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

Domain: neuroscience
Published: 2026-02-10
Editorial outcome: major_revision

Abstract
People with HIV (PWH) on combination antiretroviral therapy have an elevated risk for aging-related non-AIDS comorbidities. We assess 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 correlates with DNA methylation age and is linked to comorbidities and mortality. HIV infection accelerates systemic biological aging, with Mendelian randomization demonstrating causality between organ aging and inflammatory or metabolic complications. Accelerated aging in PWH is further related to the total HIV reservoir, and specific antiretroviral drugs reduce age acceleration. These data reveal important causal effects between chronic HIV infection, antiretroviral medication, biological aging and age-associated diseases, highlighting targets for improving healthspan in PWH.

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 inflammation1. 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 sepsis2, tuberculosis3, or COVID-194, 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 syndromes5. 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 cancer6,7, a pattern suggestive of premature aging processes8–10. 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 longevity11, with the epigenetic scores based on DNA methylation being the best known12. More recently, plasma proteomics has emerged as a promising tool for studying accelerated aging, with initial efforts focusing on whole-body aging clocks13,14. The latest advances extend this approach to the development of organ-specific aging clocks based on plasma proteomics signatures15. In the present study, we employed these 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 measurements16. 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.

Results
PlasmaproteomicagescoresarerobustpredictorsofchronologicalandbiologicalaginginPWH
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 (Fig. 1c–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) (Supplementary Data 5), 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 (Fig. 1g and Fig. S7). 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 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 (Fig. S8). 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 (Fig. S9a). 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 (Fig. S9b). 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 (Fig. S10a, b, c, e). The age advancement of all organ-specific ages was significantly correlated with creatinine (CREAT) concentration (Fig. S10d), 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, Fig. S11a, 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 (Fig. S11e). 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 (Fig. S12).

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 aging17. 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 central nervous system (CNS) complications, including multiple sclerosis, epilepsy, Alzheimer’s disease, Parkinson’s disease, and other CNS disorders (Supplementary Data 12), which represent well-established morbidities in people with HIV (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 (β=4.68, 95% CI [1.70, 7.66], FDR=0.021). In addition, the conventional age gap also showed a moderate correlation with two-year mortality (β=3.25, 95% CI [0.37, 6.13], 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 (Supplementary Data 6), 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<1×10–5, 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 eight 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 cardiovascular disease (β=0.0005, P =0.03) (Fig. 4e, Supplementary Data 7, Supplementary Data 8)18. 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 (Supplementary Data 9). Of note, there were no evidence of significant heterogeneity or pleiotropy (P<0.05, Supplementary Data 4). Furthermore, MR-PRESSO detected no outliers (P for global test of pleiotropy >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 cardiovascular disease, 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–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 cardiovascular disease, 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–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 (Supplementary Data 10 and Supplementary Data 11). 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 (Fig. 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.

Methods
Humancohorts
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). The 200HIV cohort is a cross-sectional cohort of PWH receiving combination antiretroviral therapy, established to study host immune function and inflammation during treated HIV infection. The 2000HIV cohort is a large, well-characterized observational cohort of PWH in long-term clinical follow-up, with extensive longitudinal clinical, immunological, and molecular data collected during suppressive antiretroviral therapy. The 200FG cohort comprises healthy volunteers from the general population recruited to investigate inter-individual variation in immune responses. Detailed information regarding cohort recruitment, inclusion criteria, and follow-up procedures has been previously described14. 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). The study included adults with HIV enrolled in the 200HIV and 2000HIV cohorts. Participant sex was self-reported. The number of participants, sex distribution, and age range are provided in Table 1 and in the Nature Portfolio Reporting Summary. Written informed consent was obtained from all participants prior to inclusion. Participants did not receive financial compensation for study participation. This study was complied with all relevant ethical regulations. The 2000HIV study protocol was approved by an accredited medical research ethics committee, the Independent Review Board Nijmegen (NL68056.091.81) and published at clinicaltrials.gov (ID: NCT03994835).

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)23. 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 Supplementary Data 1. 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 (Fig. S1a), 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 package24 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 Figs. S2 and S3. 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., 201925; Hamilton et al., 202315) (Fig. S4a). The predicted proteomic ages exhibited a strong Spearman correlation with those from the reference models (Fig. S4b, 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 database26 to identify organ-specific proteins, as previously described15. 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 used proteins for which the expression level of a gene in one organ was 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 (Fig. S1f), 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 (Figs. 2, 3) and corresponding out-of-bag (OOB) (Supplementary Data 2) 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 gap 36−60 = 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 described16,27. DNA was extracted from EDTA whole blood by the Radboudumc Genetics Department using the Chemagic Star 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 Methylation EPIC 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)28. 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 applied29. 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: HorvathAge12, HannumAge30, PhenoAge31, and GrimAge/GrimAge232, using the DNAm age calculator (https://dnamage.clockfoundation.org; accessed October 2024). Preliminary age advancement scores were obtained by subtracting chronological age from DNAm age. 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 (Fig. S5)—for downstream analyses.

Linear modeling 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 glmnet24 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 v1.90b33. 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 tool34. 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 GRCh38) reference panel. The imputed variants were filtered using BCFtools 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 age, sex and the first five genetic PCs as covariates to account for population stratification. Furthermore, we evaluated genomic inflation factors (λGC) to assess potential population stratification within our cohort. A λ value close to 1 indicates no detectable inflation of test statistics and therefore minimal confounding due to population structure. The calculated λ values were as follows: λ artery=0.992, λ brain=0.982, λ conventional=1.007, λ intestine=1.009, λ liver=1.021, λ organismal=1.002, λ pancreas=1.011, λ lung=1.017, and λ all_organ=1.011. All values are close to 1, confirming the absence of significant population stratification in our QTL analyses. 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 Open GWAS database35. An overview of studies extracted from the Open GWAS database included in the Mendelian randomization analyses are provided in the Supplementary Data 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<1×10–5) and, a stringent clumping was performed to extract independent SNPs. Genetic variants were clumped using a linkage disequilibrium (LD) r2 <0.001 and a window size of 10,000kb 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 (Fig. S6). 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 (Supplementary Data 4).

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 variants36. 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) 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>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 package37. 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 50 µL. 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 5 µL 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, *: FDR or P<0.05, NS: not significant.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This is a well-conceived and innovative study examining accelerated aging in people with HIV (PWH) using multi-omics approaches. The integration of epigenetic clocks, global and organ-specific proteomic clocks, and HIV reservoir measurements positions this manuscript as a valuable contribution to the HIV and aging field, with strong translational relevance. Importantly, identifying reservoir size as a determinant of biological aging highlights a factor directly relevant to guiding long-term care beyond viral suppression. Equally, the demonstration that specific organs (brain, artery, liver, intestine) show distinct aging signatures strengthens the clinical value of the work, as these systems correspond to those most affected in HIV treatment history and patient outcomes.\n**Noteworthy results and significance\n• (Key strength) Biological age acceleration associated with total—but not intact—HIV reservoir size is a key strength, suggesting defective proviruses drive chronic immune activation and aging.\n• Development of organ-specific proteomic clocks is highly novel. Brain and artery clocks diverge from systemic measures, while liver and intestine clocks align with clear translational implications for prioritizing organ-targeted versus systemic interventions in HIV care.\n• The Mendelian randomization finding linking intestinal proteomic age to coronary artery disease is compelling, consistent with the gut–vascular axis, and particularly relevant as HIV and ART disrupt the microbiome and gut barrier integrity.\nThese results are highly significant for the field of HIV and aging, extending the literature beyond global epigenetic clocks to organ-level insights with clinical relevance.\n**Areas for clarification\nOverall, the conclusions are supported by the data. However, several clarifications and methodological details would further strengthen the manuscript:\n• The strategy for adjusting age gaps should be explained in more detail, including justification for binning choices (i.e., bin size vs overall cohort characteristics) and limitations of the small control cohort (n=98) in the setting of complex genetic, gender, and other risk factors.\n• While sex was included in regression models, potential contributions of comorbidities as confounders rather than outcomes should be more explicitly addressed.\n• Additional reporting of per-cohort and per-batch sample counts and demographics is needed to ensure confidence that normalization fully addresses batch effects. In particular, clarification of the sample composition in Batch 3 ( a superset) would be valuable, as the number of HIV-positive versus control samples is not reported. While batch processing and bridge normalization are acknowledged, the lack of detail on the composition of Batch 3 makes it unclear whether the batch structure may overlap with case/control status and bias age-gap estimates.\n• The ancestry characterization of the 200HIV and 200FG cohorts is not described, and it is unclear whether imputation in the 2000HIV cohort was stratified by self-report or genetically inferred ancestry (assuming genetic ancestry based on genotyping for this cohort). Clarification would strengthen claims of generalizability.\n• In the HIV reservoir analysis, limitations of not sorting CD4+ T-cell subsets, assigning a single copy when intactness was undetectable, and reliance on cross-sectional data should be acknowledged, as these factors constrain biological interpretation.\nMethodology and reproducibility\n• The proteomic clock training set is very small relative to the feature space, raising concerns about model stability. Bootstrapping and external benchmarking mitigate this, but authors should discuss the potential instability more explicitly.\n• The methods are overall well-described and reproducible. Additional detail on how overlapping proteins between organs were handled in the GTEx–proteomics integration would improve the interpretability of the organ-specific clocks.\n• While the regression analyses for ART exposure are valuable, the use of more causal modeling approaches (e.g., marginal structural models) would strengthen causal inference.\n***Summary of Major and Minor Points***\n#Major Points (requiring revision/clarification):\n• Age-gap adjustment: clarify rationale for binning, small control cohort limitations, and residual confounding from sex/comorbidities.\n• Proteomic training set: acknowledge instability risk given small sample size versus large feature space.\n• Batch reporting: provide per-cohort, per-batch sample counts/demographics; clarify normalization adequacy.\n• Ancestry characterization: clarify whether 2000HIV imputation stratification was by self-report or genetic ancestry; describe ancestry of 200HIV and 200FG cohorts.\n• HIV reservoir quantification: acknowledge the lack of CD4+ subset sorting, the floor effect from assigning 1 intact copy, and the limitation of the cross-sectional design.\n• ART exposure analysis (causal inference): consider more causal modeling frameworks beyond standard regression.\n• Treatment sequencing and legacy exposure: the analysis focuses on cumulative drug exposure but does not consider sequencing of ART regimens. Early exposure to older agents (e.g., thymidine analogues, early protease inhibitors) may have long-lasting toxic effects not captured by cumulative duration. Stratifying analyses by first-line regimen type or ART initiation era would help clarify these legacy effects.\n#Minor Points (improvements but not prohibitive):\n• Clarify handling of overlapping proteins in GTEx–proteomics integration for organ clocks.\n• Expand discussion of lifestyle/environmental contributors to aging (smoking, alcohol, socioeconomic stress).\n• Consider including supplementary analyses on the sensitivity of organ-specific clocks to comorbidities.\n• Provide a clearer explanation of age gap correlations (artery/brain vs liver/intestine) in relation to clinical implications.\nSummary\nThis is a comprehensive, original, and clinically significant contribution to the HIV and aging literature. While certain methodological clarifications would enhance transparency, these do not undermine the robustness of the findings. With revisions, the manuscript has high potential impact.", "reviewer_2": "This paper raises an important issue in the era of cART for people with HIV and presents a comprehensive study to predict whole-body and organ-specific aging using plasma proteomic data and epigenetic age scores, and to investigate the causal relationship between biological age and comorbidities in PWH. The strength of the study is the availability of calculating and evaluating Olink-based proteomic age scores for PWH, and therefore the possibility of investigating the associations of proteomic age(s) with comorbidities and mortality.\nMajor concerns:\n1. Linear correlations were mostly used for evaluating appropriateness or slection of age clocks or age gap. Did any possibility of non-linear but meaningful relationship exist? How could such linear correlation criteria affect the study validity? This would be particularly important when, as the author stated, HIV infection was associated with accelerated aging which means non-linearly increased or accelerated impact of HIV infection on aging along with chronologically increased age. No data or figures clearly supported that only accelerated but no accentuated aging effects existed duing HIV infection. Furthermore, as biological age was defined as various ages including proteomic age, DNA methylation age, organismal age, all-organ age and organ-specific ages, the aging mode might be accelerated or accentuated or both. Clarification of these might be very useful for understanding the divergent pattern of aging dynamics across different organs (Line 462-463), the heterogeneity in organ-specific age gaps (Line 470-472), and the complexities of the overall study findings.\nSystematically examine and tabulate or diagram the divergent mediation effects of the various age or age advancement calculated as proteiomic age, DNA methylation age, organismal age, all-organ age, conventional age and organ-specific age in the causal paths from HIV infection and/or cART to comorbidities and mortality.\n2. The logic of causal hypotheses and analyses in this study seems to be that HIV infection (with cART) accelerates biological aging (using six organ-specific and three organism-wide aging clocks derived from plasma proteomics of healthy individuals) and the accelerated aging was linked or associated with comorbidities and mortality. Were any mediation analyses of certain biological age(s) performed?\n3. HIV-specific clinical status such as CD4 cell count and viral load were significanlty associated with the advancement of organismal age, all-organ age and conventional age, while no strong associations were not observed with organ-specific aging clocks. Apart from that CD4 cell count is a systemic marker, any other explanations?\n4. The potential impact of misclassification of biological age in terms of all-organ versus organ-specific ages on the associations should be more elaborated.\nMinors:\n1. The title of the manuscript stated as “… the opposite effects of chronic HIV infection and antiretroviral medication on organismal and organ-specific biological aging” seems not to fully reflect the main results, i.e., the causal relationship between biological age and comorbidities in PWH was ignored. Why?\n2. The paper could be more succint by focusing on the main objectives of the study.\n3. Carefully reexamine text typoes, e.g., (1) Line 638-639: “All these processes are disturbed in PWH, even when virally suppressed because of effective c (36)” seems to be “All these processes are disturbed in PWH, even when virally suppressed because of effective cART (36)”; (2) Ref 15 and ref 20 are duplicated, and so are ref 12 and ref 24, etc.", "reviewer_3": "Understanding the determinants of biologic age has been important insights into the mechanisms the aging process. The newly described aging hallmarks, felt to represent the fundamental mechanisms of aging, have been studied by the metrics developed to measure biologic age. These measures, also referred as clocks, help to identify diverse factors which may increase or decrease the rate of aging of the organism, or individual. The goal of these insights is to develop interventions which may alter the impact of those factors adversely affecting biologic age. Among the many measures developed to measure biologic age, various epigenetic clocks based on DNA methylation are among the most accurate surrogates for determining biologic age of the whole organism. More recently assessment of plasma proteomics has broadened these investigations from the study of the whole organism to the level of organ-specific aging clocks.\nIn HIV, persistent low-grade immune activation, due to ongoing low level HIV replication despite effective control by antiretroviral regimens, leads to persistent chronic inflammation which is felt to be the driving force behind the increased rate of the premature development of common age-related conditions. Understanding the mechanisms behind the drivers of chronic inflammation utilizing these geroscience based principles has demonstrated increased biologic age occurring at the time of acute infection with HIV. This accelerated aging rate is significantly reduced with starting effective antiretroviral therapy but does not return to pre-infection levels and persists despite ongoing treatment. These investigations have led to deeper insight into the various environmental, personal and treatment related contributors to accelerated and accentuated aging of persons living with HIV.\nThis study developed whole-body and organ specific proteomic clocks and compared the aging-related measures to those derived from the current range of epigenetic clocks in a Iarge, diverse cohort of effectively treated older adults with HIV. To better understand the relevance of the new findings in this study, certain aspects require clarification. I will limit my comments to the Results and Discussion as the detailed description of the complex methods utilized is not my area of expertise.\n1-An important issue is that the 3 cohorts are very different in their demographics. The 2000HIV group (mean age – 50) is considerably younger than the 200HIV group (77), which has a similar age to the 200FG (73). Although no statistics are provided the SD for the 2000HIV is wide enough so that there may not be a statistical difference with the 200HIV cohort but a difference of 27 years (!) surely has some clinical significance which may impact on the results. This requires an explanation.\n2-The correlations between the proteomic and DNA clocks of the intestines was weak (line 440). This may be relevant as the gut is the main target of the initial exposure of an individual to HIV and also the least likely to return to pre-HIV structure despite effective and early treatment. This impacts on the “leakiness” of the GALT which has been shown to be an important source of ongoing chronic systemic inflammation.\n3-Furthermore, using the proteomic age scores, there were either non-significant or negative correlations between chronologic age and intestine and liver specific proteomic organ scores (lines 461-463). There were positive correlations with all-organ age gap, organismal age gao and brain age (lines 458-460). These differences and their impact on the reliability of using proteomic age as a measure of biologic age needs clarification. The utility of using proteomic age for a more focussed understanding of biologic age with a goal to intervene in the future is the stated goal of these studies (lines 75-78).\n4-Similar inconsistent results occurred with strong correlations between the most recent HIV-RNA levels and chronologic age, organismal age and all-organ age clocks but not with organ-specific clocks (lines 496-498).\n5-The positive correlations described in lines 473-485 are reassuring but it would be useful to know if the DNA methylation-based age scores also showed similar correlations with the individual liver parameters.\n6-The statement (line 501) that low-level viremia contributes to accelerated aging may well be true for biologic age but the clinical implications of an increased risk of adverse clinical events between persons with consistent undetectable viremia, intermittent HIV-RNA “blippers” and chronic low-level viremia (eg 50-1200 copies) remain uncertain. It would be relevant to use these measures to better understand the possible impact on biologic aging, and on clinical sequelae of HIV-RNA levels between, for eg 1-4 copies vs 5-19 copies, using ultra-sensitive assays.\n7-The relative limited impact on biologic aging in elite controllers is reassuring but further investigation of such persons could be used to identify a subgroup who may progress and for whom earlier therapy is indicated.\n8-Given the intense interest on cognitive impairment in HIV, the association of brain age advancement with CNS complications is very important. Unfortunately, Fig 4 does not provide any details of which CNS complications were assessed. This information should however be available for the Cohort and is relevant given the intense interest of HIV-related vs age-related degenerative cognitive decline and vascular risk-related cognitive decline in persons aging with HIV. There is a substantial literature on epigenetic changes with cognitive decline in HIV.\n9-The preliminary data described herein on the interaction between various ARTs and biologic aging is clearly important. Again, the associations found with various proteomic and DNA clocks are divergent but these investigations are still early. If confirmed, then the potential clinical relevance of recommending cART regimens to regions with limited access to the latest ARVs because of access and affordability may also need to consider the long-term impact on biologic age. It is interesting that a possible “geroprotector” effect of cART has been demonstrated (Guaraldi OFID 2019) in that frailty appeared to decrease over time in > 50 yo PWH while increasing in PWH > 75.\n10-There is no discussion of the possible utility of a proteomic determined biologic age and the commonly diagnosed geriatric syndrome of frailty. The impact of geriatrics syndromes on functional status maybe more important than counting comorbidities with limited short-term impact on functon(eg stable minimal CKD, well controlled HTN etc versus falls, frailty, sarcopenia, polypharmacy etc). A proteomic profile of frailty has been described in older Italian adults (Landino, Geroscience 2021) and increased DNA methylation age was described in frail older adults with HIV (Sanchez-Conde, Epigenomics 2019)."}, "author_response": "We thank the reviewer for raising this important methodological point.\nRegarding the binning strategy, our approach was guided by both demographic conventions and precedents from previous large-scale aging studies. According to the United Nations, individuals aged over 60 are generally considered older adults, while youth typically refer to those aged 15–35, leaving the range between 35 and 60 as middle age. Consistent with previous proteomic and immunologic aging studies (e.g., Alpert et al., Nat. Med., 2019; Furman et al., Nat. Med., 2019), we adopted comparable binning ranges to ensure age strata that reflect major physiological and immunological transitions rather than arbitrary numeric divisions. This binning approach also maintains sufficient sample size within each stratum to ensure stable estimation of mean age gaps in the control cohort.\nRegarding the limitation of a relatively small control cohort of the healthy volunteers (n=98), we acknowledge this as a potential constraint of the study. Our design was based on the framework of previous work (Navas, A. et al., Aging Cell 2025), where the same healthy cohort (200FG) was used as a training reference for the aging clock, and the 200HIV cohort served as the test set. To mitigate sampling limitations, we implemented a bootstrap-based training approach to enhance model robustness (FigS.2–3), and further validated our model against published proteomic aging parameters, observing strong correlations between our predicted scores and those derived from external datasets (FigS.4).\nImportantly, our proteomic aging scores also demonstrated biologically consistent associations with multiple health indicators in people with HIV (PWH), including BMI, blood pressure, and liver stiffness (Fig. 2d), providing further evidence of model validity and generalizability. Nevertheless, we recognize that the relatively small training cohort may not fully capture the genetic, metabolic, and environmental heterogeneity of the broader population. We have accordingly clarified this limitation in the revised Discussion (lines 642): “In addition, as our training set was relatively small (comprising 98 healthy participants), the model may not fully capture the heterogeneity encountered in real-world clinical settings, and this limitation should be taken into consideration as well.”\n•2. While sex was included in regression models, potential contributions of comorbidities as confounders rather than outcomes should be more explicitly addressed.\nWe appreciate the reviewer’s insightful comment.\nIn the regression analyses, we included chronological age, sex, and ethnicity as covariates to control for major demographic confounders. Comorbidities, however, were intentionally modeled as outcomes rather than confounders, as our primary goal was to evaluate the clinical relevance and biological validity of the proteomic and epigenetic aging scores.\nSpecifically, systemic and organ-specific comorbidities were used as downstream outcomes to test whether the aging scores captured meaningful biological variation across individuals. For example, as shown in Fig. 4, central nervous system (CNS)-related diseases were strongly associated with the brain aging score, and cardiovascular diseases correlated with the artery aging score—supporting the organ-level specificity of our aging models. Including these comorbidities as confounders would have led to over-adjustment, thereby obscuring the true associations between biological aging and disease burden that our models are designed to capture.\nIn summary, comorbidities in our framework represent clinical manifestations of biological aging rather than sources of confounding. This analytic choice allows the aging scores to be interpreted as potential mediators linking HIV infection and therapeutic exposure to age-related disease outcomes.\n•3. Additional reporting of per-cohort and per-batch sample counts and demographics is needed to ensure confidence that normalization fully addresses batch effects. In particular, clarification of the sample composition in Batch 3 (a superset) would be valuable, as the number of HIV-positive versus control samples is not reported. While batch processing and bridge normalization are acknowledged, the lack of detail on the composition of Batch 3 makes it unclear whether the batch structure may overlap with case/control status and bias age-gap estimates.\nWe thank the reviewer’s suggestion and understand his confusion on the composition of batches.\nIn this study, we only used batch 1 and batch 3, which includes approximately 2000 samples of the 2000HIV cohort and 100 samples of the 200FG cohort. We used only these two batches because they contain the same number of proteins as the 200HIV cohort used in this study. To clarify, batch 3 does not include samples from the 200FG cohort but only 2000HIV samples. Therefore, to remove batch effect between panels from the different batches, we performed a standard bridging normalization as described in the OLINK vignette (https://cran.r-project.org/web/packages/OlinkAnalyze/vignettes/bridging_introduction.html) .Subsequently, we proceeded with using the normalized protein matrix in the follow-up analyses. Last, but not least, to address differences in demographics between the 2000HIV and 200FG cohort in our follow-up association analyses, we make use of linear regression models in order to correct for potential confounders.\n•4. The ancestry characterization of the 200HIV and 200FG cohorts is not described, and it is unclear whether imputation in the 2000HIV cohort was stratified by self-report or genetically inferred ancestry (assuming genetic ancestry based on genotyping for this cohort). Clarification would strengthen claims of generalizability.\nWe thank the reviewer for this valuable comment.\nWe have now clarified the ancestry composition and data source for both cohorts. Upon re-examination of the cohort metadata, we confirm that all participants in the 200FG and 200HIV cohorts are of European ancestry. The ancestry information was obtained from self-reported ethnicity collected at enrollment rather than genetically inferred ancestry. This information has been added to Table 1 of the revised manuscript.\nTo ensure robust QTL mapping and minimize the potential impact of hidden population structure within our dataset, we additionally repeated the analyses in our subset of 1,331 individuals who self-reported as being of European ancestry. Age, sex, and the first five genetic principal components (PCs), derived from autosomal variants, were included as covariates in the linear regression models to correct for residual population stratification.\nFurthermore, we evaluated genomic inflation factors (λGC) to assess potential population stratification within our cohort. A λ value close to 1 indicates no detectable inflation of test statistics and therefore minimal confounding due to population structure. The calculated λ values were as follows: λartery = 0.992, λbrain = 0.982, λconventional = 1.007, λintestine = 1.009, λliver = 1.021, λorganismal = 1.002, λpancreas = 1.011, λlung = 1.017, and λall_organ = 1.011. All values are close to 1, confirming the absence of significant population stratification in our analyses. Based on these results, we proceeded to use the identified QTLs for Mendelian randomization analyses, and we confirmed the causal relationship between the intestine age gap and cardiovascular disease. We updated the method section in line 269 and updated Fig4 e,f and Table S3, S4, S8,and S9.\n•5. In the HIV reservoir analysis, limitations of not sorting CD4+ T-cell subsets, assigning a single copy when intactness was undetectable, and reliance on cross-sectional data should be acknowledged, as these factors constrain biological interpretation.\nWe appreciate the reviewer’s thoughtful comments.\nRegarding the assignment of one intact copy when intactness was undetectable, this was not an arbitrary assumption but rather a standardized analytical convention adopted to avoid numerical artifacts caused by zero values in digital PCR–based assays. As described in our Methods section (lines 360-364), this rule was applied only when positive partitions were observed across all target regions, ensuring that the adjustment remained biologically conservative and consistent with prior studies employing similar quantification approaches.\nWhile we agree that sorting CD4⁺ T-cell subpopulations could provide more granular insight into the cellular distribution of the reservoir, the goal in our study was to estimate the size of the HIV reservoir (both total and intact) rather than to analyze which circulating cell subsets are involved. To the best of our knowledge, the 2000HIV study included the largest number of participants in whom HIV reservoir assessment has been performed, representing a massive amount of work that is already technically challenging. We therefore view this as a scope choice rather than a methodological limitation.\nFinally, we agree that the cross-sectional design constrains causal interpretation, and we have already acknowledged this in the Discussion: “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. (lines 644–646).”\nMethodology and reproducibility\n•6. The proteomic clock training set is very small relative to the feature space, raising concerns about model stability. Bootstrapping and external benchmarking mitigate this, but authors should discuss the potential instability more explicitly.\nWe thank the reviewer for this valuable comment.\nAs detailed in our response to Reviewer Comment 1, we fully acknowledge the limitation of the relatively small training cohort and have explicitly discussed its potential impact on model stability and generalizability in the revised Discussion section. To address this issue, we employed bootstrap-based model training and external benchmarking to ensure robustness and reproducibility. These analyses consistently demonstrated stable performance across repeated resampling and strong concordance with previously published proteomic aging parameters, which together mitigate the concern of model instability. We have noted this limitation in the revised Discussion section (lines 642): “In addition, as our training set was relatively small (comprising 98 healthy participants), the model may not fully capture the heterogeneity encountered in real-world clinical settings: this limitation should be taken into consideration as well.”\n•7. The methods are overall well-described and reproducible. Additional detail on how overlapping proteins between organs were handled in the GTEx–proteomics integration would improve the interpretability of the organ-specific clocks.\nWe appreciate the reviewer’s thoughtful comment.\nRegarding the handling of overlapping proteins between organs, our approach strictly followed the methodology described in a previously published study (Oh et al. Nature, 2023). Specifically, organ-specific proteins were defined as those whose expression level in a given organ was at least two-fold higher than in any other organ, as stated in the article definition: “To define organ-specific genes, we used proteins for which the expression level of a gene in one organ was at least twice as high as its expression in any other organ” (line 163)\nAccordingly, only mutually exclusive organ-specific proteins were retained for training each organ-specific proteomic clock. Proteins that were highly expressed across multiple organs were not included, to preserve the organ-level specificity of the models. While this stringent criterion enhances biological interpretability and reduces cross-organ confounding, it also limited the number of available proteins for certain organs (e.g., heart and kidney), which made model training infeasible for those tissues. We have noted this limitation in the revised Discussion section (lines 639-640): “Furthermore, we only considered the mutual organ specific proteins rather than any overlapping highly expressed protein across organs.”\nWe agree that overlapping proteins may hold important biological insights into systemic aging processes and inter-organ communication. However, given our study’s goal to construct organ-specific aging models, such proteins were excluded from the current modeling framework.\n•8. While the regression analyses for ART exposure are valuable, the use of more causal modeling approaches (e.g., marginal structural models) would strengthen causal inference. (Treatment sequencing and legacy exposure: the analysis focuses on cumulative drug exposure but does not consider sequencing of ART regimens. Early exposure to older agents (e.g., thymidine analogues, early protease inhibitors) may have long-lasting toxic effects not captured by cumulative duration. Stratifying analyses by first-line regimen type or ART initiation era would help clarify these legacy effects.)\nWe thank the reviewer for these insightful comments.\nWe fully agree that treatment sequencing and legacy effects represent important and complex aspects of ART exposure that cannot be fully captured by cumulative duration alone. In our current analysis, we accounted for the cumulative exposure to each antiretroviral agent and included all possible ART combinations as confounders to minimize collinearity effects (Tables S10–S11). However, as the reviewer noted, this approach does not explicitly model the temporal sequence or potential long-term toxicity associated with early exposure to older regimens. To further address this concern, we additionally stratified our cohort by ART initiation era and first-line regimen type. Specifically, ART initiation eras were categorized as follows:\n-Before 1997 (n = 126; pre-cART era)\n-1997–2007 (n = 555; early cART expansion)\n-2007–2015 (n = 871; introduction of INSTIs)\n-After 2015 (n = 330; immediate ART initiation era).\nWe also annotated the first-line regimen class (NRTIs, NNRTIs, PIs, and INSTIs) for each participant and incorporated these factors into the following adjusted model:\nCorrected age gap ∼ ART accumulation + Age + ART initiation era + NRTIs as first-line regimen + INSTIs as first-line regimen + NNRTI as first-line regimen + PI as first-line regimen\nThe new result shows below:\nThe inclusion of these additional variables modestly reduced the overall significance of cumulative ART associations, but did not materially alter the overall directionality of effects. This suggests that while sequencing and legacy exposure may contribute to variability, the primary associations between cumulative ART use and biological aging remain robust. Moreover, using all ART combinations as confounders represents a particularly stringent adjustment strategy. Therefore, in the absence of more detailed sequential treatment data, we believe that the current results (Fig 5f) remain robust and reliable.\nWe agree that more causal inference frameworks, such as marginal structural models or targeted maximum likelihood estimation, would be ideal to formally address time-dependent confounding and sequencing effects. However, such analyses would require detailed longitudinal treatment data that are not currently available for our cohort. We have now emphasized this limitation in the revised Discussion. (lines 644), where we included the following text:\n“Second, this is a cross-sectional, albeit large, study, including participants using long-term ART that have been facing different ART guidelines and guideline changes. As ART guidelines are more crystalized now, future longitudinal studies are warranted to strengthen the conclusions drawn here”.\n***Summary of Major and Minor Points***\n#Major Points (requiring revision/clarification):\n• Age-gap adjustment: clarify rationale for binning, small control cohort limitations, and residual confounding from sex/comorbidities.\nThis point has been clarified in the main response to Comment 1 ,2, and 6.\n• Proteomic training set: acknowledge instability risk given small sample size versus large feature space.\nThis point has been clarified in the main response to Comment 1 ,2, and 6.\n• Batch reporting: provide per-cohort, per-batch sample counts/demographics; clarify normalization adequacy.\nThis point has been clarified in the main response to Comment 3.\n• Ancestry characterization: clarify whether 2000HIV imputation stratification was by self-report or genetic ancestry; describe ancestry of 200HIV and 200FG cohorts.\nThis point has been clarified in the main response to Comment 4.\n• HIV reservoir quantification: acknowledge the lack of CD4+ subset sorting, the floor effect from assigning 1 intact copy, and the limitation of the cross-sectional design.\nThis point has been clarified in the main response to Comment 5.\n• ART exposure analysis (causal inference): consider more causal modeling frameworks beyond standard regression.\nThis point has been clarified in the main response to Comment 8.\n• Treatment sequencing and legacy exposure: the analysis focuses on cumulative drug exposure but does not consider sequencing of ART regimens. Early exposure to older agents (e.g., thymidine analogues, early protease inhibitors) may have long-lasting toxic effects not captured by cumulative duration. Stratifying analyses by first-line regimen type or ART initiation era would help clarify these legacy effects.\nThis point has been clarified in the main response to Comment 8.\n#Minor Points (improvements but not prohibitive):\n• Clarify handling of overlapping proteins in GTEx–proteomics integration for organ clocks.\nThis point has been clarified in the main response to Comment 7.\n• Expand discussion of lifestyle/environmental contributors to aging (smoking, alcohol, socioeconomic stress).\nWe thank the reviewer for this helpful suggestion. A brief discussion addressing lifestyle and environmental contributors to biological aging has been added to the Discussion section (lines 591): “In addition, lifestyle and environmental factors such as smoking, alcohol use, physical inactivity, and socioeconomic stress are also known to influence the heterogeneity of biological aging observed among PWH.”\n• Consider including supplementary analyses on the sensitivity of organ-specific clocks to comorbidities.\nOrgan-specific associations with comorbidities are presented in Fig. 4 and FigS10 and 11, where we highlight representative links such as CNS disease–brain age and CVD–artery age. We therefore believe additional analyses would be redundant.\n• Provide a clearer explanation of age gap correlations (artery/brain vs liver/intestine) in relation to clinical implications.\nWe thank the reviewer for this insightful comment.\nAs elaborated in our response to Reviewer 3, systemic aging scores better capture overall biological aging, whereas organ-specific clocks reflect organ-level metabolic or functional states. These aging measures are correlated but not necessarily concordant, reflecting the inherent heterogeneity of aging trajectories across different organs. Our study was designed to provide a system-level overview of the effects of HIV infection and ART on biological aging, rather than to dissect individual-level organ-specific differences. Therefore, we did not further explore intra-individual heterogeneity across organ-specific aging scores.\nSummary\nThis is a comprehensive, original, and clinically significant contribution to the HIV and aging literature. While certain methodological clarifications would enhance transparency, these do not undermine the robustness of the findings. With revisions, the manuscript has high potential impact.\n\nReviewer #2 (Remarks to the Author):\nThis paper raises an important issue in the era of cART for people with HIV and presents a comprehensive study to predict whole-body and organ-specific aging using plasma proteomic data and epigenetic age scores, and to investigate the causal relationship between biological age and comorbidities in PWH. The strength of the study is the availability of calculating and evaluating Olink-based proteomic age scores for PWH, and therefore the possibility of investigating the associations of proteomic age(s) with comorbidities and mortality.\nMajor concerns:\n1. Linear correlations were mostly used for evaluating appropriateness or slection of age clocks or age gap. Did any possibility of non-linear but meaningful relationship exist? How could such linear correlation criteria affect the study validity? This would be particularly important when, as the author stated, HIV infection was associated with accelerated aging which means non-linearly increased or accelerated impact of HIV infection on aging along with chronologically increased age. No data or figures clearly supported that only accelerated but no accentuated aging effects existed duing HIV infection. Furthermore, as biological age was defined as various ages including proteomic age, DNA methylation age, organismal age, all-organ age and organ-specific ages, the aging mode might be accelerated or accentuated or both. Clarification of these might be very useful for understanding the divergent pattern of aging dynamics across different organs (Line 462-463), the heterogeneity in organ-specific age gaps (Line 470-472), and the complexities of the overall study findings. Systematically examine and tabulate or diagram the divergent mediation effects of the various age or age advancement calculated as proteiomic age, DNA methylation age, organismal age, all-organ age, conventional age and organ-specific age in the causal paths from HIV infection and/or cART to comorbidities and mortality.\nWe appreciate the reviewer’s insightful comment.\nRegarding the possibility of non-linear but meaningful relationships, we fully agree that human aging is inherently non-linear. Using linear models to approximate biological age can only capture part of this complexity. Indeed, recent longitudinal studies (e.g., Xiaotao et al., Nat. Aging, 2024) have demonstrated non-linear patterns in molecular markers of aging. The organ-specific proteomic clocks used in our study were developed based on the method by Oh et al. (Nature, 2023), who also acknowledged this limitation: “We observe many instances of nonlinear dynamics in the plasma proteome and in aging phenotypes… More sophisticated nonlinear machine learning methods may further improve the accuracy and generalizability of this approach in the future.” Thus, we recognize that the linear approach inevitably loses certain non-linear patterns.\nHowever, linear modeling remains the dominant approach in the field for estimating biological age (e.g., Lehallier et al., Nat. Med., 2019, and others). In our study, the linear models performed robustly, as evidenced by their strong associations with mortality (Fig. 4d) and comorbidities (Fig. 4a–c), which supports the biological validity of our aging scores. Since our primary goal was to explore the impact of HIV infection and ART on systemic and organ-level biological aging rather than to develop new modeling algorithms, we believe that applying the existing linear correlation criteria was appropriate and sufficient for our analytical aims.\nWith respect to the reviewer’s comment on “accelerated versus accentuated aging,” in this study we defined the age gap as the difference between predicted biological age and chronological age (Methods, line 188). Therefore, our use of “age acceleration” and “age deceleration” refers to relative biological age differences within age-matched healthy controls, rather than to the rate of aging over time. This definition follows the widely accepted convention that “age acceleration” represents a cross-sectional deviation of biological age from chronological age, not a temporal measure of speed. Consequently, our study does not aim to distinguish between accelerated and accentuated aging trajectories, which would require longitudinal data.\nFinally, the reviewer’s suggestion to systematically tabulate the divergent mediation effects of various biological age measures (proteomic, epigenetic, systemic, and organ-specific) is closely related to Question 2, where we provide a detailed response.\n2. The logic of causal hypotheses and analyses in this study seems to be that HIV infection (with cART) accelerates biological aging (using six organ-specific and three organism-wide aging clocks derived from plasma proteomics of healthy individuals) and the accelerated aging was linked or associated with comorbidities and mortality. Were any mediation analyses of certain biological age(s) performed?\nWe thank the reviewer for this thoughtful question.\nFirst, to clarify our analytical logic: we used proteomic and DNA methylation data to estimate systemic and organ-specific aging scores in people with HIV (PWH). These aging scores were then correlated with clinical outcomes—including comorbidities and mortality—to evaluate their biological and clinical relevance. We observed significant associations between systemic and organ-specific aging and multiple health indicators (Fig. 2d, 2g, and Fig. 4a–d), supporting the validity of our aging measures. Subsequently, we examined the associations between these aging measures and HIV-related parameters (Fig. 2e, Fig. 3a–c). These analyses were conducted independently and were not intended to infer direct causal relationships between HIV infection, aging, and clinical outcomes.\nRegarding the reviewer’s question about mediation analyses, we did not perform formal mediation modeling in this study. As noted in the Limitations section (line 644), our dataset is cross-sectional:‘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.’ Given the lack of longitudinal follow-up data, causal mediation analysis was not methodologically appropriate at this stage. Nevertheless, we explored causal relationships through Mendelian randomization (MR) analysis, which identified a significant causal link between intestinal aging and cardiovascular disease (Fig. 4e–f). This finding provides preliminary evidence for organ-specific mediation effects and supports the potential utility of our aging measures in future longitudinal causal inference studies.\n3. HIV-specific clinical status such as CD4 cell count and viral load were significanlty associated with the advancement of organismal age, all-organ age and conventional age, while no strong associations were not observed with organ-specific aging clocks. Apart from that CD4 cell count is a systemic marker, any other explanations?\nWe thank the reviewer for this insightful comment.\nHIV infection is characterized by chronic immune activation and systemic low-grade inflammation, even under effective viral suppression. These processes reshape circulating proteomes and accelerate global biological aging. Therefore, systemic clocks—such as organismal, all-organ, and conventional proteomic ages—are sensitive to HIV-related parameters not only on CD4 count (e.g., viral load, CD4/CD8 ratio), as they reflect whole-body immune and metabolic dysregulation. In contrast, organ-specific clocks (e.g., liver, intestine, brain, and artery) quantify local functional and metabolic aging inferred from tissue-enriched protein signatures. These organ-level processes are influenced more strongly by ART exposure, metabolic comorbidities, or tissue-specific stress than by HIV replication. Also, the included participants were using long term ART which inhibits viral replication but no viral transcription. These viral products may have systemic effects that are not organ specific. Consequently, weaker associations between organ-specific aging scores and HIV markers are biologically plausible and consistent with the expected hierarchy of systemic versus organ-level aging mechanisms.\n4. The potential impact of misclassification of biological age in terms of all-organ versus organ-specific ages on the associations should be more elaborated.\nWe thank the reviewer for raising this important point.\nIn our framework, the systemic (all-organ) aging score and the six organ-specific aging clocks were trained independently. Organ-specific clocks were developed using mutually exclusive organ-enriched proteins identified from GTEx data, ensuring that each model primarily reflects organ-level molecular features rather than a mixture of systemic signals. The all-organ aging score, in contrast, was constructed from all available organ specific proteins and represents overall aging.\nWe acknowledge that aging processes across organs are inherently interconnected through shared biological pathways such as inflammation, defective autophagy, and metabolic dysregulation (among others). Therefore, some degree of molecular overlap between systemic and organ-specific clocks is expected and should be interpreted as reflecting biological interdependence rather than methodological misclassification.\nImportantly, the observed associations between organ-specific clocks and corresponding diseases (e.g., arterial aging with cardiovascular disease, brain aging with CNS complications) were biologically coherent and organ-consistent, demonstrating that these models capture localized, organ-level functional and metabolic decline. In contrast, the all-organ (systemic) aging score showed the strongest association with overall mortality within two years, suggesting that it reflects the cumulative burden of systemic biological deterioration rather than organ-specific dysfunction. Together, these patterns indicate that organ-specific and systemic aging clocks capture distinct, yet complementary, dimensions of biological aging — one mapping localized organ vulnerability, and the other summarizing global organismal resilience and survival risk.\nMinors:\n1. The title of the manuscript stated as “… the opposite effects of chronic HIV infection and antiretroviral medication on organismal and organ-specific biological aging” seems not to fully reflect the main results, i.e., the causal relationship between biological age and comorbidities in PWH was ignored. Why?\nWe thank the reviewer for this comment.\nWe agree that the causal relationship between biological aging and comorbidities in PWH is an important aspect. We did not intend in any way to ignore the causal relationships between biological age and co-morbidities, we only did not want to make the title too long. However, if the editors would suggest to add that in the title, we would be happy to do it.\n2. The paper could be more succint by focusing on the main objectives of the study.\nWe appreciate the reviewer’s suggestion.\nWe have carefully reviewed the manuscript to ensure that it remains focused on the main objectives of the study. Our analysis follows a coherent and logically connected structure: first, we trained aging models using proteomic data; second, we validated the resulting aging scores against clinical outcomes to confirm their reliability and biological relevance; third, we examined how HIV-related factors influence systemic and organ-specific aging; and finally, we evaluated the impact of different ART regimens on these aging measures. Each of these steps is closely interlinked and collectively supports our central aim — to delineate the opposing effects of chronic HIV infection and antiretroviral therapy on biological aging. For this reason, we believe the current structure appropriately reflects the main focus of the study without unnecessary redundancy.\n3. Carefully reexamine text typoes, e.g., (1) Line 638-639: “All these processes are disturbed in PWH, even when virally suppressed because of effective c (36)” seems to be “All these processes are disturbed in PWH, even when virally suppressed because of effective cART (36)”; (2) Ref 15 and ref 20 are duplicated, and so are ref 12 and ref 24, etc.\nWe thank the reviewer for carefully pointing out the typographical errors and reference duplications. These issues have been corrected in the revised manuscript.\n\nReviewer #3 (Remarks to the Author):\nUnderstanding the determinants of biologic age has been important insights into the mechanisms the aging process. The newly described aging hallmarks, felt to represent the fundamental mechanisms of aging, have been studied by the metrics developed to measure biologic age. These measures, also referred as clocks, help to identify diverse factors which may increase or decrease the rate of aging of the organism, or individual. The goal of these insights is to develop interventions which may alter the impact of those factors adversely affecting biologic age. Among the many measures developed to measure biologic age, various epigenetic clocks based on DNA methylation are among the most accurate surrogates for determining biologic age of the whole organism. More recently assessment of plasma proteomics has broadened these investigations from the study of the whole organism to the level of organ-specific aging clocks.\nIn HIV, persistent low-grade immune activation, due to ongoing low level HIV replication despite effective control by antiretroviral regimens, leads to persistent chronic inflammation which is felt to be the driving force behind the increased rate of the premature development of common age-related conditions. Understanding the mechanisms behind the drivers of chronic inflammation utilizing these geroscience based principles has demonstrated increased biologic age occurring at the time of acute infection with HIV. This accelerated aging rate is significantly reduced with starting effective antiretroviral therapy but does not return to pre-infection levels and persists despite ongoing treatment. These investigations have led to deeper insight into the various environmental, personal and treatment related contributors to accelerated and accentuated aging of persons living with HIV.\nThis study developed whole-body and organ specific proteomic clocks and compared the aging-related measures to those derived from the current range of epigenetic clocks in a Iarge, diverse cohort of effectively treated older adults with HIV. To better understand the relevance of the new findings in this study, certain aspects require clarification. I will limit my comments to the Results and Discussion as the detailed description of the complex methods utilized is not my area of expertise.\n1-An important issue is that the 3 cohorts are very different in their demographics. The 2000HIV group (mean age – 50) is considerably younger than the 200HIV group (77), which has a similar age to the 200FG (73). Although no statistics are provided the SD for the 2000HIV is wide enough so that there may not be a statistical difference with the 200HIV cohort but a difference of 27 years (!) surely has some clinical significance which may impact on the results. This requires an explanation.\nWe thank the reviewer for this valuable comment.\nWe agree that the three cohorts differ in their chronological age distributions, but they overlap and we corrected in the analysis for these age differences. To mitigate any potential confounding, chronological age was included as a covariate in all regression analyses (as stated in line 232: “To assess these relationships, we employed linear models controlling for age, sex, and ethnicity”).\nFurthermore, to account for baseline age differences between cohorts, we used biological age gaps as our main outcome measures. Specifically, the age gaps in the 2000HIV cohort were adjusted by subtracting the mean age gap of the corresponding age group in the 200FG dataset (line 192).\nThese steps effectively minimize the impact of age distribution differences on our findings.\n2-The correlations between the proteomic and DNA clocks of the intestines was weak (line 440). This may be relevant as the gut is the main target of the initial exposure of an individual to HIV and also the least likely to return to pre-HIV structure despite effective and early treatment. This impacts on the “leakiness” of the GALT which has been shown to be an important source of ongoing chronic systemic inflammation.\nWe thank the reviewer for this insightful comment.\nRegarding the statement that “the correlations between the proteomic and DNA clocks of the intestines was weak,” our original description was: “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.” Here, the term “weaker” was used in a relative sense, compared with the higher correlations observed in other organs. However, the correlation between intestinal proteomic age and DNA methylation age (r = 0.40–0.46) should more appropriately be described as a moderate correlation, which still represents a biologically meaningful association.\nAs the reviewer noted, the gut plays a key role in HIV pathogenesis. Consistently, we observed significant associations between intestinal aging and gut translocation biomarkers (Fig. 4g–i), and we have discussed this finding in the Discussion section (lines 618). This supports the idea that intestinal aging may contribute to systemic inflammation through mechanisms such as microbial translocation.\nWith respect to the reviewer’s suggestion that the gut is the main target of early HIV exposure and the least likely organ to return to pre-HIV structure despite effective ART, we fully agree that this is a biologically plausible hypothesis. However, our current data are limited to plasma proteomic measures, which estimate intestinal aging at the molecular level but do not directly assess gut structural integrity. Therefore, we cannot confirm or refute structural restoration of the gut under HIV infection or ART based on our dataset, as our study was designed to model intestinal aging using plasma proteomic data rather than to assess gut histology directly.\n3-Furthermore, using the proteomic age scores, there were either non-significant or negative correlations between chronologic age and intestine and liver specific proteomic organ scores (lines 461-463). There were positive correlations with all-organ age gap, organismal age gao and brain age (lines 458-460). These differences and their impact on the reliability of using proteomic age as a measure of biologic age needs clarification. The utility of using proteomic age for a more focused understanding of biologic age with a goal to intervene in the future is the stated goal of these studies (lines 75-78).\nWe thank the reviewer for this valuable comment.\nFirst, we would like to clarify a possible misunderstanding. The reviewer noted that “there were either non-significant or negative correlations between chronologic age and intestine and liver specific proteomic organ scores.” As shown in Fig.S7, all proteomic ages calculated from the 2000HIV cohort were significantly positively correlated with chronological age, supporting the validity of proteomic clocks as proxies of biological age. Similarly, Fig. 2b demonstrates that all age gaps (including DNA methylation, all-organ, organismal, and organ-specific age gaps) were positively correlated with each other, although the correlation strengths varied. Since these two correlations describe different relationships—namely, between chronological age and age score, and between different age gaps—we assume that the “differences” mentioned by the reviewer refer to Fig. 2c, where conventional, organismal, and brain age gaps showed positive shifts, whereas intestine, liver, and artery age gaps appeared smaller or slightly negative.\nImportantly, a shift in the intercept (overall direction of the age gap) does not imply a change in correlation slope: as shown below, the liver and conventional age gaps are in fact moderately and significantly positively correlated (r≈0.4).\nRegarding model reliability, we acknowledge that several organ-specific clocks (particularly liver, lung, and pancreas) showed lower out-of-bag R² values (Table S2), reflecting the high inter-individual variability of organ-derived proteins in healthy individuals. Consequently, pancreas and lung clocks were excluded from downstream analyses. Nevertheless, the liver aging score showed strong positive associations with BMI, CAP score, and liver stiffness (FigS. 10 a,b,e), suggesting that although the liver clock may be less predictive of chronological age, it remains biologically meaningful, reflecting metabolic and functional aging of the liver under the influence of HIV infection and ART exposure.\nIn summary, systemic aging scores better capture overall aging, whereas organ-specific clocks reflect organ-level metabolic or functional states. These two dimensions are correlated but not necessarily concordant, reflecting the inherent heterogeneity of aging across organs.\nFinally, regarding the reviewer’s question about the usefulness of proteomic age, we would like to emphasize that in Results section 4, we analyzed the associations between cumulative ART exposure, ART combinations, and aging scores. Although these findings are preliminary, they indicate that proteomic aging measures have potential clinical value for ART-related monitoring. To clarify this point, we have added the following statement to the revised Discussion (lines 659): “Importantly, these aging measures may also help to guide ART optimization, especially for individuals at higher risk of treatment-related or aging-associated complications.”\n4-Similar inconsistent results occurred with strong correlations between the most recent HIV-RNA levels and chronologic age, organismal age and all-organ age clocks but not with organ-specific clocks (lines 496-498).\nWe thank the reviewer for this insightful comment.\nWe agree that HIV-specific clinical markers such as most recent HIV-RNA levels strong associations with organismal, all-organ, and conventional proteomic aging scores, but not with organ-specific clocks. We believe that rather than being inconsistent, this pattern reflects the different biological scales captured by systemic versus organ-specific proteomic aging models.\nHIV infection is characterized by chronic immune activation and systemic low-grade inflammation, even under effective viral suppression. These processes reshape circulating proteomes and accelerate global biological aging.\nTherefore, systemic clocks—such as organismal, all-organ, and conventional proteomic ages—are sensitive to HIV-related parameters (e.g., CD4 count, viral load, CD4/CD8 ratio), as they reflect whole-body immune and metabolic dysregulation. Also, these HIV specific parameters (CD4, viral load) are related to the size of the HIV reservoir, that may not replicate (because of ART). The viral transcripts may have systemic effects but not necessarily specific organ effects.\nIn contrast, organ-specific clocks (e.g., liver, intestine, brain, and artery) quantify local functional and metabolic aging inferred from tissue-enriched protein signatures. These organ-level processes are influenced more strongly by ART exposure, metabolic comorbidities, or tissue-specific stress than by HIV replication. Consequently, weaker associations between organ-specific aging scores and HIV markers are biologically plausible and consistent with the expected hierarchy of systemic versus organ-level aging mechanisms.\n5-The positive correlations described in lines 473-485 are reassuring but it would be useful to know if the DNA methylation-based age scores also showed similar correlations with the individual liver parameters.\nWe appreciate the reviewer’s request to examine whether DNA methylation–based age scores show similar correlations with liver parameters.\nIn our data, the liver-specific proteomic age gap was significantly associated with liver stiffness (LSM), steatosis (CAP), and BMI, whereas five DNA methylation age accelerations (Hannum, Horvath, PhenoAge, GrimAge, GrimAge2) showed no material correlations with these liver measures, please check the figure below:\nWe interpret this divergence as reflecting biological scale and tissue specificity rather than inconsistency. The liver proteomic clock is enriched for liver-derived, metabolism- and inflammation-responsive proteins, and thus is expected to track hepatic functional remodeling that underlies LSM/CAP variability in PWH. By contrast, DNA methylation clocks primarily capture systemic/organismal aging of circulating leukocytes and are not designed to resolve organ-specific phenotypes such as liver stiffness or steatosis. Consequently, the lack of correlation between blood DNAm age acceleration and liver imaging/biometric readouts is biologically plausible and consistent with a framework in which systemic and organ-level aging signatures are complementary but not interchangeable.\n6-The statement (line 501) that low-level viremia contributes to accelerated aging may well be true for biologic age but the clinical implications of an increased risk of adverse clinical events between persons with consistent undetectable viremia, intermittent HIV-RNA “blippers” and chronic low-level viremia (eg 50-1200 copies) remain uncertain. It would be relevant to use these measures to better understand the possible impact on biologic aging, and on clinical sequelae of HIV-RNA levels between, for eg 1-4 copies vs 5-19 copies, using ultra-sensitive assays.\nWe thank the reviewer for this thoughtful comment.\nWe agree that the clinical implications of different viremia states—such as consistent undetectable viremia, intermittent HIV-RNA “blips,” and chronic low-level viremia (50–1200 copies/mL)—remain uncertain. As our dataset is cross-sectional, it does not allow the analysis of longitudinal trajectories of aging in PWH, which we have acknowledged as a limitation (“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.” line 644).\nRegarding the suggestion to explore the potential impact of very low HIV-RNA levels (e.g., 1–4 vs 5–19 copies/mL) using ultra-sensitive assays, our measurements did not include values below 20 copies/mL. However, all viral load values above 20 copies/mL were available. We therefore stratified participants into three groups—<20 copies/mL, 20–40 copies/mL, and >40 copies/mL—and compared the organismal age gap across these categories (see figure below).\nEven at this resolution, the results showed a trend toward higher organismal age gap in the 20–40 copies/mL group compared with <20 copies/mL, although the difference did not reach statistical significance. Importantly, a significant increase in organismal age gap was observed in participants with >40 copies/mL relative to those <20 copies/mL. This finding supports the notion that increasing levels of viremia are associated with progressive biological age acceleration, consistent with the reviewer’s hypothesis, even within the low-level viremia range.\n7-The relative limited impact on biologic aging in elite controllers is reassuring but further investigation of such persons could be used to identify a subgroup who may progress and for whom earlier therapy is indicated.\nWe thank the reviewer for this valuable comment.\nWe fully agree that elite controllers (ECs) represent a heterogeneous group. As shown in Fig. 3d and FigS. 12, we observed an overall trend toward lower age gaps in ECs compared with normal progressors; however, some age measures, such as brain age, remained higher in ECs across chronological ages. This variability may partly reflect the limited number of EC samples in our cohort, which reduces the robustness of organ-specific comparisons. Notably, heterogeneity in aging scores was also observed among normal progressors, indicating that inter-individual variability in biological aging is a general feature rather than specific to ECs. We agree that future longitudinal studies integrating proteomic and epigenetic aging measures with clinical follow-up will be essential to identify potential “progressor” subgroups among ECs and to evaluate whether early ART initiation may benefit individuals at risk of accelerated biological aging. The challenge remains the difficulty to recruit a large cohort of elite controllers, as they represent merely 1-2% of all the people with HIV.\n8-Given the intense interest on cognitive impairment in HIV, the association of brain age advancement with CNS complications is very important. Unfortunately, Fig 4 does not provide any details of which CNS complications were assessed. This information should however be available for the Cohort and is relevant given the intense interest of HIV-related vs age-related degenerative cognitive decline and vascular risk-related cognitive decline in persons aging with HIV. There is a substantial literature on epigenetic changes with cognitive decline in HIV.\nWe thank the reviewer for this valuable comment.\nWe have now included this information in the revised Supplementary Table S12. Specifically, the cohort included individuals diagnosed with multiple sclerosis (n = 1), epilepsy (n = 28), Alzheimer’s disease (n = 1), Parkinson’s disease (n = 4), and other CNS disorders (n = 127).\nDue to the small sample sizes of individual CNS conditions, we were unable to detect statistically significant associations between specific diagnoses (e.g., epilepsy or Parkinson’s disease) and brain age gap. However, as shown in Fig. 4, the presence of any CNS complication was significantly associated with increased brain age gap in the 2000HIV cohort, suggesting that our brain-specific proteomic aging score is sensitive to clinically observable CNS abnormalities.\nGiven the limited diagnostic granularity in our current dataset, we cannot differentiate between distinct etiologies of cognitive decline (e.g., HIV-associated neurocognitive disorder, age-related neurodegeneration, or vascular cognitive impairment). Nevertheless, these findings support the biological relevance of our brain aging measure and justify further investigation.\n9-The preliminary data described herein on the interaction between various ARTs and biologic aging is clearly important. Again, the associations found with various proteomic and DNA clocks are divergent but these investigations are still early. If confirmed, then the potential clinical relevance of recommending cART regimens to regions with limited access to the latest ARVs because of access and affordability may also need to consider the long-term impact on biologic age. It is interesting that a possible “geroprotector” effect of cART has been demonstrated (Guaraldi OFID 2019) in that frailty appeared to decrease over time in > 50 yo PWH while increasing in PWH > 75.\nWe thank the reviewer for this important comment.\nAs shown in Fig. 5a, some antiretroviral drugs (e.g., TDF, FTC) appeared to show opposite associations with proteomic and DNA methylation age gaps. However, these are preliminary findings, as the initial analyses only considered cumulative exposure time of individual ART agents and chronological age. After further adjustment for potential confounders—specifically, cumulative exposure to other ART drugs—most associations with DNA methylation–based age gaps were no longer significant, while only RPV, 3TC, FTC, and TAF remained significantly associated with proteomic age gaps. Therefore, the apparent divergence between proteomic and epigenetic aging measures likely reflects differences in their biological targets rather than inconsistency or unreliability.\nWe agree that clarifying the long-term impact of ART on biological aging will require more sophisticated causal modeling and longitudinal follow-up studies in PWH. Furthermore, as the reviewer highlighted, these findings may have potential clinical relevance: if confirmed, biological aging measures could inform ART regimen selection not only for efficacy but also for minimizing aging-related risks, We have incorporated this point into the revised Discussion (lines 659).\nFinally, regarding the reviewer’s reference to a potential “geroprotective” effect of cART (Guaraldi et al., OFID 2019), our results align with this concept. Specifically, we observed that cumulative exposure to certain reverse transcriptase inhibitors (e.g., 3TC, FTC, TAF) was associated with decreased systemic and organ-specific age gaps in PWH, suggesting that some ART components may exert protective or decelerating effects on biological aging.\n10-There is no discussion of the possible utility of a proteomic determined biologic age and the commonly diagnosed geriatric syndrome of frailty. The impact of geriatrics syndromes on functional status maybe more important than counting comorbidities with limited short-term impact on function (eg stable minimal CKD, well controlled HTN etc versus falls, frailty, sarcopenia, polypharmacy etc). A proteomic profile of frailty has been described in older Italian adults (Landino, Geroscience 2021) and increased DNA methylation age was described in frail older adults with HIV (Sanchez-Conde, Epigenomics 2019).\nWe thank the reviewer for highlighting the relevance of frailty in the context of aging research. We agree that frailty represents an important clinical manifestation of aging. However, the frailty phenotype defined by Fried et al. (“unintentional weight loss,” “weakness,” “lack of energy,” “slowness,” and “sedentariness”) primarily captures functional aspects of aging rather than molecular or pathological processes. In our view, frailty-based and comorbidity-based assessments represent distinct but complementary perspectives of aging: frailty reflects the functional outcomes of aging, whereas comorbidities and mortality better capture its pathological burden and long-term health consequences. Therefore, the use of comorbidity and mortality outcomes in our study does not preclude the relevance of frailty, but rather emphasizes a different and equally important dimension of aging biology.\nBecause our available clinical data do not allow the construction of a standardized frailty index, we referred to frailty-associated proteins reported by Landino et al. (Geroscience, 2021), including B-type creatine kinase (CKB), C-X-C motif chemokine ligand 13 (CXCL13), and thrombospondin 2 (THBS2). As interleukin 1 alpha (IL1A) was not available in our dataset, we used IL1B as a related inflammatory control marker.\nThe correlation analyses showed that CKB, CXCL13, and THBS2 were weakly correlated (r ≈ 0.2) with chronological age, DNAm-based aging scores, and our systemic proteomic aging score (conventional age), while IL1B showed no correlation. Future studies to assess corrections between proteomic aging scores and frailty, in which this is calculated using the validated clinical scores, are warranted."}, {"round": 2, "reviewer_comments": {"reviewer_1": "Dear Authors,\nI would like to begin by expressing my sincere appreciation for the thoughtful and comprehensive responses to the first-round comments. The manuscript has meaningfully improved. The expanded Methods, updated Discussion sections, and additional sensitivity analyses—including ART stratification, ancestry-restricted analyses, and evaluation of low-level viremia—substantially enhance clarity and strengthen the robustness of the findings. I appreciate the considerable effort invested in these revisions.\nSeveral of my earlier methodological comments overlap conceptually with those raised by other reviewers—particularly regarding model validity, age-correction, and causal interpretation. While many of these concerns have been addressed, a few areas remain important considerations for future methodological refinement.\n1. Age-gap harmonization and the binning strategy\nThank you for clarifying the use of demographic conventions rather than data-driven binning. I would encourage the authors, in future work, to empirically demonstrate that results remain stable across alternative bin definitions. Such sensitivity analyses would support reproducibility and help ensure that demographic mismatches between reference and target populations do not unintentionally influence age-gap estimates.\n2. Causal ordering of comorbidities and aging; risks of under- versus over-adjustment\nThis point was shared across multiple reviewers. Modeling comorbidities as outcomes assumes a causal sequence from HIV → biological aging → comorbidities. While plausible, alternative causal structures are also reasonable. Many comorbidities—such as metabolic syndrome, hypertension, and liver disease—directly affect systemic proteomic signatures, which are central to the modeling framework used for age estimation, organ clocks, and ART associations. Both overadjustment (if comorbidities lie on the causal pathway) and underadjustment (if they confound proteomic patterns) remain realistic possibilities. This tension is especially relevant because clock instability is most concerning when the age–protein relationship is nonlinear, and mis-specification during age-gap estimation may propagate into downstream causal interpretation.\n3. Organ-specific clock reliability and protein-selection strategy\nThe revised manuscript provides clearer justification for the organ-specific clocks, including acknowledgment of modest R² values and the removal of pancreas and lung clocks. Nonetheless, I remain cautious about the strict protein-selection approach based solely on mutually exclusive organ-enrichment thresholds. While this enhances specificity, it may exclude biologically relevant shared proteins that contribute to inter-organ pathways, potentially limiting clock stability or interpretability. Future iterations may benefit from presenting model performance metrics across the age spectrum (e.g., age-stratified R² or MAE) to better characterize reliability and guide subsequent applications of these organ clocks.\n4. ART exposure and causal inference\nThis component shows significant improvement. The expanded analyses—including stratification by ART initiation era and first-line regimen, and explicit acknowledgment of the limitations inherent to cross-sectional causal inference—appropriately contextualize the ART-related findings within the constraints of the available data. I am grateful for the clear and careful revisions addressing this point.\nOverall Assessment\nThe authors have made substantial and meaningful improvements. The revisions enhance scientific rigor, transparency, and reproducibility. My remaining comments are not barriers to publication but rather highlight opportunities for continued methodological refinement in subsequent work—particularly regarding age-harmonization strategies, causal ordering of comorbidities, organ-clock reliability assessments, and protein-selection frameworks.\nI appreciate the authors’ responsiveness and believe this manuscript represents a valuable contribution to the field.", "reviewer_2": "The authors have provided satisfactory responses or explanations to my questions or confusions.", "reviewer_3": "I thank the authors for their careful and thorough replies to my comments.\nI have no further queries or comments."}, "author_response": "Response to Reviewers:\nWe thank the reviewers for their careful evaluation of our manuscript and for their constructive feedback throughout the review process.\nReviewer #1\nWe are grateful for the reviewer’s thoughtful assessment and positive evaluation of the revised manuscript. We appreciate the recognition that the revisions have substantially improved clarity, robustness, and transparency, and that the remaining comments highlight opportunities for future methodological refinement rather than barriers to publication. As suggested, considerations regarding alternative age-bin definitions, causal ordering of comorbidities, protein-selection strategies, and organ-specific clock reliability have been acknowledged as future directions. No additional changes were required for the current manuscript.\nReviewer #2\nWe thank the reviewer for confirming that all questions and concerns have been satisfactorily addressed.\nReviewer #3\nWe appreciate the reviewer’s careful review and confirmation that no further comments are required.\nWe thank the reviewers for their time and constructive input, which have significantly strengthened the manuscript."}]

Decision letter