radboudumc Genetics Department using the ChemagicStar automated configuration (Hamilton Robotics) with magnetic polyvinyl alcohol (M-PVA) bead-based technology. DNA concentration and purity (260/280 nm ratio) were assessed using a NanoDrop spectrophotometer. Samples were normalized to 50 ng/µL in TE buffer and randomly assigned to plates. High-quality samples were analyzed using the Illumina Infinium MethylationEPIC BeadChip array (manifest B5). DNA methylation data processing Standard sample- and probe-level quality control (QC) procedures were applied. Raw IDAT files from the 2000HIV cohort were processed using the minfi package in R (v4.2.0) ( 22 ). Samples with gender mismatches or poor quality were excluded. Probes were removed if they had >10% missing values (detection P > 0.01), mapped to sex chromosomes, overlapped with common SNPs (MAF > 5% in European populations), or mapped to multiple genomic loci. Stratified quantile normalization was applied ( 23 ). Methylation β-values were calculated as β = M / (M + U + 100), where M and U represent the methylated and unmethylated signal intensities, respectively. DNA methylation age calculation Normalized methylation β-values were used to estimate DNA methylation age (DNAm age) with five blood-based epigenetic clocks: HorvathAge ( 24 ), HannumAge ( 25 ), PhenoAge ( 26 ), and GrimAge/GrimAge2 ( 27 ), using the DNAm age calculator (https://dnamage.clockfoundation.org; accessed October 2024). Preliminary age advancement scores were obtained by subtracting chronological age from DNAmage. To remove the confounding effect of chronological age, we regressed DNAm age on chronological age and used the resulting residuals—termed residual DNAm age gaps (Extended Data Fig.5)—for downstream analyses. Linear modelling and meta-analyses We examined the associations between corrected organ age gaps and various factors, including physical health indicators, HIV stages, comorbidities, medication usage, and antiretroviral therapy (ART) in the 2000HIV cohort. To assess these relationships, we employed linear models controlling for age, sex, and ethnicity, using the following formula (detailed Ethnicity information was recorded in Table 1): Corrected age gap ∼ Variable of interest + Age + Sex + Ethnicity For accumulative ART analysis, we employed linear models controlling for age and all ART co-administration: Corrected age gap ∼ ART accumulation + Age + co-administration ART Additionally, we applied the Benjamini-Hochberg method to adjust for multiple testing burden where appropriate (indicated as q-value). Meta-analyses with multiple linear regression model were conducted in R using glmnet (18) package to compare and aggregate effect sizes and confidence intervals across multiple age models. Genotyping, quality control and imputation DNA was extracted from each participant's whole blood. The Illumina Infinium Global Screening Array was used for genotyping all participants of multiple ancestries in the 2000HIV cohort. Prior to imputation, QC for raw variants and samples was performed using PLINK v1.90b ( 28 ). Genetic variants with a call rate genotype missingness of more than 5% and those deviating from Hardy-Weinberg equilibrium (HWE) with a P value < 10 -6 were excluded from the dataset. The HWE exact test was performed with variants stratified by ancestry. Samples with a call rate < 97.5% and those that showed a heterozygosity rate that deviated more than three standard deviations (SD) from the mean heterozygosity rate per self-reported ancestry were excluded. Genetic variants that passed QC were converted from GRCh37 to GRCh38 genomic build using the UCSC liftOver tool ( 29 ). Next, TOPMed Freeze5 was used on genome build GRCh38 to align strands to the TOPMed reference panel. We used the McCarthy group tools for alignment ( https://www.well.ox.ac.uk/~wrayner/tools/ ). After QC, 582,404 variants from 1864 individuals were retained for the imputation procedure. The filtered raw variants were uploaded to the TOPmed Imputation server and imputed against the TOPMed (version r2 on GRCh38) reference panel. The imputed variants were filtered using BCF tools stratified by ethnicity, excluding variants with low imputation quality scores (R2 < 0.3 or ER2 < 0.7) or MAF < 1%. This yielded 10,810,841 variants from 1864 members of the 2000HIV multi-ancestry cohort. Quantitative trait locus mapping on age advancement We performed quantitative trait locus (QTLs) mapping using the imputed genetic data and age advancement scores of 2000HIV samples of European ancestry. After imputation, 1331 samples of European ancestry had both genetic and age advancement scores. First, a standard post-imputation QC was performed using PLINK v1.90b. During QC per SNP, SNPs that deviate from HWE with a P value < 10 -6 and MAF below 5% were excluded. We mapped the age advancement scores to genotype data using a linear model with sex as a covariate. QTLs associated to age advancement were selected to perform mendelian randomization as described in detail below. Mendelian randomization To determine causality between organ-specific, all-organ, conventional and organismal age advancement and certain diseases, two sample Mendelian randomization (MR) was performed using the R package TwoSampleMR version 0.6.9 with default settings. For each exposure (organ-specific, all-organ, conventional, and organismal age advancement), genetic variants used as instrumental variables (IVs) were extracted from the summary statistics of QTLs on age advancement identified in this study and publicly available genome-wide association studies (GWASs) on disease outcomes of interest in the OpenGWAS database ( 30 ). An overview of studies extracted from the Open GWAS database included in the Mendelian randomization analyses are provided in the Supplementary Table 3 (n = 63 studies). All GWAS studies on disease outcomes used in this study were from populations of European ancestry to eliminate demographic stratification bias. The IVs were selected based on the following criteria: SNPs were significantly associated with the exposure (age advancement) in the 2000HIV cohort ( P < 1 x 10 -5 ) and, a stringent clumping was performed to extract independent SNPs. Genetic variants were clumped using a linkage disequilibrium (LD) r 2<0.001 and a window size of 10,000 kb using the 2000HIV cohort of European ancestry as a reference for clumping. SNP proxies were added automatically by the TwoSampleMR R package. In total, 148 independent SNPs were selected as instrumental variables after clumping. SNPs associated to exposure were from GRCh38 to GRCh37 genomic build before MR. Only if there were still at least six SNPs remaining per exposure, MR was performed. In addition, SNPs that were associated with the age advancement of more than 5 exposures using the 2000HIV cohort of European ancestry were considered pleiotropic and excluded (Extended Data Fig.6). Twenty-three (n = 23) SNPs showed pleiotropic effects and removed, resulting in 119 unique independent SNPs. Further, the strength of each SNP was assessed by F-statistic using the formula F = β2/Se2, whereas β and Se are the coefficient and standard error of exposure respectively. SNPs with F-statistics < 10 were regarded as weak IVs and should be discarded in the following analyses. None of the SNPs were discarded due to F-statistics. Finally, Steiger-filtering was used to exclude SNPs, which explain more variance in the outcome than the exposure, as these SNPs are likely to be invalid instruments (which either act though horizontal pleiotropy or proxy a reverse causal pathway from outcome to exposure). We harmonized exposure and outcome data to ensure that effect estimated corresponded to the same allele for each SNP (Table S4). We used the inverse variance weighted (IVW) method as the main approach to evaluate the potential causal relationship between age advancement and disease outcomes by combining the β -values and the standard errors of the causal estimate from them (forward MR-IVW). Seven additional effective methods, including MR-Egger , weighted median , weighted mode , and simple mode , simple median , maximun likelihood and inverse variance weighted with fixed effects were also applied to evaluate the possible causal relationship comprehensively. For MR results with significant IVW (P < 0.05), sensitivity analyses were performed to evaluate if the causal estimates are robust to violations of MR underlying assumptions. First, we performed the mendelian randomization pleiotropy residual sum and outlier test (MR-PRESSO) to detect potential outlier variants ( 31 ). Furthermore, the MR-Egger regression was used to evaluate the bias generated by gene pleiotropy, of which the intercept is an indicator. In addition, the Cochran’s Q statistics was applied to quantify the heterogeneity between SNPs. Thirdly, we used the leave-one-out analysis to verify whether there are SNP outliers that strongly affect the results by eliminating each SNPs and then re-calculating the causal estimates using the IVW method on the rest. Finally, to evaluate the possibility of reverse causality between age advancement and diseases, we performed MR-IVW in the other direction (reverse MR; GWAS diseases used as an exposure and age advancement as outcome). We precluded results for which less than five SNPs were available as instrumental variables per exposure. For the reverse MR, we used the exposure/outcome pairs that reached P nominal significance in the forward MR-IVW analyses (P < 0.05). We excluded any results that had a nominal significant IVW results in the reverse MR while passing all the sensitivity analyses. A strong causal relationship between the age advancement and disease was considered when the following criteria were met: (1) the IVW method demonstrated a significant difference (forward MR, P 0.05) and; (4) the reverse MR showed no significant difference (MR-IVW P > 0.05). All statistical analyses were performed using R statistical software (version 4.2.0). LD clumping was performed using rtracklayer::liftOver in R software. QTL mapping was performed using the MatrixEQTL package ( 32 ). Mendelian randomization was performed u using the ‘TwoSampleMR’, ‘MR-PRESSO’, ‘ieugwasr’ R packages. Quantification of total and intact HIV-1 DNA from CD4+ T cells Total and intact HIV-1 DNA levels were measured in CD4+ T cells from 1850 PWH by digital PCR (dPCR) using the Rainbow proviral HIV-1 DNA assay. Starting from 40 Mio cryopreserved PBMCs, CD4+ T-cells were enriched by negative selection using EasySep Human CD4+ T-cell isolation kit on the Robosep-S (Stemcell Technologies, Vancouver, Canada). Genomic DNA (gDNA) was extracted using the QiaAmp DNA mini kit on the Qiacube (Qiagen, Hilden, Germany) with two elution steps of 50µL. DNA concentrations were determined using 2 µL of the eluted DNA with the SpectraMax Quant AccuBlue HiRange dsDNA Assay Kit by using the SpectraMax i3x (Molecular Devices, San Jose, California, United States). Samples were stored at -20°C prior to dPCR quantification. HIV-1 DNA levels were quantified in triplicate by dPCR using the Rainbow proviral HIV-1 DNA assay on the QIAcuity Four platform (Qiagen, Hilden, Germany). Depending on the DNA concentration, either 18 µL of eluted gDNA was used per replicate for samples with concentrations below 90 ng/µL, or 10 µL for those exceeding 90 ng/µL. HIV-1 DNA levels were normalized by measuring the reference gene RPP30 in duplicate and reported per million CD4+ T-cells. For normalization and DNA shearing assessment, a 1/100 dilution was made for each sample and 5µL was used as input. Total HIV-1 DNA levels were assessed by the RU5 region in the Rainbow assay and were highlighted when the result was below the limit of detection (i.e. 10 copies per well). Intactness levels were obtained by the presence of at least two (psi and env) and maximum five target regions (RU5, psi, gag, pol and env) in the Rainbow assay. Automatic thresholds were calculated with the Rainbow Shiny tool and adapted if needed per sample when a threshold crossed the double-positive population. In case the intactness result was a zero-value, results of individual targets were checked. If positive partitions were observed in all target regions, the intactness result was undetectable and was artificially calculated as one intact copy in the total cell input for that sample. If there were no positive partitions in psi and/or env due to presumable signal failure, no intactness level was reported. Statistical Analysis In this study, we reported false discovery rate (FDR) values for all meta-analyses and P values for individual regression analyses. Statistical significance was indicated as follows: ***: FDR or P < 0.001, **: FDR or P < 0.01, *: P < 0.05, NS: not significant. Results Plasma proteomic age scores are robust predictors of chronological and biological aging in PWH To evaluate whether proteomic data can successfully predict biological age in healthy individuals and PWH, plasma proteins were analyzed in various cohorts using different Olink panels. 1254 proteins overlapped and were available in plasma samples across three independent cohorts: 200FG (general population), 200HIV (PWH cohort), and 2000HIV (PWH cohort). These proteins were subsequently used to train the proteomic aging clock models (Fig.1a). Specifically, data from 200FG cohort served as the training set, while data from 200HIV and 2000HIV were used as independent test set and exploration set. Four machine learning algorithms—LASSO, elastic net, ridge regression, and LightGBM with bootstrap aggregation—were employed to train the models, and their performances were systematically compared. We observed that LASSO demonstrated comparable performance to elastic net and ridge regression, and they in turn overperformed LightGBM on both the training and test sets (Extended Data Fig.1c, d, e). Given that LASSO inherently incorporates feature selection and keep less features compared to elastic net, making it more effective at retaining proteins with high importance, we ultimately selected the LASSO to construct subsequent organ-specific proteomic aging clocks. Next, we utilized tissue RNA-seq expression data from the GTEx project to identify organ-specific proteins for the construction of organ-specific proteomic aging clock models (Fig.1b). In total, we developed nine proteomic aging clocks: six organ-specific aging clocks for the artery, brain, intestine, lung, liver, and pancreas, along with three additional general clocks—a conventional aging clock (using all 1254 proteins), an all-organ aging clock (using 513 all-organ-specific proteins) (Table S5), and an organismal aging clock (using 741 non-organ-specific proteins). These models were compared to assess their performance and usefulness as a proxy in predicting biological age. All nine proteomic aging models demonstrated a significant correlation between the predicted age and chronological age (Extended Data Fig.1g and Extended Data Fig.7). However, given the relatively poor performance of the models for the pancreas and lung, we excluded these two from subsequent downstream analyses. To further validate the biological relevance of our proteomic age models, we used DNA methylation data from the 2000HIV cohort to calculate five well-established DNA methylation age scores: HorvathAge, HannumAge, PhenoAge, GrimAge, and GrimAge2, and calculated their correlations with the seven proteomic age scores (Fig.2a). We observed a high correlation between the five DNA methylation ages, with an average Pearson correlation coefficient of r=0.85, highlighting the consistency across different methylation age clocks. Notably, the conventional age, all-organ age, and organismal age clocks also exhibited high inter-correlations (r average=0.86), and subsequently also showed strong correlations with the five DNA methylation ages (r average=0.78), supporting the feasibility of predicting age using proteomic data from circulating proteins in PWH. Interestingly, we found that the artery age (r=0.58∼0.68) and brain age (r=0.67∼0.84) models demonstrated moderate to high correlations with the DNA methylation ages and the conventional age clock. In contrast, the liver (r=0.33∼0.43) and intestine (r=0.40∼0.46) age models exhibited weaker correlations with both the DNA methylation ages and the conventional age clock. Similarly, we compared the age gap derived from different age clocks (Fig.2b). The distribution of proteomic age gap is depicted in Extended Data Fig.1b. In addition to observing moderate correlations among the age gaps of various DNA methylation clocks, we found that the artery age gap exhibited only weak correlations with the age gap calculated based on the other clocks (r average=0.25). In contrast, the age gap for the liver and intestine clocks showed moderate-to-high correlations (r =0.65), suggesting common mechanisms in the aging processes of these organs. People with HIV show accelerated biological aging Next, we assessed whether chronic HIV infection influences biological aging scores. The different age gaps calculated in PWH either by proteomic or methylation scores were strongly associated with chronological age, so we applied correction of chronological age on age gaps in all downstream analysis (Extended Data Fig.8). Importantly, we observed a trend of age acceleration in PWH in four out of the five methylation age gaps examined, with the exception of the PhenoAge gap (Fig.2c). A similar acceleration of the aging process in PWH was apparent when assessing proteomic age scores, as shown by the conventional age acceleration that encompasses both the ‘all-organ age gap’ and the ‘organismal age gap’, as well as the brain age acceleration. In contrast, other organ-specific proteomic scores did not differ significantly from chronological age or displayed negative age acceleration, as in the case of intestine and liver, suggesting a divergent pattern of aging dynamics across different organs. Although we lacked statistical power to obtain significant differences, we observed a general tendency that the conventional age acceleration in PWH of Asian descent was higher compared to European or African ancestries, both in younger and older populations (Extended Data Fig.9a). Additionally, sex-specific differences in organ-specific age acceleration were evident across different age strata. Males with PWH exhibited higher intestine and organismal age acceleration compared to females, whereas females demonstrated higher artery and liver age gaps (Extended Data Fig.9b). These findings highlight the heterogeneity in organ-specific age gaps in PWH, which may be linked to distinct clinical characteristics and comorbidity susceptibilities. Next, we leveraged clinical data from the 2000HIV cohort to further examine the relationship between proteomic age and biological aging. Specifically, poorer physical health indicators (Fig.2d), HIV-related parameters (Fig.2e), different comorbidities (Fig.2g), and medication usage (Fig.2h) were all significantly correlated with age advancement. Notably, we also observed a significant impact of current use of antiretroviral medication (Fig.2f) on age advancement, suggesting their potential impact on biological aging in PWH. For organ-specific age, the age advancement of liver and intestine showed the strongest associations with body mass index (BMI) and the following hepatic parameters: CAP liver score (steatosis parameter), liver stiffness measure (LSM) and alanine aminotransferase (ALAT) concentrations (Extended Data Fig.10a, b, c, e). The age advancement of all organ-specific ages was significantly correlated with creatinine (CREAT) concentration (Extended Data Fig.10d), arguing for an important impact of kidney function on biological aging. PWH were also classified based on lowest CD4 T cell count, ranging according to the level of immunosuppression (Stage 1: ≥500 CD4 cells/µL, Stage 2: 200–499 CD4 cells/µL, and Stage 3: <200 CD4 cells/µL or an AIDS-defining diagnosis). We found that a more advanced HIV stage was significantly associated with conventional age, organismal age, and all-organ age advancement, but no significant association with organ-specific age scores (Fig.3b). This association was also reflected in key HIV parameters, including latest CD4 T cell count, lowest recorded CD4 T cell count, latest CD8 T cell count, most recent CD4/CD8 T cell ratio, and the CD4/CD8 T cell ratio before initiating cART (Fig.3a, Extended Data Fig.11a, b, c, d). Although all PHW were virally suppressed because of cART or spontaneous HIV control, some individuals (3.24%) have low level viremia (> 40 HIV-RNA copies/ml). Interestingly, we found that the latest viral load was significantly associated with the advancement of organismal age, all-organ age, and conventional age, while no strong associations were observed with organ-specific aging clocks (Fig.3c). Also, consistently undetectable viral load over the past three years was significantly associated with less organismal age acceleration, which indicate that chronic HIV infection with episodes of low-level viremia contributes to accelerated ageing (Extended Data Fig.11e). These findings strongly suggest that chronic HIV infection contributes to accelerated systemic biological aging. The importance of an undetectable plasma viral load is also underlined by comparing elite controllers (n= 21) that spontaneous control HIV infection with persistent undetectable plasma viral loads without cART and normal progressors, using cART. Elite controllers showed a trend of age deceleration on organismal age, although the difference did not reach statistical significance, most likely due to limited number of individuals in the elite controller group (Fig.3d). Furthermore, in elite controllers we also observed a trend of less age acceleration in multiple proteomic and DNA methylation aging clocks, including intestine age, artery age, liver age, HorvathAge, PhenoAge, GrimAge, GrimAge2 (Extended Data Fig.12). Although HIV replication can be spontaneously controlled in elite controllers or by cART in the other infected individuals, HIV persists in latently infected cells, mostly resting memory CD4+T cells. Around 95% of the reservoir consists of defective proviral HIV DNA that, unlike intact proviral DNA, does not contribute to viral replication after cART interruption, but may still result in HIV RNA transcripts or proteins that elicit a host response. To more precisely analyze the effect of HIV on the aging process, we investigated whether the total or the intact HIV reservoir size is associated with biological aging ( 33 ). Interestingly, we observed that all conventional, all-organ, organismal, and brain proteomic and DNA-methylation age (except HannumAge) acceleration scores were significantly associated with total HIV reservoir (Fig.3e). This is highly overlap to the age gaps we reported in Fig.2c. In contrast, proteomic and epigenetic age scores were not associated with the intact HIV reservoir levels. Mendelian randomization demonstrates a causal relationship between biological aging and co-morbidities To assess whether the impact of HIV infection on inflammatory aging scores is biologically relevant, we investigated whether age acceleration is associated with increased susceptibility to complications. Exploring organ-specific aging scores, we found that brain age advancement was significantly associated with CNS complications which are well known morbidities in PWH (Fig.4a). Furthermore, artery age advancement showed a strong correlation with cardiovascular disease, particularly deep venous thromboembolism (VTE) (Fig.4b, c). Importantly, we found that all-organ age advancement was significantly correlated with mortality within two years follow-up period (FDR= 0.021). In addition, the conventional age gap also showed a moderate correlation with two-year mortality (P = 0.027) (Fig.4d). Although most associations did not reach statistical significance due to the limited number of death cases (n = 24) and different death causes, including malignancy and infection (Table S6), these data strongly suggest that proteomic age advancement may serve as a predictive survival biomarker in PWH. Collectively, our findings confirm relation between organ-specific age clock and organ-specific morbidities, while all-organ clock relates more closely with mortality. To further investigate whether organ-specific, all-organ, conventional and organismal age advancement causally impact disease outcomes, we performed Mendelian Randomization (MR) analysis. For this, we used 119 independent SNPs as instrumental variables (IVs) significantly associated with the exposure (age gap) at P < 1x10 -5 , and extracted the summary statistics of GWAS performed in cardiovascular diseases (stroke, coronary artery disease, myocardial infarction, etc), diabetes, liver fibrosis or steatosis. We tested for causal relationship in exposures with more than five variants, performing sensitivity analyses. A strong causal relationship between age advancement and disease outcomes, which met all the above-described criteria (see Methods), was observed between intestinal age advancement and coronary artery disease (β = 0.0043, P IVW = 0.01) (Fig.4e, Table S7, Table S8) ( 34 ). Scatterplots showed a linear regression line for the positive associations between age gap and risk of corresponding disease (Fig.4f). The causal estimates obtained from the rest of MR methods (MR-Egger, weighted median, weighted mode, and simple mode, simple median, maximum likelihood and inverse variance weighted with fixed effects)showed similar direction to those from the primary IVW method (Table S9).Of note, there were no evidence of significant heterogeneity or pleiotropy (P 0.05) and the leave-one-out sensitivity analyses showed no single SNP had a substantial impact on the results. To further validate the relationship between intestinal age advancement and coronary artery diseases, we selected circulating protein markers associated with intestinal integrity measured in the 2000HIV cohort (LBP, CD14 and FABP2) and tested them for association with the intestinal age (Fig.4f, g, h). In line with the hypothesis tested, we found a significant positive association between the biomarkers associated with intestinal integrity and age advancement. Overall, Mendelian randomization analyses indicate that intestinal age advancement is linked to coronary artery diseases, and we hypothesize that translocation of gut bacterial products due to loss of mucosal integrity may contribute to systemic inflammation and accelerated atherosclerosis. Differential effect of specific ART drugs on biological and specific organ age acceleration as measured by proteomic clock in PWH As our data shows the impact of HIV on biological aging, we next assessed a possible effect of the different antiretroviral drugs on biological aging. As virologically suppressed chronic HIV infection is shown to have important impact on biological aging, and biologic age gap in our PWH was correlated to comorbidities, also during 2 year follow up, we next assessed whether there is a difference in the effect of specific antiretroviral medication on age acceleration in PWH. Cumulative drug exposure was used to investigate the impact of ART on aging, as the effect of the medication may be dose and time related, and in order to account for treatment switches. The cumulative drug exposure and all age acceleration scores were explored by regression analyses (Fig.5a). We observed that exposure duration to certain nucleoside reverse transcriptase inhibitors (NRTIs), particularly lamivudine (3TC), was significantly associated with decreased age acceleration, as estimated by either proteomic aging in the intestine or DNA methylation-based models such as HannumAge, GrimAge, and GrimAge2. In contrast, other NRTIs—including tenofovir disoproxil fumarate (TDF), tenofovir alafenamide (TAF), and emtricitabine (FTC)—exhibited divergent effects on organ-specific aging trajectories. Notably, TDF and FTC exposure duration was significantly associated with accelerated DNA methylation age. Interestingly, TDF demonstrated a stronger association with deceleration of conventional aging metrics, compared to TAF, which is a prodrug of TDF. TDF and TAF both result into the active compound tenofovir diphosphate, but TDF results in higher plasma concentrations while TAF leads to higher intracellular levels in certain cells such as lymphocytes and hepatocytes. Remarkably, increased liver ageing score was seen with accumulating TAF exposure. Furthermore, stavudine (D4T), an NRTI with well-documented toxicities, showed significant associations with age acceleration in multiple organs, including the intestine and liver, although it was also paradoxically associated with less age acceleration in the brain. These findings highlight the heterogeneous and organ-specific effects of antiretroviral therapy drugs on biological aging. Among non-nucleoside reverse transcriptase inhibitors (NNRTIs), both rilpivirine (RPV) and nevirapine (NVP) were significantly associated with lower biological aging across multiple organ-specific measures. Within the class of integrase strand transfer inhibitors (INSTIs), dolutegravir (DTG) was notably linked to lower age scores of the intestine and liver. As for protease inhibitors (PIs), cobicistat (COBI) showed significant associations with decelerated aging scores in the artery and overall organismal age, as well as in methylation-based measures such as GrimAge and GrimAge2. Considering the cumulative drug exposure time of all agents, only TDF, RPV and NVP showed a significant association with conventional age deceleration (Fig.5b, c, d,). Ritonavir (RTV), a protease inhibitor, showed significant conventional age acceleration (Fig.5e). Given that antiretroviral drugs are typically administered in combination, we cataloged both the currently and cumulative ART use in our cohort (Tables S10 and S11). To disentangle the independent contributions of individual drugs, we constructed multivariable regression models to assess the specific effects of selected ART agents—including dolutegravir (DTG), bictegravir (BIC), doravirine (DOR), nevirapine (NVP), rilpivirine (RPV), elvitegravir (EVG), darunavir (DRV), lamivudine (3TC), emtricitabine (FTC), tenofovir alafenamide (TAF), and tenofovir disoproxil fumarate (TDF)—on aging outcomes. We then highlighted the most significant associations (Figure 5f). Notably, 3TC exhibited the strongest association with deceleration of both organismal and intestinal aging scores. Another NRTI, FTC, was also significantly linked to deceleration of organismal age scores. Among the NNRTIs, RPV showed consistent associations with deceleration across conventional, multi-organ, and artery-specific aging metrics. Intriguingly, TAF was associated with acceleration of both all-organ and brain aging scores, consistent with the differential aging effects observed earlier when compared to TDF. Collectively, our findings indicate that certain classes of cumulative ART exposure—particularly NRTIs and NNRTIs—are associated with reduced age acceleration in PWH. These associations became even more pronounced for specific agents such as RPV, 3TC, and FTC after adjusting for the effects of ART co-administration, suggesting independent, protective roles against biological aging. These results provide a rationale for future randomized clinical trials to investigate the impact of specific ART regimens on biological age gaps as surrogate outcomes, and on non-AIDS comorbidities as definitive clinical endpoints in PWH. Discussion An increasing number of recent studies argue that biological age may be a better predictor of health compared to chronological age ( 35 ). Recently, organ-specific aging scores have been developed based on plasma proteomics, deepening our understanding of the aging processes ( 15 ). Many factors may contribute to premature aging, such as chronic low-grade inflammation, oxidative stress and mitochondrial dysfunction. All these processes are disturbed in PWH, even when virally suppressed because of effective c ( 36 ). In the present study, we applied a comprehensive analysis to predict whole-body and organ-specific aging using plasma proteomic data and epigenetic age scores, providing a systematic evaluation of factors influencing biological aging in PWH. This analysis resulted several novel findings: 1. people with HIV show premature biological aging, both at the level of the entire organism, as well as at the level of specific organs; 2. age acceleration is related to the total HIV reservoir size, confirming the role of HIV infection in the aging process; 3. organ and systemic proteomic aging clocks are associated with comorbidities and mortality, with a causal relationship between premature intestinal aging and CVD.; and 4. the cumulative use of certain antiretroviral drugs, in particular some belonging to the class of reverse transcriptase inhibitors, is associated with decreased systemic and organ-specific aging in PWH, while others show opposite effects in line with their well-known toxicity profile. The first major observation of our study is that PWH show premature biological aging. This is in line with a recent study in which we have shown that especially young PWH show accelerated inflammatory aging (14), and accompanies studies that showed an increased epigenetic age in HIV-infected individuals ( 37 ). Our finding that the total HIV reservoir size was associated with biological aging scores, argues for a direct role of HIV infection or more specifically of viral transcripts that originate from defective proviral DNA, but still capable to induce inflammation (14). The intactness of the viral reservoir, that mostly reflect only 5% of the total reservoir size, does not seem to be important for age acceleration. Our finding of lower age acceleration in elite controllers, that are known to have a small HIV reservoir size that is mostly locked up in transcriptionally silent DNA regions, also underscores the role of HIV infection itself for impacting biological aging. The relevance of the association between the HIV infection and biological aging is apparent at several levels. First, Mendelian randomization demonstrates that accelerated aging in PWH is not a mere epiphenomenon, but is a cause of severe co-morbidities. From this perspective, the causal link demonstrated between intestinal aging and cardiovascular diseases is especially relevant: it is tempting to speculate that intestinal aging in PWH is associated with increased leakage of microbial products, leading to systemic inflammation and subsequently cardiovascular complications. Indeed, this hypothesis is strengthened by the association of gut translocation biomarkers with intestinal aging. Second, increased organ age acceleration was associated with higher risk for co-morbidities and mortality during follow-up, demonstrating the importance of biological age acceleration for health span. Another strong argument for the relevance of chronic infection with HIV for biological aging is provided by the counter-regulatory effects of certain antiretroviral agents, notably the reverse transcriptase inhibitors, both NRTI as well as NNRTI, on age acceleration. Both the current use as well as the cumulative drug use were associated with age deceleration. Earlier studies have suggested that some anti-HIV drugs have anti-aging effects in PWH, especially dolutegravir ( 38 ). We now demonstrated that this effect is shared by several groups of anti-HIV medication, which argues that down-regulation of HIV expression and proliferation itself has a beneficial anti-aging effect. These findings raise additional important questions to be investigated in future studies: do other chronic (viral) infections have similar effects on biological aging? Could antiretroviral medication exert anti-aging effects also in non-HIV individuals, for example by inhibition of retroelements known to be activated during the aging process? Our data warrant more studies on the anti-aging effects of ART, which opens a new direction of investigation in the field of aging research. This study has also limitations. First, due to variations in cohorts and batch effects in the proteomic data, we had only a partial overlap in plasma proteins between the healthy and HIV datasets. This constrained the number of proteins available for training organ-specific aging models, leading to the omission of key organs such as the heart and kidney. Second, this is a cross-sectional study, albeit large, and future longitudinal studies with long follow-up are warranted to strengthen the conclusions drawn here. Third, the sex imbalance within the PWH cohort presents another limitation. Although sex was included as a covariate in our analyses, this imbalance may still introduce potential biases in downstream analyses. Finally, the majority of the volunteers in the cohorts studied are individuals of European descent, and therefore the conclusions of the studies should only be cautiously extrapolated to other populations. Future studies should validate these findings in non-European populations. In conclusion, we systematically revealed the impacts of HIV infection and antiretroviral drugs on biological aging in PWH. Proteomic and epigenetic aging scores demonstrate predictive power for future all-cause mortality and showed significant associations with comorbidities, highlighting their potential as biomarkers for aging and disease risk. Declarations Acknowledgments: The study was supported by an unrestricted research grant from ViiV Healthcare. 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Keller, J. Verghese, S. Sathyan, C. Franceschi, S. Milman, N. Barzilai, T. Wyss-Coray, Undulating changes in human plasma proteome profiles across the lifespan. Nat Med 25 , 1843–1850 (2019). Table 1 Table 1 is available in the Supplementary Files section. Additional Declarations There is NO Competing Interest. Supplementary Files Table1.xlsx Table 1. The demographic characteristics of the volunteers from the three cohorts: 200FG, 200-HIV and 2000-HIV. SupplementaryTable.xlsx Table S1. The Olink® Explore 3072 panels information. Table S2. OOB testing result of all proteomic aging models. Table S3. Detailed information on the genome-wide association studies extracted from the OpenGWAS database and used for Mendelian randomization analyses (n = 63 studies). Table S4. Harmonised summary data for SNPs used in Mendelian randomisation analyses of age advancement (exposure) and 63 GWAS diseases (outcomes). Also, results from the MR steiger test are presented. Table S5. Organ specific proteins used in this study and normalized expression in GTEX data Table S6. Death causes of 2000HIV participants. Table S7. Summary results from Mendelian randomization analyses using inverse variance weighted method (P-IVW < 0.05) of age advancement (exposure) on 63 disease outcomes. Results from the MR-IVW method and sensitivity analyses for heterogeneity (Cochran's Q test) and pleiotropy analyses (MR-PRESSO and Egger regression) are presented. Table S8. Reverse MR results of GWAS diseases (exposure) on age advancement (outcome). For the exposure/outcome pairs that 1cSNP remained as instrumental variable, MR statistics was not calculated using the IVW method. Sensitivity analyses were performed only for the pairs that showed a P value using forward-MR-IVW method < 0.05. REsults for which less than five SNPs were available as instrumental variables per exposure were excluded. Table S9. Full results of MR estimates (Age advancement on disease outcomes). Table S10. List of current co-administration ART in 2000HIV cohort. Table S11. List of accumulative co-administration ART in 2000HIV cohort. ExtendedDataFig.docx Cite Share Download PDF Status: Published Journal Publication published 10 Feb, 2026 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7234421","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":493386042,"identity":"754e6b9f-28d5-4a81-b4ba-b01d77045ec7","order_by":0,"name":"Mihai 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(MHH)","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Li","suffix":""},{"id":493386058,"identity":"bc85c183-d62e-41b7-821d-ab96f0ac0011","order_by":16,"name":"Linos Vandekerckhove","email":"","orcid":"https://orcid.org/0000-0002-8600-1631","institution":"Ghent University","correspondingAuthor":false,"prefix":"","firstName":"Linos","middleName":"","lastName":"Vandekerckhove","suffix":""},{"id":493386059,"identity":"1a3925b2-9503-4425-b630-7801fd442d95","order_by":17,"name":"André van der Ven","email":"","orcid":"https://orcid.org/0000-0003-1833-3391","institution":"Radboud University Medical Center","correspondingAuthor":false,"prefix":"","firstName":"André","middleName":"van der","lastName":"Ven","suffix":""}],"badges":[],"createdAt":"2025-07-28 13:27:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7234421/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7234421/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41467-026-69412-1","type":"published","date":"2026-02-10T05:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":104952648,"identity":"5c07627f-5e9f-41a9-ae1c-5ded195176cf","added_by":"auto","created_at":"2026-03-19 07:13:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":991641,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7234421/v1/4b23cb9a-972e-46a9-9e91-e52cf68b286b.pdf"},{"id":90151241,"identity":"b6b86cc9-87bb-4829-8f2d-29ea120cb575","added_by":"auto","created_at":"2025-08-29 07:13:49","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":17359,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e \u003cstrong\u003eThe demographic characteristics of the volunteers from the three cohorts: 200FG, 200-HIV and 2000-HIV.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Table1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7234421/v1/536c75d8ee31c77638fb456a.xlsx"},{"id":90151244,"identity":"b5aecaad-5a00-48e4-adcc-17057b8a9e65","added_by":"auto","created_at":"2025-08-29 07:13:52","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":42644897,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S1.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Olink® Explore 3072 panels information.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S2.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOOB testing result of all proteomic aging models.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S3.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDetailed information on the genome-wide association studies extracted from the OpenGWAS database and used for Mendelian randomization analyses (n = 63 studies).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S4.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHarmonised summary data for SNPs used in Mendelian randomisation analyses of age advancement (exposure) and 63 GWAS diseases (outcomes). Also, results from the MR steiger test are presented.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S5.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOrgan specific proteins used in this study and normalized expression in GTEX data\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S6.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDeath causes of 2000HIV participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S7.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSummary results from Mendelian randomization analyses using inverse variance weighted method (P-IVW \u0026lt; 0.05) of age advancement (exposure) on 63 disease outcomes. Results from the MR-IVW method and sensitivity analyses for heterogeneity (Cochran's Q test) and pleiotropy analyses (MR-PRESSO and Egger regression) are presented.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S8.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eReverse MR results of GWAS diseases (exposure) on age advancement (outcome). For the exposure/outcome pairs that 1cSNP remained as instrumental variable, MR statistics was not calculated using the IVW method. Sensitivity analyses were performed only for the pairs that showed a P value using forward-MR-IVW method \u0026lt; 0.05. REsults for which less than five SNPs were available as instrumental variables per exposure were excluded.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S9.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFull results of MR estimates (Age advancement on disease outcomes).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S10.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eList of current co-administration ART in 2000HIV cohort.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S11.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eList of accumulative co-administration ART in 2000HIV cohort.\u003c/p\u003e","description":"","filename":"SupplementaryTable.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7234421/v1/2cbe6ad85c467b0e9f20dd22.xlsx"},{"id":90151243,"identity":"88cb3b93-f480-41fb-bc5b-fb595eb050d6","added_by":"auto","created_at":"2025-08-29 07:13:50","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":35101375,"visible":true,"origin":"","legend":"","description":"","filename":"ExtendedDataFig.docx","url":"https://assets-eu.researchsquare.com/files/rs-7234421/v1/a7e29a9b8286f35f08ea07ff.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Opposite effects of chronic HIV infection and antiretroviral medication on organismal and organ-specific biological aging","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMany studies in the last two decades focused on the physiological and molecular mechanisms underpinning biological aging, with the aim of identifying therapeutic targets to slow or even reverse this process. Several ‘hallmarks of aging’ have been\u0026nbsp;proposed, including telomere attrition, epigenetic alterations, cellular senescence, stem cell exhaustion, and chronic inflammation (\u003cem\u003e1\u003c/em\u003e). This has led to an increased interest in understanding the host and environmental factors that modulate the kinetics of aging, in order to try to slow these processes. Interestingly, an increasing body of evidence suggests that severe infections may impact the aging process. Indeed, after severe infections such as sepsis\u0026nbsp;(\u003cem\u003e2\u003c/em\u003e), tuberculosis\u0026nbsp;(\u003cem\u003e3\u003c/em\u003e), or COVID-19\u0026nbsp;(\u003cem\u003e4\u003c/em\u003e), metabolic and epigenetic scars can lead to dysregulation of immune responses leading, on the one hand, to systemic inflammation (inflammaging), and on the other hand, to poor responses to microbial stimulation (immune paralysis). Both inflammaging and immune paralysis are associated with biological aging. These effects likely contribute to the increased susceptibility to infectious and cardiovascular complications after sepsis,\u0026nbsp;tuberculosis, or post-viral syndromes\u0026nbsp;(\u003cem\u003e5\u003c/em\u003e). A comprehensive understanding of the effects and mechanisms through which chronic infections impact biological aging and age-dependent complications is however missing.\u003c/p\u003e\n\u003cp\u003eHuman immunodeficiency virus (HIV) is a lentivirus that causes chronic infection, leading to loss of CD4\u0026nbsp;T-lymphocytes and eventually to severe opportunistic complications. With the advent of combination antiretroviral therapy (cART), people with HIV (PWH) can now achieve long-term viral suppression, leading to substantially increased life expectancy, close to individuals without HIV. However, PWH using cART are at increased risk to develop chronic inflammatory\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp;non-AIDS comorbidities, such as cardiovascular diseases, liver steatosis and fibrosis, and cancer (\u003cem\u003e6\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e), a pattern suggestive of premature aging processes (\u003cem\u003e8\u003c/em\u003e–\u003cem\u003e10\u003c/em\u003e). Therefore, understanding which biological processes underly premature aging in well-treated PWH may provide important insights into age-related comorbidities, the effect of chronic inflammation on aging, and future therapies to slow the aging process.\u003c/p\u003e\n\u003cp\u003eProgress in the methodologies used to characterize the aging process led to the development of molecular scores that mirror biological aging and longevity (\u003cem\u003e11\u003c/em\u003e), with the epigenetic scores based on DNA methylation being the best known (\u003cem\u003e12\u003c/em\u003e). More recently, plasma proteomics has emerged as a promising tool for studying accelerated aging, with initial efforts focusing on whole-body aging clocks (\u003cem\u003e13\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e). The latest advances extend this approach to the development of organ-specific aging clocks based on plasma proteomic signatures (\u003cem\u003e15\u003c/em\u003e). In the present study we employed these novel molecular tools to investigate organismal and organ-specific aging in PWH treated with long-term cART.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe study is based on the 2000HIV project (n = 1850), a prospective longitudinal cohort of virally suppressed PWH using cART, integrating in-depth multi-omics data and clinical measurements (\u003cem\u003e16\u003c/em\u003e). We developed whole-body and organ-specific proteomic aging clocks using blood plasma proteomic data from a healthy cohort, we validated them in an independent PWH cohort (200HIV), and applied them thereafter to the entire 2000HIV cohort. We systematically analyzed the associations between proteomic age with DNA methylation age and with key clinical features, such as HIV disease stage, cART, comorbidities, and medication use. We used Mendelian randomization to identify causality relationship between aging scores and comorbidities. Finally, we assessed the effect of the HIV reservoir and cART on the biological aging of PWH.\u0026nbsp;\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eHuman cohorts\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur study investigated two independent cohorts of PWH, the 200HIV and 2000HIV cohorts, as well as a population-based cohort of individuals without HIV from the general population (the 200FG cohort) recruited within the Human Functional Genomics Project (HFGP). Detailed information regarding these cohorts has been previously described (\u003cem\u003e14\u003c/em\u003e). In this study, we utilized proteomic data from 98 volunteers from the 200FG cohort, 205 volunteers from the 200HIV cohort, and 1850 volunteers from the 2000HIV cohort, along with DNA methylation data, genetic data, and clinical information (Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProteomic profiling of circulating plasma proteins\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePlasma proteins were measured using a proximity extension assay coupled with next generation sequencing as a readout method by OLINK Proteomics AB (Uppsala Sweden) (\u003cem\u003e17\u003c/em\u003e). Protein measurements are delivered as Normalized Protein expression (NPX) values, which is Olink’s relative protein quantification unit on log2 scale. Olink has developed a built-in quality control (QC) system using internal controls to control over technical performance of assays and samples.\u003c/p\u003e\n\u003cp\u003ePlasma proteins from the 2000HIV and 200FG cohort were measured in three batches. The first batch (n = 692 samples) was measured using the library Olink® Explore 1536 consisting of 1472 proteins divided into four 384-plex panels focused on inflammation, oncology, cardiometabolic and neurology proteins (panels I). The second batch (n = 692 samples) was measured using the Olink® Explore Expansion 1536 consisting of 1472 proteins divided into four 384-plex panels focused on additional inflammation II, oncology II, cardiometabolic II and neurology II proteins (panels II). The third batch was measured using the full library (Olink® Explore 3072) consisting of 3500 proteins divided into eight 384-plex panels focused on inflammation, oncology, cardiometabolic and neurology proteins (panels I and II).\u0026nbsp;Detailed\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;panel\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;information can be found in Table S1. For this study, we used the first and third batch (1500 proteins). \u0026nbsp;Bridging normalization was performed to remove batch effect between panels by following the next steps for each protein: (1) we first calculated the median of the bridging samples for each protein in the two batches; (2) subsequently, we calculated the median difference keeping one batch as a reference; (3) finally, we subtracted the median difference from each protein in the non-reference batch. Limit of detection (LOD) values per protein were re-adjusted by the same adjustment factor as the respective protein measurements after bridging normalization.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAfter removing batch effects using bridging normalization, standard QC per protein and sample was performed prior to statistical data analysis. In each of the four panels from the Olink® Explore 1536 platform, IL6, TNF, CXCL8 proteins were measured as technical duplicates for QC purposes. Strong correlations were observed between the technical duplicates among panels (Spearman rho correlation r \u0026gt; 0.9), and therefore, we selected the measurements from the inflammatory panel. Next, we excluded proteins with lower limit of detection (LOD) \u0026gt;= 25 of the samples, resulting in 1306 proteins in the 2000HIV and 200FG cohort for follow-up analysis. In addition, to detect outliers, we performed principal component analysis (PCA) using the NPX values. \u0026nbsp; Outliers were defined as those samples falling above or below four standard deviations (SD) from the mean of principal component one (PC1) and/or two (PC2). After removing outliers, 1850 and 98 samples remained in the 2000HIV and 200FG cohort, respectively. Proteomic profiling of the 200HIV was performed using the library Olink Explore 1536 platform. QC per protein and sample was performed as described above. After excluding proteins with LOD \u0026gt;= 25 and samples based on PCA (Extended Data Fig.1a), 1254 protein measurements of 205 samples remained for follow-up analysis of the 200HIV cohort.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModel benchmarking\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe compared the performance of four machine learning methods—LASSO, elastic net, ridge regression, and LightGBM—on the training set 200FG (n = 98) and age was predicted into two independent cohorts of PWH, the 200HIV (n = 205) and 2000HIV cohort (n = 1,850). For all models, the normalized expression levels of 1254 proteins measured using the Olink platform were used as input features to predict the chronological age of each sample. During model training, the training set was subjected to 500 rounds of bootstrap resampling, for each bootstrap iteration hyperparameter tuning was conducted using five-fold cross-validation implemented through the caret package (18) in R. The final predicted age values are derived by averaging the outputs from all 500 optimized models. The final model selection was based on the average R2 and average RMSE in both training set and validation cohorts. The robustness test of\u0026nbsp;all\u0026nbsp;bootstrap aggregated LASSO aging models was shown in Extended Data Fig.2 and Extended Data Fig.3.\u0026nbsp;Given the relatively small size of our training dataset, we sought to enhance the credibility of our aging clock by benchmarking its performance against previously published proteomic aging clocks trained using LASSO regression (Lehallier et al., 2019\u0026nbsp;(39); Hamilton et al., 2023 (15)) (Extended Data Fig.4a). The predicted proteomic ages exhibited a strong Spearman correlation with those from the reference models (Extended Data Fig.4b, c), providing robust support for the reliability of our approach.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOrgan-specific protein selection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used normalized bulk tissue RNA-seq expression data from the GTEx database (\u003cem\u003e19\u003c/em\u003e) to identify organ-specific proteins, as previously described (\u003cem\u003e20\u003c/em\u003e). Since the GTEx dataset includes a large number of sub-organs, we consolidated these sub-organs into corresponding main organs followed by the same categories as previous research and defined the expression level of each gene in a main organ as the highest expression value observed among its sub-organs. Considering that the LASSO model inherently applies stringent feature selection, we aimed to retain as many input protein features as possible. To define organ-specific genes, we required the expression level of a gene in one organ to be at least twice as high as its expression in any other organ. These organ-specific genes were then mapped to our Olink proteomic data. As a result, we identified 513 organ-specific proteins that successfully mapped to our dataset, accounting for approximately 41% of the total proteins analyzed. Based on the distribution of organ-specific protein counts (Extended Data Fig.1f), we first selected the brain, lung, artery, liver, pancreas, and intestine (at least with over 15 features) as target organs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOrgan-specific aging clock training and age gap calculation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe applied the bootstrap LASSO model using the expression levels of organ-specific proteins identified for the artery, brain, intestine, lung, liver, and pancreas in the training set (200FG) as input features to predict their biological age. The predicted values for the training set were calculated as the mean of the outputs from 500 bootstrap iterations. As a supplementary analysis, we also trained three additional aging clock models for comparison: a conventional age clock with all proteomic expression, an all-organ age clock with all organ-specific protein, and an organismal age clock with all non-organ specific protein. After evaluating the average performance across 500 bootstrap-trained models (Extended Data Fig.2,3) and corresponding out-of-bag (OOB) (Table S2) estimates for each organ, we selected the brain, artery, liver, and intestine for downstream analyses based on their consistently robust predictive accuracy. These models were included to evaluate and contrast their predictive performance with the organ-specific aging clocks.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe predicted age obtained from each model was designated as the organ-specific age. The trained models were then applied to the 200HIV and 2000HIV datasets to calculate the organ-specific age for PWH. The age gap for each sample in each model was defined as the difference between the predicted age and the chronological age:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAge\u0026nbsp;gap = Predicted age – Chronological age\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo account for baseline age gaps in healthy individuals and their variation across age groups, we adjusted the age gap in the 2000HIV cohort by subtracting the mean age gap of the corresponding age group in the 200FG dataset, as follows:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCorrected\u0026nbsp;age\u0026nbsp;gap≤35=Age\u0026nbsp;gap−E[(Predicted\u0026nbsp;age−Chronological\u0026nbsp;age)\u003c/em\u003e\u003cem\u003e∣\u003c/em\u003e\u003cem\u003eFG200≤35]\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCorrected\u0026nbsp;age\u0026nbsp;gap36−60=Age\u0026nbsp;gap−E[(Predicted\u0026nbsp;age−Chronological\u0026nbsp;age)\u003c/em\u003e\u003cem\u003e∣\u003c/em\u003e\u003cem\u003eFG200 36−60]\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCorrected\u0026nbsp;age\u0026nbsp;gap\u0026gt;60=Age\u0026nbsp;gap−E[(Predicted\u0026nbsp;age−Chronological\u0026nbsp;age)\u003c/em\u003e\u003cem\u003e∣\u003c/em\u003e\u003cem\u003eFG200\u0026gt;60]\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ewhere E[\u003c/em\u003e\u003cstrong\u003e\u003cem\u003e⋅\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e]represents the expected value (mean) within each respective age group in the 200FG dataset (≤35 years, 36–60 years, and \u0026gt;60 years).\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis adjusted 2000HIV age gap was used in all subsequent analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDNA methylation profiling\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDNA methylation profiling was performed on 1914 samples as previously described (\u003cem\u003e16\u003c/em\u003e, \u003cem\u003e21\u003c/em\u003e). DNA was extracted from EDTA whole blood by the Radboudumc Genetics Department using the ChemagicStar automated configuration (Hamilton Robotics) with magnetic polyvinyl alcohol (M-PVA) bead-based technology. DNA concentration and purity (260/280 nm ratio) were assessed using a NanoDrop spectrophotometer. Samples were normalized to 50 ng/µL in TE buffer and randomly assigned to plates. High-quality samples were analyzed using the Illumina Infinium MethylationEPIC BeadChip array (manifest B5).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDNA methylation data processing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStandard sample- and probe-level quality control (QC) procedures were applied. Raw IDAT files from the 2000HIV cohort were processed using the minfi package in R (v4.2.0) (\u003cem\u003e22\u003c/em\u003e). Samples with gender mismatches or poor quality were excluded. Probes were removed if they had \u0026gt;10% missing values (detection P \u0026gt; 0.01), mapped to sex chromosomes, overlapped with common SNPs (MAF \u0026gt; 5% in European populations), or mapped to multiple genomic loci. Stratified quantile normalization was applied (\u003cem\u003e23\u003c/em\u003e). Methylation β-values were calculated as β = M / (M + U + 100), where M and U represent the methylated and unmethylated signal intensities, respectively.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDNA methylation age calculation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNormalized methylation β-values were used to estimate DNA methylation age (DNAm age) with five blood-based epigenetic clocks: HorvathAge (\u003cem\u003e24\u003c/em\u003e), HannumAge (\u003cem\u003e25\u003c/em\u003e), PhenoAge (\u003cem\u003e26\u003c/em\u003e), and GrimAge/GrimAge2 (\u003cem\u003e27\u003c/em\u003e), using the DNAm age calculator (https://dnamage.clockfoundation.org; accessed October 2024). Preliminary age advancement scores were obtained by subtracting chronological age from DNAmage. To remove the confounding effect of chronological age, we regressed DNAm age on chronological age and used the resulting residuals—termed residual DNAm age gaps (Extended Data Fig.5)—for downstream analyses.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLinear modelling and meta-analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe examined the associations between corrected organ age gaps and various factors, including physical health indicators, HIV stages, comorbidities, medication usage, and \u0026nbsp;antiretroviral therapy (ART) in the 2000HIV cohort. To assess these relationships, we employed linear models controlling for age, sex, and ethnicity, using the following formula (detailed Ethnicity information was recorded in Table 1):\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCorrected age gap\u0026nbsp;\u003c/em\u003e\u003cem\u003e∼\u003c/em\u003e\u003cem\u003e\u0026nbsp;Variable of interest + Age + Sex +\u0026nbsp;\u003c/em\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u003cem\u003eEthnicity\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFor accumulative ART analysis, we employed linear models controlling for age and all ART co-administration:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCorrected age gap\u0026nbsp;\u003c/em\u003e\u003cem\u003e∼\u003c/em\u003e\u003cem\u003e\u0026nbsp;ART accumulation + Age + co-administration ART\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAdditionally, we applied the Benjamini-Hochberg method to adjust for multiple testing burden where appropriate (indicated as q-value). Meta-analyses with multiple linear regression model were conducted in R using glmnet (18) package to compare and aggregate effect sizes and confidence intervals across multiple age models.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenotyping, quality control and imputation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDNA was extracted from each participant's whole blood. The Illumina Infinium Global Screening Array was used for genotyping all participants of multiple ancestries in the 2000HIV cohort. Prior to imputation, QC for raw variants and samples was performed using PLINK v1.90b (\u003cem\u003e28\u003c/em\u003e). Genetic variants with a call rate genotype missingness of more than 5% and those deviating from Hardy-Weinberg equilibrium (HWE) with a P value \u0026lt; 10\u003csup\u003e-6\u003c/sup\u003e were excluded from the dataset. The HWE exact test was performed with variants stratified by ancestry. Samples with a call rate \u0026lt; 97.5% and those that showed a heterozygosity rate that deviated more than three standard deviations (SD) from the mean heterozygosity rate per self-reported ancestry were excluded. Genetic variants that passed QC were converted from GRCh37 to GRCh38 genomic build using the UCSC liftOver tool (\u003cem\u003e29\u003c/em\u003e). Next, TOPMed Freeze5 was used on genome build GRCh38 to align strands to the TOPMed reference panel. We used the McCarthy group tools for alignment (\u003cu\u003ehttps://www.well.ox.ac.uk/~wrayner/tools/\u003c/u\u003e). After QC, 582,404 variants from 1864 individuals were retained for the imputation procedure. The filtered raw variants were uploaded to the TOPmed Imputation server and imputed against the TOPMed (version r2 on GRCh38) reference panel. The imputed variants were filtered using BCF tools stratified by ethnicity, excluding variants with low imputation quality scores (R2 \u0026lt; 0.3 or ER2 \u0026lt; 0.7) or MAF \u0026lt; 1%. This yielded 10,810,841 variants from 1864 members of the 2000HIV multi-ancestry cohort.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQuantitative trait locus mapping on age advancement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe performed quantitative trait locus (QTLs) mapping using the imputed genetic data and age advancement scores of 2000HIV samples of European ancestry. After imputation, 1331 samples of European ancestry had both genetic and age advancement scores. First, a standard post-imputation QC was performed using PLINK v1.90b. During QC per SNP, SNPs that deviate from HWE with a P value \u0026lt; 10\u003csup\u003e-6\u003c/sup\u003e and MAF below 5% were excluded. We mapped the age advancement scores to genotype data using a linear model with sex as a covariate. QTLs associated to age advancement were selected to perform mendelian randomization as described in detail below.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMendelian randomization\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo determine causality between organ-specific, all-organ, conventional and organismal age advancement and certain diseases, two sample Mendelian randomization (MR) was performed using the R package TwoSampleMR version 0.6.9 with default settings. For each exposure (organ-specific, all-organ, conventional, and organismal age advancement), genetic variants used as instrumental variables (IVs) were extracted from the summary statistics of QTLs on age advancement identified in this study and publicly available genome-wide association studies (GWASs) on disease outcomes of interest in the OpenGWAS database (\u003cem\u003e30\u003c/em\u003e). An overview of studies extracted from the Open GWAS database included in the Mendelian randomization analyses are provided in the Supplementary Table 3 (n = 63 studies). All GWAS studies on disease outcomes used in this study were from populations of European ancestry to eliminate demographic stratification bias.\u003c/p\u003e\n\u003cp\u003eThe IVs were selected based on the following criteria: SNPs were significantly associated with the exposure (age advancement) in the 2000HIV cohort (\u003cem\u003eP\u003c/em\u003e \u0026lt; 1 x 10\u003csup\u003e-5\u003c/sup\u003e) and, a stringent clumping was performed to extract independent SNPs. Genetic variants were clumped using a linkage disequilibrium (LD) \u003cem\u003er\u003c/em\u003e2\u0026lt;0.001 and a window size of 10,000 kb using the 2000HIV cohort of European ancestry as a reference for clumping. SNP proxies were added automatically by the TwoSampleMR R package. In total, 148 independent SNPs were selected as instrumental variables after clumping. SNPs associated to exposure were from GRCh38 to GRCh37 genomic build before MR.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOnly if there were still at least six SNPs remaining per exposure, MR was performed. In addition, SNPs that were associated with the age advancement of more than 5 exposures using the 2000HIV cohort of European ancestry were considered pleiotropic and excluded (Extended Data Fig.6). \u0026nbsp;Twenty-three (n = 23) SNPs showed pleiotropic effects and removed, resulting in 119 unique independent SNPs. Further, the strength of each SNP was assessed by F-statistic using the formula \u003cem\u003eF\u0026nbsp;\u003c/em\u003e= β2/Se2, whereas β and Se are the coefficient and standard error of exposure respectively. SNPs with F-statistics \u0026lt; 10 were regarded as weak IVs and should be discarded in the following analyses. None of the SNPs were discarded due to F-statistics. Finally, Steiger-filtering was used to exclude SNPs, which explain more variance in the outcome than the exposure, as these SNPs are likely to be invalid instruments (which either act though horizontal pleiotropy or proxy a reverse causal pathway from outcome to exposure). We harmonized exposure and outcome data to ensure that effect estimated corresponded to the same allele for each SNP (Table S4).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe used the inverse variance weighted (IVW) method as the main approach to evaluate the potential causal relationship between age advancement and disease outcomes by combining the \u003cem\u003eβ\u003c/em\u003e-values and the standard errors of the causal estimate from them (forward MR-IVW). Seven additional effective methods, including \u003cem\u003eMR-Egger\u003c/em\u003e, \u003cem\u003eweighted median\u003c/em\u003e, \u003cem\u003eweighted mode\u003c/em\u003e, and \u003cem\u003esimple mode\u003c/em\u003e, \u003cem\u003esimple median\u003c/em\u003e, \u003cem\u003emaximun likelihood\u003c/em\u003e and \u003cem\u003einverse variance weighted with fixed effects\u0026nbsp;\u003c/em\u003ewere also applied to evaluate the possible causal relationship comprehensively. For MR results with significant IVW (P \u0026lt; 0.05), sensitivity analyses were performed to evaluate if the causal estimates are robust to violations of MR underlying assumptions.\u003c/p\u003e\n\u003cp\u003eFirst, we performed the mendelian randomization pleiotropy residual sum and outlier test (MR-PRESSO) to detect potential outlier variants (\u003cem\u003e31\u003c/em\u003e). Furthermore, the MR-Egger regression was used to evaluate the bias generated by gene pleiotropy, of which the intercept is an indicator. In addition, the Cochran’s Q statistics was applied to quantify the heterogeneity between SNPs. Thirdly, we used the leave-one-out analysis to verify whether there are SNP outliers that strongly affect the results by eliminating each SNPs and then re-calculating the causal estimates using the IVW method on the rest. Finally, to evaluate the possibility of reverse causality between age advancement and diseases, we performed MR-IVW in the other direction (reverse MR; GWAS diseases used as an exposure and age advancement as outcome). We precluded results for which less than five SNPs were available as instrumental variables per exposure. For the reverse MR, we used the exposure/outcome pairs that reached P nominal significance in the forward MR-IVW analyses (P \u0026lt; 0.05). We excluded any results that had a nominal significant IVW results in the reverse MR while passing all the sensitivity analyses. A strong causal relationship between the age advancement and disease was considered when the following criteria were met: (1) the IVW method demonstrated a significant difference (forward MR, P \u0026lt; 0.05); (2) the seven MR methods provided consistent estimations (forward-MR); (3) the Cochran’s Q test, MR-Egger test, and MR-PRESSO global test had no significance (forward MR, P \u0026gt; 0.05) and; (4) the reverse MR showed no significant difference (MR-IVW P \u0026gt; 0.05).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll statistical analyses were performed using R statistical software (version 4.2.0). LD clumping was performed using rtracklayer::liftOver in R software. QTL mapping was performed using the MatrixEQTL package (\u003cem\u003e32\u003c/em\u003e). Mendelian randomization was performed u using the ‘TwoSampleMR’, ‘MR-PRESSO’, ‘ieugwasr’ R packages.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQuantification of total and intact HIV-1 DNA from CD4+ T cells\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTotal and intact HIV-1 DNA levels were measured in CD4+ T cells from 1850 PWH by digital PCR (dPCR) using the Rainbow proviral HIV-1 DNA assay. Starting from 40 Mio cryopreserved PBMCs, CD4+ T-cells were enriched by negative selection using EasySep Human CD4+ T-cell isolation kit on the Robosep-S (Stemcell Technologies, Vancouver, Canada). Genomic DNA (gDNA) was extracted using the QiaAmp DNA mini kit on the Qiacube (Qiagen, Hilden, Germany) with two elution steps of 50µL. \u0026nbsp;DNA concentrations were determined using 2 µL of the eluted DNA with the SpectraMax Quant AccuBlue HiRange dsDNA Assay Kit by using the SpectraMax i3x (Molecular Devices, San Jose, California, United States). Samples were stored at -20°C prior to dPCR quantification. HIV-1 DNA levels were quantified in triplicate by dPCR using the Rainbow proviral HIV-1 DNA assay on the QIAcuity Four platform (Qiagen, Hilden, Germany). Depending on the DNA concentration, either 18 µL of eluted gDNA was used per replicate for samples with concentrations below 90 ng/µL, or 10 µL for those exceeding 90 ng/µL. HIV-1 DNA levels were normalized by measuring the reference gene RPP30 in duplicate and reported per million CD4+ T-cells. For normalization and DNA shearing assessment, a 1/100 dilution was made for each sample and 5µL was used as input. Total HIV-1 DNA levels were assessed by the RU5 region in the Rainbow assay and were highlighted when the result was below the limit of detection (i.e. 10 copies per well). Intactness levels were obtained by the presence of at least two (psi and env) and maximum five target regions (RU5, psi, gag, pol and env) in the Rainbow assay. Automatic thresholds were calculated with the Rainbow Shiny tool and adapted if needed per sample when a threshold crossed the double-positive population. In case the intactness result was a zero-value, results of individual targets were checked. If positive partitions were observed in all target regions, the intactness result was undetectable and was artificially calculated as one intact copy in the total cell input for that sample. If there were no positive partitions in psi and/or env due to presumable signal failure, no intactness level was reported. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, we reported false discovery rate (FDR) values for all meta-analyses and P values for individual regression analyses. Statistical significance was indicated as follows: ***: FDR or P \u0026lt; 0.001, **: FDR or P \u0026lt; 0.01, *: P \u0026lt; 0.05, NS: not significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003ePlasma proteomic age scores are robust predictors of chronological and biological aging in PWH\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo evaluate whether proteomic data can successfully predict biological age in healthy individuals and PWH, plasma proteins were analyzed in various cohorts using different Olink panels. 1254 proteins overlapped and were available in plasma samples across three independent cohorts: 200FG (general population), 200HIV (PWH cohort), and 2000HIV (PWH cohort). These proteins were subsequently used to train the proteomic aging clock models (Fig.1a). Specifically, data from 200FG cohort served as the training set, while data from 200HIV and 2000HIV were used as independent test set and exploration set. Four machine learning algorithms—LASSO, elastic net, ridge regression, and LightGBM with bootstrap aggregation—were employed to train the models, and their performances were systematically compared. We observed that LASSO demonstrated comparable performance to elastic net and ridge regression, and they in turn overperformed LightGBM on both the training and test sets (Extended Data Fig.1c, d, e). Given that LASSO inherently incorporates feature selection and keep less features compared to elastic net, making it more effective at retaining proteins with high importance, we ultimately selected the LASSO to construct subsequent organ-specific proteomic aging clocks.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNext, we utilized tissue RNA-seq expression data from the GTEx project to identify organ-specific proteins for the construction of organ-specific proteomic aging clock models (Fig.1b). In total, we developed nine proteomic aging clocks: six organ-specific aging clocks for the artery, brain, intestine, lung, liver, and pancreas, along with three additional general clocks—a conventional aging clock (using all 1254 proteins), an all-organ aging clock (using 513 all-organ-specific proteins) (Table S5), and an organismal aging clock (using 741 non-organ-specific proteins). These models were compared to assess their performance and usefulness as a proxy in predicting biological age. All nine proteomic aging models demonstrated a significant correlation between the predicted age and chronological age (Extended Data Fig.1g and Extended Data Fig.7). However, given the relatively poor performance of the models for the pancreas and lung, we excluded these two from subsequent downstream analyses.\u003c/p\u003e\n\u003cp\u003eTo further validate the biological relevance of our proteomic age models, we used DNA methylation data from the 2000HIV cohort to calculate five well-established DNA methylation age scores: HorvathAge, HannumAge, PhenoAge, GrimAge, and GrimAge2, and calculated their correlations with the seven proteomic age scores (Fig.2a). We observed a high correlation between the five DNA methylation ages, with an average Pearson correlation coefficient of\u0026nbsp;r=0.85, highlighting the consistency across different methylation age clocks. Notably, the conventional age, all-organ age, and organismal age clocks also exhibited high inter-correlations (r average=0.86), and subsequently also showed strong correlations with the five DNA methylation ages (r average=0.78), supporting the feasibility of predicting age using proteomic data from circulating proteins in PWH. Interestingly, we found that the artery age (r=0.58∼0.68) and brain age (r=0.67∼0.84) models demonstrated moderate to high correlations with the DNA methylation ages and the conventional age clock. In contrast, the liver (r=0.33∼0.43) and intestine (r=0.40∼0.46) age models exhibited weaker correlations with both the DNA methylation ages and the conventional age clock. Similarly, we compared the age gap derived from different age clocks (Fig.2b).\u0026nbsp;The distribution of proteomic age gap is depicted in Extended Data Fig.1b. In addition to observing moderate correlations among the age gaps of various DNA methylation clocks, we found that the artery age gap exhibited only weak correlations with the age gap calculated based on the other clocks (r average=0.25). In contrast, the age gap for the liver and intestine clocks showed moderate-to-high correlations (r =0.65), suggesting common mechanisms in the aging processes of these organs.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePeople with HIV show accelerated biological aging\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNext, we assessed whether chronic HIV infection influences biological aging scores. The different age gaps calculated in PWH either by proteomic or methylation scores were strongly associated with chronological age, so we applied correction of chronological age on age gaps in all downstream analysis (Extended Data Fig.8). Importantly, we observed a trend of age acceleration in PWH in four out of the five methylation age gaps examined, with the exception of the PhenoAge gap (Fig.2c). A similar acceleration of the aging process in PWH was apparent when assessing proteomic age scores, as shown by the conventional age acceleration that encompasses both the ‘all-organ age gap’ and the ‘organismal age gap’, as well as the brain age acceleration. In contrast, other organ-specific proteomic scores did not differ significantly from chronological age or displayed negative age acceleration, as in the case of intestine and liver, suggesting a divergent pattern of aging dynamics across different organs.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAlthough we lacked statistical power to obtain significant differences, we observed a general tendency that the conventional age acceleration in PWH of Asian descent was higher compared to European or African ancestries, both in younger and older populations (Extended Data Fig.9a). Additionally, sex-specific differences in organ-specific age acceleration were evident across different age strata. Males with PWH exhibited higher intestine and organismal age acceleration compared to females, whereas females demonstrated higher artery and liver age gaps (Extended Data Fig.9b). These findings highlight the heterogeneity in organ-specific age gaps in PWH, which may be linked to distinct clinical characteristics and comorbidity susceptibilities.\u003c/p\u003e\n\u003cp\u003eNext, we leveraged clinical data from the 2000HIV cohort to further examine the relationship between proteomic age and biological aging. Specifically, poorer physical health indicators (Fig.2d), HIV-related parameters (Fig.2e), different comorbidities (Fig.2g), and medication usage (Fig.2h) were all significantly correlated with age advancement. Notably, we also observed a significant impact of current use of antiretroviral medication (Fig.2f) on age advancement, suggesting their potential impact on biological aging in PWH. For organ-specific age, the age advancement of liver and intestine showed the strongest associations with body mass index (BMI) and the following hepatic parameters: CAP liver score (steatosis parameter), liver stiffness measure (LSM) and alanine aminotransferase (ALAT) concentrations (Extended Data Fig.10a, b, c, e). The age advancement of all organ-specific ages was significantly correlated with creatinine (CREAT) concentration (Extended Data Fig.10d), arguing for an important impact of kidney function on biological aging.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePWH were also classified based on lowest CD4 T cell count, ranging according to the level of immunosuppression (Stage 1: ≥500 CD4 cells/µL, Stage 2: 200–499 CD4 cells/µL, and Stage 3: \u0026lt;200 CD4 cells/µL or an AIDS-defining diagnosis). We found that a more advanced HIV stage was significantly associated with conventional age, organismal age, and all-organ age advancement, but no significant association with organ-specific age scores (Fig.3b). This association was also reflected in key HIV parameters, including latest CD4\u0026nbsp;T cell count, lowest recorded CD4\u0026nbsp;T cell count, latest CD8\u0026nbsp;T cell count, most recent CD4/CD8 T cell ratio, and the CD4/CD8\u0026nbsp;T cell ratio before initiating cART (Fig.3a, Extended Data Fig.11a, b, c, d). Although all PHW were virally suppressed because of cART or spontaneous HIV control,\u0026nbsp;some individuals (3.24%) have low level viremia (\u0026gt; 40 HIV-RNA copies/ml). Interestingly, we found that the latest viral load was significantly associated with the advancement of organismal age, all-organ age, and conventional age, while no strong associations were observed with\u0026nbsp;organ-specific aging clocks (Fig.3c). Also, consistently undetectable viral load over the past three years was significantly associated with less organismal age acceleration, which indicate that chronic HIV infection with episodes of low-level viremia contributes to accelerated ageing (Extended Data Fig.11e). These findings strongly suggest that chronic HIV infection contributes to\u0026nbsp;accelerated\u0026nbsp;systemic biological aging. The importance of an undetectable plasma viral load is also underlined by comparing elite controllers (n= 21) that spontaneous control HIV infection with persistent undetectable plasma viral loads without cART and normal progressors, using cART. Elite controllers showed a trend of\u0026nbsp;age deceleration on organismal age, although the difference did not reach statistical significance, most likely due\u0026nbsp;to limited number of individuals in the elite controller group (Fig.3d).\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp;Furthermore, in elite controllers we also observed a trend of less age acceleration in multiple proteomic and DNA methylation aging clocks, including intestine age, artery age, liver age, HorvathAge, PhenoAge, GrimAge, GrimAge2 (Extended Data Fig.12).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAlthough HIV replication can be spontaneously controlled in elite controllers or by cART in the other infected individuals, HIV persists in latently infected cells, mostly resting memory CD4+T cells. Around 95% of the reservoir consists of defective proviral HIV DNA that, unlike intact proviral DNA, does not contribute to viral\u0026nbsp;replication after cART interruption, but may still result in HIV RNA transcripts or\u0026nbsp;proteins that elicit a host response. To more precisely analyze the effect of HIV on the aging process, we investigated whether the total or the intact HIV reservoir size is associated with biological aging (\u003cem\u003e33\u003c/em\u003e). Interestingly, we observed that all conventional, all-organ, organismal, and brain proteomic and DNA-methylation age (except HannumAge) acceleration scores were significantly associated with total HIV reservoir (Fig.3e). This is highly overlap to the age gaps we reported in Fig.2c. In contrast, proteomic and epigenetic age scores were not associated with the intact HIV reservoir levels.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMendelian randomization demonstrates a causal relationship between biological aging and co-morbidities\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo assess whether the impact of HIV infection on inflammatory aging scores is biologically relevant, we investigated whether age acceleration is associated with increased susceptibility to complications. Exploring organ-specific aging scores, we found that brain age advancement was significantly associated with CNS complications which are well known morbidities in PWH (Fig.4a). Furthermore, artery age advancement showed a strong correlation with cardiovascular disease, particularly\u0026nbsp;deep venous thromboembolism (VTE) (Fig.4b, c). Importantly, we found that all-organ age advancement was significantly correlated with mortality within two years follow-up period (FDR=\u0026nbsp;0.021). In addition, the conventional age gap also showed a moderate correlation with two-year mortality (P = 0.027) (Fig.4d). Although most associations did not reach statistical significance due to the limited number of death cases (n = 24) and different death causes, including malignancy and infection (Table S6), these data strongly suggest that proteomic age advancement may serve as a predictive survival biomarker in PWH. Collectively, our findings confirm relation between organ-specific age clock and organ-specific morbidities, while all-organ clock relates more closely with mortality.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo further investigate whether organ-specific, all-organ, conventional and organismal age advancement causally impact disease outcomes, we performed Mendelian Randomization (MR) analysis. For this, we used 119 independent SNPs as instrumental variables (IVs) significantly associated with the exposure (age gap) at P \u0026lt; 1x10\u003csup\u003e-5\u003c/sup\u003e, and extracted the summary statistics of GWAS performed in cardiovascular diseases (stroke, coronary artery disease, myocardial infarction, etc), diabetes, liver fibrosis or steatosis. We tested for causal relationship in exposures with more than five variants, performing sensitivity analyses. A strong causal relationship between age advancement and disease outcomes, which met all the above-described criteria (see Methods), was observed between intestinal age advancement and coronary artery disease (β = 0.0043, P\u003csub\u003eIVW\u003c/sub\u003e= 0.01) (Fig.4e, Table S7, Table S8) (\u003cem\u003e34\u003c/em\u003e). Scatterplots showed a linear regression line for the positive associations between age gap and risk of corresponding disease (Fig.4f). The causal estimates obtained from the rest of MR methods (MR-Egger, weighted median, weighted mode, and simple mode, simple median, maximum likelihood and inverse variance weighted with fixed effects)showed similar direction to those from the primary IVW method (Table S9).Of note, there were no evidence of significant heterogeneity or pleiotropy (P \u0026lt; 0.05, Table S4). Furthermore, MR-PRESSO detected no outliers (P for global test of pleiotropy \u0026gt; 0.05) and the leave-one-out sensitivity analyses showed no single SNP had a substantial impact on the results.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo further validate the relationship between intestinal age advancement and coronary artery diseases, we selected circulating protein markers associated with intestinal integrity measured in the 2000HIV cohort (LBP, CD14 and FABP2) and tested them for association with the intestinal age (Fig.4f, g, h). In line with the hypothesis tested, we found a significant positive association between the biomarkers associated with intestinal integrity and age advancement. Overall, Mendelian randomization analyses indicate that intestinal age advancement is linked to coronary artery diseases, and we hypothesize that translocation of gut bacterial products due to loss of mucosal integrity may contribute to systemic inflammation and accelerated atherosclerosis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDifferential effect of specific ART drugs on biological and specific organ age acceleration as measured by proteomic clock in PWH\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs our data shows the impact of HIV on biological aging, we next assessed a possible effect of the different antiretroviral drugs on biological aging. As virologically suppressed chronic HIV infection is shown to have important impact on biological aging, and biologic age gap in our PWH was correlated to comorbidities, also during 2 year follow up, we next assessed whether there is a difference in the effect of specific antiretroviral medication on age acceleration in PWH. Cumulative drug exposure was used to investigate the impact of ART on aging, as the effect of the medication may be dose and time related, and in order to account for treatment switches. The cumulative drug exposure and all age acceleration scores were explored by regression analyses (Fig.5a).\u003c/p\u003e\n\u003cp\u003eWe observed that exposure duration to certain nucleoside reverse transcriptase inhibitors (NRTIs), particularly lamivudine (3TC), was significantly associated with decreased age acceleration, as estimated by either proteomic aging in the intestine or DNA methylation-based models such as HannumAge, GrimAge, and GrimAge2. In contrast, other NRTIs—including tenofovir disoproxil fumarate (TDF), tenofovir alafenamide (TAF), and emtricitabine (FTC)—exhibited divergent effects on organ-specific aging trajectories. Notably, TDF and FTC exposure duration was significantly associated with accelerated DNA methylation age. Interestingly, TDF demonstrated a stronger association with deceleration of conventional aging metrics, compared to TAF, which is a prodrug of TDF. TDF and TAF both result into the active compound tenofovir diphosphate, but TDF results in higher plasma concentrations while TAF leads to higher intracellular levels in certain cells such as lymphocytes and hepatocytes. Remarkably, increased liver ageing score was seen with accumulating TAF exposure. Furthermore, stavudine (D4T), an NRTI with well-documented toxicities, showed significant associations with age acceleration in multiple organs, including the intestine and liver, although it was also paradoxically associated with less age acceleration in the brain. These findings highlight the heterogeneous and organ-specific effects of antiretroviral therapy drugs on biological aging. Among non-nucleoside reverse transcriptase inhibitors (NNRTIs), both rilpivirine (RPV) and nevirapine (NVP) were significantly associated with lower biological aging across multiple organ-specific measures. Within the class of integrase strand transfer inhibitors (INSTIs), dolutegravir (DTG) was notably linked to lower age scores of the intestine and liver. As for protease inhibitors (PIs), cobicistat (COBI) showed significant associations with decelerated aging scores in the artery and overall organismal age, as well as in methylation-based measures such as GrimAge and GrimAge2.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConsidering the cumulative drug exposure time of all agents, only TDF, RPV and NVP showed a significant association with conventional age deceleration (Fig.5b, c, d,). Ritonavir (RTV), a protease inhibitor, showed significant conventional age acceleration (Fig.5e).\u003c/p\u003e\n\u003cp\u003eGiven that antiretroviral drugs are typically administered in combination, we cataloged both the currently and cumulative ART use in our cohort (Tables S10 and S11). To disentangle the independent contributions of individual drugs, we constructed multivariable regression models to assess the specific effects of selected ART agents—including dolutegravir (DTG), bictegravir (BIC), doravirine (DOR), nevirapine (NVP), rilpivirine (RPV), elvitegravir (EVG), darunavir (DRV), lamivudine (3TC), emtricitabine (FTC), tenofovir alafenamide (TAF), and tenofovir disoproxil fumarate (TDF)—on aging outcomes. We then highlighted the most significant associations (Figure 5f). Notably, 3TC exhibited the strongest association with deceleration of both organismal and intestinal aging scores. Another NRTI, FTC, was also significantly linked to deceleration of organismal age scores. Among the NNRTIs, RPV showed consistent associations with deceleration across conventional, multi-organ, and artery-specific aging metrics. Intriguingly, TAF was associated with acceleration of both all-organ and brain aging scores, consistent with the differential aging effects observed earlier when compared to TDF.\u003c/p\u003e\n\u003cp\u003eCollectively, our findings indicate that certain classes of cumulative ART exposure—particularly NRTIs and NNRTIs—are associated with reduced age acceleration in PWH. These associations became even more pronounced for specific agents such as RPV, 3TC, and FTC after adjusting for the effects of ART co-administration, suggesting independent, protective roles against biological aging. These results provide a rationale for future randomized clinical trials to investigate the impact of specific ART regimens on biological age gaps as surrogate outcomes, and on non-AIDS comorbidities as definitive clinical endpoints in PWH.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eAn increasing number of recent studies argue that biological age may be a better predictor of health compared to chronological age (\u003cem\u003e35\u003c/em\u003e). Recently, organ-specific aging scores have been developed based on plasma proteomics, deepening our understanding of the aging processes (\u003cem\u003e15\u003c/em\u003e). Many factors may contribute to premature aging, such as chronic low-grade inflammation, oxidative stress and mitochondrial dysfunction. All these processes are disturbed in PWH, even when virally suppressed because of effective c (\u003cem\u003e36\u003c/em\u003e). In the present study, we applied a comprehensive analysis to predict whole-body and organ-specific aging using plasma proteomic data and epigenetic age scores, providing a systematic evaluation of factors influencing biological aging in PWH. This analysis resulted several novel findings: 1. people with HIV show premature biological aging, both at the level of the entire organism, as well as at the level of specific organs; 2. age acceleration is related to the total HIV reservoir size, confirming the role of HIV infection in the aging process; 3. organ and systemic proteomic aging clocks are associated with comorbidities and mortality, with a causal relationship between premature intestinal aging and CVD.; and 4. the cumulative use of certain antiretroviral drugs, in particular some belonging to the class of reverse transcriptase inhibitors, is associated with decreased systemic and organ-specific aging in PWH, while others show opposite effects in line with their well-known toxicity profile.\u003c/p\u003e\n\u003cp\u003eThe first major observation of our study is that PWH show premature biological aging. This is in line with a recent study in which we have shown that especially young PWH show accelerated inflammatory aging (14), and accompanies studies that showed an increased epigenetic age in HIV-infected individuals (\u003cem\u003e37\u003c/em\u003e). Our finding that the total HIV reservoir size was associated with biological aging scores, argues for a direct role of HIV infection or more specifically of viral transcripts that originate from defective proviral DNA, but still capable to induce inflammation (14). The intactness of the viral reservoir, that mostly reflect only 5% of the total reservoir size, does not seem to be important for age acceleration. Our finding of lower age acceleration in elite controllers, that are known to have a small HIV reservoir size that is mostly locked up in transcriptionally silent DNA regions, also underscores the role of HIV infection itself for impacting biological aging.\u003c/p\u003e\n\u003cp\u003eThe relevance of the association between the HIV infection and biological aging is apparent at several levels. First, Mendelian randomization demonstrates that accelerated aging in PWH is not a mere epiphenomenon, but is a cause of severe co-morbidities. From this perspective, the causal link demonstrated between intestinal aging and cardiovascular diseases is especially relevant: it is tempting to speculate that intestinal aging in PWH is associated with increased leakage of microbial products, leading to systemic inflammation and subsequently cardiovascular complications. Indeed, this hypothesis is strengthened by the association of gut translocation biomarkers with intestinal aging. Second, increased organ age acceleration was associated with higher risk for co-morbidities and mortality during follow-up, demonstrating the importance of biological age acceleration for health span.\u003c/p\u003e\n\u003cp\u003eAnother strong argument for the relevance of chronic infection with HIV for biological aging is provided by the counter-regulatory effects of certain antiretroviral agents, notably the reverse transcriptase inhibitors, both NRTI as well as NNRTI, on age acceleration. Both the current use as well as the cumulative drug use were associated with age deceleration. Earlier studies have suggested that some anti-HIV drugs have anti-aging effects in PWH, especially dolutegravir (\u003cem\u003e38\u003c/em\u003e). We now demonstrated that this effect is shared by several groups of anti-HIV medication, which argues that down-regulation of HIV expression and proliferation itself has a beneficial anti-aging effect. These findings raise additional important questions to be investigated in future studies: do other chronic (viral) infections have similar effects on biological aging? Could antiretroviral medication exert anti-aging effects also in non-HIV individuals, for example by inhibition of retroelements known to be activated during the aging process? Our data warrant more studies on the anti-aging effects of ART, which opens a new direction of investigation in the field of aging research.\u003c/p\u003e\n\u003cp\u003eThis study has also limitations. First, due to variations in cohorts and batch effects in the proteomic data, we had only a partial overlap in plasma proteins between the healthy and HIV datasets. This constrained the number of proteins available for training organ-specific aging models, leading to the omission of key organs such as the heart and kidney. Second, this is a cross-sectional study, albeit large, and future longitudinal studies with long follow-up are warranted to strengthen the conclusions drawn here. Third, the sex imbalance within the PWH cohort presents another limitation. Although sex was included as a covariate in our analyses, this imbalance may still introduce potential biases in downstream analyses. Finally, the majority of the volunteers in the cohorts studied are individuals of European descent, and therefore the conclusions of the studies should only be cautiously extrapolated to other populations. Future studies should validate these findings in non-European populations.\u003c/p\u003e\n\u003cp\u003eIn conclusion, we systematically revealed the impacts of HIV infection and antiretroviral drugs on biological aging in PWH. Proteomic and epigenetic aging scores demonstrate predictive power for future all-cause mortality and showed significant associations with comorbidities, highlighting their potential as biomarkers for aging and disease risk.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e The study was\u0026nbsp;supported by an unrestricted research grant from ViiV Healthcare. MGN was supported by an ERC Advanced Grant (833247) and a Spinoza Grant of the Netherlands Organization for Scientific Research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConceptualization: MGN, AvdV, YZ, VM\u003c/p\u003e\n\u003cp\u003eMethodology: YZ, VM, NV, MB, WV, AG, LvE, JS, MBe, MD\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInvestigation: YZ, VM, NV, MGN, AvdV\u003c/p\u003e\n\u003cp\u003eVisualization: YZ, VM, NV\u003c/p\u003e\n\u003cp\u003eFunding acquisition: MGN, AvdV\u003c/p\u003e\n\u003cp\u003eProject administration: MGN, AvdV\u003c/p\u003e\n\u003cp\u003eSupervision: MGN, AvdV\u003c/p\u003e\n\u003cp\u003eWriting – original draft: YZ, VM, NV, MGN, AvdV\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWriting – review \u0026amp; editing: YZ, VM, NV, MGN, AvdV, CR, TO, LJ, CJX, YL, LV\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e MGN is a scientific founder of TTxD, Lemba, Biotrip and Salvina.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData and materials availability:\u003c/strong\u003e Further information and request for data resources should be directed to 2000 HIV study principal investigators: Prof. Dr. Andre van der Ven and Prof. Dr. Mihai G. 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Wyss-Coray, Undulating changes in human plasma proteome profiles across the lifespan. \u003cem\u003eNat Med\u003c/em\u003e\u003cstrong\u003e25\u003c/strong\u003e, 1843\u0026ndash;1850 (2019).\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 1","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7234421/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7234421/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"People with HIV (PWH) on combination antiretroviral therapy have an elevated risk for aging-related non-AIDS comorbidities. We assessed whether HIV infection accelerates biological aging in two independent cohorts of PWH using six organ-specific and three organism-wide aging clocks derived from plasma proteomics of healthy individuals. Proteomic age acceleration significantly correlated with DNA methylation age and was linked to comorbidities and mortality. HIV infection accelerated systemic biological aging, with Mendelian randomization demonstrating causality between organ aging and inflammatory or metabolic complications. Accelerated aging in PWH was further related to the total HIV reservoir, and specific antiretroviral drugs decreased age acceleration. These data reveal important causal effects between chronic HIV infection, antiretroviral medication, biological aging and age-associated diseases, highlighting targets for improving health span in PWH.","manuscriptTitle":"Opposite effects of chronic HIV infection and antiretroviral medication on organismal and organ-specific biological aging","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-29 07:13:45","doi":"10.21203/rs.3.rs-7234421/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"392025b2-5be4-431e-9400-8c8a4558f1fa","owner":[],"postedDate":"August 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":52396497,"name":"Health sciences/Medical research/Translational research"},{"id":52396498,"name":"Biological sciences/Computational biology and bioinformatics"}],"tags":[],"updatedAt":"2026-03-19T07:12:52+00:00","versionOfRecord":{"articleIdentity":"rs-7234421","link":"https://doi.org/10.1038/s41467-026-69412-1","journal":{"identity":"nature-communications","isVorOnly":false,"title":"Nature Communications"},"publishedOn":"2026-02-10 05:00:00","publishedOnDateReadable":"February 10th, 2026"},"versionCreatedAt":"2025-08-29 07:13:45","video":"","vorDoi":"10.1038/s41467-026-69412-1","vorDoiUrl":"https://doi.org/10.1038/s41467-026-69412-1","workflowStages":[]},"version":"v1","identity":"rs-7234421","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7234421","identity":"rs-7234421","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}