Telomere Integrity, Epigenetic Aging, and Genetic Burden Shape Biological Aging Trajectories in Idiopathic Pulmonary Fibrosis

Telomere Integrity, Epigenetic Aging, and Genetic Burden Shape Biological Aging Trajectories in Idiopathic Pulmonary Fibrosis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Telomere Integrity, Epigenetic Aging, and Genetic Burden Shape Biological Aging Trajectories in Idiopathic Pulmonary Fibrosis Manuela Campisi, Luana Cannella, Filippo Liviero, Federico Tamiazzo, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7686711/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Idiopathic pulmonary fibrosis (IPF) is a paradigmatic aging-related lung disorder. We studied 101 treatment-naïve patients at diagnosis (T0) and a subgroup (n = 31) after one year of antifibrotic therapy (T1) including leukocyte telomere length (LTL), DNA methylation age (DNAmAge by Horvath, Levine/PhenoAge, and a 5-CpGs panel), age acceleration (AgeAcc), and 17 IPF-associated SNPs summarized as Effect Allele Count (EAC). Multiple regression models showed that at T1, LTL was independently predicted by baseline LTL (p = 0.0004) and treatment duration (p = 0.0056). ΔLTL increased in nintedanib- versus pirfenidone-treated patients (p = 0.0402) and with treatment duration (p = 0.0233). ΔAgeAcc decreased at follow-up (p = 0.0435), while was higher in males (p = 0.0204). Among epigenetic clocks, Levine’s PhenoAge was the most therapy-responsive (p < 0.0001), whereas the 5-CpGs panel show clinical scalability. Genotyping revealed enrichment of MUC5B , TERT , TOLLIP , DPP9 , and ATP11A variants, and higher EAC associated with lower FVC (p = 0.0136). These findings frame IPF as an aging-aligned disorder and support biomarker-informed precision medicine. Health sciences/Biomarkers Health sciences/Diseases Biological sciences/Genetics Health sciences/Medical research Biological sciences/Molecular biology Health sciences/Molecular medicine Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Aging represents a major global health challenge with profound societal and economic implications, driving chronic diseases, performance status decline, and premature mortality 1 . Yet, aging is far from uniform: while some individuals maintain physiological resilience, others undergo accelerated biological aging leading to early dysfunction. This heterogeneity highlights the limitations of chronological age as a predictor of health. Biological aging—the cumulative burden of molecular damage and systemic dysregulation—offers a more precise and clinically relevant measure 2 . Among its central mechanisms are telomere attrition and epigenetic drift 3 . Within this framework, Idiopathic Pulmonary Fibrosis (IPF) emerges as a paradigmatic model to study biological aging. IPF is a rare, chronic, and progressive interstitial lung disease primarily affecting older adults, characterized by fibrotic remodeling, dyspnea, and progressive loss of respiratory function 4 , 5 , affecting ~ 3 million people worldwide 5 . It is the most common form among all different interstitial lung diseases, with incidence increasing—partly due to population aging and possibly post-COVID-19 fibrotic sequelae 6 . The prognosis remains poor: median survival is ~ 3 years, and the disease imposes high healthcare costs 4 and a substantial psychosocial burden 7 . Although defined as “idiopathic,” the pathogenesis of IPF reflects the interplay of multiple risk factors. Aging represents a major determinant 8 , but cumulative exposures and environmental insults also play a key role. Robust evidence from systematic reviews and meta-analyses highlights occupational exposures—particularly to metal, wood, silica/stone, and agricultural or organic dusts, including livestock—as strong contributors to disease risk 9 – 12 . Environmental pollutants, notably air pollution, further exacerbate susceptibility 10 – 14 . Other exposures—including pesticides, hairdressing-related chemicals, bird breeding, molds, textile dust, and fire smoke—have also been reported, although their associations with IPF are less consistent across studies 10 , 11 . Lifestyle determinants such as cigarette smoking and dietary habits, together with genetic predisposition, add to this complex risk profile 12 . IPF is also the most frequent clinical manifestation of telomere-mediated disease, directly linking it to mitotic aging mechanisms 15 – 17 . Telomeres—repetitive DNA sequences protecting chromosomal integrity—shorten with each cell division. Telomere attrition, measurable as leukocyte telomere length (LTL), represents a recognized biomarker of biological age and systemic stress 18 , 19 . Genetic susceptibility further shapes IPF risk. Genome-wide association studies (GWAS) have identified variants in genes involved in telomere maintenance (TERT, TERC, OBFC1 20,21 ) , epithelial integrity (MUC5B, DSP) , immune regulation (DPP9) , and fibrotic remodeling (FAM13A, ATP11A) . Among these, the MUC5B rs35705950 promoter polymorphism confers a four-fold increased risk of IPF, particularly in Europeans, while shorter LTL predicts worse clinical outcomes 15 , 22 , 23 . Strikingly, epigenetic inheritance of shortened telomeres has been observed even without causal mutations, suggesting non-Mendelian transmission mechanisms in IPF 24 . Alongside telomere biology, epigenetic mechanisms—especially DNA methylation age (DNAmAge)—capture non-mitotic aspects of biological aging. DNAmAge, derived from methylation patterns at selected CpGs, correlates strongly with chronological age 25 , 26 . Accelerated epigenetic aging (AgeAcc)—when DNAmAge exceeds chronological age—has been linked to frailty, multimorbidity, and mortality 27 – 32 . However, no studies have yet explored DNAmAge and AgeAcc in IPF, representing a critical gap in knowledge. Therapeutically, nintedanib and pirfenidone remain the only approved antifibrotic agents. They slow functional decline but are not curative 33 , 34 . Nintedanib inhibits receptor tyrosine kinases that drive fibroblast activation 35 , whereas pirfenidone modulates TGF-β signaling and attenuates oxidative stress 36 , potentially limiting telomere shortening and delaying senescence. By intersecting with aging-related pathways, these agents reinforce the need to integrate biological aging biomarkers into IPF clinical monitoring and therapeutic development. Finally, the hallmark-based decomposition analyses—linking diseases to the 12 hallmarks of aging—place IPF among the most aging-aligned conditions 37 . IPF disrupts multiple hallmarks early, including telomere attrition, senescence, deregulated nutrient sensing, genomic instability, and mitochondrial dysfunction 38 , 39 . This makes IPF a unique model for investigating biological aging mechanisms and testing geroprotective strategies. To better understand the contribution of biological aging to IPF pathogenesis and prognosis, this study adopts a multimodal approach integrating clinical, molecular, and genetic data. Specifically, we aimed: To analyze LTL at diagnosis (T0) in a well-characterized cohort of treatment-naïve IPF patients with clinical outcomes, pulmonary function, hematochemical parameters and occupational exposures. To longitudinally assess biological aging biomarkers—LTL, DNAmAge, and AgeAcc—at the first year of treatment (T1). To genotype selected IPF patients for known susceptibility variants to support genetic stratification and refine disease risk assessment. To compare three DNA methylation-based epigenetic clocks (Horvath, Levine/PhenoAge, and a targeted 5-CpG panel) for consistency and feasibility, aiming to identify a minimal, robust CpG panel for future NGS-based clinical applications. Through this integrated analysis, we seek to elucidate the role of biomarkers of biological aging in IPF pathogenesis and progression, and explore their potential as predictive tools to inform precision medicine strategies. Results Characteristics of IPF patients Table 1 summarizes characteristics of IPF patients at enrollment (T0). The majority were male (83.2%) with a mean age of 69 ± 9 years and LTL at T0 averaged 1.27 ± 0.34 (T/S). Nonparametric linear regression analysis revealed no significant correlation between LTL and patient age at T0 (p = 0.780). Multiple linear regression analysis did not identify any significant correlations between LTL and various demographic and clinical variables (Supplementary Table 2). Table 1 Characteristics of IPF patients (n = 101) at diagnosis (T0). Variables Mean ± SD Number (%) Gender (male %) 84 (83) Age (years) 69.0 ± 9.00 BMI (kg/m 2 ) 26.0 ± 4.00 Pack years ((cigarettes/20)×years) 21.0 ± 23.0 Leucocytes (10 3 /ml) 7.94 ± 3.55 Neutrophils (10 3 /ml) 5.00 ± 2.00 % Neutrophils 58.0 ± 8.00 Lymphocytes (10 3 /ml) 2.30 ± 0.75 % Lymphocytes 30.0 ± 8.00 Monocytes (10 3 /ml) 0.67 ± 0.20 % Monocytes 8.52 ± 2.21 FVC (L) 2.61 ± 0.73 FVC (% pred) 76.0 ± 20.0 Therapy No therapy 4 (4) Nintedanib 39 (39) Pirfenidone 58 (57) LTL 1.27 ± 0.34 Acronyms: BMI = body mass index; FVC = forced vital capacity; FVC(%pred) = forced vital capacity (percentage of the predicted normal value) Characteristic and biological aging markers of follow-up Patient characteristics As reported in Table 2 , a total of 31 IPF patients underwent the follow-up assessment after a median time of 310 days of antifibrotics treatment (T1). The majority were male (77.4%), with a mean chronological age of 70.9 ± 7.10 years. Table 2 Main characteristics of n = 31 IPF patients at follow-up (T1). Variables Mean ± SD Number (%) Gender (Males %) 24 (77.4) Age (years) 70.9 ± 7.10 BMI (Kg/m 2 ) 26.9 ± 3.65 Pack-years ((cigarettes/20)×years) 22.3 ± 21.8 Leucocytes (10 3 /ml) 11.6 ± 14.3 Neutrophils (10 3 /ml) 4.92 ± 1.17 % Neutrophils 58.7 ± 7.08 Lymphocytes (10 3 /ml) 2.17 ± 0.65 % Lymphocytes 29.2 ± 6.47 Monocytes (10 3 /ml) 0.63 ± 0.18 % Monocytes 8.23 ± 1.90 Occupational risk factor 6 (19.35) Months from diagnosis to therapy 3.42 ± 7.09 Follow-up (days of treatment) 310.6 ± 110.4 Decline (FVC %pred T1- T0) -3.02 ± 7.35 LTL at beginning of follow-up (T0) 1.19 ± 0.26 LTL at end of follow-up (T1) 1.23 ± 0.27 DNAmAge at beginning of follow-up (T0) 60.42 ± 6.94 DNAmAge at end of follow-up (T1) 60.42 ± 6.44 AgeAcc at beginning of follow-up (T0) -9.55 ± 3.91 AgeAcc at end of follow-up (T1) -10.45 ± 4.22 Acronyms: BMI = body mass index; FVC(%pred) = forced vital capacity (percentage of the predicted normal value); LTL = leukocyte telomere length; DNAmAge = DNA methylation age; AgeAcc = Age acceleration. Leukocyte DNA Biological Aging: Telomere Length (LTL) At follow-up (T1), a slight, non-significant increase in LTL was observed compared with baseline (T0) (p = 0.458, Table 2 ). Multiple linear regression analysis (Table 3 ), including BMI, gender, pack-years, decline in lung function (FVC%pred. T1–T0), age at baseline (T0), baseline values of LTL, treatment duration, and occupational risk factors, indicated that LTL at T1 was significantly associated with both treatment duration (p = 0.0056) and baseline LTL (p = 0.0004). Table 3 Multiple regression analysis: the influence of BMI (Kg/m 2 ), gender (M = 1; F = 0), pack-years ((cigarettes/20) x years), lung function decline (FVC%pred T1-T0), age at beginning of follow-up T0 (years), LTL / DNAmAge / AgeAcc at (T0), duration of treatment (days), and occupational risk factor on LTL, DNAmAge and AgeAcc at follow up (T1). Variables b r t P-Value T1 LTL BMI b1 = -0.006804 r = -0.125815 t = -0.59485 P = 0.558 Gender (M = 1) b2 = 0.079917 r = 0.15917 t = 0.756214 P = 0.4575 Pack-years b3 = 0.000227 r = 0.023696 t = 0.111173 P = 0.9125 Decline (FVC %pred T1-T0) b4 = -0.004313 r = -0.150604 t = -0.714545 P = 0.4824 Age T0 b5 = 0.003384 r = 0.110268 t = 0.520375 P = 0.608 LTL T0 b6 = 0.642611 r = 0.66351 t = 4.159686 P = 0.0004 Duration of treatment (days) b7 = 0.001157 r = 0.547459 t = 3.068487 P = 0.0056 Occupation b8 = 0.209806 r = 0.367963 t = 1.856121 P = 0.0769 T1 DNAmAge Variables b r t P-Value BMI b1 = 0.153467 r = 0.292132 t = 1.432718 P = 0.166 Gender (M = 1) b2 = 2.083691 r = 0.401535 t = 2.056428 P = 0.0518 Pack-years b3 = 0.004116 r = 0.04564 t = 0.214294 P = 0.8323 Decline (FVC %pred T1-T0) b4 = -0.003256 r = -0.011911 t = -0.055871 P = 0.9559 Age T0 b5 = 0.06019 r = 0.124768 t = 0.589824 P = 0.5613 DNAmAge T0 b6 = 0.791651 r = 0.860308 t = 7.915657 P < 0.0001 Duration of treatment (days) b7 = -0.003525 r = -0.203398 t = -0.974389 P = 0.3405 Occupation b8 = -1.663566 r = -0.310627 t = -1.532792 P = 0.1396 T1 AgeAcc* Variables b r t P-Value BMI b1 = 0.184112 r = 0.324503 t = 1.645299 P = 0.1135 Gender (M = 1) b2 = 2.826082 r = 0.493077 t = 2.718107 P = 0.0123 Pack-years b3 = 0.000397 r = 0.004113 t = 0.019727 P = 0.9844 Decline (FVC %pred T1-T0) b4 = 0.052576 r = 0.191305 t = 0.93473 P = 0.3596 AgeAcc T0 b5 = 0.861377 r = 0.871537 t = 8.524344 P < 0.0001 Duration of treatment (days) b6 = -0.004858 r = -0.259676 t = -1.2896 P = 0.21 Occupation b7 = -0.602101 r = -0.123908 t = -0.598855 P = 0.5551 *The variable Age is not considered for AgeAcc because of its own definition. Bold character is displayed only for significant values. Acronyms: BMI = body mass index; FVC(%pred) = forced vital capacity (percentage of the predicted normal value); LTL = leukocyte telomere length; DNAmAge = DNA methylation age; AgeAcc = Age acceleration. Further regression analysis on longitudinal changes (Table 4 ) including BMI, gender, antifibrotic therapy (0 = pirfenidone; 1 = nintedanib), pack-years, age at baseline, lung function decline (FVC%pred T1-T0), and duration of treatment (days) on Δ LTL (LTL T1-T0) revealed that the increase in ΔLTL was significantly greater in patients treated with nintedanib compared with those receiving pirfenidone (p = 0.0402) and showed a positive association with treatment duration (p = 0.0233). Table 4 Multiple regression analysis: the influence of BMI (Kg/m 2 ), gender (M = 1; F = 0), antifibrotic therapy (0 = pirfenidone; 1 = nintedanib), pack-years ((cigarettes/20)×years), age at T0 (years), lung function decline (FVC%pred T1-T0), and duration of treatment (days) on Δ LTL (LTL T1-T0), ΔDNAmAge (DNAmAge T1-T0), ΔAgeAcc (AgeAcc T1-T0). Variables b r t P-Value ΔLTL BMI b1 = -0.017549 r = -0.301378 t = -1.515836 P = 0.1432 Gender (M = 1) b2 = 0.026736 r = 0.050506 t = 0.242528 P = 0.8105 Therapy (P = 0; N = 1) b3 = 0.197156 r = 0.413044 t = 2.175101 P = 0.0402 Pack-years b4 = -0.000796 r = -0.070951 t = -0.341129 P = 0.7361 Age T0 b5 = -0.003064 r = -0.106249 t = -0.512451 P = 0.6132 Decline (FVC %pred T1-T0) b6 = -0.011056 r = -0.347259 t = -1.775912 P = 0.089 Duration of treatment (days) b7 = 0.00093 r = 0.451936 t = 2.429691 P = 0.0233 ΔDNAmAge Variables b r t P-Value BMI b1 = 0.188671 r = 0.319576 t = 1.617452 P = 0.1194 Gender (M = 1) b2 = 2.125313 r = 0.370654 t = 1.913919 P = 0.0682 Therapy (P = 0; N = 1) b3 = 0.450862 r = 0.102271 t = 0.493061 P = 0.6266 Pack-years b4 = -0.006255 r = -0.055343 t = -0.265824 P = 0.7927 Age T0 b5 = -0.067492 r = -0.22748 t = -1.120328 P = 0.2741 Decline (FVC %pred T1-T0) b6 = 0.018415 r = 0.060917 t = 0.292693 P = 0.7724 Duration of treatment (days) b7 = -0.003082 r = -0.164296 t = -0.798792 P = 0.4326 ΔAgeAcc* Variables b r t P-Value BMI b1 = 0.193313 r = 0.326992 t = 1.69511 P = 0.103 Gender (M = 1) b2 = 2.621248 r = 0.45209 t = 2.483017 P = 0.0204 Therapy (P = 0; N = 1) b3 = 0.411273 r = 0.093707 t = 0.461098 P = 0.6489 Pack-years b4 = -0.006248 r = -0.055237 t = -0.27102 P = 0.7887 Decline (FVC %pred T1-T0) b5 = 0.043846 r = 0.152086 t = 0.753836 P = 0.4583 Duration of treatment (days) b6 = -0.005322 r = -0.277333 t = -1.414121 P = 0.1702 *The variable Age is not considered for AgeAcc because of its own definition. Bold character is displayed only for significant values. Acronyms: BMI = body mass index; FVC(%pred) = forced vital capacity (percentage of the predicted normal value); LTL = leukocyte telomere length; DNAmAge = DNA methylation age; AgeAcc = Age acceleration. Leukocyte DNA Biological Aging: Epigenetic Age (DNAmAge) and Age Acceleration (AgeAcc) At T1, a significant reduction in epigenetic age acceleration was detected compared with baseline (–10.45 ± 4.22 at T1 vs − 9.55 ± 3.91 at T0, p = 0.0435; Fig. 2 ). The unadjusted DNAmAge (i.e., not corrected for chronological age) showed no difference between T1 and T0 (p > 0.9999). The multiple linear regression analysis (Table 3 ) demonstrated that both DNAmAge and AgeAcc at T1 were higher in males (p = 0.0518 and p = 0.0123, respectively) and were strongly determined by their baseline values (p < 0.0001). No other covariates reached statistical significance. When analyzing longitudinal changes by multiple regression analysis (Table 4 ), the increase in AgeAcc (ΔAgeAcc = AgeAcc T1–T0) was significantly associated with male sex (p = 0.0204), whereas no significant predictors emerged for ΔDNAmAge. DNA methylation analysis of CpG sites within the five genes included in the Qiagen DNAmAge estimation panel (ELOVL2, C1orf132, KLF14, TRIM59, and FHL2) revealed a significant reduction in methylation at C1orf132 after treatment (p = 0.0044; Fig. 3 ). Correlation between LTL and DNAmAge Simple linear regression analysis confirmed the negative correlation between the reduction in non-mitotic DNAmAge and the increase in LTL (ΔLTL = LTL T1-T0) (p = 0.0115), as illustrated in Fig. 4 . Comparison of Qiagen system and Illumina Methylome Epic Array (Horvath and Levine) methods As depicted in Fig. 5 A, a significant positive correlation was confirmed between chronological age and DNAmAge estimated by all three independent methodologies — Horvath (p < 0.0001), Levine (p = 0.0007), and Qiagen (p < 0.0001) — in the control group. A similar trend was observed in the IPF patient’s subgroup at both baseline (T0) and follow-up (T1), as reported in Figs. 5 B and 5 C (p < 0.0001 for all methods). The discordance between number of samples analyzed by Qiagen method in Table 2 and those analyzed by all three independent methodologies in Figs. 4 has to be ascribed to the insufficient amount of DNA available to perform further the analyses of DNAmAge for four samples. As shown in Fig. 6 , a significant post-treatment reduction in DNAmAge was observed using the Levine clock (p < 0.0001), while no changes were detected by Horvath and Qiagen methods. Genotyping Identification of genetic variants associated with IPF Based on a literature review, 17 SNPs previously associated with IPF and included in the Infinium Global Screening Array (GSA) were selected for analysis. Supplementary Table 3 reports the rs and IDs of the 17 variants, the corresponding risk alleles (EA) according to the analyzed literature, and the respective references. The allelic frequencies (%) of the EA were also determined for each of the 17 SNPs in the 48 genotyped IPF patients and their distribution is presented in Fig. 7. In our cohort, EA frequencies ranged from a maximum of 84.3% for rs1981997 ( MAPT ) to a minimum of 1% for rs3893252 ( DAZAP1 ). Among the most represented alleles, rs1981997 ( MAPT , 84.3%) and rs5743890 ( TOLLIP , 82.2%) showed high prevalence but were relatively comparable to reference datasets, whereas rs1278769 ( ATP11A , 83.3%) demonstrated a clearly elevated frequency compared to both European and worldwide populations. Conversely, rs3893252 ( DAZAP1 ) and rs4387287 ( OBFC1 ) displayed markedly reduced frequencies in Italian IPF patients. As shown in Table 5, the EA frequencies observed in the 48 Italian IPF patients were compared with those reported for worldwide and European populations in the dbSNP database (NCBI). Using a threshold of frequency differences > 0.05, 7 out of the 17 investigated alleles (41%) were more frequent in the Italian cohort, whereas the remaining variants displayed lower frequencies. Among these, five SNPs—rs12610495 ( DPP9 ), rs1278769 ( ATP11A ), rs35705950 ( MUC5B ), rs5743894 ( TOLLIP ), and rs7725218 ( TERT )—showed the strongest enrichment, with EA frequencies consistently higher in the Italian IPF cohort compared with both worldwide and European reference datasets. Table 5 Comparison of allele effect frequencies in 48 Italian IPF patients with those in the worldwide and European populations. The final two columns (D1 and D2) indicate corresponding increases (↑), decreases (↓) or no change (=). rsID Gene Risk allele Effect allele frequency (Worldwide) Effect allele frequency (EUR) Effect allele frequency (48 Italian IPF) D1 (Worldwide vs Italian IPF)* D2 (EUR vs Italian IPF)* rs11191865 OBFC1 A 0.4716 0.4989 0.5417 ↑ = rs12610495 DPP9 G 0.2705 0.2837 0.4062 ↑ ↑ rs1278769 ATP11A G 0.7569 0.7567 0.8333 ↑ ↑ rs1981997 MAPT G 0.8049 0.7865 0.8437 = ↑ rs2034650 _ / Intronic A 0.5105 0.5141 0.5208 = = rs2076295 DSP G 0.4480 0.4494 0.4791 = = rs2609255 FAM13A G 0.2350 0.2237 0.1563 ↓ ↓ rs2736100 TERT A 0.4938 0.4903 0.5208 = = rs35705950 MUC5B T 0.0264 0.0352 0.4583 ↑ ↑ rs4387287 OBFC1 A 0.1890 0.1749 0.1250 ↓ = rs4727443 Intergenic C 0.6096 0.6045 0.5313 ↓ ↓ rs5743890 TOLLIP T 0.8634 0.8523 0.8229 = = rs5743894 TOLLIP C 0.0935 0.1211 0.3020 ↑ ↑ rs6793295 LRRC34 C 0.2810 0.2615 0.2604 = = rs7725218 TERT G 0.6388 0.6422 0.6979 ↑ ↑ rs7934606 MUC2 C 0.6371 0.5975 0.4583 ↓ ↓ rs3893252 DAZAP1 T 0.0452 0.0095 0.0104 = = * Threshold of frequency differences > 0.05 to distinguish between increase (↑), decrease (↓) or no change (=). Table 6 Multiple regression analysis: the influence of age (years), BMI (Kg/m2), gender (M = 1; F = 0), LTL at diagnosis and total effect alleles (EAs) on Forced Vital Capacity (FVC). Variables b r t P-Value Forced vital Capacity (FVC). Age (years) -0.018713 -0.238295 -1.590134 0.1193 BMI (Kg/m2) -0.01139 -0.065749 -0.427024 0.6715 Gender (m = 1; F = 0) 0.917286 0.52177 3.963787 0.0003 Total EAs -0.085527 -0.369452 -2.576621 0.0136 LTL (T/S) 0.398756 0.210682 1.396729 0.1698 Acronyms: BMI = body mass index; FVC = forced vital capacity; LTL = leukocyte telomere length. Effect Allele Count (EAC) and Clinical Correlates The cumulative distribution of EA per individual revealed considerable inter-individual variability within the IPF cohort. As shown in Fig. 8, the number of EAs per subject ranged from 10 to 22, with a cohort mean of 16 EAs, underscoring the broad genetic heterogeneity among patients. A multiple regression analysis (Table 7) was performed to assess the influence of age, BMI, gender, LTL at diagnosis, and total EAs on forced vital capacity (FVC). The results demonstrated that the total number of EAs was a significant determinant of decreased FVC (p = 0.0136), together with gender, with female patients showing a stronger association with reduced FVC (p = 0.0003). Discussion In this study, we conducted a comprehensive, multidimensional investigation into IPF by integrating telomere biology, epigenetic age acceleration metrics, and genetic susceptibility profiling in a well-characterized Italian cohort. Our approach provides new insights into the molecular heterogeneity of IPF and its modulation by antifibrotic treatment over time. Our cohort—predominantly elderly men (mean age ~ 69 years), as expected for IPF 40 —showed no baseline association between chronological age and LTL on nonparametric and multivariable analyses, likely reflecting the narrow age range. Conventional covariates (age, BMI, smoking) were not independent determinants of baseline LTL. Importantly, IPF patients had shorter LTL than age- and gender- matched controls from our laboratory (Table 4 supplementary), consistent with landmark studies 15 , 23 , 41 . These data reinforce telomere attrition as a central IPF hallmark 23 , 42 , implicating premature senescence and defective alveolar repair mechanisms as key contributors to disease onset and progression 39 . Clinically, shorter baseline LTL correlated with lower reduced diffusing capacity of the lung for carbon monoxide (DLCO) 23 , supporting LTL as a baseline biomarker of disease severity and early prognostic stratification 23 . At follow-up (T1), we performed a comprehensive longitudinal assessment of biological aging biomarkers—including LTL, DNAmAge, and AgeAcc—to investigate their associations with clinical outcomes, treatment exposure, and demographic or occupational factors. LTL remained generally stable or exhibited a slight, non-significant increase compared with baseline. However, multiple regression analyses revealed that LTL at T1 was significantly influenced by both treatment duration and baseline LTL values. Notably, patients receiving nintedanib showed a greater increase in LTL compared with those treated with pirfenidone, and this effect was positively correlated with treatment duration. These findings suggest that nintedanib may exert a protective effect on telomere maintenance, an intriguing hypothesis that warrants further mechanistic exploration. Nintedanib, a tyrosine kinase inhibitor, targets multiple growth factor receptors involved in fibrotic processes, including the platelet-derived growth factor receptor (PDGFR), fibroblast growth factor receptor (FGFR), and vascular endothelial growth factor receptor (VEGFR) 35 . Its antifibrotic activity is primarily mediated through inhibition of fibroblast proliferation, differentiation, and migration, as well as by reducing extracellular matrix (ECM) deposition. Moreover, nintedanib has been reported to attenuate vascular remodeling 43 . By dampening fibroblast activation and turnover, nintedanib may mitigate the excessive cellular stress and replicative demand typical of fibrotic tissues, thereby slowing telomere attrition and contributing to the preservation of telomere length in patients with IPF. Recent preclinical evidence further suggests that nintedanib may exert senolytic properties by promoting apoptosis of senescent fibroblasts through inhibition of the STAT3 pathway 44 . Taken together, these data raise the possibility that the protective effects of nintedanib on telomere dynamics may extend beyond lung fibroblasts to circulating leukocytes, where we measured LTL in the present study. In line with our findings, these observations support a broader role for nintedanib in modulating aging-related mechanisms beyond fibrosis control and highlight LTL as a potential biomarker not only of disease progression 41 but also of treatment response 45 . Furthermore, emerging therapeutic strategies aimed at preserving telomere integrity—such as sex hormone–based telomerase activation 46 or intercellular telomere transfer via extracellular vesicles 47 —underscore the relevance of LTL monitoring within the framework of personalized medicine for IPF. Collectively, our findings open new avenues for precision medicine and aging modulation in fibrotic lung disease. In parallel, we observed a significant reduction in epigenetic age acceleration (AgeAcc) following antifibrotic therapy, while unadjusted DNAmAge remained largely unchanged. This pattern suggests that antifibrotic treatment may contribute to a deceleration of biological aging in IPF patients, potentially through mechanisms involving epigenetic remodelling rather than a direct resetting of the epigenetic clock. One plausible explanation is that therapy-induced attenuation of chronic oxidative and inflammatory stress—well-established drivers of accelerated epigenetic aging 48 —could underlie this effect. Interestingly, we also found that male patients exhibited higher DNAmAge and AgeAcc values at T1 compared with females, suggesting potential sex-specific trajectories in biological aging among individuals with IPF. This finding is consistent with previous evidence, including our own work in pauci- and asymptomatic COVID-19 healthcare workers 49 and the study by Gallego-Fabrega et al. 50 in patients with ischemic stroke, and Oblak et al. 29 , which together support the broader concept of the male-female health–survival paradox. According to this paradigm, males typically experience faster biological aging, despite often presenting lower disability rates compared with females 51 . Moreover, recent findings by Tondo et al. 52 reinforce the relevance of sex differences in IPF, demonstrating that male sex significantly influences disease progression and long-term treatment response, with men showing poorer survival rates and reduced therapy tolerance compared with women. Taken together, these observations highlight the necessity of incorporating sex-specific considerations into clinical management and therapeutic decision-making for IPF. Furthermore, site-specific DNA methylation analysis of the five CpG loci used to compute DNAmAge (ELOVL2, C1orf132, KLF14, TRIM59, FHL2) revealed a significant hypomethylation at the C1orf132 locus (chromosome 1 open reading frame 132) following antifibrotic treatment. C1orf132, also known as MIR29B2C, encodes a non-coding RNA located at 1q32.2 and is recognized as one of the most robust epigenetic predictors of chronological age. Consistent with previous studies 26 , 53 , 54 , methylation at C1orf132 typically declines with aging, and such hypomethylation is thought to enhance transcriptional activity, potentially modulating downstream regulatory pathways involving age-sensitive microRNAs. Interestingly, in specific pathological contexts—such as following hematopoietic stem cell transplantation (HSCT)—C1orf132 has instead been reported as hypermethylated, where it may influence graft function 55 . In our study, the observed therapy-associated hypomethylation at this locus suggests a potential remodeling of age-related epigenetic pathways, possibly impacting microRNA-mediated mechanisms of aging and tissue repair. Finally, we observed a significant inverse correlation between ΔLTL and ΔDNAmAge, supporting the existence of a shared biological aging axis that integrates telomeric and epigenetic mechanisms. Several investigations 56 , 57 , including our previous study 58 , have reported similar associations in blood-based measurements, further reinforcing the biological plausibility of this relationship. This convergence strengthens the potential utility of LTL and DNAmAge as complementary, integrative biomarkers for monitoring disease progression and assessing therapeutic efficacy in IPF. To better understand the genetic underpinnings of IPF, we genotyped a subgroup of 48 patients for 17 SNPs previously identified in large GWAS 20 , 21 , 59 – 63 . Our findings reveal a complex genetic landscape defined by distinct allelic enrichments and substantial inter-individual variability, offering novel insights into mechanisms of IPF susceptibility and disease progression. Among the 17 SNPs analyzed, seven variants (41%) displayed allele frequency differences greater than 5% compared with both European and global reference datasets. Five of these — rs12610495 ( DPP9 ), rs1278769 ( ATP11A ), rs35705950 ( MUC5B ), rs5743894 ( TOLLIP ), and rs7725218 ( TERT ) — exhibited a marked enrichment within our patients, confirming their strong contribution to IPF risk. Among the analyzed genetic variants, the MUC5B promoter polymorphism rs35705950 emerged as the most prevalent and clinically significant, in agreement with previous reports demonstrating its strong association with IPF in European cohorts 20 , 60 , 64 . MUC5B encodes a gel-forming mucin critical for mucociliary clearance (MCC) and respiratory host defense 65 , 66 . Individuals carrying the rs35705950 T-allele exhibit markedly elevated MUC5B expression in the airways, which may initially enhance mucosal barrier protection. However, excessive mucin production ultimately becomes maladaptive: it impairs MCC efficiency, promotes mucus retention, and hampers the clearance of inhaled particles and pathogens. These changes lead to repetitive epithelial injury and persistent activation of alveolar epithelial cells (AECs), thereby triggering profibrotic remodeling pathway 67 . Furthermore, hyperactivation of MUC5B expression has been linked to the induction of endoplasmic reticulum (ER) stress, unresolved inflammation, and epithelial cell death, which together contribute to the progressive deposition of extracellular matrix and fibrosis 68 – 70 . Intriguingly, while the rs35705950 T-allele confers a significantly increased susceptibility to IPF, several studies have reported an association with improved survival outcomes, suggesting a dual and context-dependent role of this variant in disease onset and prognosis 62 , 71 – 74 . One possible explanation is that enhanced mucociliary defense mechanisms may delay disease progression in early stages, even as chronic overproduction of MUC5B drives fibrotic remodeling over time. This paradox underscores the complex interplay between host defense pathways and fibrogenesis in IPF pathobiology. Enriched frequencies of DPP9 , ATP11A , and TOLLIP variants in our cohort further underscore the involvement of complementary biological pathways in IPF pathogenesis. DPP9 encodes dipeptidyl peptidase 9, a cytoplasmic serine protease expressed in epithelial tissues and implicated in cell adhesion, migration and apoptosis 75 . Although overall DPP9 expression appears only nominally elevated in IPF lungs compared with healthy controls, the rs12610495 variant has not been directly associated with altered gene expression 20 . This suggests the involvement of indirect, as yet unidentified molecular mechanisms that may disrupt epithelial integrity, affect cell–cell adhesion, and contribute to aberrant tissue remodeling in IPF. Similarly, the ATP11A gene (ATPase phospholipid transporting 11A), identified as a potential susceptibility locus for IPF, encodes an ATP-binding cassette (ABC) transporter belonging to the P4-ATPase family 76 . This transmembrane ATPase is predicted to regulate phospholipid translocation and ion transport, potentially affecting intracellular calcium homeostasis and activating downstream Rho GTPase–mediated signaling pathways 76 , 77 . In line with previous findings of Fingerlin and colleagues 20 , we observed a high frequency of the rs1278769 variant in our Italian IPF cohort, reinforcing its role as a genetic marker of susceptibility. Interestingly, ATP11A expression does not differ significantly between cases and controls or across genotypes, suggesting that the contribution of this locus may arise from context-dependent regulatory effects rather than direct transcriptional changes 20 . TOLLIP (Toll-interacting protein), a multifunctional intracellular protein, plays a central role in modulating innate immunity by tempering pro-inflammatory signaling 78 , 79 and promoting autophagy 80 . TOLLIP is expressed in alveolar type II cells, macrophages, and basal epithelial cells, where it protects against oxidative stress, mitochondrial dysfunction, and apoptosis induced by bleomycin and other insults 81 . This corroborates the hypothesis that TOLLIP protects several cell populations against fibrosis and oxidative damage, explaining the link between TOLLIP SNPs and worse outcomes in IPF. Reduced expression of TOLLIP , equivalent to a 50% as observed in carriers of the rs5743894 minor allele 62 , has been associated with increased IPF susceptibility and, in some reports, with poorer clinical outcomes 62 , 82 and increased mortality 62 . Our findings, showing enrichment of this variant, support its potential contribution to impaired epithelial defense and heightened fibrosis risk. The enrichment of TERT (Telomerase Reverse Transcriptase) variants in our patients is also consistent with previous report linking telomere attrition to IPF pathogenesis 83 . Dysfunctional telomere maintenance promotes premature epithelial senescence and impaired regeneration, reinforcing the importance of telomere biology as a driver of fibrosis 76 , 84 . In our Italian IPF cohort, we observed a marked enrichment of the TERT rs7725218 variant, consistent with findings reported by Allen R. et al. 61 . However, to date, no other studies have specifically focused on this variant. The distribution of risk alleles revealed striking genetic heterogeneity within our cohort, with the total number of EA per individual ranging from 10 to 22 (mean = 16), underscoring the polygenic nature of IPF. Notably, patients carrying a higher burden of EA tended to exhibit more severe phenotypes. Multiple regression analysis identified the total number of EA as an independent predictor of reduced FVC, indicating a cumulative genetic effect on disease severity. Interestingly, female gender emerged as an additional independent predictor of lower FVC, suggesting possible sex-specific biological influences on disease progression, potentially mediated by hormonal, metabolic, or immune factors. FVC, which is physiologically higher in men than in women, represents the primary clinical parameter used to assess pulmonary function in IPF. Its decline occurs at a rate nearly ten times faster in IPF patients compared with healthy individuals, making it a robust marker of disease severity and survival 85 , 86 . Moreover, change in FVC is currently established as the primary endpoint in clinical trials evaluating pharmacological interventions 33 , 34 , 87 , underscoring its central role in monitoring disease progression. These findings support the feasibility of SNP-based patient stratification to identify individuals at higher genetic risk for IPF and enable risk-adapted surveillance strategies. This approach may also help explain part of the clinical heterogeneity observed among patients, including variability in disease course and treatment response. Recent advances in aging biology provide a unifying lens for our IPF findings. Using hallmark decomposition 37 , cohort SNPs map to core aging axes—telomere maintenance/genomic stability ( TERT, TERC, RTEL1, OBFC1) , intercellular/immune–senescence signaling ( DPP9, FAM13A, MUC5B ), and nutrient sensing (DEPTOR)—supporting IPF as an aging-aligned disease and motivating biomarker-based stratification. In parallel, multiple phase II–III programs targeting these hallmarks (senolytics, telomere-directed and epigenetic modulators, metabolic/kinase inhibitors) aim to slow progression and confer geroprotection. Our integrative framework—combining LTL, epigenetic age, and polygenic burden—enables stratification by biological aging profile and prospective testing of hallmark-targeted interventions with measurable effects on biological age and clinical outcomes, positioning IPF at the interface of aging science and precision medicine. In this study, we compared two methodologies for epigenetic age estimation—a targeted pyrosequencing assay of five well-established CpG sites (ELOVL2, FHL2, KLF14, PENK, TRIM59) 88 , 89 and the Illumina MethylationEPIC BeadChip interrogating over 850,000 CpGs—using two independent clocks: Horvath 25 and Levine (PhenoAge) 90 . Across healthy controls and IPF patients, both before and after antifibrotic therapy, DNAmAge estimates were strongly correlated with chronological age for all methods, supporting the robustness and reproducibility of these approaches. However, only the Levine clock detected a significant post-treatment reduction in the unadjusted DNAmAge, suggesting that DNA PhenoAge is more sensitive to therapy-induced biological aging modulation. This greater responsiveness likely stems from its design: unlike other clocks, Levine’s algorithm integrates CpGs associated not only with chronological aging but also with mortality and disease risk 90 . Validation data from the NHANES IV cohort showed that a 1-year increase in DNA PhenoAge corresponds to a 9% rise in all-cause mortality and chronic respiratory disease mortality, underscoring its clinical predictive power 90 . Therefore, the observed deceleration of PhenoAge following antifibrotic therapy may reflect an epigenetic benefit of treatment, reinforcing its potential as a sensitive biomarker for monitoring therapeutic efficacy and predicting long-term outcomes in IPF. From a translational perspective, while the Illumina EPIC array offers broader genomic coverage and high-resolution insights, it requires higher costs, greater DNA input, and complex bioinformatic workflows. In contrast, the 5-CpG pyrosequencing panel developed by Zbieć-Piekarska et al. 53 and optimized by Pavanello et al. 88 provides excellent feasibility, reproducibility, and minimal DNA requirements, allowing for rapid, cost-effective analyses suitable for routine clinical implementation. Notably, despite its simplified design, this targeted assay remained effective in detecting biological aging acceleration in IPF patients, further supporting its translational applicability. Together, these findings highlight the Levine DNA PhenoAge clock as a clinically relevant, therapy-sensitive biomarker, while the 5-CpG pyrosequencing method emerges as a pragmatic and scalable tool for integrating epigenetic aging assessments into personalized medicine strategies for IPF. This study has limitations—including sample size for patients in follow-up, single-country enrollment, follow-up duration, and platform heterogeneity—that warrant caution and independent validation. Prospective multicenter trials should embed LTL and DNAmAge endpoints to test whether biomarker improvements translate into slower functional decline and better survival, and mechanistic work should dissect telomere/epigenetic pathways. Such efforts will determine whether aging-informed, sex-aware, and genotype-guided strategies can operationalize precision medicine in IPF. A well-phenotyped Italian cohort with longitudinal sampling; harmonized acquisition of three aging domains (telomere, epigenetic clocks, genetics) in the same individuals; replication of established IPF signals (e.g., MUC5B ) alongside a polygenic burden–FVC association; convergence of LTL and DNAmAge dynamics (ΔLTL↔ΔDNAmAge); and demonstration that PhenoAge is therapy-responsive while a low-input 5-CpG assay offers pragmatic clinical scalability. Together, these features enhance internal validity and translational relevance. This work integrates telomere biology (LTL), epigenetic aging (DNAmAge/AgeAcc), and genetic susceptibility to provide a cohesive view of IPF as an aging-aligned disorder. At diagnosis, shorter LTL aligns with worse gas-exchange capacity, supporting LTL as a baseline severity marker. Longitudinally, antifibrotic therapy—particularly nintedanib—is associated with stabilization or modest gains in LTL and a reduction in AgeAcc, consistent with treatment-linked remodeling of aging pathways. The inverse coupling between ΔLTL and ΔDNAmAge indicates a shared biological aging axis. On the genetic side, enrichment of MUC5B, TOLLIP, TERT, ATP11A , and DPP9 variants and the cumulative effect-allele burden relate to lower FVC, supporting polygenic risk as a driver of clinical heterogeneity. Collectively, these findings support a precision framework in which LTL, therapy-responsive clocks (e.g., PhenoAge), and genotype are combined to refine risk, tailor surveillance, and track therapeutic benefit in IPF. METHODS Study Population The study population comprised n = 101 individuals diagnosed with Idiopathic Pulmonary Fibrosis (IPF) according to the ATS/ERS/JRS/ALAT guidelines 91 . We conducted a longitudinal retrospective cohort study comprising 101 consecutive patients affected by IPF—according to ATS/ERS/JRS/ALAT guidelines 91 enrolled from July 2014 and followed- up in our centre. The study was ethically approved by the local Ethics Committee - University of Padova, in accordance with principles of the Helsinki Declaration (practice number 3843/AO/16). All patients recruited, before starting treatment with antifibrotic drugs (nintedanib and pirfenidone), provided their written informed consent to participate to this study. Pulmonary Function Tests (PFTs) including Forced Vital Capacity (FVC% predicted) were recorded at diagnosis and during follow- up. These measurements were performed in accordance with the recommendations of the American Thoracic Society/European Respiratory Society 5 , 91 . At enrolment (T0), characteristics of the patients including demographic data (age, gender), lifetime history of smoking (pack-years) and body mass index (BMI), age at diagnosis and antifibrotic therapy were acquired through a questionnaire specifically structured. Blood samples were collected in vacutainers K3EDTA tubes and Paxgene tubes, for laboratory tests (total white blood cell counts) and LTL determination. After the first year of treatment a subgroup of 31 patients underwent a second blood analysis (T1) using the same methodology as at enrollment (T0). Data from these follow-up visits were integrated with occupational history, and biomarkers of biological aging, including leukocyte telomere length (LTL), DNA methylation age (DNAmAge), and Age Acceleration (AgeAcc), were analyzed. Occupational history was systematically collected, and patients were considered as having an “occupational risk factor” (Table 2 ) if they reported at least ten years of exposure to agents documented in the literature as associated with IPF. Exposures of shorter duration or not previously linked to IPF were not included in this category. Of the 101 IPF patients, a subset of n = 48 subjects were also genotyped to identify known genetic variants associated with the disease. Figure 1 provides an overview of the study population, detailing the number of IPF patients enrolled and summarising the genetic and epigenetic assessments performed in each analytical subgroup. DNA extraction DNA extraction was performed on all whole blood samples using an automated QIAcube System (QIAGEN, Milano, Italy) according to the DNAeasy Blood&Tissue kit (QIAGEN, Milano, Italy) as previously described 92 . After extraction, all DNA samples were quantified and checked for quality and integrity using QIAexpert Quantification System (Qiagen, Milano, Italy). We obtained genetic material suitable for subsequent analytical procedures from a quantitative (mean DNA 151.15 ng/µL) and qualitative (mean 260/280 = 1.84) point of view. Leukocyte telomere length (LTL) analysis LTL in genomic DNA was measured by the real-time quantitative PCR method developed by Cawthon 93 and previously described 94 , 95 . This method measures the relative LTL by determining the ratio of telomere repeat copy number (T) to single-copy gene (S) (T:S ratio) in experimental samples relative to the T/S ratio of a reference pooled sample 94 . The single copy gene employed in this investigation was the human β-globin (hbg). A “seven-points” standard curve was generated from a serially diluted DNA pool (obtained from DNA samples randomly selected) varying from 40 to 0.625 ng in each plate, in order to determine relative quantities of T and S (in nanograms). All samples and standards were analyzed in triplicate and the average of the 3 T/S ratio measurements was considered in the statistical analyses. A measure of T/S ratio was considered acceptable if the SD among triplicate measures was < 0.25. To test the reproducibility of measurements, PCR runs of these samples were replicated in different days and the coefficient of variation (CV) for the average T/S ratio should be at least 10% 94,95 . DNAmAge analysis and AgeAcc Estimation DNAmAge was determined by using three different epigenetic clocks such as a forensic method 53 , 88 , the Horvath pan-tissue 25 and the PhenoAge 90 clocks. According to the first method, DNAmAge was assessed by analyzing the methylation levels of five selected markers (ELOVL2, C1orf132, KLF14, TRIM59, and FHL2) in genomic DNA using bisulfite conversion and Pyrosequencing methodology as previously described 89 , 96 . According the last two methods DNAmAge was assessed by analyzing the methylation levels of 353 25 and 519 90 CpGs. Sodium bisulfite conversion of genomic DNA was performed using the EZ DNA Methylation™ Kit according to the provided manual. The Illumina Infinium MethylationEPIC v.2.0 BeadChip was used according to the manufacturer's protocol to analyze genome-wide DNA methylation. Illumina iScan SQ scanner was utilized for chip imaging to receive intensities of hybridized CpG probes. The methylation levels were expressed as a percentage of methylated cytosines at the specific CpG sites considered and were used for the estimation of DNAmAge as previously reported 89 , 96 . AgeAcc was calculated as the difference between the DNA age of blood leucocytes and the chronological age of the subjects. Selection of IPF-Associated SNPs A literature-based SNP selection strategy was employed to identify genetic variants significantly associated with IPF susceptibility. The selection was informed by results from GWAS and meta-analyses published between 2008 and 2023, including the landmark study by Fingerlin et al. 20 and more recent large-scale investigations 59 – 61 that identified associations with telomere-related genes ( TERT , TERC , OBFC1 ), mucosal defense ( MUC5B ), immune regulation ( DPP9 ), and fibrosis pathways ( FAM13A , ATP11A ), as well as intergenic loci such as 7q22 and 15q14–15. From this targeted review, a panel of 44 SNPs (Supplementary table 1 ) was initially selected based on the following criteria: genome-wide significance (p < 5 × 10⁻⁸), strong biological plausibility and functional annotation, and compatibility with the genotyping platform used. This initial panel was cross-referenced with the content of the Illumina Infinium Global Screening Array (GSA) BeadChip, which interrogates over 650,000 genomic markers. Through this comparison, a final set of 17 SNPs present on the GSA array and suitable for genotyping was identified. These included SNPs in genes previously linked to IPF susceptibility and progression, enabling both replication of known findings and investigation of genotype–phenotype correlations. Genotyping and Allelic Frequency Analysis Genotyping was conducted on a selected subset of 48 patients diagnosed with IPF, specifically those for whom biological samples were available. The Infinium Global Screening Array (Illumina, USA) was used, following the manufacturer’s protocols. Rigorous quality control procedures were applied at both the sample and SNP levels to ensure high call rate accuracy and to minimize genotyping errors. For each of the 17 selected SNPs, the effect allele (EA) frequency was determined in the study population and compared with corresponding reference frequencies derived from the dbSNP database (NCBI), focusing on global and European population data. Additionally, the total number of EA per individual was calculated as a proxy of cumulative genetic burden. This metric was used to assess inter-individual variability and support exploratory analyses aimed at stratifying patients based on genetic risk profiles. Sample size estimation For sample size estimation, we predicted a 10% to 15% decline in at least three biomarkers of biological age over the study period, with an expected standard deviation of 20%. To achieve sufficient power (two-sided alpha = 0.05/3, beta = 0.20, Bonferroni correction), we planned to enrol 25 subjects. Statistical analyses Statistical analyses were performed on data based on information collected for each patient enrolled in the study. Continuous data were expressed as mean and standard deviation (SD), while categorical variables were given as percentages. Univariate and multivariate regressions were used selecting the appropriate models. The analyses were performed using the statistical software Stata and StastDirects and GraphPad Prism 8 software (GraphPad, USA). Comparisons between data were made by using (two-tailed) Paired t test for paired samples of the same patient (T0 and T1) in the subgroup patients (n = 31) underwent a follow up medical examination. Correlation was evaluated by a simple linear regression model in order to provide a measure of the strength of dependence between two variables. Multiple linear regression analysis was carried out at baseline on all n = 101 patients to assess the influence of independent variables ̶ such as BMI, gender, pack-years, age, FVC, FVC%pred, blood count ̶ on the dependent variable LTL. Multiple linear regression analysis in a first model was also performed to evaluate the influence of the independent variables on the dependent variables LTL, DNAmAge and AgeAcc at follow up (T1). A second model of multiple linear regression analysis was performed to assess their influence on ΔLTL, ΔDNAmAge and ΔAgeAcc (T1-T0). Genetic risk profile data were integrated with clinical and molecular markers (e.g., LTL, DNAmAge) to explore potential associations with disease severity and progression. Results were considered significant when a p value of ≤ 0.05 was obtained. Effect allele count (EAC) To estimate the cumulative genetic predisposition to idiopathic pulmonary fibrosis (IPF), an effect allele count (EAC) was calculated for each genotyped individual. The EAC was constructed by summing the number of risk alleles (EA) carried by each subject across the 17 selected IPF-associated SNPs, identified through prior GWAS and meta-analyses. Each SNP was assumed to contribute additively to disease risk, and alleles were weighted equally, consistent with a simplified, unweighted EAC approach. The score for each individual was computed as: EAC = Σ (number of effect alleles per SNP) This score provides an approximation of individual genetic burden, allowing for stratification of patients based on their cumulative load of susceptibility variants. The distribution of EAC values across the cohort was analyzed to explore potential associations with clinical parameters, LTL, and epigenetic aging indices. Additionally, EAC values were compared to population-level allele frequency data to contextualize the genetic risk observed in our Italian IPF cohort. DATA AVAILABILITY The data that support the findings of this study are available from the corresponding author, SP, upon reasonable request. Declarations ACKNOWLEDGEMENTS This research was supported by funding from Boehringer Ingelheim International GmbH in Germany (contract number: 418810). Additional funding was provided by Next Generation EU, in the context of the National Recovery and Resilience Plan, Investment PE8—Project Age-It: “Ageing Well in an Ageing Society” [DM 1557 11.10.2022]—Ministry of University and Research. PSC Veneto, FSC 2021-2027 Stralcio DGR 1570, ID 10504895. The funders played no role in study design, data collection, analysis and interpretation of data, or the writing of this manuscript. AUTHOR INFORMATION Authors and Affiliations Department of Cardiac, Thoracic and Vascular Sciences and Public Health -DSCTV, University of Padua, Italy Manuela Campisi, Filippo Liviero, Elisabetta Balestro, Paolo Spagnolo & Sofia Pavanello University - Hospital of Padova, Padova, Italy. Luana Cannella, Filippo Liviero, Elisabetta Balestro, Paolo Spagnolo & Sofia Pavanello BMR Genomics, via Redipuglia 21/A - 35131 Padova Federico Tamiazzo Contributions PS: patient recruitment, funding acquisition, data evaluation, contribution to manuscript writing. EB: patient recruitment, data evaluation, contribution to manuscript writing. SP: study conception and design, project coordination, funding acquisition, data evaluation, manuscript writing. FL: collection of occupational history, data evaluation, contribution to manuscript writing. MC: biological age analysis, data collection and evaluation, manuscript writing. LC: biological age analysis, data evaluation, manuscript writing. FT: genetic analysis, data evaluation, contribution to manuscript writing. All authors approved the final version of the manuscript. Corresponding author Correspondence to S. Pavanello ETHICS DECLARATIONS The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of the University of Padova (practice number 3843/AO/16). Competing interests PS reports consulting fees from Novartis and PPM Services, honoraria for lectures from Boehringer-Ingelheim, honoraria for participation to advisory board from AstraZeneca, BMS, Trevi, Merck, Novartis, and Structure Therapeutics, support for attending meetings from PPM Services, and institutional grants from Roche, Boehringer-Ingelheim, Chiesi and PPM Services. His wife is an employee of AstraZeneca. EB reports honoraria for lectures and for participation to advisory boards from Boehringer-Ingelheim and Roche. FT is an employee of the SNP genotyping department at BMR Genomics. 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Competing interests PS reports consulting fees from Novartis and PPM Services, honoraria for lectures from Boehringer-Ingelheim, honoraria for participation to advisory board from AstraZeneca, BMS, Trevi, Merck, Novartis, and Structure Therapeutics, support for attending meetings from PPM Services, and institutional grants from Roche, Boehringer-Ingelheim, Chiesi and PPM Services. His wife is an employee of AstraZeneca. EB reports honoraria for lectures and for participation to advisory boards from Boehringer-Ingelheim and Roche. FT is an employee of the SNP genotyping department at BMR Genomics. The other authors report no conflicts of interest. 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02:35:51","extension":"png","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":85024,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-7686711/v1/1c558cdd46a079f54cce47d3.png"},{"id":93543659,"identity":"fe78ef14-d4d8-4740-809e-fa596c4ada92","added_by":"auto","created_at":"2025-10-15 02:43:51","extension":"png","order_by":22,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":10983,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-7686711/v1/b85946e137fdb335c9d05682.png"},{"id":93541745,"identity":"9b8b622a-4fe3-4464-b651-30c625d84f43","added_by":"auto","created_at":"2025-10-15 02:35:52","extension":"png","order_by":23,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1271,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinegroupimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7686711/v1/951a43f93e38e350fb97e2dc.png"},{"id":93543660,"identity":"f8b9631a-06c4-47db-82cb-a680fcd7ab15","added_by":"auto","created_at":"2025-10-15 02:43:52","extension":"xml","order_by":24,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":247516,"visible":true,"origin":"","legend":"","description":"","filename":"4a1444cdf35c428f80269bc4a66cabd51structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7686711/v1/822ce6e2182adc470f03d4da.xml"},{"id":93541732,"identity":"a4435ba9-decd-4f9b-8186-a1ae4ea7d40f","added_by":"auto","created_at":"2025-10-15 02:35:51","extension":"html","order_by":25,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":271338,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7686711/v1/169601880f41cbd8898898dc.html"},{"id":93541716,"identity":"d871099b-68a5-46c7-9f45-fa0950533b68","added_by":"auto","created_at":"2025-10-15 02:35:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":99158,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverview of the Study Population and molecular assessments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe diagram illustrates the composition of the study population and the molecular analyses performed. A total of 101 patients with idiopathic pulmonary fibrosis (IPF) were initially enrolled and assessed for leukocyte telomere length (LTL). Among them, a subgroup of 31 patients underwent longitudinal follow-up and were analyzed for both LTL and epigenetic age using the Qiagen methylation assay. Within this subgroup, 27 patients with sufficient DNA were further evaluated using epigenetic clocks based on the models proposed by Horvath (2013) and Levine (2018). In parallel, a distinct subset of 48 patients was genotyped for a panel of 17 SNPs associated with IPF susceptibility. The arrows indicate the derivation of analytical subsets contributing to specific biomarker analyses.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7686711/v1/b24b900d4de28470f847adf5.png"},{"id":93541712,"identity":"91d798e1-01d0-4628-b6c1-9d44dad957fe","added_by":"auto","created_at":"2025-10-15 02:35:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":11552,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAgeAcc of blood leukocytes at T0 and T1 in the subgroup of IPF patients (n=31).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBox plots show levels of blood leukocytes AgeAcc at T0 and T1 in the subgroup of IPF patients (n=31). In box plots, the boundary of the box closest to the x-axis indicates the 25th percentile, the line within the box marks the mean, and the boundary of the box farthest from the x-axis indicates the 75th percentile. Whiskers (error bars) above and below the box indicate the 95 and 5th percentiles. The horizontal bar with asterisk indicates the significant comparison between blood leukocytes AgeAcc at T0 and T1 (*Paired t-test: mean AgeAcc (years) at T1, -10.45 ± 4.22 vs mean AgeAcc (years) at T0, -9.55 ± 3.91; p=0.0435).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7686711/v1/8781e83b2dd7c3c438d93094.png"},{"id":93543652,"identity":"98aadd18-684c-4926-8db1-06ee5ae89303","added_by":"auto","created_at":"2025-10-15 02:43:51","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":193471,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDNA methylation levels at 5 CpG sites of the Qiagen method before and after treatment in IPF patients (n=31).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDNA methylation status at the CpG sites of the five genes (ELOVL2, C1orf132, KLF14, TRIM59, and FHL2) analyzed for the DNAmAge determination, reported as percentage of methylation (%), before (T0) and after (T1) treatment in n=31 IPF patients. Data are reported as mean ± SD. Student’s T-test was used for statistical analyses; **, p \u0026lt; 0.01.\u003ca href=\"https://link.springer.com/article/10.1007/s11357-025-01580-2/figures/5\"\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/a\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7686711/v1/ed7850d9164879491df791a4.png"},{"id":93544077,"identity":"05c82f66-d9a8-4637-8f20-a669332492f8","added_by":"auto","created_at":"2025-10-15 02:51:51","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":23207,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation curve between \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eΔDNAmAge and ΔLTL\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e of blood leukocytes in the subgroup of IPF with follow up (n=31).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSimple linear regression plot shows the correlation between ΔDNAmAge and ΔTL of blood leukocytes in the subgroup of IPF with follow up (n=31) (Correlation coefficient (r) = -0.448). Mean, Standard Error (SE) and 95% coefficient intervals (CI) are represented as orange, pink and black lines, respectively.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7686711/v1/019108646d4ceaa5047f21a1.png"},{"id":93543657,"identity":"f8e63a47-92fc-47b4-ae37-dcd0bb1d6fec","added_by":"auto","created_at":"2025-10-15 02:43:51","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":348025,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation between chronological age and DNAmAge in controls and IPF patients before (T0) and after treatment (T1), using different methods and epigenetic clocks.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) DNAmAge estimates in healthy controls obtained using the Horvath (Pearson r=0.954, p\u0026lt;0.0001) and Levine (r=0.882, p=0.0007) clocks with the Illumina MethylationEPIC array, and the Qiagen methylation panel (r=0.974, p\u0026lt;0.0001), plotted against chronological age. (B) DNAmAge estimates in IPF patients at baseline (T0) using the Horvath (Pearson r=0.781, p\u0026lt;0.0001), Levine (r=0.759, p\u0026lt;0.0001), and Qiagen (r=0.895, p\u0026lt;0.0001) methods. (C) DNAmAge estimates in IPF patients at follow-up (T1) using the Horvath (Pearson r=0.756, p\u0026lt;0.0001), Levine (r=0.735, p\u0026lt;0.0001), and Qiagen (r=0.847, p\u0026lt;0.001) methods. Dotted lines represent identity lines corresponding to chronological\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7686711/v1/2db892131127d56f69b8b18f.png"},{"id":93541727,"identity":"889c0411-9f77-4e5d-99d5-416d9daf4922","added_by":"auto","created_at":"2025-10-15 02:35:51","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":236516,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDNAmAge in IPF patients before (T0) and after (T1) antifibrotic treatment, estimated using the Qiagen system and the Illumina MethylationEPIC array (Horvath and Levine clocks).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBar plots illustrate DNAmAge estimates obtained using three distinct approaches: the Horvath (yellow) and Levine (blue) algorithms applied to the Illumina MethylationEPIC array, and the Qiagen custom methylation panel (green). For each method, DNAmAge at baseline (T0) and after antifibrotic treatment (T1) is shown. While DNAmAge values remained stable for the Horvath and Qiagen-based estimates, a statistically significant decrease in DNAmAge was observed with the Levine clock at follow-up (****p \u0026lt; 0.0001).\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7686711/v1/81268a8b2622b4eab96bc868.png"},{"id":93541719,"identity":"3d96090e-6326-4055-96ae-e8a661033047","added_by":"auto","created_at":"2025-10-15 02:35:51","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":312627,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe allelic frequencies in percentage of the effect (EA) and non-effect alleles (NON-EA) in the 48 IPF patients genotyped.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe allelic frequency (%) for each rs (n=17) analysed was plotted in the above chart showing the effect allele (EA) in red and the non-effect allele in blue.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7686711/v1/a9de6dd95e681c42b24d822b.png"},{"id":93541726,"identity":"49a4a752-1ecd-4107-ac15-a28cec8dce66","added_by":"auto","created_at":"2025-10-15 02:35:51","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":233073,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution of effect allele (EA) counts per individual among the 48 IPF patients.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe chart plots the distribution of EA counts per individual among the IPF cohort (n=48 patients). Each bar represents the total number of EA carried by a single subject across the 17 SNPs (rs) analyzed. The number of EA ranged from 10 to 22, with a cohort mean of 16 (horizontal blue line), indicating inter-individual variability and potential differences in genetic susceptibility profiles.\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-7686711/v1/0cdf851a1bdb504a59359e66.png"},{"id":93544792,"identity":"80bdda9d-5ab5-4e2f-ab09-1fc171c56386","added_by":"auto","created_at":"2025-10-15 02:59:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4050200,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7686711/v1/c6ca9795-0b62-418e-8e16-103480eb6ff6.pdf"},{"id":93541720,"identity":"9cc6605f-ef55-42c4-9f9c-5aa82810e0b4","added_by":"auto","created_at":"2025-10-15 02:35:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":176022,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryinformation22092025.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7686711/v1/d4186ed22d556f17e0ae2b39.pdf"}],"financialInterests":"Competing interest reported. Competing interests\nPS reports consulting fees from Novartis and PPM Services, honoraria for lectures from Boehringer-Ingelheim, honoraria for participation to advisory board from AstraZeneca, BMS, Trevi, Merck, Novartis, and Structure Therapeutics, support for attending meetings from PPM Services, and institutional grants from Roche, Boehringer-Ingelheim, Chiesi and PPM Services. His wife is an employee of AstraZeneca. EB reports honoraria for lectures and for participation to advisory boards from Boehringer-Ingelheim and Roche. FT is an employee of the SNP genotyping department at BMR Genomics. \nThe other authors report no conflicts of interest.","formattedTitle":"Telomere Integrity, Epigenetic Aging, and Genetic Burden Shape Biological Aging Trajectories in Idiopathic Pulmonary Fibrosis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAging represents a major global health challenge with profound societal and economic implications, driving chronic diseases, performance status decline, and premature mortality\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Yet, aging is far from uniform: while some individuals maintain physiological resilience, others undergo accelerated biological aging leading to early dysfunction. This heterogeneity highlights the limitations of chronological age as a predictor of health. Biological aging\u0026mdash;the cumulative burden of molecular damage and systemic dysregulation\u0026mdash;offers a more precise and clinically relevant measure\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Among its central mechanisms are telomere attrition and epigenetic drift\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eWithin this framework, Idiopathic Pulmonary Fibrosis (IPF) emerges as a paradigmatic model to study biological aging. IPF is a rare, chronic, and progressive interstitial lung disease primarily affecting older adults, characterized by fibrotic remodeling, dyspnea, and progressive loss of respiratory function\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, affecting\u0026thinsp;~\u0026thinsp;3\u0026nbsp;million people worldwide\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. It is the most common form among all different interstitial lung diseases, with incidence increasing\u0026mdash;partly due to population aging and possibly post-COVID-19 fibrotic sequelae\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. The prognosis remains poor: median survival is ~\u0026thinsp;3 years, and the disease imposes high healthcare costs\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e and a substantial psychosocial burden\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAlthough defined as \u0026ldquo;idiopathic,\u0026rdquo; the pathogenesis of IPF reflects the interplay of multiple risk factors. Aging represents a major determinant\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, but cumulative exposures and environmental insults also play a key role. Robust evidence from systematic reviews and meta-analyses highlights occupational exposures\u0026mdash;particularly to metal, wood, silica/stone, and agricultural or organic dusts, including livestock\u0026mdash;as strong contributors to disease risk\u003csup\u003e\u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Environmental pollutants, notably air pollution, further exacerbate susceptibility\u003csup\u003e\u003cspan additionalcitationids=\"CR11 CR12 CR13\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Other exposures\u0026mdash;including pesticides, hairdressing-related chemicals, bird breeding, molds, textile dust, and fire smoke\u0026mdash;have also been reported, although their associations with IPF are less consistent across studies\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Lifestyle determinants such as cigarette smoking and dietary habits, together with genetic predisposition, add to this complex risk profile\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIPF is also the most frequent clinical manifestation of telomere-mediated disease, directly linking it to mitotic aging mechanisms\u003csup\u003e\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Telomeres\u0026mdash;repetitive DNA sequences protecting chromosomal integrity\u0026mdash;shorten with each cell division. Telomere attrition, measurable as leukocyte telomere length (LTL), represents a recognized biomarker of biological age and systemic stress\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Genetic susceptibility further shapes IPF risk. Genome-wide association studies (GWAS) have identified variants in genes involved in telomere maintenance \u003cem\u003e(TERT, TERC, OBFC1\u003c/em\u003e\u003csup\u003e20,21\u003c/sup\u003e\u003cem\u003e)\u003c/em\u003e, epithelial integrity \u003cem\u003e(MUC5B, DSP)\u003c/em\u003e, immune regulation \u003cem\u003e(DPP9)\u003c/em\u003e, and fibrotic remodeling \u003cem\u003e(FAM13A, ATP11A)\u003c/em\u003e. Among these, the MUC5B rs35705950 promoter polymorphism confers a four-fold increased risk of IPF, particularly in Europeans, while shorter LTL predicts worse clinical outcomes\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Strikingly, epigenetic inheritance of shortened telomeres has been observed even without causal mutations, suggesting non-Mendelian transmission mechanisms in IPF\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAlongside telomere biology, epigenetic mechanisms\u0026mdash;especially DNA methylation age (DNAmAge)\u0026mdash;capture non-mitotic aspects of biological aging. DNAmAge, derived from methylation patterns at selected CpGs, correlates strongly with chronological age\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Accelerated epigenetic aging (AgeAcc)\u0026mdash;when DNAmAge exceeds chronological age\u0026mdash;has been linked to frailty, multimorbidity, and mortality\u003csup\u003e\u003cspan additionalcitationids=\"CR28 CR29 CR30 CR31\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. However, no studies have yet explored DNAmAge and AgeAcc in IPF, representing a critical gap in knowledge.\u003c/p\u003e\u003cp\u003eTherapeutically, nintedanib and pirfenidone remain the only approved antifibrotic agents. They slow functional decline but are not curative\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Nintedanib inhibits receptor tyrosine kinases that drive fibroblast activation\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e, whereas pirfenidone modulates TGF-β signaling and attenuates oxidative stress\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e, potentially limiting telomere shortening and delaying senescence. By intersecting with aging-related pathways, these agents reinforce the need to integrate biological aging biomarkers into IPF clinical monitoring and therapeutic development.\u003c/p\u003e\u003cp\u003eFinally, the hallmark-based decomposition analyses\u0026mdash;linking diseases to the 12 hallmarks of aging\u0026mdash;place IPF among the most aging-aligned conditions\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. IPF disrupts multiple hallmarks early, including telomere attrition, senescence, deregulated nutrient sensing, genomic instability, and mitochondrial dysfunction\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. This makes IPF a unique model for investigating biological aging mechanisms and testing geroprotective strategies.\u003c/p\u003e\u003cp\u003eTo better understand the contribution of biological aging to IPF pathogenesis and prognosis, this study adopts a multimodal approach integrating clinical, molecular, and genetic data. Specifically, we aimed:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eTo analyze LTL at diagnosis (T0) in a well-characterized cohort of treatment-na\u0026iuml;ve IPF patients with clinical outcomes, pulmonary function, hematochemical parameters and occupational exposures.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eTo longitudinally assess biological aging biomarkers\u0026mdash;LTL, DNAmAge, and AgeAcc\u0026mdash;at the first year of treatment (T1).\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eTo genotype selected IPF patients for known susceptibility variants to support genetic stratification and refine disease risk assessment.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eTo compare three DNA methylation-based epigenetic clocks (Horvath, Levine/PhenoAge, and a targeted 5-CpG panel) for consistency and feasibility, aiming to identify a minimal, robust CpG panel for future NGS-based clinical applications.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eThrough this integrated analysis, we seek to elucidate the role of biomarkers of biological aging in IPF pathogenesis and progression, and explore their potential as predictive tools to inform precision medicine strategies.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eCharacteristics of IPF patients\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes characteristics of IPF patients at enrollment (T0). The majority were male (83.2%) with a mean age of 69\u0026thinsp;\u0026plusmn;\u0026thinsp;9 years and LTL at T0 averaged 1.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34 (T/S). Nonparametric linear regression analysis revealed no significant correlation between LTL and patient age at T0 (p\u0026thinsp;=\u0026thinsp;0.780). Multiple linear regression analysis did not identify any significant correlations between LTL and various demographic and clinical variables (Supplementary Table\u0026nbsp;2).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCharacteristics of IPF patients (n\u0026thinsp;=\u0026thinsp;101) at diagnosis (T0).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNumber (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender (male %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e84 (83)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e69.0\u0026thinsp;\u0026plusmn;\u0026thinsp;9.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e26.0\u0026thinsp;\u0026plusmn;\u0026thinsp;4.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePack years ((cigarettes/20)\u0026times;years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e21.0\u0026thinsp;\u0026plusmn;\u0026thinsp;23.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLeucocytes (10\u003csup\u003e3\u003c/sup\u003e/ml)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e7.94\u0026thinsp;\u0026plusmn;\u0026thinsp;3.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeutrophils (10\u003csup\u003e3\u003c/sup\u003e/ml)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e5.00\u0026thinsp;\u0026plusmn;\u0026thinsp;2.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e% Neutrophils\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e58.0\u0026thinsp;\u0026plusmn;\u0026thinsp;8.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLymphocytes (10\u003csup\u003e3\u003c/sup\u003e/ml)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e2.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e% Lymphocytes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e30.0\u0026thinsp;\u0026plusmn;\u0026thinsp;8.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMonocytes (10\u003csup\u003e3\u003c/sup\u003e/ml)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e0.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e% Monocytes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e8.52\u0026thinsp;\u0026plusmn;\u0026thinsp;2.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFVC (L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e2.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFVC (% pred)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e76.0\u0026thinsp;\u0026plusmn;\u0026thinsp;20.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eTherapy\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo therapy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNintedanib\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e39 (39)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePirfenidone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e58 (57)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLTL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e1.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cb\u003eAcronyms: BMI\u003c/b\u003e\u0026thinsp;=\u0026thinsp;body mass index; \u003cb\u003eFVC\u003c/b\u003e\u0026thinsp;=\u0026thinsp;forced vital capacity; \u003cb\u003eFVC(%pred)\u003c/b\u003e\u0026thinsp;=\u0026thinsp;forced vital capacity (percentage of the predicted normal value)\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eCharacteristic and biological aging markers of follow-up\u003c/h3\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003ePatient characteristics\u003c/h2\u003e\u003cp\u003eAs reported in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, a total of 31 IPF patients underwent the follow-up assessment after a median time of 310 days of antifibrotics treatment (T1). The majority were male (77.4%), with a mean chronological age of 70.9\u0026thinsp;\u0026plusmn;\u0026thinsp;7.10 years.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMain characteristics of n\u0026thinsp;=\u0026thinsp;31 IPF patients at follow-up (T1).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNumber (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender (Males %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e24 (77.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e70.9\u0026thinsp;\u0026plusmn;\u0026thinsp;7.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI (Kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e26.9\u0026thinsp;\u0026plusmn;\u0026thinsp;3.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePack-years ((cigarettes/20)\u0026times;years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e22.3\u0026thinsp;\u0026plusmn;\u0026thinsp;21.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLeucocytes (10\u003csup\u003e3\u003c/sup\u003e/ml)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e11.6\u0026thinsp;\u0026plusmn;\u0026thinsp;14.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeutrophils (10\u003csup\u003e3\u003c/sup\u003e/ml)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e4.92\u0026thinsp;\u0026plusmn;\u0026thinsp;1.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e% Neutrophils\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e58.7\u0026thinsp;\u0026plusmn;\u0026thinsp;7.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLymphocytes (10\u003csup\u003e3\u003c/sup\u003e/ml)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e2.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e% Lymphocytes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e29.2\u0026thinsp;\u0026plusmn;\u0026thinsp;6.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMonocytes (10\u003csup\u003e3\u003c/sup\u003e/ml)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e0.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e% Monocytes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e8.23\u0026thinsp;\u0026plusmn;\u0026thinsp;1.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOccupational risk factor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6 (19.35)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMonths from diagnosis to therapy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e3.42\u0026thinsp;\u0026plusmn;\u0026thinsp;7.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFollow-up (days of treatment)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e310.6\u0026thinsp;\u0026plusmn;\u0026thinsp;110.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDecline (FVC %pred T1- T0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e-3.02\u0026thinsp;\u0026plusmn;\u0026thinsp;7.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLTL at beginning of follow-up (T0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e1.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLTL at end of follow-up (T1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e1.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDNAmAge at beginning of follow-up (T0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e60.42\u0026thinsp;\u0026plusmn;\u0026thinsp;6.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDNAmAge at end of follow-up (T1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e60.42\u0026thinsp;\u0026plusmn;\u0026thinsp;6.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAgeAcc at beginning of follow-up (T0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e-9.55\u0026thinsp;\u0026plusmn;\u0026thinsp;3.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAgeAcc at end of follow-up (T1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e-10.45\u0026thinsp;\u0026plusmn;\u0026thinsp;4.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cb\u003eAcronyms: BMI\u003c/b\u003e\u0026thinsp;=\u0026thinsp;body mass index; \u003cb\u003eFVC(%pred)\u003c/b\u003e\u0026thinsp;=\u0026thinsp;forced vital capacity (percentage of the predicted normal value); \u003cb\u003eLTL\u003c/b\u003e\u0026thinsp;=\u0026thinsp;leukocyte telomere length; \u003cb\u003eDNAmAge\u003c/b\u003e\u0026thinsp;=\u0026thinsp;DNA methylation age; \u003cb\u003eAgeAcc\u003c/b\u003e\u0026thinsp;=\u0026thinsp;Age acceleration.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eLeukocyte DNA Biological Aging: Telomere Length (LTL)\u003c/h3\u003e\n\u003cp\u003eAt follow-up (T1), a slight, non-significant increase in LTL was observed compared with baseline (T0) (p\u0026thinsp;=\u0026thinsp;0.458, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMultiple linear regression analysis (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), including BMI, gender, pack-years, decline in lung function (FVC%pred. T1\u0026ndash;T0), age at baseline (T0), baseline values of LTL, treatment duration, and occupational risk factors, indicated that LTL at T1 was significantly associated with both treatment duration (p\u0026thinsp;=\u0026thinsp;0.0056) and baseline LTL (p\u0026thinsp;=\u0026thinsp;0.0004).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cb\u003eMultiple regression analysis: the influence of BMI (Kg/m\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e), gender (M\u0026thinsp;=\u0026thinsp;1; F\u0026thinsp;=\u0026thinsp;0), pack-years ((cigarettes/20) x years), lung function decline (FVC%pred T1-T0), age at beginning of follow-up T0 (years), LTL / DNAmAge / AgeAcc at (T0), duration of treatment (days), and occupational risk factor on LTL, DNAmAge and AgeAcc at follow up (T1).\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eb\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003er\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003et\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP-Value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e\u003cp\u003e\u003cb\u003eT1 LTL\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eb1 = -0.006804\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003er = -0.125815\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003et = -0.59485\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.558\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGender (M\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eb2\u0026thinsp;=\u0026thinsp;0.079917\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003er\u0026thinsp;=\u0026thinsp;0.15917\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003et\u0026thinsp;=\u0026thinsp;0.756214\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.4575\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePack-years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eb3\u0026thinsp;=\u0026thinsp;0.000227\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003er\u0026thinsp;=\u0026thinsp;0.023696\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003et\u0026thinsp;=\u0026thinsp;0.111173\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.9125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDecline (FVC %pred T1-T0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eb4 = -0.004313\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003er = -0.150604\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003et = -0.714545\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.4824\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAge T0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eb5\u0026thinsp;=\u0026thinsp;0.003384\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003er\u0026thinsp;=\u0026thinsp;0.110268\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003et\u0026thinsp;=\u0026thinsp;0.520375\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.608\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eLTL T0\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eb6\u0026thinsp;=\u0026thinsp;0.642611\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003er\u0026thinsp;=\u0026thinsp;0.66351\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003et\u0026thinsp;=\u0026thinsp;4.159686\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003eP\u0026thinsp;=\u0026thinsp;0.0004\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eDuration of treatment (days)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eb7\u0026thinsp;=\u0026thinsp;0.001157\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003er\u0026thinsp;=\u0026thinsp;0.547459\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003et\u0026thinsp;=\u0026thinsp;3.068487\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003eP\u0026thinsp;=\u0026thinsp;0.0056\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOccupation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eb8\u0026thinsp;=\u0026thinsp;0.209806\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003er\u0026thinsp;=\u0026thinsp;0.367963\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003et\u0026thinsp;=\u0026thinsp;1.856121\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.0769\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"8\" rowspan=\"9\"\u003e\u003cp\u003e\u003cb\u003eT1 DNAmAge\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eVariables\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eb\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003er\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003et\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003eP-Value\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eb1\u0026thinsp;=\u0026thinsp;0.153467\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003er\u0026thinsp;=\u0026thinsp;0.292132\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003et\u0026thinsp;=\u0026thinsp;1.432718\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.166\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eGender (M\u0026thinsp;=\u0026thinsp;1)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eb2\u0026thinsp;=\u0026thinsp;2.083691\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003er\u0026thinsp;=\u0026thinsp;0.401535\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003et\u0026thinsp;=\u0026thinsp;2.056428\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003eP\u0026thinsp;=\u0026thinsp;0.0518\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePack-years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eb3\u0026thinsp;=\u0026thinsp;0.004116\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003er\u0026thinsp;=\u0026thinsp;0.04564\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003et\u0026thinsp;=\u0026thinsp;0.214294\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.8323\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDecline (FVC %pred T1-T0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eb4 = -0.003256\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003er = -0.011911\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003et = -0.055871\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.9559\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAge T0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eb5\u0026thinsp;=\u0026thinsp;0.06019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003er\u0026thinsp;=\u0026thinsp;0.124768\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003et\u0026thinsp;=\u0026thinsp;0.589824\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.5613\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eDNAmAge T0\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eb6\u0026thinsp;=\u0026thinsp;0.791651\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003er\u0026thinsp;=\u0026thinsp;0.860308\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003et\u0026thinsp;=\u0026thinsp;7.915657\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDuration of treatment (days)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eb7 = -0.003525\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003er = -0.203398\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003et = -0.974389\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.3405\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOccupation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eb8 = -1.663566\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003er = -0.310627\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003et = -1.532792\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.1396\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e\u003cp\u003e\u003cb\u003eT1 AgeAcc*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eVariables\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eb\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003er\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003et\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003eP-Value\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eb1\u0026thinsp;=\u0026thinsp;0.184112\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003er\u0026thinsp;=\u0026thinsp;0.324503\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003et\u0026thinsp;=\u0026thinsp;1.645299\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.1135\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eGender (M\u0026thinsp;=\u0026thinsp;1)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eb2\u0026thinsp;=\u0026thinsp;2.826082\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003er\u0026thinsp;=\u0026thinsp;0.493077\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003et\u0026thinsp;=\u0026thinsp;2.718107\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003eP\u0026thinsp;=\u0026thinsp;0.0123\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePack-years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eb3\u0026thinsp;=\u0026thinsp;0.000397\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003er\u0026thinsp;=\u0026thinsp;0.004113\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003et\u0026thinsp;=\u0026thinsp;0.019727\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.9844\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDecline (FVC %pred T1-T0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eb4\u0026thinsp;=\u0026thinsp;0.052576\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003er\u0026thinsp;=\u0026thinsp;0.191305\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003et\u0026thinsp;=\u0026thinsp;0.93473\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.3596\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eAgeAcc T0\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eb5\u0026thinsp;=\u0026thinsp;0.861377\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003er\u0026thinsp;=\u0026thinsp;0.871537\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003et\u0026thinsp;=\u0026thinsp;8.524344\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDuration of treatment (days)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eb6 = -0.004858\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003er = -0.259676\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003et = -1.2896\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOccupation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eb7 = -0.602101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003er = -0.123908\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003et = -0.598855\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.5551\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e*The variable Age is not considered for AgeAcc because of its own definition.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eBold character is displayed only for significant values.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cb\u003eAcronyms: BMI\u003c/b\u003e\u0026thinsp;=\u0026thinsp;body mass index; \u003cb\u003eFVC(%pred)\u003c/b\u003e\u0026thinsp;=\u0026thinsp;forced vital capacity (percentage of the predicted normal value); \u003cb\u003eLTL\u003c/b\u003e\u0026thinsp;=\u0026thinsp;leukocyte telomere length; \u003cb\u003eDNAmAge\u003c/b\u003e\u0026thinsp;=\u0026thinsp;DNA methylation age; \u003cb\u003eAgeAcc\u003c/b\u003e\u0026thinsp;=\u0026thinsp;Age acceleration.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFurther regression analysis on longitudinal changes (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) including BMI, gender, antifibrotic therapy (0\u0026thinsp;=\u0026thinsp;pirfenidone; 1\u0026thinsp;=\u0026thinsp;nintedanib), pack-years, age at baseline, lung function decline (FVC%pred T1-T0), and duration of treatment (days) on \u003cb\u003eΔ\u003c/b\u003eLTL (LTL T1-T0) revealed that the increase in ΔLTL was significantly greater in patients treated with nintedanib compared with those receiving pirfenidone (p\u0026thinsp;=\u0026thinsp;0.0402) and showed a positive association with treatment duration (p\u0026thinsp;=\u0026thinsp;0.0233).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cb\u003eMultiple regression analysis: the influence of BMI (Kg/m\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e), gender (M\u0026thinsp;=\u0026thinsp;1; F\u0026thinsp;=\u0026thinsp;0), antifibrotic therapy (0\u0026thinsp;=\u0026thinsp;pirfenidone; 1\u0026thinsp;=\u0026thinsp;nintedanib), pack-years ((cigarettes/20)\u0026times;years), age at T0 (years), lung function decline (FVC%pred T1-T0), and duration of treatment (days) on\u003c/b\u003e \u003cb\u003eΔ\u003c/b\u003e\u003cb\u003eLTL (LTL T1-T0), ΔDNAmAge (DNAmAge T1-T0), ΔAgeAcc (AgeAcc T1-T0).\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eb\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003er\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003et\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP-Value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e\u003cp\u003e\u003cb\u003eΔLTL\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eb1 = -0.017549\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003er = -0.301378\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003et = -1.515836\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.1432\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGender (M\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eb2\u0026thinsp;=\u0026thinsp;0.026736\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003er\u0026thinsp;=\u0026thinsp;0.050506\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003et\u0026thinsp;=\u0026thinsp;0.242528\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.8105\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eTherapy (P\u0026thinsp;=\u0026thinsp;0; N\u0026thinsp;=\u0026thinsp;1)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eb3\u0026thinsp;=\u0026thinsp;0.197156\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003er\u0026thinsp;=\u0026thinsp;0.413044\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003et\u0026thinsp;=\u0026thinsp;2.175101\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003eP\u0026thinsp;=\u0026thinsp;0.0402\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePack-years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eb4 = -0.000796\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003er = -0.070951\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003et = -0.341129\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.7361\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAge T0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eb5 = -0.003064\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003er = -0.106249\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003et = -0.512451\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.6132\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDecline (FVC %pred T1-T0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eb6 = -0.011056\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003er = -0.347259\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003et = -1.775912\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.089\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eDuration of treatment (days)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eb7\u0026thinsp;=\u0026thinsp;0.00093\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003er\u0026thinsp;=\u0026thinsp;0.451936\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003et\u0026thinsp;=\u0026thinsp;2.429691\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003eP\u0026thinsp;=\u0026thinsp;0.0233\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e\u003cp\u003e\u003cb\u003eΔDNAmAge\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eVariables\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eb\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003er\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003et\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003eP-Value\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eb1\u0026thinsp;=\u0026thinsp;0.188671\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003er\u0026thinsp;=\u0026thinsp;0.319576\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003et\u0026thinsp;=\u0026thinsp;1.617452\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.1194\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGender (M\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eb2\u0026thinsp;=\u0026thinsp;2.125313\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003er\u0026thinsp;=\u0026thinsp;0.370654\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003et\u0026thinsp;=\u0026thinsp;1.913919\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.0682\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTherapy (P\u0026thinsp;=\u0026thinsp;0; N\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eb3\u0026thinsp;=\u0026thinsp;0.450862\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003er\u0026thinsp;=\u0026thinsp;0.102271\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003et\u0026thinsp;=\u0026thinsp;0.493061\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.6266\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePack-years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eb4 = -0.006255\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003er = -0.055343\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003et = -0.265824\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.7927\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAge T0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eb5 = -0.067492\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003er = -0.22748\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003et = -1.120328\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.2741\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDecline (FVC %pred T1-T0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eb6\u0026thinsp;=\u0026thinsp;0.018415\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003er\u0026thinsp;=\u0026thinsp;0.060917\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003et\u0026thinsp;=\u0026thinsp;0.292693\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.7724\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDuration of treatment (days)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eb7 = -0.003082\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003er = -0.164296\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003et = -0.798792\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.4326\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e\u003cp\u003e\u003cb\u003eΔAgeAcc*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eVariables\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eb\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003er\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003et\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003eP-Value\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eb1\u0026thinsp;=\u0026thinsp;0.193313\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003er\u0026thinsp;=\u0026thinsp;0.326992\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003et\u0026thinsp;=\u0026thinsp;1.69511\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eGender (M\u0026thinsp;=\u0026thinsp;1)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eb2\u0026thinsp;=\u0026thinsp;2.621248\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003er\u0026thinsp;=\u0026thinsp;0.45209\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003et\u0026thinsp;=\u0026thinsp;2.483017\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003eP\u0026thinsp;=\u0026thinsp;0.0204\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTherapy (P\u0026thinsp;=\u0026thinsp;0; N\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eb3\u0026thinsp;=\u0026thinsp;0.411273\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003er\u0026thinsp;=\u0026thinsp;0.093707\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003et\u0026thinsp;=\u0026thinsp;0.461098\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.6489\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePack-years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eb4 = -0.006248\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003er = -0.055237\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003et = -0.27102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.7887\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDecline (FVC %pred T1-T0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eb5\u0026thinsp;=\u0026thinsp;0.043846\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003er\u0026thinsp;=\u0026thinsp;0.152086\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003et\u0026thinsp;=\u0026thinsp;0.753836\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.4583\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDuration of treatment (days)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eb6 = -0.005322\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003er = -0.277333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003et = -1.414121\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.1702\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e*The variable Age is not considered for AgeAcc because of its own definition.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eBold character is displayed only for significant values.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cb\u003eAcronyms: BMI\u003c/b\u003e\u0026thinsp;=\u0026thinsp;body mass index; \u003cb\u003eFVC(%pred)\u003c/b\u003e\u0026thinsp;=\u0026thinsp;forced vital capacity (percentage of the predicted normal value); \u003cb\u003eLTL\u003c/b\u003e\u0026thinsp;=\u0026thinsp;leukocyte telomere length; \u003cb\u003eDNAmAge\u003c/b\u003e\u0026thinsp;=\u0026thinsp;DNA methylation age; \u003cb\u003eAgeAcc\u003c/b\u003e\u0026thinsp;=\u0026thinsp;Age acceleration.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003eLeukocyte DNA Biological Aging: Epigenetic Age (DNAmAge) and Age Acceleration (AgeAcc)\u003c/h3\u003e\n\u003cp\u003eAt T1, a significant reduction in epigenetic age acceleration was detected compared with baseline (\u0026ndash;10.45\u0026thinsp;\u0026plusmn;\u0026thinsp;4.22 at T1 vs \u0026minus;\u0026thinsp;9.55\u0026thinsp;\u0026plusmn;\u0026thinsp;3.91 at T0, p\u0026thinsp;=\u0026thinsp;0.0435; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The unadjusted DNAmAge (i.e., not corrected for chronological age) showed no difference between T1 and T0 (p\u0026thinsp;\u0026gt;\u0026thinsp;0.9999).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe multiple linear regression analysis (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) demonstrated that both DNAmAge and AgeAcc at T1 were higher in males (p\u0026thinsp;=\u0026thinsp;0.0518 and p\u0026thinsp;=\u0026thinsp;0.0123, respectively) and were strongly determined by their baseline values (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). No other covariates reached statistical significance.\u003c/p\u003e\u003cp\u003eWhen analyzing longitudinal changes by multiple regression analysis (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), the increase in AgeAcc (ΔAgeAcc\u0026thinsp;=\u0026thinsp;AgeAcc T1\u0026ndash;T0) was significantly associated with male sex (p\u0026thinsp;=\u0026thinsp;0.0204), whereas no significant predictors emerged for ΔDNAmAge.\u003c/p\u003e\u003cp\u003eDNA methylation analysis of CpG sites within the five genes included in the Qiagen DNAmAge estimation panel (ELOVL2, C1orf132, KLF14, TRIM59, and FHL2) revealed a significant reduction in methylation at C1orf132 after treatment (p\u0026thinsp;=\u0026thinsp;0.0044; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eCorrelation between LTL and DNAmAge\u003c/h2\u003e\u003cp\u003eSimple linear regression analysis confirmed the negative correlation between the reduction in non-mitotic DNAmAge and the increase in LTL (ΔLTL\u0026thinsp;=\u0026thinsp;LTL T1-T0) (p\u0026thinsp;=\u0026thinsp;0.0115), as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eComparison of Qiagen system and Illumina Methylome Epic Array (Horvath and Levine) methods\u003c/h3\u003e\n\u003cp\u003eAs depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, a significant positive correlation was confirmed between chronological age and DNAmAge estimated by all three independent methodologies \u0026mdash; Horvath (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), Levine (p\u0026thinsp;=\u0026thinsp;0.0007), and Qiagen (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) \u0026mdash; in the control group. A similar trend was observed in the IPF patient\u0026rsquo;s subgroup at both baseline (T0) and follow-up (T1), as reported in Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eB and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eC (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001 for all methods). The discordance between number of samples analyzed by Qiagen method in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and those analyzed by all three independent methodologies in Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e has to be ascribed to the insufficient amount of DNA available to perform further the analyses of DNAmAge for four samples.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e, a significant post-treatment reduction in DNAmAge was observed using the Levine clock (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), while no changes were detected by Horvath and Qiagen methods.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eGenotyping\u003c/h3\u003e\n\u003cdiv id=\"Sec11\"\u003e\n \u003ch2\u003eIdentification of genetic variants associated with IPF\u003c/h2\u003e\n \u003cp\u003eBased on a literature review, 17 SNPs previously associated with IPF and included in the Infinium Global Screening Array (GSA) were selected for analysis. Supplementary Table\u0026nbsp;3 reports the rs and IDs of the 17 variants, the corresponding risk alleles (EA) according to the analyzed literature, and the respective references.\u003c/p\u003e\n \u003cp\u003eThe allelic frequencies (%) of the EA were also determined for each of the 17 SNPs in the 48 genotyped IPF patients and their distribution is presented in Fig.\u0026nbsp;7. In our cohort, EA frequencies ranged from a maximum of 84.3% for rs1981997 (\u003cem\u003eMAPT\u003c/em\u003e) to a minimum of 1% for rs3893252 (\u003cem\u003eDAZAP1\u003c/em\u003e). Among the most represented alleles, rs1981997 (\u003cem\u003eMAPT\u003c/em\u003e, 84.3%) and rs5743890 (\u003cem\u003eTOLLIP\u003c/em\u003e, 82.2%) showed high prevalence but were relatively comparable to reference datasets, whereas rs1278769 (\u003cem\u003eATP11A\u003c/em\u003e, 83.3%) demonstrated a clearly elevated frequency compared to both European and worldwide populations. Conversely, rs3893252 (\u003cem\u003eDAZAP1\u003c/em\u003e) and rs4387287 (\u003cem\u003eOBFC1\u003c/em\u003e) displayed markedly reduced frequencies in Italian IPF patients.\u003c/p\u003e\n \u003cp\u003eAs shown in Table 5, the EA frequencies observed in the 48 Italian IPF patients were compared with those reported for worldwide and European populations in the dbSNP database (NCBI). Using a threshold of frequency differences \u0026gt; 0.05, 7 out of the 17 investigated alleles (41%) were more frequent in the Italian cohort, whereas the remaining variants displayed lower frequencies. Among these, five SNPs—rs12610495 (\u003cem\u003eDPP9\u003c/em\u003e), rs1278769 (\u003cem\u003eATP11A\u003c/em\u003e), rs35705950 (\u003cem\u003eMUC5B\u003c/em\u003e), rs5743894 (\u003cem\u003eTOLLIP\u003c/em\u003e), and rs7725218 (\u003cem\u003eTERT\u003c/em\u003e)—showed the strongest enrichment, with EA frequencies consistently higher in the Italian IPF cohort compared with both worldwide and European reference datasets.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 5\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eComparison of allele effect frequencies in 48 Italian IPF patients with those in the worldwide and European populations. The final two columns (D1 and D2) indicate corresponding increases (↑), decreases (↓) or no change (=).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ersID\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGene\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRisk allele\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEffect allele frequency\u003c/p\u003e\n \u003cp\u003e(Worldwide)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEffect allele frequency\u003c/p\u003e\n \u003cp\u003e(EUR)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEffect allele frequency\u003c/p\u003e\n \u003cp\u003e(48 Italian IPF)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eD1 (Worldwide vs Italian IPF)*\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eD2 (EUR vs Italian IPF)*\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers11191865\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOBFC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.4716\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.4989\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.5417\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e↑\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e=\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers12610495\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDPP9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2705\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2837\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.4062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e↑\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e↑\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers1278769\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eATP11A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.7569\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.7567\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.8333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e↑\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e↑\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers1981997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMAPT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.8049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.7865\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.8437\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e=\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e↑\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers2034650\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e_ / Intronic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.5105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.5141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.5208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e=\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e=\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers2076295\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDSP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.4480\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.4494\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.4791\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e=\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e=\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers2609255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFAM13A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1563\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e↓\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e↓\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers2736100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTERT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.4938\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.4903\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.5208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e=\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e=\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers35705950\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMUC5B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0352\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.4583\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e↑\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e↑\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers4387287\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOBFC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1890\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1749\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e↓\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e=\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers4727443\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntergenic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.6096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.6045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.5313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e↓\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e↓\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers5743890\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTOLLIP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.8634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.8523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.8229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e=\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e=\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers5743894\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTOLLIP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0935\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e↑\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e↑\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers6793295\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLRRC34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2810\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2615\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2604\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e=\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e=\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers7725218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTERT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.6388\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.6422\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.6979\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e↑\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e↑\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers7934606\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMUC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.6371\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.5975\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.4583\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e↓\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e↓\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers3893252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDAZAP1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e=\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e=\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003e* Threshold of frequency differences \u0026gt; 0.05 to distinguish between increase (↑), decrease (↓) or no change (=).\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 6\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eMultiple regression analysis: the influence of age (years), BMI (Kg/m2), gender (M = 1; F = 0), LTL at diagnosis and total effect alleles (EAs) on Forced Vital Capacity (FVC).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eb\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003er\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP-Value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eForced vital Capacity (FVC).\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.018713\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.238295\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.590134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1193\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI (Kg/m2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.01139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.065749\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.427024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.6715\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender (m = 1; F = 0)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.917286\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.52177\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.963787\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal EAs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.085527\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.369452\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e-2.576621\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0136\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLTL (T/S)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.398756\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.210682\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.396729\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1698\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003e\u003cstrong\u003eAcronyms: BMI\u003c/strong\u003e = body mass index; \u003cstrong\u003eFVC\u003c/strong\u003e = forced vital capacity; \u003cstrong\u003eLTL\u003c/strong\u003e = leukocyte telomere length.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\"\u003e\n \u003ch2\u003eEffect Allele Count (EAC) and Clinical Correlates\u003c/h2\u003e\n \u003cp\u003eThe cumulative distribution of EA per individual revealed considerable inter-individual variability within the IPF cohort. As shown in Fig.\u0026nbsp;8, the number of EAs per subject ranged from 10 to 22, with a cohort mean of 16 EAs, underscoring the broad genetic heterogeneity among patients.\u003c/p\u003e\n \u003cp\u003eA multiple regression analysis (Table\u0026nbsp;7) was performed to assess the influence of age, BMI, gender, LTL at diagnosis, and total EAs on forced vital capacity (FVC). The results demonstrated that the total number of EAs was a significant determinant of decreased FVC (p = 0.0136), together with gender, with female patients showing a stronger association with reduced FVC (p = 0.0003).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we conducted a comprehensive, multidimensional investigation into IPF by integrating telomere biology, epigenetic age acceleration metrics, and genetic susceptibility profiling in a well-characterized Italian cohort. Our approach provides new insights into the molecular heterogeneity of IPF and its modulation by antifibrotic treatment over time.\u003c/p\u003e\u003cp\u003eOur cohort—predominantly elderly men (mean age ~ 69 years), as expected for IPF\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e—showed no baseline association between chronological age and LTL on nonparametric and multivariable analyses, likely reflecting the narrow age range. Conventional covariates (age, BMI, smoking) were not independent determinants of baseline LTL. Importantly, IPF patients had shorter LTL than age- and gender- matched controls from our laboratory (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e supplementary), consistent with landmark studies\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. These data reinforce telomere attrition as a central IPF hallmark\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e, implicating premature senescence and defective alveolar repair mechanisms as key contributors to disease onset and progression\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Clinically, shorter baseline LTL correlated with lower reduced diffusing capacity of the lung for carbon monoxide (DLCO)\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, supporting LTL as a baseline biomarker of disease severity and early prognostic stratification\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAt follow-up (T1), we performed a comprehensive longitudinal assessment of biological aging biomarkers—including LTL, DNAmAge, and AgeAcc—to investigate their associations with clinical outcomes, treatment exposure, and demographic or occupational factors. LTL remained generally stable or exhibited a slight, non-significant increase compared with baseline. However, multiple regression analyses revealed that LTL at T1 was significantly influenced by both treatment duration and baseline LTL values. Notably, patients receiving nintedanib showed a greater increase in LTL compared with those treated with pirfenidone, and this effect was positively correlated with treatment duration. These findings suggest that nintedanib may exert a protective effect on telomere maintenance, an intriguing hypothesis that warrants further mechanistic exploration. Nintedanib, a tyrosine kinase inhibitor, targets multiple growth factor receptors involved in fibrotic processes, including the platelet-derived growth factor receptor (PDGFR), fibroblast growth factor receptor (FGFR), and vascular endothelial growth factor receptor (VEGFR)\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Its antifibrotic activity is primarily mediated through inhibition of fibroblast proliferation, differentiation, and migration, as well as by reducing extracellular matrix (ECM) deposition. Moreover, nintedanib has been reported to attenuate vascular remodeling\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. By dampening fibroblast activation and turnover, nintedanib may mitigate the excessive cellular stress and replicative demand typical of fibrotic tissues, thereby slowing telomere attrition and contributing to the preservation of telomere length in patients with IPF. Recent preclinical evidence further suggests that nintedanib may exert senolytic properties by promoting apoptosis of senescent fibroblasts through inhibition of the STAT3 pathway\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Taken together, these data raise the possibility that the protective effects of nintedanib on telomere dynamics may extend beyond lung fibroblasts to circulating leukocytes, where we measured LTL in the present study. In line with our findings, these observations support a broader role for nintedanib in modulating aging-related mechanisms beyond fibrosis control and highlight LTL as a potential biomarker not only of disease progression\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e but also of treatment response\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Furthermore, emerging therapeutic strategies aimed at preserving telomere integrity—such as sex hormone–based telomerase activation\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e or intercellular telomere transfer via extracellular vesicles\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e—underscore the relevance of LTL monitoring within the framework of personalized medicine for IPF. Collectively, our findings open new avenues for precision medicine and aging modulation in fibrotic lung disease.\u003c/p\u003e\u003cp\u003eIn parallel, we observed a significant reduction in epigenetic age acceleration (AgeAcc) following antifibrotic therapy, while unadjusted DNAmAge remained largely unchanged. This pattern suggests that antifibrotic treatment may contribute to a deceleration of biological aging in IPF patients, potentially through mechanisms involving epigenetic remodelling rather than a direct resetting of the epigenetic clock. One plausible explanation is that therapy-induced attenuation of chronic oxidative and inflammatory stress—well-established drivers of accelerated epigenetic aging\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e—could underlie this effect.\u003c/p\u003e\u003cp\u003eInterestingly, we also found that male patients exhibited higher DNAmAge and AgeAcc values at T1 compared with females, suggesting potential sex-specific trajectories in biological aging among individuals with IPF. This finding is consistent with previous evidence, including our own work in pauci- and asymptomatic COVID-19 healthcare workers\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e and the study by Gallego-Fabrega et al.\u003csup\u003e50\u003c/sup\u003e in patients with ischemic stroke, and Oblak et al.\u003csup\u003e29\u003c/sup\u003e, which together support the broader concept of the male-female health–survival paradox. According to this paradigm, males typically experience faster biological aging, despite often presenting lower disability rates compared with females\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. Moreover, recent findings by Tondo et al.\u003csup\u003e52\u003c/sup\u003e reinforce the relevance of sex differences in IPF, demonstrating that male sex significantly influences disease progression and long-term treatment response, with men showing poorer survival rates and reduced therapy tolerance compared with women. Taken together, these observations highlight the necessity of incorporating sex-specific considerations into clinical management and therapeutic decision-making for IPF.\u003c/p\u003e\u003cp\u003eFurthermore, site-specific DNA methylation analysis of the five CpG loci used to compute DNAmAge (ELOVL2, C1orf132, KLF14, TRIM59, FHL2) revealed a significant hypomethylation at the C1orf132 locus (chromosome 1 open reading frame 132) following antifibrotic treatment. C1orf132, also known as MIR29B2C, encodes a non-coding RNA located at 1q32.2 and is recognized as one of the most robust epigenetic predictors of chronological age. Consistent with previous studies\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e,\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e, methylation at C1orf132 typically declines with aging, and such hypomethylation is thought to enhance transcriptional activity, potentially modulating downstream regulatory pathways involving age-sensitive microRNAs. Interestingly, in specific pathological contexts—such as following hematopoietic stem cell transplantation (HSCT)—C1orf132 has instead been reported as hypermethylated, where it may influence graft function\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. In our study, the observed therapy-associated hypomethylation at this locus suggests a potential remodeling of age-related epigenetic pathways, possibly impacting microRNA-mediated mechanisms of aging and tissue repair.\u003c/p\u003e\u003cp\u003eFinally, we observed a significant inverse correlation between ΔLTL and ΔDNAmAge, supporting the existence of a shared biological aging axis that integrates telomeric and epigenetic mechanisms. Several investigations\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e,\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e, including our previous study\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e, have reported similar associations in blood-based measurements, further reinforcing the biological plausibility of this relationship. This convergence strengthens the potential utility of LTL and DNAmAge as complementary, integrative biomarkers for monitoring disease progression and assessing therapeutic efficacy in IPF.\u003c/p\u003e\u003cp\u003eTo better understand the genetic underpinnings of IPF, we genotyped a subgroup of 48 patients for 17 SNPs previously identified in large GWAS\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan additionalcitationids=\"CR60 CR61 CR62\" citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e–\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e. Our findings reveal a complex genetic landscape defined by distinct allelic enrichments and substantial inter-individual variability, offering novel insights into mechanisms of IPF susceptibility and disease progression. Among the 17 SNPs analyzed, seven variants (41%) displayed allele frequency differences greater than 5% compared with both European and global reference datasets. Five of these — rs12610495 (\u003cem\u003eDPP9\u003c/em\u003e), rs1278769 (\u003cem\u003eATP11A\u003c/em\u003e), rs35705950 (\u003cem\u003eMUC5B\u003c/em\u003e), rs5743894 (\u003cem\u003eTOLLIP\u003c/em\u003e), and rs7725218 (\u003cem\u003eTERT\u003c/em\u003e) — exhibited a marked enrichment within our patients, confirming their strong contribution to IPF risk. Among the analyzed genetic variants, the MUC5B promoter polymorphism rs35705950 emerged as the most prevalent and clinically significant, in agreement with previous reports demonstrating its strong association with IPF in European cohorts\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e,\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eMUC5B\u003c/em\u003e encodes a gel-forming mucin critical for mucociliary clearance (MCC) and respiratory host defense\u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e,\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e. Individuals carrying the rs35705950 T-allele exhibit markedly elevated \u003cem\u003eMUC5B\u003c/em\u003e expression in the airways, which may initially enhance mucosal barrier protection. However, excessive mucin production ultimately becomes maladaptive: it impairs MCC efficiency, promotes mucus retention, and hampers the clearance of inhaled particles and pathogens. These changes lead to repetitive epithelial injury and persistent activation of alveolar epithelial cells (AECs), thereby triggering profibrotic remodeling pathway\u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e. Furthermore, hyperactivation of \u003cem\u003eMUC5B\u003c/em\u003e expression has been linked to the induction of endoplasmic reticulum (ER) stress, unresolved inflammation, and epithelial cell death, which together contribute to the progressive deposition of extracellular matrix and fibrosis\u003csup\u003e\u003cspan additionalcitationids=\"CR69\" citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e–\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e. Intriguingly, while the rs35705950 T-allele confers a significantly increased susceptibility to IPF, several studies have reported an association with improved survival outcomes, suggesting a dual and context-dependent role of this variant in disease onset and prognosis\u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e,\u003cspan additionalcitationids=\"CR72 CR73\" citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e–\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e. One possible explanation is that enhanced mucociliary defense mechanisms may delay disease progression in early stages, even as chronic overproduction of \u003cem\u003eMUC5B\u003c/em\u003e drives fibrotic remodeling over time. This paradox underscores the complex interplay between host defense pathways and fibrogenesis in IPF pathobiology. Enriched frequencies of \u003cem\u003eDPP9\u003c/em\u003e, \u003cem\u003eATP11A\u003c/em\u003e, and \u003cem\u003eTOLLIP\u003c/em\u003e variants in our cohort further underscore the involvement of complementary biological pathways in IPF pathogenesis. \u003cem\u003eDPP9\u003c/em\u003e encodes dipeptidyl peptidase 9, a cytoplasmic serine protease expressed in epithelial tissues and implicated in cell adhesion, migration and apoptosis\u003csup\u003e\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u003c/sup\u003e. Although overall DPP9 expression appears only nominally elevated in IPF lungs compared with healthy controls, the rs12610495 variant has not been directly associated with altered gene expression\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. This suggests the involvement of indirect, as yet unidentified molecular mechanisms that may disrupt epithelial integrity, affect cell–cell adhesion, and contribute to aberrant tissue remodeling in IPF. Similarly, the \u003cem\u003eATP11A\u003c/em\u003e gene (ATPase phospholipid transporting 11A), identified as a potential susceptibility locus for IPF, encodes an ATP-binding cassette (ABC) transporter belonging to the P4-ATPase family\u003csup\u003e\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e. This transmembrane ATPase is predicted to regulate phospholipid translocation and ion transport, potentially affecting intracellular calcium homeostasis and activating downstream Rho GTPase–mediated signaling pathways\u003csup\u003e\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e,\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u003c/sup\u003e. In line with previous findings of Fingerlin and colleagues\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e, we observed a high frequency of the rs1278769 variant in our Italian IPF cohort, reinforcing its role as a genetic marker of susceptibility. Interestingly, \u003cem\u003eATP11A\u003c/em\u003e expression does not differ significantly between cases and controls or across genotypes, suggesting that the contribution of this locus may arise from context-dependent regulatory effects rather than direct transcriptional changes\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eTOLLIP\u003c/em\u003e (Toll-interacting protein), a multifunctional intracellular protein, plays a central role in modulating innate immunity by tempering pro-inflammatory signaling\u003csup\u003e\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e,\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e\u003c/sup\u003e and promoting autophagy\u003csup\u003e\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eTOLLIP\u003c/em\u003e is expressed in alveolar type II cells, macrophages, and basal epithelial cells, where it protects against oxidative stress, mitochondrial dysfunction, and apoptosis induced by bleomycin and other insults\u003csup\u003e\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e. This corroborates the hypothesis that \u003cem\u003eTOLLIP\u003c/em\u003e protects several cell populations against fibrosis and oxidative damage, explaining the link between \u003cem\u003eTOLLIP\u003c/em\u003e SNPs and worse outcomes in IPF. Reduced expression of \u003cem\u003eTOLLIP\u003c/em\u003e, equivalent to a 50% as observed in carriers of the rs5743894 minor allele\u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e, has been associated with increased IPF susceptibility and, in some reports, with poorer clinical outcomes\u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e,\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e\u003c/sup\u003e and increased mortality\u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e. Our findings, showing enrichment of this variant, support its potential contribution to impaired epithelial defense and heightened fibrosis risk. The enrichment of \u003cem\u003eTERT\u003c/em\u003e (Telomerase Reverse Transcriptase) variants in our patients is also consistent with previous report linking telomere attrition to IPF pathogenesis\u003csup\u003e\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e\u003c/sup\u003e. Dysfunctional telomere maintenance promotes premature epithelial senescence and impaired regeneration, reinforcing the importance of telomere biology as a driver of fibrosis\u003csup\u003e\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e,\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e\u003c/sup\u003e. In our Italian IPF cohort, we observed a marked enrichment of the \u003cem\u003eTERT\u003c/em\u003e rs7725218 variant, consistent with findings reported by Allen R. et al.\u003csup\u003e61\u003c/sup\u003e. However, to date, no other studies have specifically focused on this variant.\u003c/p\u003e\u003cp\u003eThe distribution of risk alleles revealed striking genetic heterogeneity within our cohort, with the total number of EA per individual ranging from 10 to 22 (mean = 16), underscoring the polygenic nature of IPF. Notably, patients carrying a higher burden of EA tended to exhibit more severe phenotypes. Multiple regression analysis identified the total number of EA as an independent predictor of reduced FVC, indicating a cumulative genetic effect on disease severity. Interestingly, female gender emerged as an additional independent predictor of lower FVC, suggesting possible sex-specific biological influences on disease progression, potentially mediated by hormonal, metabolic, or immune factors. FVC, which is physiologically higher in men than in women, represents the primary clinical parameter used to assess pulmonary function in IPF. Its decline occurs at a rate nearly ten times faster in IPF patients compared with healthy individuals, making it a robust marker of disease severity and survival\u003csup\u003e\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e,\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e\u003c/sup\u003e. Moreover, change in FVC is currently established as the primary endpoint in clinical trials evaluating pharmacological interventions\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e\u003c/sup\u003e, underscoring its central role in monitoring disease progression. These findings support the feasibility of SNP-based patient stratification to identify individuals at higher genetic risk for IPF and enable risk-adapted surveillance strategies. This approach may also help explain part of the clinical heterogeneity observed among patients, including variability in disease course and treatment response.\u003c/p\u003e\u003cp\u003eRecent advances in aging biology provide a unifying lens for our IPF findings. Using hallmark decomposition\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e, cohort SNPs map to core aging axes—telomere maintenance/genomic stability (\u003cem\u003eTERT, TERC, RTEL1, OBFC1)\u003c/em\u003e, intercellular/immune–senescence signaling (\u003cem\u003eDPP9, FAM13A, MUC5B\u003c/em\u003e), and nutrient sensing (DEPTOR)—supporting IPF as an aging-aligned disease and motivating biomarker-based stratification. In parallel, multiple phase II–III programs targeting these hallmarks (senolytics, telomere-directed and epigenetic modulators, metabolic/kinase inhibitors) aim to slow progression and confer geroprotection. Our integrative framework—combining LTL, epigenetic age, and polygenic burden—enables stratification by biological aging profile and prospective testing of hallmark-targeted interventions with measurable effects on biological age and clinical outcomes, positioning IPF at the interface of aging science and precision medicine.\u003c/p\u003e\u003cp\u003eIn this study, we compared two methodologies for epigenetic age estimation—a targeted pyrosequencing assay of five well-established CpG sites (ELOVL2, FHL2, KLF14, PENK, TRIM59)\u003csup\u003e\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e,\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e\u003c/sup\u003e and the Illumina MethylationEPIC BeadChip interrogating over 850,000 CpGs—using two independent clocks: Horvath\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e and Levine (PhenoAge)\u003csup\u003e\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e\u003c/sup\u003e. Across healthy controls and IPF patients, both before and after antifibrotic therapy, DNAmAge estimates were strongly correlated with chronological age for all methods, supporting the robustness and reproducibility of these approaches. However, only the Levine clock detected a significant post-treatment reduction in the unadjusted DNAmAge, suggesting that DNA PhenoAge is more sensitive to therapy-induced biological aging modulation. This greater responsiveness likely stems from its design: unlike other clocks, Levine’s algorithm integrates CpGs associated not only with chronological aging but also with mortality and disease risk\u003csup\u003e\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e\u003c/sup\u003e. Validation data from the NHANES IV cohort showed that a 1-year increase in DNA PhenoAge corresponds to a 9% rise in all-cause mortality and chronic respiratory disease mortality, underscoring its clinical predictive power\u003csup\u003e\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e\u003c/sup\u003e. Therefore, the observed deceleration of PhenoAge following antifibrotic therapy may reflect an epigenetic benefit of treatment, reinforcing its potential as a sensitive biomarker for monitoring therapeutic efficacy and predicting long-term outcomes in IPF.\u003c/p\u003e\u003cp\u003eFrom a translational perspective, while the Illumina EPIC array offers broader genomic coverage and high-resolution insights, it requires higher costs, greater DNA input, and complex bioinformatic workflows. In contrast, the 5-CpG pyrosequencing panel developed by Zbieć-Piekarska et al.\u003csup\u003e53\u003c/sup\u003e and optimized by Pavanello et al.\u003csup\u003e88\u003c/sup\u003e provides excellent feasibility, reproducibility, and minimal DNA requirements, allowing for rapid, cost-effective analyses suitable for routine clinical implementation. Notably, despite its simplified design, this targeted assay remained effective in detecting biological aging acceleration in IPF patients, further supporting its translational applicability.\u003c/p\u003e\u003cp\u003eTogether, these findings highlight the Levine DNA PhenoAge clock as a clinically relevant, therapy-sensitive biomarker, while the 5-CpG pyrosequencing method emerges as a pragmatic and scalable tool for integrating epigenetic aging assessments into personalized medicine strategies for IPF.\u003c/p\u003e\u003cp\u003eThis study has limitations—including sample size for patients in follow-up, single-country enrollment, follow-up duration, and platform heterogeneity—that warrant caution and independent validation. Prospective multicenter trials should embed LTL and DNAmAge endpoints to test whether biomarker improvements translate into slower functional decline and better survival, and mechanistic work should dissect telomere/epigenetic pathways. Such efforts will determine whether aging-informed, sex-aware, and genotype-guided strategies can operationalize precision medicine in IPF.\u003c/p\u003e\u003cp\u003eA well-phenotyped Italian cohort with longitudinal sampling; harmonized acquisition of three aging domains (telomere, epigenetic clocks, genetics) in the same individuals; replication of established IPF signals (e.g., \u003cem\u003eMUC5B\u003c/em\u003e) alongside a polygenic burden–FVC association; convergence of LTL and DNAmAge dynamics (ΔLTL↔ΔDNAmAge); and demonstration that PhenoAge is therapy-responsive while a low-input 5-CpG assay offers pragmatic clinical scalability. Together, these features enhance internal validity and translational relevance.\u003c/p\u003e\u003cp\u003eThis work integrates telomere biology (LTL), epigenetic aging (DNAmAge/AgeAcc), and genetic susceptibility to provide a cohesive view of IPF as an aging-aligned disorder. At diagnosis, shorter LTL aligns with worse gas-exchange capacity, supporting LTL as a baseline severity marker. Longitudinally, antifibrotic therapy—particularly nintedanib—is associated with stabilization or modest gains in LTL and a reduction in AgeAcc, consistent with treatment-linked remodeling of aging pathways. The inverse coupling between ΔLTL and ΔDNAmAge indicates a shared biological aging axis. On the genetic side, enrichment of \u003cem\u003eMUC5B, TOLLIP, TERT, ATP11A\u003c/em\u003e, and \u003cem\u003eDPP9\u003c/em\u003e variants and the cumulative effect-allele burden relate to lower FVC, supporting polygenic risk as a driver of clinical heterogeneity. Collectively, these findings support a precision framework in which LTL, therapy-responsive clocks (e.g., PhenoAge), and genotype are combined to refine risk, tailor surveillance, and track therapeutic benefit in IPF.\u003c/p\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"METHODS","content":"\u003ch2\u003eStudy Population\u003c/h2\u003e\u003cp\u003eThe study population comprised n = 101 individuals diagnosed with Idiopathic Pulmonary Fibrosis (IPF) according to the ATS/ERS/JRS/ALAT guidelines\u003csup\u003e\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e\u003c/sup\u003e. We conducted a longitudinal retrospective cohort study comprising 101 consecutive patients affected by IPF—according to ATS/ERS/JRS/ALAT guidelines\u003csup\u003e\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e\u003c/sup\u003e enrolled from July 2014 and followed- up in our centre. The study was ethically approved by the local Ethics Committee - University of Padova, in accordance with principles of the Helsinki Declaration (practice number 3843/AO/16). All patients recruited, before starting treatment with antifibrotic drugs (nintedanib and pirfenidone), provided their written informed consent to participate to this study. Pulmonary Function Tests (PFTs) including Forced Vital Capacity (FVC% predicted) were recorded at diagnosis and during follow- up. These measurements were performed in accordance with the recommendations of the American Thoracic Society/European Respiratory Society\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e\u003c/sup\u003e. At enrolment (T0), characteristics of the patients including demographic data (age, gender), lifetime history of smoking (pack-years) and body mass index (BMI), age at diagnosis and antifibrotic therapy were acquired through a questionnaire specifically structured. Blood samples were collected in vacutainers K3EDTA tubes and Paxgene tubes, for laboratory tests (total white blood cell counts) and LTL determination.\u003c/p\u003e\u003cp\u003eAfter the first year of treatment a subgroup of 31 patients underwent a second blood analysis (T1) using the same methodology as at enrollment (T0). Data from these follow-up visits were integrated with occupational history, and biomarkers of biological aging, including leukocyte telomere length (LTL), DNA methylation age (DNAmAge), and Age Acceleration (AgeAcc), were analyzed.\u003c/p\u003e\u003cp\u003eOccupational history was systematically collected, and patients were considered as having an “occupational risk factor” (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) if they reported at least ten years of exposure to agents documented in the literature as associated with IPF. Exposures of shorter duration or not previously linked to IPF were not included in this category.\u003c/p\u003e\u003cp\u003eOf the 101 IPF patients, a subset of n = 48 subjects were also genotyped to identify known genetic variants associated with the disease.\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e1\u003c/span\u003e provides an overview of the study population, detailing the number of IPF patients enrolled and summarising the genetic and epigenetic assessments performed in each analytical subgroup.\u003c/p\u003e\u003ch2\u003eDNA extraction\u003c/h2\u003e\u003cp\u003eDNA extraction was performed on all whole blood samples using an automated QIAcube System (QIAGEN, Milano, Italy) according to the DNAeasy Blood\u0026amp;Tissue kit (QIAGEN, Milano, Italy) as previously described\u003csup\u003e\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e\u003c/sup\u003e. After extraction, all DNA samples were quantified and checked for quality and integrity using QIAexpert Quantification System (Qiagen, Milano, Italy). We obtained genetic material suitable for subsequent analytical procedures from a quantitative (mean DNA 151.15 ng/µL) and qualitative (mean 260/280 = 1.84) point of view.\u003c/p\u003e\u003ch2\u003eLeukocyte telomere length (LTL) analysis\u003c/h2\u003e\u003cp\u003eLTL in genomic DNA was measured by the real-time quantitative PCR method developed by Cawthon\u003csup\u003e\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e\u003c/sup\u003e and previously described\u003csup\u003e\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e,\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e\u003c/sup\u003e. This method measures the relative LTL by determining the ratio of telomere repeat copy number (T) to single-copy gene (S) (T:S ratio) in experimental samples relative to the T/S ratio of a reference pooled sample\u003csup\u003e\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e\u003c/sup\u003e. The single copy gene employed in this investigation was the human β-globin (hbg). A “seven-points” standard curve was generated from a serially diluted DNA pool (obtained from DNA samples randomly selected) varying from 40 to 0.625 ng in each plate, in order to determine relative quantities of T and S (in nanograms). All samples and standards were analyzed in triplicate and the average of the 3 T/S ratio measurements was considered in the statistical analyses. A measure of T/S ratio was considered acceptable if the SD among triplicate measures was \u0026lt; 0.25. To test the reproducibility of measurements, PCR runs of these samples were replicated in different days and the coefficient of variation (CV) for the average T/S ratio should be at least 10%\u003csup\u003e94,95\u003c/sup\u003e.\u003c/p\u003e\u003ch2\u003eDNAmAge analysis and AgeAcc Estimation\u003c/h2\u003e\u003cp\u003eDNAmAge was determined by using three different epigenetic clocks such as a forensic method\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e,\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e\u003c/sup\u003e, the Horvath pan-tissue\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e and the PhenoAge\u003csup\u003e\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e\u003c/sup\u003e clocks.\u003c/p\u003e\u003cp\u003eAccording to the first method, DNAmAge was assessed by analyzing the methylation levels of five selected markers (ELOVL2, C1orf132, KLF14, TRIM59, and FHL2) in genomic DNA using bisulfite conversion and Pyrosequencing methodology as previously described\u003csup\u003e\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e,\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e\u003c/sup\u003e. According the last two methods DNAmAge was assessed by analyzing the methylation levels of 353\u003csup\u003e25\u003c/sup\u003e and 519\u003csup\u003e90\u003c/sup\u003e CpGs. Sodium bisulfite conversion of genomic DNA was performed using the EZ DNA Methylation™ Kit according to the provided manual. The Illumina Infinium MethylationEPIC v.2.0 BeadChip was used according to the manufacturer's protocol to analyze genome-wide DNA methylation. Illumina iScan SQ scanner was utilized for chip imaging to receive intensities of hybridized CpG probes. The methylation levels were expressed as a percentage of methylated cytosines at the specific CpG sites considered and were used for the estimation of DNAmAge as previously reported\u003csup\u003e\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e,\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAgeAcc was calculated as the difference between the DNA age of blood leucocytes and the chronological age of the subjects.\u003c/p\u003e\u003ch2\u003eSelection of IPF-Associated SNPs\u003c/h2\u003e\u003cp\u003eA literature-based SNP selection strategy was employed to identify genetic variants significantly associated with IPF susceptibility. The selection was informed by results from GWAS and meta-analyses published between 2008 and 2023, including the landmark study by Fingerlin et al.\u003csup\u003e20\u003c/sup\u003e and more recent large-scale investigations\u003csup\u003e\u003cspan additionalcitationids=\"CR60\" citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e–\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e that identified associations with telomere-related genes (\u003cem\u003eTERT\u003c/em\u003e, \u003cem\u003eTERC\u003c/em\u003e, \u003cem\u003eOBFC1\u003c/em\u003e), mucosal defense (\u003cem\u003eMUC5B\u003c/em\u003e), immune regulation (\u003cem\u003eDPP9\u003c/em\u003e), and fibrosis pathways (\u003cem\u003eFAM13A\u003c/em\u003e, \u003cem\u003eATP11A\u003c/em\u003e), as well as intergenic loci such as 7q22 and 15q14–15.\u003c/p\u003e\u003cp\u003eFrom this targeted review, a panel of 44 SNPs (Supplementary table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) was initially selected based on the following criteria:\u003c/p\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003egenome-wide significance (p \u0026lt; 5 × 10⁻⁸),\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003estrong biological plausibility and functional annotation,\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eand compatibility with the genotyping platform used.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003cp\u003eThis initial panel was cross-referenced with the content of the Illumina Infinium Global Screening Array (GSA) BeadChip, which interrogates over 650,000 genomic markers. Through this comparison, a final set of 17 SNPs present on the GSA array and suitable for genotyping was identified. These included SNPs in genes previously linked to IPF susceptibility and progression, enabling both replication of known findings and investigation of genotype–phenotype correlations.\u003c/p\u003e\u003ch2\u003eGenotyping and Allelic Frequency Analysis\u003c/h2\u003e\u003cp\u003eGenotyping was conducted on a selected subset of 48 patients diagnosed with IPF, specifically those for whom biological samples were available. The Infinium Global Screening Array (Illumina, USA) was used, following the manufacturer’s protocols. Rigorous quality control procedures were applied at both the sample and SNP levels to ensure high call rate accuracy and to minimize genotyping errors.\u003c/p\u003e\u003cp\u003eFor each of the 17 selected SNPs, the effect allele (EA) frequency was determined in the study population and compared with corresponding reference frequencies derived from the dbSNP database (NCBI), focusing on global and European population data.\u003c/p\u003e\u003cp\u003eAdditionally, the total number of EA per individual was calculated as a proxy of cumulative genetic burden. This metric was used to assess inter-individual variability and support exploratory analyses aimed at stratifying patients based on genetic risk profiles.\u003c/p\u003e\u003ch2\u003eSample size estimation\u003c/h2\u003e\u003cp\u003eFor sample size estimation, we predicted a 10% to 15% decline in at least three biomarkers of biological age over the study period, with an expected standard deviation of 20%. To achieve sufficient power (two-sided alpha = 0.05/3, beta = 0.20, Bonferroni correction), we planned to enrol 25 subjects.\u003c/p\u003e\u003ch2\u003eStatistical analyses\u003c/h2\u003e\u003cp\u003eStatistical analyses were performed on data based on information collected for each patient enrolled in the study. Continuous data were expressed as mean and standard deviation (SD), while categorical variables were given as percentages. Univariate and multivariate regressions were used selecting the appropriate models. The analyses were performed using the statistical software Stata and StastDirects and GraphPad Prism 8 software (GraphPad, USA). Comparisons between data were made by using (two-tailed) Paired t test for paired samples of the same patient (T0 and T1) in the subgroup patients (n = 31) underwent a follow up medical examination. Correlation was evaluated by a simple linear regression model in order to provide a measure of the strength of dependence between two variables. Multiple linear regression analysis was carried out at baseline on all n = 101 patients to assess the influence of independent variables ̶ such as BMI, gender, pack-years, age, FVC, FVC%pred, blood count ̶ on the dependent variable LTL.\u003c/p\u003e\u003cp\u003eMultiple linear regression analysis in a first model was also performed to evaluate the influence of the independent variables on the dependent variables LTL, DNAmAge and AgeAcc at follow up (T1). A second model of multiple linear regression analysis was performed to assess their influence on ΔLTL, ΔDNAmAge and ΔAgeAcc (T1-T0). Genetic risk profile data were integrated with clinical and molecular markers (e.g., LTL, DNAmAge) to explore potential associations with disease severity and progression.\u003c/p\u003e\u003cp\u003eResults were considered significant when a p value of ≤ 0.05 was obtained.\u003c/p\u003e\u003ch2\u003eEffect allele count (EAC)\u003c/h2\u003e\u003cp\u003eTo estimate the cumulative genetic predisposition to idiopathic pulmonary fibrosis (IPF), an effect allele count (EAC) was calculated for each genotyped individual. The EAC was constructed by summing the number of risk alleles (EA) carried by each subject across the 17 selected IPF-associated SNPs, identified through prior GWAS and meta-analyses. Each SNP was assumed to contribute additively to disease risk, and alleles were weighted equally, consistent with a simplified, unweighted EAC approach.\u003c/p\u003e\u003cp\u003eThe score for each individual was computed as:\u003c/p\u003e\u003cp\u003eEAC = Σ (number of effect alleles per SNP)\u003c/p\u003e\u003cp\u003eThis score provides an approximation of individual genetic burden, allowing for stratification of patients based on their cumulative load of susceptibility variants. The distribution of EAC values across the cohort was analyzed to explore potential associations with clinical parameters, LTL, and epigenetic aging indices. Additionally, EAC values were compared to population-level allele frequency data to contextualize the genetic risk observed in our Italian IPF cohort.\u003c/p\u003e\u003ch2\u003eDATA AVAILABILITY\u003c/h2\u003e\u003cp\u003eThe data that support the findings of this study are available from the corresponding author, SP, upon reasonable request.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eACKNOWLEDGEMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by funding from Boehringer Ingelheim International GmbH in Germany (contract number: 418810). Additional funding was provided by Next Generation EU, in the context of the National Recovery and Resilience Plan, Investment PE8\u0026mdash;Project Age-It: \u0026ldquo;Ageing Well in an Ageing Society\u0026rdquo; [DM 1557 11.10.2022]\u0026mdash;Ministry of University and Research. PSC Veneto, FSC 2021-2027 Stralcio DGR 1570, ID 10504895. The funders played no role in study design, data collection, analysis and interpretation of data, or the writing of this manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUTHOR INFORMATION\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors and Affiliations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDepartment of Cardiac, Thoracic and Vascular Sciences and Public Health -DSCTV, University of Padua, Italy\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eManuela Campisi, Filippo Liviero, Elisabetta Balestro, Paolo Spagnolo \u0026amp; Sofia Pavanello\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eUniversity - Hospital of Padova, Padova, Italy.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eLuana Cannella, Filippo Liviero, Elisabetta Balestro, Paolo Spagnolo \u0026amp; Sofia Pavanello\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eBMR Genomics, via Redipuglia 21/A - 35131 Padova\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFederico Tamiazzo\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePS: patient recruitment, funding acquisition, data evaluation, contribution to manuscript writing. EB: patient recruitment, data evaluation, contribution to manuscript writing. SP: study conception and design, project coordination, funding acquisition, data evaluation, manuscript writing. FL: collection of occupational history, data evaluation, contribution to manuscript writing. MC: biological age analysis, data collection and evaluation, manuscript writing. LC: biological age analysis, data evaluation, manuscript writing. FT: genetic analysis, data evaluation, contribution to manuscript writing. All authors approved the final version of the manuscript. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding author\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondence to S. Pavanello\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eETHICS DECLARATIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of the University of Padova (practice number 3843/AO/16).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePS reports consulting fees from Novartis and PPM Services, honoraria for lectures from Boehringer-Ingelheim, honoraria for participation to advisory board from AstraZeneca, BMS, Trevi, Merck, Novartis, and Structure Therapeutics, support for attending meetings from PPM Services, and institutional grants from Roche, Boehringer-Ingelheim, Chiesi and PPM Services. His wife is an employee of AstraZeneca. EB reports honoraria for lectures and for participation to advisory boards from Boehringer-Ingelheim and Roche. FT is an employee of the SNP genotyping department at BMR Genomics.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe other authors report no conflicts of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSUPPLEMENTARY INFORMATION\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupplementary information file\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAgeing and health. https://www.who.int/news-room/fact-sheets/detail/ageing-and-health.\u003c/li\u003e\n\u003cli\u003eKhan, S. S., Singer, B. D. \u0026amp; Vaughan, D. E. Molecular and physiological manifestations and measurement of aging in humans. \u003cem\u003eAging Cell\u003c/em\u003e \u003cstrong\u003e16\u003c/strong\u003e, 624\u0026ndash;633 (2017).\u003c/li\u003e\n\u003cli\u003eL\u0026oacute;pez-Ot\u0026iacute;n, C., Blasco, M. A., Partridge, L., Serrano, M. \u0026amp; Kroemer, G. 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We studied 101 treatment-naïve patients at diagnosis (T0) and a subgroup (n = 31) after one year of antifibrotic therapy (T1) including leukocyte telomere length (LTL), DNA methylation age (DNAmAge by Horvath, Levine/PhenoAge, and a 5-CpGs panel), age acceleration (AgeAcc), and 17 IPF-associated SNPs summarized as Effect Allele Count (EAC). Multiple regression models showed that at T1, LTL was independently predicted by baseline LTL (p = 0.0004) and treatment duration (p = 0.0056). ΔLTL increased in nintedanib- versus pirfenidone-treated patients (p = 0.0402) and with treatment duration (p = 0.0233). ΔAgeAcc decreased at follow-up (p = 0.0435), while was higher in males (p = 0.0204). Among epigenetic clocks, Levine’s PhenoAge was the most therapy-responsive (p \u0026lt; 0.0001), whereas the 5-CpGs panel show clinical scalability. Genotyping revealed enrichment of \u003cem\u003eMUC5B\u003c/em\u003e, \u003cem\u003eTERT\u003c/em\u003e, \u003cem\u003eTOLLIP\u003c/em\u003e, \u003cem\u003eDPP9\u003c/em\u003e, and \u003cem\u003eATP11A\u003c/em\u003e variants, and higher EAC associated with lower FVC (p = 0.0136). These findings frame IPF as an aging-aligned disorder and support biomarker-informed precision medicine.\u003c/p\u003e","manuscriptTitle":"Telomere Integrity, Epigenetic Aging, and Genetic Burden Shape Biological Aging Trajectories in Idiopathic Pulmonary Fibrosis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-15 02:35:46","doi":"10.21203/rs.3.rs-7686711/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-09T14:22:45+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-08T20:23:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"330779064567730706420207810299881358040","date":"2025-12-08T19:12:01+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-16T13:28:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"203264153478972647991681177086754158514","date":"2025-09-30T16:28:27+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-30T16:24:12+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-30T13:39:56+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-29T03:20:57+00:00","index":"","fulltext":""},{"type":"submitted","content":"npj Aging","date":"2025-09-22T15:48:01+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"npj-aging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [npj Aging](https://www.nature.com/npjamd/)","snPcode":"41514","submissionUrl":"https://submission.springernature.com/new-submission/41514/3","title":"npj Aging","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f5a7acb8-f2c7-40d1-8a4a-c2fb024ef6f8","owner":[],"postedDate":"October 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":56106869,"name":"Health sciences/Biomarkers"},{"id":56106870,"name":"Health sciences/Diseases"},{"id":56106871,"name":"Biological sciences/Genetics"},{"id":56106872,"name":"Health sciences/Medical research"},{"id":56106873,"name":"Biological sciences/Molecular biology"},{"id":56106874,"name":"Health sciences/Molecular medicine"}],"tags":[],"updatedAt":"2026-05-15T14:54:27+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-15 02:35:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7686711","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7686711","identity":"rs-7686711","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}