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# Integrative Multiparametric Analysis of Circulating Cell-Free Nucleic Acids of Plasma in Healthy Individuals During Aging

Nicolas P Tessier, Lise M Hardy, Florence Mauger, Antoine Daunay, Christian

Daviaud, Ilef Hchaichi, Caroline Horgues, Mourad Sahbatou, Hélène Le

Buanec, Jean-françois Deleuze, et al.

► To cite this version:

Nicolas P Tessier, Lise M Hardy, Florence Mauger, Antoine Daunay, Christian Daviaud, et al. Integrative Multiparametric Analysis of Circulating Cell-Free Nucleic Acids of Plasma in Healthy Individuals During Aging. Aging Cell, 2025, 10.1111/acel.70133. hal-05159922

HAL Id: hal-05159922

https://hal.science/hal-05159922v1

Submitted on 12 Jul 2025

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RESEARCH ARTICLE

OPEN ACCESS

# Integrative Multiparametric Analysis of Circulating Cell-Free Nucleic Acids of Plasma in Healthy Individuals During Aging

Nicolas P. Tessier<fnref n="1" /> | Lise M. Hardy<fnref n="1" /> | Florence Mauger² ID | Antoine Daunay<fnref n="1" /> | Christian Daviaud² | Ilef Hchaichi<fnref n="1" /> |
Caroline Horgues² | Mourad Sahbatou<fnref n="1" /> | Hélène Le Buanec³ | Jean-François Deleuze<fnref n="1" />,² | Alexandre How-Kit<fnref n="1" /> ID

**Correspondence:** Alexandre How-Kit (alexandre.how-kit@fjd-ceph.org)

**Received:** 25 February 2025 | **Revised:** 4 May 2025 | **Accepted:** 1 June 2025

**Funding:** This work was supported by the Agenomics project (Les Mutuelles AXA, programme de mécénat santé 2022—Axe grand âge) and the GENMED Laboratory of Excellence on Medical Genomics [ANR-10-LABX-0013] for the project and/or publication fees.

**Keywords:** aging | circulating cell-free DNA | circulating cell-free nucleic acids | circulating cell-free RNA | DNA methylation | miRNA | plasma

## ABSTRACT

Plasma circulating cell-free nucleic acids (ccfNAs) provide an exceptional source of information about an individual's health, yet their biology in healthy individuals during aging remains poorly understood. Here, we present the first integrative multiparametric analysis of the major types of plasma ccfNAs, including nuclear (ccfnDNA) and mitochondrial (ccfmtDNA) DNA, as well as ribosomal (ccfrRNA), messenger (ccfmRNA) and micro-RNA (ccfmiRNA) in 139 healthy donors aged 19–66 years. We focused on quantity, integrity, and DNA methylation using an optimized experimental workflow that combines highly sensitive analytical methods with the detection of highly repetitive DNA and highly abundant RNA sequences, thereby reducing the required amount of ccfNAs per analysis. We showed a highly significant increase in ccfnDNA levels during aging (p < 0.001), associated with a decrease in its integrity (p < 0.05), while no significant changes were detected in ccfmtDNA levels and ccfDNA methylation. Moreover, a significant increase in ccfmRNA and ccfrRNA (p < 0.05), as well as *miR-483-5p* (p < 0.001) levels was detected during aging, but without any changes in ccfRNA integrity. Finally, we also showed that ccfDNA and ccfRNA levels were correlated (p < 0.001), and a similar pattern was observed for ccfmtDNA and ccfRNA levels, suggesting a possible common release, maintenance, and/or clearance mechanism. Therefore, our study provides an optimized workflow for the global analysis of ccfNAs, enhances the understanding of their biology during aging, and identifies several potential ccfNA-based biomarkers of aging.

## 1 | Introduction

Although circulating cell-free nucleic acids (ccfNAs) were first described by Mandel and Metais (1948) in 1948, significant interest has only emerged in recent decades due to their potential as non-invasive biomarkers in various diseases and health conditions, particularly ccfDNA and ccfmiRNA (Armand-Labit

and Pradines 2017; Bruno et al. 2020; Pozniak et al. 2022). Thus, since the 2000s, studies on ccfNAs from serum and plasma have increased exponentially, with plasma being the preferred matrix for analysis (Peng et al. 2024). They are now crucial tools for prenatal screening and oncology, given the high proportion of fetal and tumoral ccfNAs in the bloodstream (Szilagyi et al. 2020; Thierry et al. 2016). However, only a few studies have gone

<footnote n="1">¹Laboratory for Genomics, Foundation Jean Dausset—CEPH, Paris, France | ²Commissariat à L'énergie Atomique et Aux Energies Alternatives, Centre National de Recherche en Génomique Humaine, Université Paris-Saclay, Gif-sur-Yvette, France | ³Saint-Louis Research Institute, INSERM U976—HIPI Unit, University of Paris, Paris, France</footnote>

<footnote>This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.</footnote>

<footnote>© 2025 The Author(s). Aging Cell published by Anatomical Society and John Wiley & Sons Ltd.</footnote>
beyond the disease context to explore the biology of ccfNAs and their dynamics in healthy individuals during aging, examining inter-individual variations that could improve their use in many applications (Tessier et al. 2023).

ccfNAs include ccfnDNA and ccfmtDNA, as well as ccfRNAs, including both coding RNA (ccfmRNA) and non-coding RNA, such as ccfRNA and ccfmiRNA (Tessier et al. 2023). Their forms are variable, including linear or circular, single (ss) or double-stranded (ds). Moreover, they can be free, enclosed in extra-cellular vesicles (EVs) (microvesicles, exosomes or apop- totic bodies), or associated with virtosomes or proteins (Thierry et al. 2016). Apoptosis is a primary mechanism for ccfNA re- lease (Grabuschnig et al. 2020), resulting in the main mono- nucleosome peak and the residual di- and tri-nucleosome peaks, typical of ccfDNA profile (Ungerer et al. 2020). Other mecha- nisms can result from accidental (e.g., necrosis) or regulated deaths (pyroptosis and NETosis), and from passive (e.g., necro- sis) or active (e.g., secretion) mechanisms (Aucamp et al. 2018; Grabuschnig et al. 2020; Stejskal et al. 2023; Thierry et al. 2016; Tzimagiorgis et al. 2011). The active secretion of exosomes (~50– 150 nm) and microvesicles (~100–1000 nm) is implicated in long- distance intercellular communication (Mathieu et al. 2019). These EVs contain various proteins, lipids, and nucleic acids that contribute to ccfNAs, reflecting the molecular functions and physiological conditions of the secreting cells, tissues, and/ or organs in both healthy and pathological contexts (De Toro et al. 2015). They can also mediate biological functions in the targeted acceptor cells after cargo delivery, such as miRNA- mediated mRNA silencing (Mathieu et al. 2019). Hematopoietic- derived cells have been identified as the major contributors of plasma ccfNAs. Thus, epigenetic profiling reported leukocytes, erythrocyte progenitors, and vascular endothelial cells as the three main contributors of ccfDNA (Moss et al. 2018; Snyder et al. 2016). Platelets, erythrocyte progenitors, and leukocytes were identified as primary sources of ccfRNA from ccfRNA-seq data (Vorperian et al. 2022), while RT-qPCR experiments identi- fied red blood cells for ccfmiRNA (Pritchard et al. 2012).

Due to their low concentration in the plasma of healthy individ-
uals, most studies have been limited to analyzing a single type
of ccfNA and/or a single measured parameter. Some highlighted
the positive correlation of total and nuclear ccfDNA with age
among adults, including nonagenarians (Jylhava et al. 2011,
2013; Li et al. 2024; Meddeb et al. 2019). Furthermore, a lower
ccfnDNA level was correlated to good health, exercise, and nutri-
tional supplementation in healthy individuals aged 65–98 years
old (y.o) (Tosevska et al. 2016), while elevated ccfDNA levels
were correlated with inflammatory status (Jylhava et al. 2012)
and frailty (Nidadavolu et al. 2022), supporting the poten-
tial of the low ccfDNA level as a biomarker of healthy aging.
Regarding ccfDNA methylation, hypomethylation of *Line1* and
*Alu* retroelements has been described during aging (Erichsen
et al. 2018), as well as the identification of 2000 age-related dif-
ferentially methylated CpGs (Li et al. 2024). Conversely, studies
investigating ccfmtDNA levels in aging yielded conflicting re-
sults, with some showing a positive correlation (Padilla-Sanchez
et al. 2020; Pinti et al. 2014) and others reporting no association
(Jylhava et al. 2013; Meddeb et al. 2019). Finally, ccfmiRNAs
are the only type of ccfRNAs already studied in healthy indi-
viduals during aging. Age-related ccfmiRNAs either increased

or decreased in older individuals (Accardi et al. 2022; Ameling
et al. 2015; Olivieri et al. 2014, 2012) and some showed lower lev-
els in individuals with better health status and could potentially
be biomarkers of healthy aging (Balzano et al. 2017; Morsiani
et al. 2021).

Despite their promise, the study of ccfNAs in aging in healthy individuals remains limited to either ccfDNA or ccfmiRNAs. Other types of ccfRNA, including mRNA and rRNA, as well as the correlations between different ccfNA types, remain largely unexplored, although they may also carry important biological information. This constrains our understanding of the biological processes underlying ccfNAs during aging. To address this gap, we performed an integrative multiparametric analysis of both ccfDNA and ccfRNA, including miRNA, mRNA, and rRNA, in plasma samples from a cohort of 139 healthy donors aged 19–66 years divided into three age groups (G1: 19–34 years, G2: 35–49 years, G3: 50–66 years). An experimental workflow was specifically developed to enable the analysis of most ccfNA types from a single plasma sample, using several optimized high-resolution methods and assays. This study provides a comprehensive and integrative overview of ccfNA variation in healthy individuals during aging, which might improve the understanding of the biology of ccfNAs and of their potential as biomarkers of aging.

2 | Material and Methods

2.1 | Ethics Statement

The study was conducted in accordance with current ethical
and legal frameworks. Anonymized blood samples were ob-
tained after informed consent from healthy donors through
French blood bank, EFS (Etablissement Français du Sang,
Paris, France—research agreement 15/EFS/012). All methods
were performed in accordance with the recommendations of the
French National Committee of Ethics.

## 2.2 | Study Population

The study was conducted on 139 healthy donors from the French Blood bank (Etablissement Français du sang, Paris, France) aged between 19 and 66 y.o, including 69 women (F) and 70 men (M). EFS donors must be free of ongoing infections (tested after blood collection by the EFS for HBV, HCV, HIV, syphilis, HTLV, malaria, tetanus and Chagas disease), and have no history of cancer, chronic diseases (including insulin-treated diabetes, inflammatory bowel disease, or autoimmune disease), or cardiovascular problems. Additional eligibility criteria can be found at: https://dondesang.efs.sante.fr/quiz. Study participants were divided into three different age groups for analysis: young (G1: 19–34 y.o., n = 53, 49.1% F), middle-aged (G2: 35–48 y.o., n = 43, 46.5% F) and older adults (G3: 50–66 y.o, n = 43, 53.5% F).

2.3 | Plasma Isolation

6 mL of blood per donor was collected between 10:00 AM and
01:00 PM in BD Vacutainer K2 EDTA tubes and was stored
A

<figure><img src="image_4.png" /></figure>

B

<figure><img src="image_5.png" /><figcaption>**FIGURE 1** | Overview of the experimental design used for the multiparametric analysis of circulating cell-free nucleic acids from plasma. (A) Whole experimental workflow from blood collection to ccfNA analysis. (B) Sankey plot showing the volume equivalents of ccfNAs used for the different analyses in this study. ExoV, exonuclease V; HS, high-sensitivity; rt., room temperature; RT, reverse transcription.</figcaption></figure>

at room temperature from 2 to 4h before plasma isolation. 3.8 mL of plasma was isolated by two successive centrifugations (Figure 1A). After the first centrifugation at 1,600 g for 10 min at room temperature, plasma was carefully collected to avoid collecting the buffy coat and transferred to a new tube. The second centrifugation was conducted at 16,000 g for 10 min at room temperature to remove any remaining leukocytes and platelets. Plasma samples were stored at -80°C in LoBind tubes (Eppendorf) prior to ccfNA extraction.

## 2.4 | ccfNA Extraction

Plasma samples were thawed at room temperature and centrifuged at 16,000g for 10 min at room temperature (Figure 1A). 3.5 mL of plasma was then used to isolate ccfNAs, including ccfDNAs and ccfRNAs, using the column-based QIAamp ccfDNA/RNA kit (Qiagen) according to the manufacturer's instructions. 50 µL of H₂O was used for elution, and the eluate was aliquoted in twintec PCR Plates LoBind (Eppendorf) and stored at -80°C until use (2 × 24-µL).
## 2.5 | Plasma Hemolysis Measurement by UV Absorbance

Plasma hemolysis was assessed using a previously described spectrophotometric-based method (Appierto et al. 2014), using a NanoDrop 2000c Spectrophotometer (Thermo Scientific). We first assessed the method on calibration samples made by spiking a pool of very clear non-hemolyzed plasma samples with red blood cells (RBC) at different proportions: 0.002%, 0.004%, 0.008%, 0.0016%, 0.031%, 0.0625%, 0.125%, 0.25% and 2.5%. Spiked RBC was prepared after isolation from plasma using multiple cycles of freezing, thawing and vortex mixing. Absorbance measurement were carried out using 2µL of plasma. Absorbances at 385 nm (A<sub>385</sub>) and 415 nm (A<sub>414</sub>) were measured to calculate the Hemolysis Score (H.S.) with the published formula: H.S. = (A<sub>414</sub> - A<sub>385</sub>) / (A<sub>414</sub> + A<sub>385</sub>) + 0.16 (Appierto et al. 2014). A H.S. threshold of 0.062 was used to consider a sample hemolyzed, calculated by the following equation: H.S. = ((A<sub>414</sub> - A<sub>385</sub>) / (A<sub>414</sub> + A<sub>385</sub>)) + 3 × SD<sub>H.S. non-hemolyzed samples</sub> as recommended (Appierto et al. 2014). Thus, very clear non-hemolyzed plasma samples were used to calculate the H.S. threshold of 0.062 for hemolysis, and the calibration plasma samples spiked with 0.002%–0.0625% RBC were thereby found non-hemolyzed.

## 2.6 | Quantification of Total ccfDNA by High-Sensitivity Fluorometry

Total ccfDNA was measured by fluorometric quantification using the Qubit dsDNA HS Assay Kit on a Qubit 3 Fluorometer (Life Technologies) according to the manufacturer's instructions. 2 µL of ccfNA was used for measurement and each sample was analyzed in duplicate. The mean value of the two measurements was used as the total ccfDNA concentration.

## 2.7 | Total ccfDNA Profiling by High-Sensitivity Capillary Electrophoresis

2µL of total ccfDNA was analyzed by high-sensitivity pulse-field capillary electrophoresis using the Ultra Sensitivity NGS kit on a Femto Pulse system (Agilent), following the manufacturer's instructions. After analysis, ccfDNA profiles were obtained, and the concentrations of total ccfDNA as well as the 1st and 2nd nucleosome peaks were obtained using the calibrated ladders for quantification.

## 2.8 | Primer and Probe Design and Efficiency Determination

All primers and probes are listed in Table S1, including commercial assays, previously published and newly designed primers and probes. New qPCR primers and probes were designed using Beacon Designer 8 (PREMIER Biosoft). New bisulfite sequencing PCR and pyrosequencing primers were designed using MethPrimer (http://www.urogene.org/cgi-bin/methprimer/methprimer.cgi) and the SNP Primer design software (Qiagen), respectively. All oligonucleotides were purchased from Eurogentec using RP-Gold purification for unmodified oligonucleotides and HPLC purification for modified oligonucleotides.

All qPCR assay efficiencies were determined from standard curves generated from serial dilutions of a control sample (human leukocyte gDNA (Promega) for ccfDNA qPCR assays, control cDNA prepared from 1:1 PBMC/liver RNA (Ambion) for ccfrRNA, ccfmRNA and ccfmiRNA qPCR assays), and exhibited efficiencies between 1.72 and 2.12 (Table S1).

## 2.9 | Reverse Transcription

### 2.9.1 | ccfmRNA and ccfrRNA

ccfRNA was reverse transcribed using the Quantitect Reverse Transcription Kit (Qiagen). 6 µL of input ccfNA was mixed with 2 µL of gDNA wipeout buffer and 6 µL of RNase-free water. The mixture was incubated for 2 min at 42°C to eliminate DNA. Then, a 20-µL reverse transcription (RT) reaction was prepared, containing the initial 14 µL mix, 1 µL of reverse transcriptase, 4 µL of RT buffer 5X, and 1 µL of RT primer mix. The RT reaction was incubated at 42°C for 30 min, followed by 3 min at 95°C to inactivate the reverse transcriptase. cDNA was stored at -20°C before ccfrRNA and ccfmRNA analysis. A control cDNA sample was also prepared from 1 µg of pooled 1:1 PBMC/liver RNA (Ambion) using the same protocol described above.

### 2.9.2 | ccfmiRNA

cccfmiRNAs were reverse transcribed using the miRCURY LNA RT Kit (Qiagen). The RT reaction contained 5 µL ccfNAPs, 2 µL 5X miRCURY SybrGreen RT reaction Buffer, 1 µL 10X miRCURY RT Enzyme, 0.5 µL UniSp6 SpikeIn (10⁸ copies/µL) and 1.5 µL RNase-free water. The mixture was incubated for 60 min at 42°C followed by 5 min at 95°C. Samples were stored at -20°C. A control cDNA sample was also prepared from 1 µg of pooled 1:1 PBMC/liver RNA (Ambion) using the same protocol described above.

## 2.10 | Quantitative PCR

Real-time PCR was used for absolute quantification (AQ) of ccfNAs. All PCR reactions were performed in 384-well plates (Roche) with a final reaction volume of 10 µL on a LightCycler 480 II Real-Time PCR System (Roche). During PCR preparation, samples were always processed in 96-well LoBind plates (Eppendorf). PCR mix and ccfNA samples were distributed in 384-well PCR plates by a Bravo Automated Liquid Handling Platform (Agilent). ccfDNA (i), ccfmRNA, and ccfrRNA (ii), and ccfmiRNA (iii) were analyzed on separate PCR plates.

### 2.10.1 | ccfDNA, ccfmRNA and ccfrRNA Assays

ccfDNA (Kpn I, APP and mtDNA) and 18S ccfrRNA assays were based on Syto 9 detection, while ccfmRNA assays (GAPDH, B2M and GSTA1/GSTA2) were based on hydrolysis probe detection. PCR reactions contained 1X Buffer, 1.625 mM MgCl₂, 200 µM dNTPs, 200 nM PCR primers, 1.6 µM Syto 9 or 100 nM hydrolysis probes, 0.05 U/µL HotStarTaq DNA polymerase (Qiagen), and 2 µL of a 20-fold dilution of ccfNA for ccfDNA analysis or
2µL of a 4-fold dilution of cDNA for ccfRNA analysis. Cycling conditions included an initial denaturation for 10 min at 95°C, followed by 50 cycles of amplification (95°C for 30 s, 60°C for 30 s for ccfDNA or 60 s for ccfRNA, and 72°C for 45 s), and a final melting curve analysis from 65°C to 95°C (5 acquisition per °C) to assess amplicon specificity for PCR assays based on Syto 9 detection. A control human genomic DNA (Promega) and a control cDNA sample were added to PCR plates with ccfDNA and ccfRNA assays, respectively. These samples were used as interplate calibrators for AQ.

AQ in ng/mL of plasma was performed for each ccfDNA PCR assay using standard curves obtained from serial dilutions of human genomic DNA (Promega) ranging from 2 to 0.0002 ng/µL for Kpn I assays, and 9–0.0009 ng/µL for APP assays. Absolute concentrations were obtained using these standard curves and two interplate calibrators included in the PCR reactions, using the AQ module of the LightCycler480 Software (Roche).

AQ in copies/mL of plasma was also performed for each ccfDNA, ccfmRNA, and ccfrRNA PCR assay using standard curves generated from serial dilutions of the respective purified PCR amplicon. Briefly, a first PCR reaction was performed for each assay in a final PCR reaction volume of 20 µL, using the same concentrations and cycling conditions as above. After amplification, PCR product profiles were verified by 2% agarose gel electrophoresis and purified using AMPure XP beads (Beckman Coulter) with different bead ratios, from 1X to 3X, to eliminate unspecific PCR products. The purified PCR products were quantified in triplicate using the Qubit dsDNA HS Assay Kit (Life Technologies) and verified for their length and absence of unspecific amplification products on a Fragment Analyzer system (Agilent) using the DNF-477 HS Small Fragment Kit, according to the manufacturer's instructions. Finally, for each amplicon, qPCR reactions were performed in duplicate using the purified PCR products serially diluted from 10 to 10, from 25,000,000 to 25 copies/µL. PCR reactions and cycling conditions were the same as above and included two PCR replicates of the interplate calibrators. The absolute concentrations of all amplicons of each ccfNA sample were calculated using these standard curves and the interplate calibrators using the AQ module of the LightCycler480 Software (Roche). Each calculated amplicon copy number was thereby reported per mL of plasma.

### 2.10.2 | ccfmiRNA Assays

10 miRCURY LNA miRNA PCR Assays (Qiagen) were used for ccfmiRNA analysis (Table S1). PCR reactions were prepared using the miRCURY LNA SYBR Green PCR Kit (Qiagen). The PCR mix contained 3 µL of 30-fold diluted cDNA, 1X miRCURY SYBR Green Master Mix, 1 µL miRCURY LNA miRNA PCR assay, and 1 µL RNase-free water in a final volume of 10 µL. Cycling conditions included an initial denaturation for 2 min at 95°C, followed by 50 cycles of 10 s denaturation at 95°C, a combined annealing/elongation step at 56°C for 1 min, and a final melting curve analysis from 65°C to 95°C (5 acquisition per °C) to assess amplicon specificity for SYBR detection PCR assays.

AQ of each miRNA in copies/mL of plasma was performed using the Ct value obtained with the UniSp6 spike-in. Due

to the known and fixed quantity of this synthetic small RNA spiked in each ccfNA sample before reverse transcription and to miRNA assay efficiencies that were close to 100% (Table S1), it was possible to calculate the quantity of each miRNA in a sample using the following formula: miRNA quantity = quantity of UniSp6 × $2^{Ct_{miRNA} - Ct_{miSp6}}$. Relative expression of age-associated miRNAs (*miR-21-5p*, *miR-126-3p* and *miR-483-5p*), and liver-specific *miR-122-5p* could also be calculated by normalizing their concentrations by the geometric mean of the concentrations of the three housekeeping-miRNA, that is, *mir-93-5p*, *miR-22-3p*, and *miR-320d*.

Plasma hemolysis was also evaluated by qPCR using *miR-451a* and *miR-23a-3p* Ct values. *miR-23a* is not affected by hemolysis, while *miR-451a* is enriched in erythrocytes (Smith et al. 2022). For each sample, we calculated the $Ct_{hsa-miR-23a-3p} - Ct_{hsa-miR-451a}$ value. A difference higher than 7 indicated hemolysis (Smith et al. 2022). This miR-based delta Ct method recommended by Qiagen was compared to the hemolysis score (H.S.) based on plasma UV absorbance (Appierto et al. 2014) (Figure S1A,D). Using calibration plasma samples spiked with increasing percentages of red blood cells, both methods detected hemolysis and were strongly correlated (Figure S1B). However, the qPCR method proved more sensitive when using the recommended threshold. Applied to our samples, the H.S. correlated positively with $Ct_{miR-23a-3p} - Ct_{miR-451a}$ and the ratio of *miR-451a/miR-23a-3p* copies/mL, but not with each miRNA Ct or concentration alone (Figure S1C,D). Using the miRNA-based method, six samples showed $Ct_{miR-23a-3p} - Ct_{miR-451a} > 7$ and were excluded for downstream analyses (Figure S1C).

### 2.11 | Integrity Analysis

The integrity of ccfDNA and ccfRNA was determined by calculating the integrity indexes from qPCR results, and also from capillary electrophoresis data for ccfDNA. The qPCR integrity index was defined as the ratio of a large amplicon quantity to the smallest. Integrity values close to 1 indicated unfragmented ccfNAs, whereas values close to zero indicated fragmented ccfNAs. For capillary electrophoresis, the 1st and the 1st + 2nd peak concentrations were divided by the total amount of ccfDNA to calculate the integrity indexes. Integrity indexes close to 0 indicated unfragmented ccfNAs, while values close to 1 indicated fragmented ccfNAs.

### 2.12 | DNA Methylation Analysis by Pyrosequencing

Bisulfite conversion of ccfDNA was performed using 6 µL of plasma ccfNAs and the EZ-96 DNA Methylation-Gold Kit (Zymo Research), according to the manufacturer's instructions. Converted ccfDNA was stored in LoBind plates at -20°C before use. For optimization experiments, DNA methylation standards with 0%, 25%, 50%, 75%, and 100% DNA methylation levels and concentrations of 0.01, 0.1, 1, and 10 ng/µL were prepared using converted methylated and unmethylated human DNA from the EpiTect PCR Control DNA Set (Qiagen). Two *Line1* amplicons were amplified in a 25 µL reaction by standard PCR on a LightCycler 480 Real-Time PCR System (Roche).
PCR reactions contained 2µL converted ccfDNA (or 1µL of DNA methylation standard), 1X Buffer, 1.625 mM MgCl₂, 200µM dNTPs, 200nM PCR primers, 1.6µM Syto 9, and 0.08 U/µL Hot Start Taq DNA polymerase (Qiagen). Cycling conditions included an initial 10-min denaturation at 95°C, followed by 45 cycles of amplification (30s denaturation at 95°C, 30s primer annealing at 60°C, and 30s elongation at 72°C for), and a final elongation step at 72°C for 5min. PCR products were stored at -20°C before sequencing. 10µL of PCR products were purified using a previously described protocol (How-Kit et al. 2015). Pyrosequencing was performed using the PyroMark Gold SQA Q96 Kit (Qiagen) on a PyroMark Q96 MD (Qiagen) using pyrosequencing sequences (Table S1), and the DNA methylation data were analyzed with PyroMark CpG software (Qiagen). We excluded one, seven, and thirteen DNA methylation outliers for CpGs 1, 2, and 4, respectively, as their DNA methylation percentages were close to or equal to 100%, which is much higher than the 100% DNA methylation standard.

**2.13 | Statistical Analysis**

All analyses were performed using GraphPad Prism version 10.2.3 for Windows (GraphPad Software) and R (GPL) for PCA analysis. Correlation analysis was performed using the Pearson test with the associated *r* correlation coefficient and *p*-values. Significant differences between groups were assessed using the Mann–Whitney *U* test, either by comparing age groups of women, men, and all individuals pairwise, or by considering both sexes within the same age range. *p*-values are two-sided, and *p*-value <0.05 is considered significant; *<0.05, **<0.01, ***<0.001, ****<0.0001. PCA analysis was performed using the *FactoMineR_2.3* package using reduced centered data from our analysis. Missing values were replaced by the median of the variable in the data matrix. ccfmiRNA quantifications were included for hemolyzed plasma samples to avoid missing values in the data matrix.

**3 | Results**

**3.1 | Description of the Overall Strategy Used for Multiparametric Analysis**

We optimized an experimental workflow for the comprehensive analysis of variations in several plasma ccfNA parameters occurring during aging. It included two centrifugation steps for plasma isolation from EDTA blood before -80°C storage, and one centrifugation after thawing before the simultaneous isolation of all ccfNAs using 3.5 mL of plasma and the QIAamp ccfDNA/RNA kit (Figure 1A). To drastically reduce the amount of ccfNAs required for each analysis without losing analytical sensitivity, especially for the longest amplicons, we targeted highly repetitive DNA (*Kpn I*, *Line1* and mtDNA) and highly abundant RNA (*18S* rRNA and mRNA from housekeeping genes) sequences. Highly sensitive (HS) fluorometric quantification was used to assess total ccfDNA quantity, while HS capillary electrophoresis was used to assess both its quantity and integrity. Global ccfDNA methylation was measured by pyrosequencing, and qPCR assays were

used to quantify and assess the integrity of both ccfnDNA and ccfmtDNA. RT-qPCR was used to quantify ccfrRNA, ccfmRNA, and ccfmiRNA, and assess the integrity of ccfRNA. Each sample was also controlled for hemolysis, either directly on the plasma or by RT-qPCR using two specific miRNAs. The different volumes of ccfNAs used for each analysis are given in Figure 1B.

**3.2 | Comparison of ccfDNA Quantification Methods**

The quantification of ccfDNA is a critical step in the analysis workflow and could sometimes be challenging (Mauger et al. 2015; Ungerer et al. 2020). We therefore measured and compared ccfDNA concentrations using three reference methods: dsDNA fluorometric quantification (Qubit fluorometer), high-sensitivity pulse-field capillary electrophoresis (Femto Pulse), and qPCR assays targeting a small segment (60–67 bp) of either *APP*, a single-copy gene, or *Kpn I* repeats (Figure S2A). ccfDNA quantifications exhibited mean values of 4.18 ± 2.04, 4.79 ± 3.23, and 2.30 ± 1.45/2.25 ± 1.47 ng per mL of plasma with Qubit, Femto Pulse, and *Kpn I/APP* qPCR quantification, respectively. All correlations were highly significant (2.43 × 10⁻⁴¹ ~ p ~ 2.32 × 10⁻⁴), with strong correlations (r > 0.5) found between the three quantification methods, except for the Femto Pulse and *APP* qPCR assay, exhibiting moderate correlation (r = 0.3) (Figure S2B). Interestingly, the best correlation (r = 0.88, p = 2.43 × 10⁻⁴¹) was found between the Qubit and *Kpn I* qPCR quantification, and not between the two qPCR assays, suggesting that *Kpn I* qPCR and Qubit could be used interchangeably for ccfDNA quantification. Notably, an overestimation of ccfDNA concentration was generally found with the Femto Pulse compared to the other methods, and also with the Qubit compared to PCR assays. qPCR assays quantified amplifiable ccfDNA of a minimum length and should always present lower values compared to whole DNA quantification methods. This was also clearly illustrated by the quantifications performed with the other qPCR assays amplifying larger DNA fragments (Figure S3).

**3.3 | Levels of ccfnDNA, but Not ccfmtDNA Levels, Increase With Age and Decrease in Integrity**

Absolute quantification was performed using different sets of qPCR assays amplifying DNA sequences of different lengths ranging from 60 to 421 bp and 100–417 bp for ccfnDNA and ccfmtDNA, respectively (Figure 2A). Copy number analysis showed a slight increase in nuclear ccfDNA level between 19 and 66 y.o. with each assay, which was only significant for the smallest *Kpn I* amplicons (60–168 bp, 0.002 < *p* < 0.02, Figures 2B and S4A,B). Using the 60-bp *Kpn I* assay, values ranged from 5.83 × 10⁵ to 9.99 × 10⁶, with a mean value of 2.45 × 10⁶ copies per plasma mL (RSD = 61.25%). When comparing ccfnDNA levels considering the three age groups, the most significant change (*p* = 0.0003) was found with the 60-bp assay between G3 and the younger group. This significant difference was also found in women (*p* = 0.0003) but not in men when the sex was considered. Similar differences were also found with the larger assays, but their significance
<figure><img src="image_6.png" /><figcaption>**FIGURE 2** | Plasma ccfDNA quantity and integrity variation during aging (n = 139). (A) ccfnDNA and ccfmtDNA qPCR assays used. (B) ccfnDNA quantification using Set 1 *Kpn I* repeat assays. (C) ccfmtDNA quantification. (D) ccfDNA integrity assessment using the integrity index based on the ratio of a large PCR amplicon to the smallest one. Mann–Whitney *U* tests were performed in box-plots between each age group and between men and women of a same age group. The ‘+’ symbol indicates the mean value obtained for each group. Pearson’s *r* coefficients and associated *p*-values as well as linear regression lines are shown in scatterplots. *p*-values < 0.05 are considered significant; \* < 0.05, \*\* < 0.01, \*\*\* < 0.001.</figcaption></figure>
gradually disappeared with increasing length of the ampli-
cons (Figures 2B and S4B).

We also analyzed quantification data obtained with other
methods, which yielded similar results to those obtained with
the *Kpn I* repeat. Thus, all methods showed an increase in
ccfDNA levels with age, although only Qubit quantification
reached significance (*p* = 0.012) in the correlation analysis
(Figures S4C and S5). Furthermore, significant differences
were found between G1 and G3 (all, *p* = 0.0026, and women,
*p* = 0.0076) using the *APP* 67-bp qPCR assay and Qubit quanti-
fication (Figures S4C and S5). In contrast, Femto Pulse quan-
tification, using either the first peak, the first two peaks, or
the total ccfDNA amount, showed a significant increase only
in women.

Contrary to ccfnDNA, the two qPCR assays targeting ccfmtDNA
showed no significant variation in their copy number with
age in correlation analyses or group comparisons (Figure 2C).
Calculation of the ccfmtDNA copy number per genome equiva-
lent using the 100-bp mtDNA and the 67-bp APP assays revealed
highly variable values ranging from 8 to 12,903 copies, with a
mean value of 696 copies per genome equivalent (RSD = 240.5%).
Moreover, a clear and nearly significant decrease in ccfmtDNA
copies per genome equivalent (r = -0.16, p = 0.056) was ob-
served during aging (Figure 2C). The levels of linear and cir-
cular ccfmtDNA were also assessed following Exonuclease V
treatment of ccfNAs. Despite a median ratio between the two
forms close to 1, there was high inter-individual variability,
with a few individuals presenting only one form of ccfmtDNA
(Figure S6A,B). The proportion of circular ccfmtDNA showed
significant positive correlations with total ccfmtDNA levels
(r = 0.23, p = 0.011) and copy number per genome equivalent
(r = 0.26, p = 0.0039, Figure S6C). Circular mtDNA also exhib-
ited a decreasing trend with age in both mtDNA levels and cop-
ies per genome equivalent (Figure S6D,E). Of note, buffy coat
mtDNA copies per genome equivalent did not vary with age or
correlate with plasma values (Figure S7).

We also assessed ccfDNA integrity using an integrity index
based on the ratio of a large amplicon to the smallest from each
set of qPCR assays (Figure 2A). *Kpn I* 421/149 and 249/149 integ-
rity indexes showed a significant decrease with age (r = -0.20,
p = 0.0197 and r = -0.21, p = 0.0156, respectively), which was not
visible with the 168/149, 134/60, and 300/60 indexes (Figures 2D
and S4B). The decrease in the 421/149 integrity index was signif-
icant in G3 compared to G1, whether all individuals (p = 0.0148)
or only women (p = 0.0455) were considered (Figures 2D and
S4B), while the 134/60 and 300/60 integrity indexes were signifi-
cantly higher in young women than in young men (p = 0.0224
and p = 0.0175, respectively). On the contrary, this loss of integ-
rity was not observed for ccfmtDNA during aging (Figure 2D).
We also evaluated the ability of the Femto Pulse to assess
ccfDNA integrity by calculating an integrity index using the
ratio of the 1st and/or 2nd peak to the total ccfDNA concentra-
tion (Figure S8). The 1st peak/Total and 1st + 2nd peaks/Total
integrity indexes significantly correlated with age (r = 0.28,
p = 0.00096 and r = 0.25, p = 0.003, respectively), indicating a
loss of ccfDNA integrity during aging. Age-group comparisons
showed significant differences in integrity indexes between
G3 and G1 (all: p = 6.9 × 10⁻⁵ and p = 0.0003, respectively; and

women: *p* = 0.0013 and *p* = 0.0012, respectively) and sometimes
revealed significant sex differences (Figure S8).

3.4 | Global ccfDNA Methylation Did Not Vary During Aging

To assess the compatibility of our protocol with minute and
degraded DNA, we optimized two *Line1* repeat PCR and py-
rosequencing assays for global ccfDNA methylation estimation
using decreasing amounts (10, 1, 0.1, and 0.01 ng) of DNA meth-
ylation standards ranging from 0% to 100% (Figure 3A,B). *Line1*
DNA methylation profiles obtained with 10 ng of template DNA
revealed three CpGs (*CpG<sub>2</sub>*, *CpG<sub>8</sub>*, and *CpG<sub>11</sub>*) with reduced
DNA methylation percentages, which were excluded from all
analyses. Analysis of the eight remaining CpGs showed that pi-
cogram quantities of DNA had little effect on the DNA methyla-
tion levels (Figure 3C), indicating that the two assays should be
compatible with plasma ccfDNA. Global ccfDNA methylation
was thereby assessed in the entire cohort and, despite a weak
positive correlation (*r* = 0.1376), showed no significant changes
during aging from correlation analyses and in age and sex group
comparisons (Figure 3D). Considering each CpG separately, a
significant increase in ccfDNA methylation was observed in G3
compared to G1 with CpG<sub>3</sub> (*p* = 0.0174) and in G2 men compared
to G2 women with CpG<sub>1</sub> (*p* = 0.0119, Figure S9). Moreover, when
analyzing buffy coat samples, *Line1* DNA methylation did not
show any correlation with age or plasma values (Figure S10).
Overall, no change in global ccfDNA methylation was observed
during aging.

3.5 | Levels of ccfRNA Increase With Age but Do Not Vary in Integrity

Analysis of plasma ccfRNA abundance and integrity was per-
formed using RT-qPCR assays targeting 18S rRNA and four
mRNAs (Figure 4A). We used *GAPDH* and *B2M*, two ubiqui-
tously expressed housekeeping genes, as proxies for total ccfm-
RNA load, and also evaluated the mRNA levels of *GSTA1/GSTA2*,
which are specifically expressed in the liver, kidney, and adrenal
glands (Mlakar et al. 2021; Ng et al. 2021).

The results first showed significant moderate to strong linear
correlations (0.57 < *r* < 0.84, 3.8 × 10⁻³⁹ < *p* < 7.5 × 10⁻¹⁴) between
*GAPDH* mRNA, *B2M* mRNA, and 18S rRNA concentrations
(Figure 4B), indicating that plasma ccmRNA and ccfrRNA
levels are closely related. The number of 18S rRNA, *GAPDH*,
and *B2M* mRNA copies per plasma mL were also very variable
among individuals, from 9.50 × 10⁵ to 3.15 × 10⁷ (59-bp assay),
3.18 × 10³–7.86 × 10⁴, and 2.17 × 10³–1.19 × 10⁶, with a mean
value of 6.73 × 10⁶ (RSD = 70.82%), 1.59 × 10⁴ (RSD = 77.02%) and
6.87 × 10⁴ (RSD = 262.5%), respectively. 18S rRNA quantifica-
tion results with the four assays revealed a significant increase
with age (0.176 < *r* < 0.195 and 0.021 < *p* < 0.039), while age-
group comparisons were significant between G1 and G3 using
the 59 (*p* = 0.0195) and 115-bp (*p* = 0.0339) assays (Figures 4C
and S11A). A significant increase was also observed for *GAPDH*
ccfmRNA (*r* = 0.19, *p* = 0.02) but not with *B2M* ccfmRNA during
aging (Figure 4C). This increase in *GAPDH* ccfmRNA level was
significant between the youngest and oldest groups, whether all
A

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C

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D

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**FIGURE 3** | Global plasma ccfDNA methylation variation during aging. (A) *Line1* pyrosequencing assays. (B) DNA methylation profile obtained by pyrosequencing using 10 ng of DNA methylation standards ranging from 0 to 100% methylation (duplicate experiments). Shaded gray bands indicate excluded CpGs. (C) *Line1* DNA methylation difference calculated between two different quantities of DNA methylation standards used as input (duplicate experiments). (D) Global ccfDNA methylation variation during aging measured using the mean DNA methylation value of *Line1* (CpG<sub>1,3-7,9-10</sub>) (*n* = 139). Mann–Whitney *U* tests were performed in box-plots between each age group and between men and women of the same age group. The ‘+’ symbol indicates the mean value obtained for each group. Pearson’s *r* coefficients and associated *p*-values, as well as linear regression lines, are indicated in scatterplots. *p*-values < 0.05 are considered significant; \* < 0.05, \*\* < 0.01, \*\*\* < 0.001.

individuals (*p* = 0.012) or only women (*p* = 0.021) were consid-
ered. Furthermore, by targeting *GSTA1* and *GSTA2* in a single
assay, we were able to detect these transcripts in 16.5% of the
plasma samples. Depending on their expression level, either ex-
pressed in relative expression or in copies/mL (Figure 4C), no
change or a slight increase (*r* = 0.35) was observed during aging,
respectively. Despite their low abundance, ccfmRNAs from
minor contributing organs can be detected in plasma, suggest-
ing potential applications as surrogate, non-invasive biomark-
ers of internal tissues and organs, particularly in the context of
disease. Integrity analysis was also performed with 18S rRNA
assays using the integrity index (Figures 4D and S11B). In con-
trast to ccfDNA, no significant correlation was found between
ccfrRNA integrity and aging, regardless of the combination of
assays used.

3.6 | Evaluation of Age-Related and Liver-Specific ccfmiRNA Levels in Plasma

We assessed the expression of several housekeeping (mir-93-5p, miR-22-3p, and miR-320d) (Faraldi et al. 2019; Korma et al. 2020), age-associated (miR-126-3p, miR-21-5p, miR-483-5p) (Rusanova et al. 2018; Tessier et al. 2023), liver-specific (miR-122-5p) (Hu et al. 2022) and hemolysis control (miR-23a-3p and miR-451a) ccfmiRNA in plasma samples of the entire cohort. Six samples considered to be hemolyzed were excluded from our analyses (Figure S1).

Absolute quantification analysis revealed that the levels
of housekeeping miRNAs (mir-93-5p, miR-22-3p, and miR-
320d) did not vary with age, as expected (Chen et al. 2020)
<figure><img src="image_11.png" /></figure>

C

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B

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D

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<figure><img src="image_24.png" /><figcaption>**FIGURE 4** | Variation of plasma ccfRNA quantity and integrity during aging (n=139). (A) mRNA and rRNA qPCR assays used for ccfRNA quantification. (B) Correlation of *B2M* ccfmRNA, *GAPDH* ccfmRNA, and *18S* ccfrRNA quantity in plasma. (C) Variation of *18S* ccfrRNA and *B2M*, *GAPDH*, and *GSTA1/GSTA2* ccfmRNAs during aging. (D) Assessment of ccfrRNA integrity using the integrity index based on the ratio of a large *18S* amplicon to the smallest one. Mann-Whitney *U* tests were performed in box-plots between each age group and between men and women of the same age group. The '+' symbol indicates the mean value obtained for each group. Pearson's *r* coefficients and associated *p*-values, as well as linear regression lines, are indicated in scatterplots. *p*-values < 0.05 are considered significant; \* < 0.05, \*\* < 0.01, \*\*\* < 0.001.</figcaption></figure>
(Figure S12A). These miRNAs were subsequently used as normalizers to calculate the relative expression of other miRNAs. Among the age-related ccfmiRNAs tested, only *miR-483-5p* correlated significantly with age using either absolute quantification (*r* = 0.196, *p* = 0.024, Figure S12B) or relative expression (*r* = 0.2974, *p* = 5.1 × 10⁻⁴, Figure 5). Group comparisons showed significantly higher concentrations in G3 compared with G1 for all individuals (*p* = 0.0089) and for men (*p* = 0.032). Liver-specific *miR-122-5p* was detected in all plasma samples and showed significantly lower levels in G2 compared to G1, for both all adults (*p* = 0.0374) and women (*p* = 0.0284), but only when considering absolute concentration (Figure S12C).

As our targeted RT-qPCR approach may have missed several age-associated miRNAs, we performed miRNA-sequencing of ccfmiRNAs from 24 additional plasma samples: 12 from young adults (29–39 years old) and 12 from older adults (50–68 years old). Differential expression analysis identified 32 over-expressed and 15 under-expressed miRNAs in older compared to younger individuals, using thresholds of 2-fold expression change and *p* < 0.05 (Figure S13 and Table S2). Of these, 4 (*miR-30b-5p*, *miR-885-5p*, *miR-23b-3p* and *miR-3659*) and 1 (*miR-20a-3p*), respectively, remained significant after applying

a 10% FDR correction. Notably, among the three candidate age-associated miRNAs investigated, only *miR-483-5p* was significantly increased in older individuals (*p* = 0.00993, without FDR correction), which confirms our RT-qPCR results.

## 3.7 | ccfDNA and ccfRNA Levels Are Correlated but Display Different Patterns

We first investigated whether there was a correlation between the amounts of ccfDNA and ccfRNA among healthy individuals. Figure 6A shows that ccfnDNA concentrations using the *Kpn I* repeat 60-bp assay correlated weakly but significantly with ccfmRNA *GAPDH* assay (*r* = 0.35, *p* = 1.8 × 10⁻⁵) and with ccfrRNA using the 18S 59-bp assay (*r* = 0.34, *p* = 3.9 × 10⁻⁵). Moreover, ccfnDNA was also significantly correlated to the only ccfmiRNA that increased significantly during aging in our study, *miR-483-5p* (*r* = 0.40, *p* = 2.1 × 10⁻⁶), and also to liver-specific *miR-122-5p* (*r* = 0.26, *p* = 0.003). Using the mtDNA 100-bp assay, ccfmtDNA concentrations correlated significantly and strongly with *B2M* ccfmRNA levels (*r* > 0.8, 3.1 × 10⁻³³), and significantly but moderately with *GAPDH* ccfmRNA (*r* = 0.5, *p* = 2.2 × 10⁻¹⁰) and 18S ccfrRNA using the 59-bp assay (*r* = 0.42,

<figure><img src="image_25.png" /><figcaption>**FIGURE 5** | Variation in the levels of different plasma ccfmiRNAs during aging (n = 133 samples). Age-associated (*miR-126-3p*, *miR-21-5p*, *miR-483-5p*) and liver-specific (*miR-122-5p*) ccfmiRNA relative expression levels during aging. Mann–Whitney U tests were performed in box-plots between each age group and between men and women of the same age group. The ‘+’ symbol indicates the mean value obtained for each group. Pearson’s *r* coefficients and associated *p*-values, as well as linear regression lines, are shown in scatterplots. *p*-values < 0.05 are considered significant; \* < 0.05, \*\* < 0.01, \*\*\* < 0.001.</figcaption></figure>
A

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B

<figure><img src="image_27.png" /><figcaption>**FIGURE 6** | Legend on next page.</figcaption></figure>
**FIGURE 6** | Comparison of ccfDNA and ccfRNA variations in plasma (n = 139 samples). (A) Correlation of ccfnDNA and ccfmtDNA, using *Kpn I* 60 bp and mtDNA 100 bp assays respectively, with ccfmRNA (*GAPDH* 102 bp, *B2M* 102 bp), ccfrRNA (*18S* 59 pb), and ccfmiRNA (*miR-126-3p*, *miR-21-5p*, *miR-483-5p*, *miR-122-5p*). Linear regression lines are shown, Pearson's *r* correlation coefficients and associated *p*-values are indicated in each graph. *p*-values < 0.05 are considered significant. (B) Principal component analysis of the different variables used. The contribution of the variables to the first two components is indicated by a color scale. II, integrity index.

$p=2×10^{-7}$). We also observed a significant correlation of ccfmtDNA with *miR-126-3p* (*r*=0.42, *p*=4.7×10⁻⁷) and *miR-21-5p* (*r*=0.43, *p*=2.2×10⁻⁷) (Figure 6A). Notably, ccfnDNA and ccfmtDNA showed similar age-related patterns to those of miRNAs with which they correlate, ccfnDNA and *miR-483-5p* increasing during aging, whereas ccfmtDNA, *miR-126-3p*, and *miR-21-5p* showed no correlation with age.

We further performed a principal component analysis (PCA) and heatmap clustering analysis using most of the parameters measured in our study. Both analyses showed no separation of the samples according to age and/or sex (Figure S14). In contrast, the variable correlation plots showed two clusters: one on the lower right composed of ccfnDNA quantification by different qPCR assays and a second on the upper right including mtDNA, mRNA, and rRNA qPCR-based quantifications, while integrity indexes had little contribution to the variance of the first two principal components and were localized in the central region (Figure 6B). Similar variable clusters were also visible in the heatmap clustering analysis (Figure S14B).

4 | Discussion

In this study, we developed an optimized workflow for the com-
prehensive analysis of several types of plasma ccfNAs during
aging in healthy individuals. It enabled us to perform an integra-
tive and multiparametric analysis of different ccfNAs, includ-
ing ccfDNA (nuclear and mitochondrial) and ccfRNA (mRNA,
rRNA, and miRNA), to investigate the age-related changes in
ccfNA levels and integrities, as well as in ccfDNA methyla-
tion. To our knowledge, this is the first study investigating both
ccfDNA and ccfRNA variations in this context.

Thus, we have reported for the first time correlation analyses
between plasma ccfDNA and ccfRNA levels, as well as integra-
tive analyses using PCA and 2D heatmap clustering of the abun-
dance, integrity, and methylation of plasma ccfNAs (Figures 6
and S14). We showed that ccfmRNA and 18S-ccfrRNA levels
correlated positively with ccfnDNA and ccfmtDNA levels, with
the latter exhibiting the strongest correlation with both ccfRNA
species. In addition, PCA and heatmap clustering analysis re-
vealed two clearly separated variable clusters characterized by
the abundance and integrity of (i) ccfmtDNA, ccfmRNA, and
ccfrRNA, or (ii) ccfnDNA. This suggests that ccfmtDNA, ccfm-
RNA, and ccfrRNA share a similar pattern in plasma, distinct
from that of ccfnDNA. Collectively, all our results highlight
that total ccfDNA and ccfRNA levels in an individual's plasma
are closely related, possibly through a global and common phe-
nomenon such as the release mechanism in the blood. Thus, an
increase in cellular apoptosis, necrosis, and/or secretion might
potentially explain the elevated plasma levels of both ccfDNA
and ccfRNA species. Furthermore, the similar pattern observed

in refined analyses for ccfmtDNA and ccfRNA, but not ccfnDNA,
leads us to speculate that there is also a common release, main-
tenance, and/or clearance mechanism into the bloodstream for
these specific types of ccfNAs. For example, we could speculate
that platelets, which have recently been identified as a major
contributor of plasma ccfRNA (Vorperian et al. 2022), could re-
lease both RNA and mtDNA but not nDNA into the blood.

We quantified total ds ccfDNA, total linear ds ccfDNA, and ccfnDNA using the Qubit fluorometer, the Femto Pulse system, and nuclear DNA qPCR assays, respectively. These three types of ccfDNA are generally considered equivalent in most studies as most ccfDNA is in linear form and double-stranded (Thierry et al. 2016), and due to the extremely high contribution of ccfnDNA to total ccfDNA (Meddeb et al. 2019; Panagopoulou et al. 2024). We observed a significant increase in ccfnDNA levels during aging with Qubit and qPCR assays (*Kpn I* repeat and *APP-67bp*) quantification, respectively. Significant correlations were obtained only with the shortest *Kpn I* assays up to 168 bp (Figure 2 and Figure S4), while most of the significant differences were observed between the oldest group (50–66 y.o) and the youngest (19–39 y.o) in age-group comparisons (Figures 2, S4 and S5). Significant differences were mainly captured by smaller qPCR assays, indicating that the inter-group and inter-individual variabilities of ccfDNA levels might predominantly reside in highly fragmented DNA. Our results align with those of Meddeb et al. (2019), who reported increased ccfDNA levels in plasma of healthy individuals aged 18–69 years. In addition, the increased ccfnDNA levels observed here in older women are also consistent with previous studies (Jylhava et al. 2011; Zhong et al. 2007). The increase in ccfDNA levels with age has been linked to increased inflammation, which is associated with increased cell death in the elderly (Jylhava et al. 2011; Meddeb et al. 2019). Furthermore, integrity analyses revealed a significant decrease in total ccfDNA and ccfnDNA integrity, using the Femto Pulse and *Kpn I* qPCR assays, respectively (Figures 2 and S8). The higher apoptosis-to-necrosis ratio in older individuals previously described (Behrens et al. 2011) may explain an increased contribution of apoptosis to ccfDNA release, resulting in smaller ccfnDNA fragments around 166 bp (Ungerer et al. 2020). Our global ccfDNA methylation results revealed no significant change with age (Figure 3), contrasting with another study that identified hypomethylation of *Line1* elements using MS-PCR (Erichsen et al. 2018). These discrepancies could be due to the different CpGs analyzed and require further validation. Collectively, our findings suggest the potential of ccfnDNA level and integrity as a biomarker of aging.

Although not protected by a nucleosome structure, we were
able to easily detect 100-bp and 400-bp ccfmtDNA fragments
at up to 3.49×10⁶ and 1.91×10⁶ copies/mL of plasma, respec-
tively (Figure 2), as well as very high proportions (up to 100%)
and copy numbers (up to 1.2×10⁴) of circular mtDNA in some
plasma samples (Figure S6). In contrast to ccfnDNA, ccfmtDNA showed no significant age-related variations (Figure 2), which is consistent with some studies reporting no age-related changes in ccfmtDNA levels (Jylhava et al. 2013; Meddeb et al. 2019). However, other studies described a significant increase in ccfmtDNA levels with age, either among healthy participants aged 20–69 years (Padilla-Sanchez et al. 2020) or aged from 1 to 104 years with a peak value at 90 (Pinti et al. 2014). Interestingly, we also observed a highly variable number of ccfmtDNA copies per genome equivalent among participants (RSD = 240.5%), with a clear but non-significant decrease with age for both total and circular ccfmtDNA (Figures 2 and S6). Such variability has also been reported in another study (Meddeb et al. 2019) and might potentially be related to the forms of cell-free mtDNA in blood, which could be free (Bisserier et al. 2021; Szilagyi et al. 2020), in an intact form in circulating mitochondria (Al Amir Dache et al. 2020; Szilagyi et al. 2020), or encapsulated in circulating vesicles (Al Amir Dache et al. 2020; Bisserier et al. 2021; Szilagyi et al. 2020). It could also be related to the particular status of an individual. For example, the inflammatory status was shown to correlate with ccfmtDNA level (Pinti et al. 2014). It could also be related to release, maintenance, and/or degradation mechanisms, which could be distinct from those of ccfnDNA. The detection of large ccfmtDNA fragments (400 bp) and of the circular form at very high genome equivalent levels in some individuals, as well as the positive correlation between the proportions of circular ccfmtDNA and total ccfmtDNA levels, support the hypothesis of protective mechanisms (Al Amir Dache et al. 2020).

Little was known about ccfRNA variations in plasma samples of healthy individuals, and here we report for the first time a significant age-related increase of both ccfmRNA and ccfrRNA levels (Figure 4), similar to ccfnDNA. Quantities measured with GAPDH and 18S qPCR assays showed a positive correlation with age, and higher ccfRNA levels in the oldest participants compared to the youngest. Older women also had higher plasma ccfmRNA levels compared to younger women, as was found with ccfnDNA. Given the global deterioration of transcriptomic activity (Gupta et al. 2021; Perez-Gomez et al. 2020) and decrease in ribosome abundance with aging (Anisimova et al. 2018, 2020), this increase might be potentially caused by an increase in the release mechanism. Unlike ccfnDNA, we did not observe any age-related variation in 18S rRNA integrity. In addition, we observed a high proportion of the large 270-bp rRNA fragment compared to the small 115-bp and 59-bp fragments, and also a high inter-individual variability of 18S rRNA, GAPDH, and B2M mRNA copies in plasma (Figure 4). We speculate that the maintenance of relatively large ccfrRNA fragments in blood might be due to a protective mechanism or structure. However, we cannot ensure that this integrity also includes ccfmRNAs, although they have been shown to be protected in EVs (Kim et al. 2023). Moreover, we could also speculate that the high variability in ccfrRNA and ccfmRNA levels among individuals could be caused by different release, maintenance, and/or clearance mechanisms. Despite the lack of attention given to ccfmRNA and ccfrRNA in ccfnDNA research, they might display a potential as plasma biomarkers of aging.

Among the candidate age-related miRNAs analyzed by RT-qPCR, only *miR-483-5p* exhibited a positive correlation with age, with significantly higher levels in the two oldest groups

and in the oldest men compared to Group 1 and the youngest men, respectively (Figure 5). This result was also observed in miRNA-sequencing analysis of 24 additional plasma samples (Figure S13 and Table S2). Our results are consistent with a previous study (Rusanova et al. 2018), where higher levels of *miR-483* were found in aged individuals (mean age = 76.6 y.o) compared to young subjects (mean age = 20.5 y.o). *miR-483-5p* also correlated with the pro-inflammatory cytokine IL-8 in the same study, while it is also related to the melatonin synthesis pathway (Clokie et al. 2012), and could therefore be implicated in the melatonin decay observed in aging (Martin Gimenez et al. 2022; Rusanova et al. 2018). Therefore, *miR-483-5p* could both be a potential biomarker of inflammation and of healthy aging, as melatonin was described as a hormone with anti-aging properties (Martin Gimenez et al. 2022). In contrast to previous studies (Accardi et al. 2022; Ameling et al. 2015; Olivieri et al. 2014, 2012), surprisingly, we did not observe significant changes in *miR-21-5p* and *miR-126-3p* levels during aging in both RT-qPCR and miRNA-sequencing experiments. However, the design of these studies included oldest participants, that is, octogenarians and nonagenarians, which could explain these discrepancies. Our miRNA-sequencing experiments also identified several miRNAs associated with aging, including *miR-23b-5p*, *miR-30c-5p*, and *miR-30b-5p*, which were previously reported as age-associated ccfmRNAs (Ameling et al. 2015). These associations with aging will require further validation as most of them lost significance after the FDR correction.

While our results provide novel insights into age-related changes in both ccfDNA and ccfRNA levels, their high inter-individual variability observed in our cohort of healthy individuals—also reported for ccfDNA in previous studies (Barbany et al. 2019; Meddeb et al. 2019)—may limit their applicability as biomarkers of aging and/or healthy aging. However, this limitation could be mitigated by different strategies, including the use of (i) optimal (pre-)analytical conditions, (ii) combined biomarkers, and (iii) longitudinal samples from the same individuals. Thus, some studies using lower centrifugation speeds for plasma isolation (700g and 1,000g) and quantifying directly total ccfDNA in plasma in some analyses showed lower inter-individual variability while maintaining significant age-related differences (Jylhava et al. 2012, 2013). Moreover, a composite biomarker could be developed by combining absolute ccfDNA and ccfRNA concentrations with other parameters showing less inter-individual variations but remaining significantly associated with aging, such as ccfDNA integrity indexes (Figure 2D). Additionally, absolute ccfDNA and ccfRNA concentrations could be used in longitudinal plasma samples from the same individuals to detect changes over time and aging that could be indicative of health conditions (Tosevska et al. 2016). Further studies are needed to evaluate the potential of absolute ccfDNA and ccfRNA concentrations as biomarkers of aging and/or healthy aging.

Finally, we would like to highlight some limitations of our study. Although larger than most similar studies, the cohort (n = 139) and group sizes remain limited, especially when considering sex and age together. The absence of individuals over 66 years of age may have prevented the identification of age-related changes sometimes observed in other studies (Accardi et al. 2022; Ameling et al. 2015; Olivieri et al. 2014, 2012). Furthermore, as
our study was mainly based on PCR analysis targeting specific
sequences, we did not fully exploit the information contained in
ccfNAs, such as the nucleosomal footprint present in ccfDNA.
A deeper investigation of the genome, epigenome, and transcrip-
tome by sequencing would provide a more comprehensive un-
derstanding of ccfNA variations during aging and would also
enable multi-omic analyses. Another limitation of our study
concerns the analysis of ccfNAs without considering their spe-
cific origin, particularly those enclosed in actively secreted EVs.
The analysis of ccfNAs contained in EVs would have been im-
portant, as they play a role in intercellular communication and
can carry information about the healthy or pathological state
of the donor cells, tissues, and/or organs (De Toro et al. 2015;
Mathieu et al. 2019).

In conclusion, our optimized multiparametric analysis work-
flow allowed highlighting numerous ccfNA variations occur-
ring during aging and identifying potential biomarkers of aging.
It enables a better understanding of ccfNA biology in healthy
individuals during aging, which will also improve their use in
pathological contexts.

Author Contributions

N.P.T., L.M.H., A.D., C.D., I.H., and C.H. performed the experiments. N.P.T., A.D., M.S., and A.H.-K. analyzed the data, made the figures and tables. A.H.-K. conceived and supervised the project. N.P.T. and A.H.-K. drafted the first version of the manuscript. All authors read, edited, and approved the final version of the submitted manuscript.

Acknowledgments

We acknowledge the financial support from the Agenomics project (Les Mutuelles AXA, programme de mécénat santé 2022—Axe grand âge) and the GENMED Laboratory of Excellence on Medical Genomics [ANR-10-LABX-0013] for the project and/or publication fees. N.P.T. and I.H. are financially supported by the Agenomics project.

Conflicts of Interest

The authors declare no conflicts of interest.

Data Availability Statement

The data that support the findings of this study are available from the
corresponding author upon reasonable request.

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## Supporting Information

Additional supporting information can be found online in the Supporting Information section.