# Computational and digital analyses in the INSPIRE mouse cohort to define sex-specific functional determinants of biological aging

Yohan Santin, Mattia Chiesa, Amélie Alfonso, Yosra Doghri, Ryeonshi Kang, Fraha Haidar, Pilar Oreja-Fuentes, Occiane Fousset, Rana Zahreddine, Mégane Guardia, et al.

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Yohan Santin, Mattia Chiesa, Amélie Alfonso, Yosra Doghri, Ryeonshi Kang, et al. Computational and digital analyses in the INSPIRE mouse cohort to define sex-specific functional determinants of biological aging. Science Advances, 2024, 10 (50), pp.eadt1670. 10.1126/sciadv.adt1670. hal-04938242

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# Computational and digital analyses in the INSPIRE mouse cohort to define sex-specific functional determinants of biological aging

Yohan Santin<fnref n="1" />,²*, Mattia Chiesa³,⁴, Amélie Alfonso⁵, Yosra Doghri², Ryeonshi Kang², Fraha Haidar², Pilar Oreja-Fuentes², Occiane Fousset⁵, Rana Zahreddine², Mégane Guardia², Lucas Lemmel², Mara Rigamonti⁶, Giorgio Rosati⁶, Cédrick Florian⁵, Sébastien Gauzin⁵, Sophie Guyonnet<fnref n="1" />,⁷,⁸,⁹, Yves Rolland<fnref n="1" />,⁷,⁸,⁹, Philipe de Souto Barreto<fnref n="1" />,⁷,⁸,⁹, Bruno Vellas<fnref n="1" />,⁷,⁸,⁹, Bruno Guiard<fnref n="1" />,⁵, Angelo Parini<fnref n="1" />,²*

Biological age, which reflects the physiological state of an individual, offers a better predictive value than chronological age for age-related diseases and mortality. Nonetheless, determining accurate functional features of biological age remains challenging due to the multifactorial nature of aging. Here, we established a unique mouse cohort comprising 1576 male and female outbred SWISS mice subjected or not to high-fat, high-sucrose diet to investigate multiorgan/system biological aging throughout adulthood. Comprehensive functional and biological phenotyping at ages of 6, 12, 18, and 24 months revealed notable sex-specific disparities in longitudinal locomotion patterns and multifunctional aging parameters. Topological data analysis enabled the identification of functionally similar mouse clusters irrespective of chronological age. Moreover, our study pinpointed critical functional markers of biological aging such as muscle function, anxiety characteristics, urinary patterns, reticulocyte maturation, cardiac remodeling and function, and metabolic alterations, underscoring muscle function as an early indicator of biological age in male mice.

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## INTRODUCTION

Chronological age, denoting the time elapsed from birth to a given date, does not reflect an individual's functional status. It falls short in capturing the heterogeneity of the aging process and the concept of healthy aging, which involves maintaining functional capacities (1). This evidence has given rise to the concept of biological age, defining the actual physiological state of individuals (2). As compared to chronological age, biological age better predicts functional decline, age-related diseases, and mortality (3). Biological age depends on changes in cells, tissues and organs, and involves numerous mechanisms, including inflammation (4), persistent cell senescence (5, 6), alteration of the epigenetic clock (7), metabolic dysfunction (8, 9), and impaired tissue regeneration (10). These biological mechanisms of aging lead to the progressive impairment of functional status and to the increased susceptibility to age-related diseases. Although the concept of biological age is now well accepted, accurate determinants for its evaluation are lacking (11). Difficulties in biological age determination mostly stem from its complexity due to nonmodifiable (racial/ethnic and sex) (12, 13) and modifiable (environmental and lifestyle) (14) factors. For instance, studies reported that women live longer than men potentially due to an overall lower biological rate of aging (15), but are frailer than men in late life (16), which suggests

that they are biologically older. In addition, dietary interventions can strongly modify biological aging as demonstrated by calorie restriction (17) or lipid- and sugar-rich diets (18, 19), which respectively slow down or hasten the aging process. Additional issues are related to the influence of specific organs/systems on normal or accelerated biological aging, their reciprocal interactions, and the period of life in which the first signs of heterogeneous biological age appear. Recent studies in mice and humans demonstrated that organs age at different rates within the same individual and organ-specific diseases are associated with the accelerated aging of those particular organs (20–22). However, the contribution of organ-specific aging in defining individual biological age is still to be determined. One of the most fundamental aspects of age-related functional decline is diminished spontaneous locomotion, as it not only hampers independence but also serves as a key precursor to a heightened burden of age-related diseases (23, 24). Nonetheless, the timing relationship between mobility decline and multiorgan changes during aging remains unclear. Addressing this question requires the development of comprehensive approaches that extend beyond traditional metrics, incorporating a diverse array of health parameters to embrace a broader understanding of biological age. In this context, mouse models can be highly valuable if important parameters are carefully selected and assessed. Special consideration should be given to selecting mice that accurately reflect human heterogeneity, including genetic variability and sex-related differences. These considerations are crucial for enhancing the relevance of experimental models to human populations, thereby facilitating better translation between human and animal studies and accelerating the discovery process. No study has performed an exhaustive functional phenotyping of mice across their adult life span, reflecting the functional parameters that determine healthy or accelerated aging in humans.

In this work, we conducted an in-depth study of the trajectories of functional decline and features of biological aging in 1576 mice

<footnote n="1">¹Institut Hospitalo-Universitaire (IHU) HealthAge, Toulouse, France. ²Institute of Metabolic and Cardiovascular Diseases (I2MC), INSERM, University of Toulouse, UPS, Toulouse, France. ³Bioinformatics and Artificial Intelligence Facility, Centro Cardiologico Monzino IRCCS, Milan, Italy. ⁴Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy. ⁵Research Center on Animal Cognition (CRCA), Center of Integrative Biology (CBI), CNRS, University of Toulouse, UPS, Toulouse, France. ⁶Tecniplast S.p.A., Buguggiate, Italy. ⁷Gerontopole of Toulouse, Institute of Aging, Toulouse University Hospital (CHU Toulouse), Toulouse, France. ⁸University of Toulouse III, Toulouse, France. ⁹CERPOP Inserm UMR 1295, Toulouse, France.</footnote>

<footnote>*Corresponding author. Email: yohan.santin@inserm.fr (Y.S.); angelo.parini@inserm.fr (A.P.)</footnote>
from 6 to 24 months old (Fig. 1A). Substantial emphasis was placed on enhancing translational relevance to human clinical phenotyping and biology. To this end, outbred SWISS mice were preferred to inbred strains to better reflect human genetic diversity. In addition, both males and females were studied to address the distinct requirements and nuances of each sex (Fig. 1A). Given its key role as a precursor of age-related functional deterioration, the primary objective of our study was to longitudinally evaluate the patterns of spontaneous locomotion decline and to understand its timing in relation to multifunctional parameters, including anxiety, memory, cardiac, metabolic, muscular, urinary, immune, and hematological domains (Fig. 1, B and 1C). Our goal was then to analyze individual data using advanced computational methods to identify clusters of phenotypically similar mouse populations and define the functional criteria determining biological age (Fig. 1B). A key objective was to further validate these results using a model of accelerated biological aging, specifically through a high-fat, high-sucrose (HFHS) diet.

## RESULTS

### Longitudinal tracking of locomotor activity unveils sex specificities in aging outbred mice

Locomotor activity decline was evaluated as a primary outcome, as it is a crucial determinant of age-related functional deterioration (23).

Locomotion was longitudinally assessed between 6 and 24 months in male and female mice exhibiting natural aging through digital home-cage monitoring, via an electromagnetic platform installed beneath the cages (Fig. 2A). Our findings indicated that aging was associated with a gradual decrease in spontaneous mobility in both males and females (Fig. 2, B and C). Locomotor data underscored notable sex disparities, with females having overall higher locomotion levels in comparison to males (Fig. 2D), particularly between 6 and 18 months (Fig. 2E). During this period, females showed a statistically significant faster decline in motor activity (slope = -0.0042) than males (slope = -0.0028) (fig. S1A). Consequently, starting from 18 months of age, sex differences were no longer detectable (slope = -0.0036 for females versus -0.0034 for males) (Fig. 2F and fig. S1B). It is noteworthy that similar profiles were observed when assessing voluntary exercise through automated monitoring of mice using running wheels (Fig. 2, A and G to K). Female voluntary activity was overall higher than males during the 6 to 18 months period (Fig. 2J), but not during the 18- to 24-month time frame (Fig. 2K). Likewise, females exhibited a sharper decline compared to males (slope = -0.077 for females versus -0.015 for males) in the initial stages (fig. S1C), whereas these differences were attenuated in later stages (slope = -0.040 for females versus -0.013 for males) (fig. S1D). These findings emphasize a remarkable aging-dependent decline in spontaneous and

<figure><img src="image_2.png" /><figcaption>**Fig. 1.** Outline of experimental design. (A) Schematic overview of mouse features studied in the article. Cross-sectional cohorts of male and female outbred SWISS mice were subjected or not to HFHS diet at 6 months, and comprehensively characterized at 6, 12, 18, or 24 months, before euthanasia. (B) Experimental workflow used to define multifunctional aging trajectories and biological aging parameters. (C) Functional and biological features measured and categorized into eight specific domains, including anxiety, cognitive, cardiac, metabolic, physical performance, urinary, immune, and hematological. Created with BioRender.com. HFHS, high-fat, high-sucrose; mo, months old.</figcaption></figure>
<figure><img src="image_3.png" /><figcaption>**Fig. 2.** Longitudinal home-cage monitoring highlights aging-related progressive decline of locomotion with sex nuances in outbred SWISS mice. (A) Schematic representation of home-cage monitoring of spontaneous and voluntary activity in SWISS mice. Data were automatically recorded through the DVC system (Tecniplast SpA) from 6 to 24 months. Spontaneous mobility data were collected from an electronic board underneath each cage measuring mouse capacitance. Running wheels were used as an indicator of voluntary activity. Created with Biorender.com. (B) Heatmaps of spontaneous locomotor activity and (C) corresponding quantification of animal locomotion index of male and female SWISS mice between 6 and 24 months (n = 16 to 17 cages, corresponding to 64 to 68 mice). (D to F) Overall animal locomotion index of male and female SWISS mice (n = 16 to 17 cages, corresponding to 64 to 68 mice). (D) Between 6 and 24 months; (E) between 6 and 18 months; and (F) between 18 and 24 months. (G) Heatmaps of voluntary activity and (H) corresponding quantification of running wheel distance of male and female SWISS mice between 6 and 24 months (n = 17 cages, corresponding to 68 mice). (I to K) Overall running wheel distance of male and female SWISS mice (n = 17 cages, corresponding to 68 mice). (I) Between 6 and 24 months; (J) between 6 and 18 months; and (K) between 18 and 24 months. Differences were considered significant when *P* < 0.05. N, night; D, day; F, females; M, males.</figcaption></figure>
voluntary locomotor activities, characterized by sex-related hetero-
morphic kinetics.

Assessment of multiorgan/system functions shows diverse
aging- and sex-related patterns

Given that locomotion decline encompasses the complex interaction
of various organs and systems, we then conducted a comprehensive
assessment of evidence-based markers of various physiological com-
ponents. To this purpose, we initiated cross-sectional cohorts at in-
termediate ages (6, 12, 18, and 24 months) to better understand the
nature of muscular, anxiety, memory, metabolic, urinary, cardiac,
immune, and hematological age-related alterations (Fig. 3, A and B).
Our data emphasize that older mice experienced greater degrada-
tion in most features compared to younger mice, with important
sex-specific variations (Fig. 3, C to J, and table S1; see Table 1 for
abbreviations). While both sexes manifested an expected age-related
muscular dysfunction (table S1), aging females exhibited a worse
endurance compared to males (R<sub>NET RUNNING TIME</sub> = -0.26 for males
versus -0.44 for females, P = 0.0328) (Fig. 3C and table S1), consis-
tent with the longitudinal observations of locomotor activity (Fig. 2,
A to K). The evaluation of anxiety-related behavior did not show
aging-related modifications (R<sub>PERIPHERY-TO-CENTER RATIO</sub> = -0.13 for
males versus -0.03 for females, P = 0.4712) (Fig. 3D and table S1), while
aging mice displayed progressive memory decline without evident sex
differences (R<sub>PERCENTAGE OF ALTERNATION</sub> = -0.3 for males versus -0.33
for females, P = 0.7960) (Fig. 3E and table S1). Intriguingly, metabolic
parameters showed that aging-linked body weight loss was more pro-
nounced in males (R<sub>BODY WEIGHT</sub> = -0.38 for males versus -0.14 for
females, P = 0.0412) (Fig. 3F and table S1), while voiding patterns
were more altered in females (R<sub>TOTAL NUMBER OF URINE SPOTS</sub> = -0.20
for males versus -0.45 for females, P < 0.0001) (Fig. 3G; fig. S2, A and
B; and table S1). In addition, female mice exhibited higher alterations
in diastolic cardiac function and remodeling (R<sub>E/A RATIO</sub> = -0.08
for males versus -0.3 for females, P = 0.0005) (Fig. 3H and table
S1). Circulating parameters revealed an increased number and/or
proportion of myeloid lineage cells alongside a concurrent decrease
in lymphocyte proportions, which were more pronounced in aging
females than males (R<sub>LYMPHOCYTES PROPORTION</sub> = -0.25 for males
versus -0.61 for females, P = 0.0004) (Fig. 3I and table S1). Simi-
larly, red blood cells and reticulocytes exhibited greater changes
in females (R<sub>RED BLOOD CELLS</sub> = -0.03 for males versus -0.6 for
females, P < 0.0001) (Fig. 3J and table S1), while no sex differences
were observed in age-related platelets modifications (table S1). Col-
lectively, these findings present a comprehensive picture of age-
related traits, emphasizing sex-specific variations in multifunctional
aging trajectories.

HFHS diet-induced obesity exacerbates age-related decline of multiple organs/systems with sex nuances

Next, we used obesity as a major risk factor of accelerated aging, as
previously documented in humans and mice (19, 25–27). Seeking
deeper insights into the temporal and sex-related aspects of changes
of functional phenotypes, we assessed the influence of a HFHS diet
and consequent overweight on age-related multifunctional decline
(fig. S3A). HFHS diet-induced obesity was confirmed in the differ-
ent age groups by body weight gain (fig. S3, B to G), increased ingui-
nal and perigonadal fat weights (fig. S3, H to M), elevated blood
glucose (fig. S3, N to S) and hepatomegaly (fig. S3, T to Y) in both
males and females, with the exception of 24-month-old mice. HFHS

diet accelerated and amplified the decline in mouse locomotion
in both sexes (Fig. 4, A and B), while voluntary activity decline was
accentuated in females only, as control (CT) males already exhib-
ited very low activity levels (fig. S4, A and B). Obesity further af-
fected age-related decline of additional trajectories, with notable
sex-specific impacts (tables S2 and S3). For instance, obesity exacer-
bated age-related trajectory of muscular performance deterioration
in females (R<sub>RUNNING DISTANCE</sub> = -0.34 for females CT versus -0.58
for females HFHS, P = 0.0060) (Fig. 4C and table S3), whereas
cardiac remodeling (R<sub>HEART WEIGHT</sub> = -0.24 for males CT versus
0.17 for males HFHS, P = 0.0004) (Fig. 4D and table S3), hemato-
logical parameters (R<sub>IMMATURE RETICULOCYTE FRACTION</sub> = -0.07 for
males CT versus 0.18 for males HFHS, P = 0.0298) (Fig. 4E and
table S3) and neutrophil count (R<sub>NEUTROPHILS COUNT</sub> = 0.06 for
males CT versus 0.21 for males HFHS, P = 0.0110) (Fig. 4F and
table S3) trajectories were significantly modified in males only.
Collectively, these findings show that obesity aggravates specific
age-related dysfunctions and emphasize notable sex distinctions in
the exacerbation of age-related effects.

Topological data analysis identifies mouse phenoclusters regardless of chronological age

To delve deeper into aging-related traits within specific groups of
mice, we used a cutting-edge Big Data tool, named topological data
analysis (TDA), which allows the interpretation of complex high-
dimensional and noisy datasets (28). TDA on the overall mouse
population including CT and HFHS-subjected 6-, 12-, 18-, and
24-month-old mice was performed as illustrated in fig. S5, facilitat-
ing the classification of mice into phenotypically similar popula-
tions, termed phenoclusters. A total of seven phenoclusters were
identified (Fig. 5A). Intriguingly, while 6-month-old mice were
mostly found in cluster 6, the other clusters encompassed mice of all
ages (Fig. 5B). Building upon these observations, we then stratified
mice by each group of age, and conducted a comprehensive analysis
of the functional heterogeneity among same-aged populations. Our
data highlighted two phenoclusters at 6 months, five phenoclusters
at 12 months, six phenoclusters at 18 months, and four phenoclus-
ters at 24 months (Fig. 5C). To gain a deeper understanding of the
various aging patterns, we then aimed to further characterize the
identified clusters. We primarily focused on physical performance
given its conventional use in delineating frailty status both in mouse
models and clinical settings (Fig. 5D) (29–31). Our data showed
that low, intermediate, or high performers were randomly distrib-
uted among clusters at 6 (adjusted *P* value = 0.14) and 12 months
(adjusted *P* value = 0.95) (Fig. 5E). The repartition of mice across
clusters correlated with physical performance score at 18 months
(adjusted *P* value < 0.001) (Fig. 5E), while this correlation was lost
in the 24-month-old group (adjusted *P* value = 1) (Fig. 5E).

Considering the importance of findings at 18 months, we pri-
marily focused on this age group. Clusters 1 and 2 exhibited an over-
representation of low-performance mice, clusters 3, 4, and 5 mostly
featured intermediate performers, while cluster 6 predominantly
comprised high-performance mice (Fig. 5E). We further investigated
the cluster-specific features that exhibited parallel shifts with physical
performance score. To this purpose, parameters that showed signifi-
cant modifications in more than two clusters were not considered dis-
criminant and were therefore not taken into consideration. Mice from
cluster 6, with the best physical performance, were characterized by
higher endurance, muscle strength, and increased walking distance,
A

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

B

<figure><img src="image_5.png" /></figure>

C

Muscular function
Net running time (s)

<figure><img src="image_6.png" /></figure>

D

Anxiety
Periphery-to-center ratio (%)

<figure><img src="image_7.png" /></figure>

E

Memory function
Percentage of alternation (%)

<figure><img src="image_8.png" /></figure>

F

Metabolic function
BW (g)

<figure><img src="image_9.png" /></figure>

G

Urinary function
Total number of urine spots

<figure><img src="image_10.png" /></figure>

H

Cardiac function
E/A ratio

<figure><img src="image_11.png" /></figure>

I

Immune function
Lymphocytes proportion (%)

<figure><img src="image_12.png" /></figure>

J

Hematological function
Red blood cells (M/µl)

<figure><img src="image_13.png" /><figcaption>**Fig. 3.** Assessment of multiorgan trajectories underlines sex-specific traits during aging. (A) Schematic representation and (B) functional parameters used for the determination of physical performance in cross-sectional cohorts of naturally aging mice at 6, 12, 18, and 24 months. (C to J) Trajectories of parameters in male and female naturally aging mice at 6, 12, 18, and 24 months including (C) muscular function indicated by Net running time on a treadmill (s); (D) anxiety indicated by periphery-to-center ratio in an open field (%); (E) memory function indicated by the percentage of alternation in a Y-maze (%); (F) metabolic function indicated by body weight (g); (G) urinary function indicated by the total number of urine spots on an absorbent paper; (H) cardiac function indicated by E/A ratio as an index of diastolic function; (I) immune function indicated by lymphocyte proportions (%); and (J) hematological function indicated by the count of red blood cells (M/µl). For all graphs, *n* = 26 to 42 per group for males and *n* = 32 to 42 per group for females. Differences were considered significant when *P* < 0.05. BW, body weight; g, grams; s, seconds.</figcaption></figure>
<table><caption>**Table 1. List of abbreviations used for variables.** The table includes the names of the tests performed, the corresponding variable abbreviations, their units, and concise descriptions defining each variable.</caption><thead><tr><th>Tests performed</th><th>Variable abbreviation</th><th>Variable unit</th><th>Variable definition</th></tr></thead><tbody><tr><td rowspan="5">Void spot Assay</td><td>SPerif</td><td></td><td>Number of urine spots in the periphery</td></tr><tr><td>SCenter</td><td></td><td>Number of urine spots in the center</td></tr><tr><td>STotal</td><td></td><td>Total number of urine spots</td></tr><tr><td>AreaTot</td><td>cm²</td><td>Total area of all urine spots</td></tr><tr><td>AreaMean</td><td>cm²</td><td>Mean area of each urine spots</td></tr><tr><td rowspan="3">Y-maze</td><td>NbEntry</td><td></td><td>Total entries</td></tr><tr><td>NbAlt</td><td></td><td>Nb alternation</td></tr><tr><td>PerAlt</td><td>%</td><td>% Alternation</td></tr><tr><td rowspan="3">Body weight</td><td>BWD30</td><td>g</td><td>Body weight 30 days before phenotyping</td></tr><tr><td>BWD0</td><td>g</td><td>Body weight at the beginning of phenotyping</td></tr><tr><td>BWch</td><td>%</td><td>Body weight change over 1 month</td></tr><tr><td rowspan="3">Grip strength test</td><td>GPFmean</td><td>gf</td><td>Mean peak of force at the grip strength test (over 3 trials)</td></tr><tr><td>GNFmean</td><td>f</td><td>Mean peak of force at the grip strength test relative to the weight of mouse (over 3 trials)</td></tr><tr><td>GPFmax</td><td>gf</td><td>Max peak of force at the grip strength test (over 3 trials)</td></tr><tr><td rowspan="2">Tight rope</td><td>TRfmean</td><td>s</td><td>Mean time before falling from the rope (over 3 trials)</td></tr><tr><td>TRpmean</td><td>s</td><td>Mean time to reach the platform (over 3 trials)</td></tr><tr><td rowspan="6">Open field</td><td>OFDist</td><td>cm</td><td>Walking distance</td></tr><tr><td>OFSpeed</td><td>cm/s</td><td>Walking speed</td></tr><tr><td>OFPeriD</td><td>s</td><td>Time in periphery</td></tr><tr><td>OFCenD</td><td>s</td><td>Time in the center</td></tr><tr><td>OFPerCenD</td><td>s</td><td>Time in the center out of total duration of OF</td></tr><tr><td>OFHCenD</td><td>s</td><td>Time in the hypercenter</td></tr><tr><td rowspan="10">Treadmill</td><td>OFPerHCenD</td><td>s</td><td>Time in the hypercenter out of total duration of OF</td></tr><tr><td>TMDist</td><td>m</td><td>Distance</td></tr><tr><td>TMSpeed</td><td>m/min</td><td>Speed</td></tr><tr><td>TMnrt</td><td>s</td><td>Net running time</td></tr><tr><td>EF</td><td>%</td><td>Ejection fraction</td></tr><tr><td>LVAWd</td><td>mm</td><td>LVAW diastole</td></tr><tr><td>LVAWs</td><td>mm</td><td>LVAW systole</td></tr><tr><td>LVIDd</td><td>mm</td><td>LVID diastole</td></tr><tr><td>LVIDs</td><td>mm</td><td>LVID systole</td></tr><tr><td>LVPWd</td><td>mm</td><td>LVPW diastole</td></tr><tr><td>LVPWs</td><td>mm</td><td>LVPW systole</td></tr><tr><td rowspan="5">Echocardiographies</td><td>FS</td><td>%</td><td>Fractional shortening</td></tr><tr><td>E</td><td>mm/s</td><td>MV E wave</td></tr><tr><td>A</td><td>mm/s</td><td>MV A wave</td></tr><tr><td>E-to-A ratio</td><td></td><td>MV E/A</td></tr><tr><td>IVRT</td><td>ms</td><td>Isovolumic relaxation time</td></tr><tr><td rowspan="3">IVRT-to-RR ratio</td><td>RR</td><td>ms</td><td>RR intervals</td></tr><tr><td></td><td></td><td>IVRT/RR</td></tr></tbody></table>

(Continued)
(Continued)

<table><thead><tr><th>Tests performed</th><th>Variable abbreviation</th><th>Variable unit</th><th>Variable definition</th></tr></thead><tbody><tr><td rowspan="20">Complete blood count</td><td>RBC</td><td>M/µl</td><td>Red blood cells</td></tr><tr><td>HGB</td><td>g/dl</td><td>Hemoglobin</td></tr><tr><td>HCT</td><td>%</td><td>Hematocrit</td></tr><tr><td>MCV</td><td>fl</td><td>Mean corpuscular volume</td></tr><tr><td>MCH</td><td>pg</td><td>Mean corpuscular hemoglobin</td></tr><tr><td>MCHC</td><td>g/dl</td><td>Mean corpuscular hemoglobin concentration</td></tr><tr><td>RDW SD</td><td>fl</td><td>Red cell distribution width (Standard deviation)</td></tr><tr><td>RDW CV</td><td>%</td><td>Red cell distribution width (Coefficient of variation)</td></tr><tr><td>RET</td><td>K/µl</td><td>Reticulocyte number</td></tr><tr><td>RET prop</td><td>%</td><td>Reticulocyte percentage</td></tr><tr><td>IRF</td><td>%</td><td>Immature reticulocyte fraction</td></tr><tr><td>LFR</td><td>%</td><td>Low fluorescence reticulocyte fraction</td></tr><tr><td>MFR</td><td>%</td><td>Medium fluorescence reticulocyte fraction</td></tr><tr><td>HFR</td><td>%</td><td>High fluorescence reticulocyte fraction</td></tr><tr><td>PLT O</td><td>K/µl</td><td>Platelets number (optical measurement)</td></tr><tr><td>PLT</td><td>K/µl</td><td>Platelets</td></tr><tr><td>PDW</td><td>fl</td><td>Platelet distribution width</td></tr><tr><td>MPV</td><td>fl</td><td>Mean platelet volume</td></tr><tr><td>P LCR</td><td>%</td><td>Platelet-large cell ratio</td></tr><tr><td>PCT</td><td>%</td><td>Procalcitonine</td></tr><tr><td>WBC</td><td>K/µl</td><td>White blood cells</td></tr><tr><td>NEUT</td><td>K/µl</td><td>Neutrophil number</td></tr><tr><td>LYMPH</td><td>K/µl</td><td>Lymphocyte number</td></tr><tr><td>MONO</td><td>K/µl</td><td>Monocyte number</td></tr><tr><td>EO</td><td>K/µl</td><td>Eosinophil number</td></tr><tr><td>BASO</td><td>K/µl</td><td>Basophil number</td></tr><tr><td>NEUT prop</td><td>%</td><td>Neutrophil percentage</td></tr><tr><td>LYMPH prop</td><td>%</td><td>Lymphocyte percentage</td></tr><tr><td>MONO prop</td><td>%</td><td>Monocyte percentage</td></tr><tr><td>EO prop</td><td>%</td><td>Eosinophil percentage</td></tr><tr><td>BASO prop</td><td>%</td><td>Basophil percentage</td></tr><tr><td rowspan="10">Anatomical data</td><td>BAT</td><td>mg</td><td>Weight of the interscapular brown adipose tissue</td></tr><tr><td>ss cutWAT</td><td>mg</td><td>Weight of the inguinal white adipose tissue (subcutaneous)</td></tr><tr><td>Liver</td><td>g</td><td>Weight of the liver</td></tr><tr><td>Spleen</td><td>mg</td><td>Weight of the spleen</td></tr><tr><td>Thymus</td><td>mg</td><td>Weight of the thymus</td></tr><tr><td>Heart</td><td>mg</td><td>Weight of the heart (ventricles + atria)</td></tr><tr><td>Ventricles</td><td>mg</td><td>Weight of the cardiac ventricles (without atria)</td></tr><tr><td>Atria</td><td>mg</td><td>Weight of the cardiac atria (without ventricles)</td></tr><tr><td>pgWAT</td><td>mg</td><td>Weight of the perigonadal white adipose tissue (visceral)</td></tr><tr><td>Gastrocnemius</td><td>mg</td><td>Weight of one gastrocnemius</td></tr><tr><td>Blood glucose</td><td>mg/dl</td><td>Glycemia</td></tr></tbody></table>
together with increased voiding patterns and changes in red blood cells parameters (Fig. 5E and table S4). On the other hand, low-performance mice from clusters 1 and 2 exhibited low endurance, muscle weakness, and slowness, together with anxiety-like behavior, altered voiding pattern, cardiac diastolic dysfunction, and hematological alterations (Fig. 5E and table S4). Then, on the basis of the data obtained from the 18-month-old mice, we searched to identify parameters at 12 months that could discriminate small populations of mice displaying features of low performance. We noticed that parameters associated with low performance at 18 months such as low endurance, muscle weakness, and slowness were also found in cluster 1 of 12-month-old mice (Fig. 5E and table S4). Mice belonging to this cluster additionally exhibited heightened anxiety, along with modifications in immune and hematological parameters (Fig. 5E and table S4), which could allow the early detection of mice that exhibit low performance. Analysis including diet and sex factors better underlined the nuanced features of various aging-related conditions (fig. S6, A and B). Of note, voluntary activity (presence of running wheels) did not affect clustering at any age (fig. S6C).

## Refined clusters characterization identifies key markers of biological aging

Last, we aimed at better understanding the significance of parameters belonging to the previously identified clusters in the sex-specific definition of biological aging (fig. S7A). Subsequently, by identifying parameters that varied among these clusters, we were able to differentiate features affected by HFHS regardless of aging (fig. S7B) from those changed by aging with variations amplified by HFHS (fig. S7C), making these parameters indicative of normal versus accelerated biological aging. At 18 months, clusters encompassing male mice were primarily analyzed (Fig. 6A). In this goal, clusters 5 and 6 mostly encompassing mice subjected to CT diet (CT18) were compared with cluster 2 encompassing HFHS diet mice (HFHS18), and further compared to cluster 2 at 6 months (CT6), encompassing CT male mice (fig. S6, A and B). This highlighted that HFHS modifies 12 parameters independently of aging (Fig. 6A). Twelve parameters were affected by aging but showed greater modifications with HFHS (Fig. 6A). These parameters, including physical performance metrics, anxiety features, urinary patterns, cardiac diastolic dysfunction, and reticulocyte maturation, represent markers of biological aging that are exacerbated in HFHS male mice (Fig. 6A). Similar analysis was performed on females by comparing cluster 4 mostly encompassing mice subjected to CT diet (CT18) with cluster 1 consisting of HFHS diet mice (HFHS18), and further compared to cluster 1 at 6 months (CT6), comprising CT female mice (fig. S6, A and B). This analysis revealed 11 features linked to HFHS-related alterations independent of aging (Fig. 6B). On the contrary, 14 parameters were altered by aging but showed bigger alterations with HFHS (Fig. 6B). These parameters mainly included physical performance, cardiac remodeling and dysfunction, metabolic changes, and reticulocyte maturation (Fig. 6B). Last, we sought to identify parameters at 12 months that could be predictive for altered functional status at more advanced ages in males. We therefore compared cluster 4 comprising mice subjected to CT diet (CT12) with cluster 1 made up of HFHS diet mice (HFHS12) and further compared to cluster 2 at 6 months (CT6), consisting of CT male mice (fig. S6, A and B). This analysis revealed that fewer data allowed to discriminate the groups. Intriguingly, five features were linked to HFHS-related changes independent of aging (Fig. 6C). These features concerned metabolic

parameters, highlighting that at 12 months, HFHS induces the initial signs of metabolic syndrome (Fig. 6C). Furthermore, three parameters were altered by both aging and HFHS, with greater modifications by HFHS than solely aging (Fig. 6C). These parameters, related to muscular alterations, were also found in male mice at 18 months, suggesting that they could be early indicators of future broader dysfunctions (Fig. 6, A and C). It is noteworthy that this type of analysis was not feasible in 12-month-old females and in 24-month-old mice due to insufficient clustering (fig. S6B).

## DISCUSSION

Our study provides insights into the heterogeneity of the aging process through functional and biological phenotyping of a large number of SWISS male and female mice. This allowed us to map the trajectories of functional decline across multiple organs and systems. Intriguingly, our data highlight significant sex-specific differences in the digital follow-up of longitudinal locomotion patterns. In addition, we show pronounced variations between sexes in the aging trajectories of muscle function, metabolic parameters, urinary patterns, cardiac function, and hematological profiles. TDA uncovered functionally similar mouse populations regardless of chronological age. Besides, we identified key determinants of biological aging including muscle function, anxiety features, urinary patterns, reticulocyte maturation, cardiac remodeling and function, and metabolic changes, with muscle function emerging as an early determinant of biological age in male mice.

A key strength of our study is its examination of aging effects through a sex-specific perspective. Although our primary focus was on health span, our findings on life span align with common trends in aging research. Cheng *et al.* (32) highlighted that female mice have a longer median life span than males, based on data collected over a decade from three geographically distinct locations and six separate cohorts. Our results corroborate this, showing median life spans of 18.72 months for females and 17.52 months for males. Of note, the lack of significant sex differences in our data (P = 0.18) likely reflects the limited power of our life span cohorts, stemming from our emphasis on health span. We believe our effort on health span offers valuable insights into the functional aspects of aging beyond life span alone. In this line, our data revealed notable sex disparities in aging-related spontaneous mobility decline, a common precursor to functional deterioration and disease (23, 24). Previous studies have investigated age-dependent decline of motor activities in male and female mice (33, 34), often using spot tests like grip strength, walking speed, and endurance. Using these tests, we also observed an age-dependent decline in motor activities in both male and female SWISS mice. However, although these tests provided interesting information, they did not reflect the kinetics of motor activity in animals' daily living conditions. Our approach advances the field through the use of innovative digital technologies for continuous monitoring of mobility in mice, offering unprecedented insights. We found an age-dependent decrease in both spontaneous and voluntary motor activities in males and females, with functional decline starting early in life, between 10 and 14 months corresponding approximately to 40 and 50 years in humans (35). However, the kinetics of activity decline strongly differed between sexes. Despite females displayed higher physical activities than males up to 18 months, they showed a steeper age-dependent mobility decline than males. As a result, differences between sexes were not further detectable starting from the age of
18 months. This is particularly intriguing and might be related to changes in hormonal impregnation, as suggested by a recent study showing that 18-month-old female mice are acyclic and ovaries are depleted of primordial follicles (36). This phenomenon, defined as oopause, mimics some of the features of menopause in humans (37). Menopause decreases differences between men and women regarding the susceptibility to age-related diseases (38, 39). As far as we know, our study is the first one monitoring real-time spontaneous and voluntary motor activities in male and female mice from 6 to 24 months of age in experimental conditions favoring their social interactions (four animals per cage). By integrating digital biomarkers into our research framework, we not only refine our understanding of age-related mobility impairments but also lay a foundation for more precise assessments in future aging studies.

Our data further emphasize sex-specific patterns in the aging trajectories of functional decline across multiple organs and systems. Integration of these multifunctional data into advanced computational analysis revealed significant diversity among mice across the described parameters, highlighting individual variability in how aging affects health. We showed that same-aged mice exhibit both shared and divergent characteristics, challenging the reliability of chronological age alone in indicating health status. Our data underline sex-specific clustering patterns at 6 months, while age-related increased number of clusters suggested a heightened functional heterogeneity within the mouse population, with the exception of the 24-month-old group. The latter observation may be attributed to the likelihood that 24-month-old mice represent an artificially selected more robust population, as frailer mice experienced substantial mortality (approximately 80%). These observations underscore the need of early investigations to better depict the aging process. Many aging studies focus on old individuals, but this approach has inherent limitations. Clinical markers currently used to characterize the aging phenotype become evident only when compensatory mechanisms start to falter, as seen in frailty (29, 40). Here, we adapted an existing frailty score (30, 41) to use it in young individuals and named it “physical performance score,” as frailty is a syndrome associated with older age. Our physical performance score exhibited a strong correlation with mouse phenoclusters at 18 months, whereas such correlation was not observed at earlier ages. This emphasizes the importance of comprehensive evaluations encompassing multifaceted aging traits, as biological aging can manifest in diverse ways beyond physical performance decline. Therefore, multiorgan functional characterization can help to elucidate factors that drive the divergence of an organ's biological age from chronological age, facilitate therapeutic opportunities, and yield disease-specific risk calculators.

Our cutting-edge computational analysis highlighted that parameters related to muscle alterations, cardiac diastolic dysfunction, and reticulocyte immaturity emerged as key determinants of biological age at 18 months in both males and females. We observed that parameters linked to muscle function were altered as early as 12 months in males and further degraded by HFHS diet. This highlights the potential importance of assessing muscle strength and coordination at younger ages to anticipate later disorders. Muscle strength alterations, independent of muscle weight loss, could serve as an early indicator measurable in young individuals, as they might underlie the premature onset of the pathophysiology of sarcopenia. Parameters determining biological aging were further validated by their amplification through HFHS diet, a known aging accelerator.

However, it is often tricky to decipher the effects that are strictly linked to aging-independent obesity-related metabolic modifications, versus aging-associated effects exacerbated by obesity. Our refined cluster analysis helped distinguish between these effects, providing deeper insights into the role of obesity in accelerating biological aging. These findings highlight the importance of early assessments and tailored analytical methods in aging research.

Overall, our study enhances understanding of the aging process, in particular with respect to sex differences. We believe that our approach will help bridge the gap between mouse and human studies by aligning functional measurements that are applicable in both species. Notably, several of our findings parallel those in human studies, underscoring the translational potential of our research. For instance, the decline in spontaneous motor activity observed in mice reflects reduced physical activity levels in older adults (42), where women experience a greater reduction than men (43), supporting our findings of a steeper locomotion decrease in female mice. Similar to humans, these different trajectories of age-dependent mobility decline might be related to different hormonal impregnation. We also observed muscle function decline in older mice, paralleling well-known human aging characteristics, such as reduced handgrip strength in older adults (44) and lower muscle endurance with age highlighted in the INSPIRE-T human cohort (45, 46). Concerning cognition, the lack of notable significant sex differences in our study is not unexpected and finds its equivalent in human reports. Data from the Integrated Care for Older People (ICOPE) Care initiative (47) in France indicate reduced cognitive functions (memory and time orientation) with age. Notably, this decline did not follow a marked sex-specific pattern, as men were more impaired until age 90, after which women were more affected (48). In addition, the INSPIRE-T cohort showed no baseline difference in Mini-Mental State Examination scores between sexes [mean 28.6 ± 1.6 (SD) for men (n = 412); and 28.5 ± 2.1 for women (n = 679)], although prior studies reported slightly higher cognitive scores in women (49). In contrast to our findings in mice, human studies, including ICOPE Care data, suggest that unintentional weight loss is more common in older women than in men (48). Further comparative investigations are needed to clarify the translational potential of our functional aging assessments. In the future, it would be valuable to examine sex differences in mice by combining multiple functional domains such as cognition, mood/anxiety, mobility, cardiovascular, urinary, and nutritional/metabolic aspects (e.g., weight loss) into a single composite metric. This alignment is particularly crucial as it resonates with the World Health Organization's ICOPE program, which prioritizes intrinsic capacity over disease-based assessments (50, 51). By focusing on functional parameters such as muscle strength, coordination, and physical performance, our methodology aligns with the ICOPE program's emphasis on maintaining and improving the intrinsic capacity and functional health of older individuals.

## Strengths and limitations of the study

This study presents a number of strengths and limitations that warrant consideration. Using a robust sample size of 1576 outbred SWISS mice, our study effectively incorporates genetic diversity alongside diet and exercise regimens that better mirror human lifestyles, thereby enhancing translational relevance. Another key strength is our unprecedented longitudinal digital follow-up of spontaneous mobility throughout adulthood, which offers insights into sex-specific functional decline over time. In addition, the comprehensive assessment
A

<figure><img src="image_14.png" /></figure>

B

<figure><img src="image_15.png" /></figure>

C

Muscular function
Running distance (m)

<figure><img src="image_16.png" /></figure>

D

Cardiac remodeling
Heart weight (mg)

<figure><img src="image_17.png" /></figure>

E

Hematological function
Immature reticulocyte fraction (%)

<figure><img src="image_18.png" /></figure>

F

Immune function
Neutrophils count (K/µl)

<figure><img src="image_19.png" /><figcaption>**Fig. 4.** HFHS-induced obesity accentuates aging-related multiple dysfunctions with sex specificities. (A and B) Heatmaps of spontaneous locomotor activity and corresponding quantification of animal locomotion index of (A) male and (B) female SWISS mice subjected or not to HFHS diet between 6 and 24 months (n = 17 cages corresponding to 68 mice for CT group and n = 27 cages for HFHS group corresponding to 108 mice). (C to F) Trajectories of parameters in male and female mice naturally aging or subjected to HFHS at 6, 12, 18, and 24 months including (C) muscular function indicated by running distance on a treadmill (m); (D) cardiac remodeling indicated by heart weight (mg); (E) hematological function indicated by immature reticulocyte fraction (%); (F) immune function indicated by neutrophils count (K/µl). For all graphs, n = 26 to 42 per group for males and n = 32 to 42 per group for females. Differences were considered significant when *P* < 0.05. HFHS, High fat high sucrose; F, females; M, males.</figcaption></figure>
<figure><img src="image_20.png" /><figcaption>**Fig. 5.** TDA of multiorgan/system parameters identifies distinct phenoclusters of mice that differentially correlate with physical performance. (A) Representation of the phenoclusters identified from the overall mouse population (including CT- and HFHS-subjected 6-, 12-, 18-, and 24-month-old mice). (B) Illustration of mouse distribution in phenoclusters based on chronological age. (C) Representation of the phenoclusters identified from mice stratified by each group of age, i.e., 6-, 12-, 18-, and 24-month-old. (D) Illustration of mouse distribution in phenoclusters based on chronological age. (E) Left panel: features, parameters and thresholds used to evaluate physical performance in mice; right panel: physical performance score determination based on the number of failed tests. (E) Illustration of mouse distribution in phenoclusters based on physical performance score. Differences were considered significant when *P* < 0.05.</figcaption></figure>
<figure><img src="image_21.png" /><figcaption>**Fig. 6.** Refined analysis identified markers of biological aging. (A) Analysis of 18-month-old males showing parameters affected by HFHS regardless of aging (in blue) and features of biological aging (in gray). (B) Analysis of 18-month-old females showing parameters affected by HFHS regardless of aging (in blue) and features of biological aging (in gray). (C) Analysis of 12-month-old males showing parameters affected by HFHS regardless of aging (in blue) and features of biological aging (in gray).</figcaption></figure>

of multiple organ systems, along with TDA to identify phenoclusters,
reveals complex aging patterns that traditional methods may over-
look. Our study involved the collection of a vast array of tissues and
organs from mice at various ages, creating a valuable biobank of bio-
logical samples and a comprehensive database, both available to the
aging research community upon request. However, some limitations
must be acknowledged. Our results are mostly based on cross-
sectional data, which may not fully capture the dynamic nature of ag-
ing. Longitudinal studies are therefore needed to confirm our findings
and assess their predictive value for health span, morbidity, and mor-
tality. Additionally, cognitive assessment relied solely on the Y-maze
test for short-term memory. To minimize overall testing demands and
reduce animal stress, additional assessments like the Barnes or water
maze were omitted, even though they could have contributed to a
more comprehensive cognitive profile. Of note, our findings did not
enable the identification of specific female traits at 12 months due to
limited clustering. Lastly, multiomics analyses will be crucial for ex-
ploring the molecular mechanisms behind the observed functional
changes, while this aspect was beyond the scope of the current paper.

METHODS

Animals and procedures

All animal procedures were performed in accordance with Interna-
tional Guidelines on Animal Experimentation and with the French
Ministry of Agriculture license. Mice experimentation protocol has

been approved by Toulouse University local ethic committee and by
the Ministry of Superior Education and Research under the agree-
ments APAFIS2019120614331282 and APAFIS2020022409196014.
This investigation complied with the Guide for Care and Use of
Laboratory Animals published by Directive 2010/63/EU of the
European Parliament. All mice (mixed-sex SWISS) were housed
four per cage in temperature-controlled environment (21° ± 2°C)
with a 12-hour light-dark cycle and given free access to water and
food. Mice were checked daily. Mice were randomized and fed either
with a normocaloric diet (CT: 10% fat, 20% proteins, and 70% carbo-
hydrate; SSNIFF Spezialdiäten GmBH, Germany) or a HFHS (40% fat,
20% proteins, and 40% carbohydrate; SSNIFF Spezialdiäten GmBH,
Germany). Mice were studied at either 6, 12, 18, or 24 months of
age. For mice subjected to HFHS, the diet was administered as fol-
lows: from 6 to 12 months for 12-month-old mice (6 months of HFHS),
from 6 to 18 months for 18-month-old mice (12 months of HFHS),
from 6 to 24 months for 24-month-old mice (18 months of HFHS). The
number of mice was adapted in each group to anticipate age- and
HFHS-induced mortality. Additional mice (60 males and 60 females)
under normocaloric diet were used to assess natural life expectancy
in our husbandry and were left until spontaneous death. In case of
discomfort/pain was observed, mice were euthanized. Following
phenotyping, mice were anesthetized using 2% isoflurane, and in-
tracardiac blood sampling was conducted. Subsequently, euthanasia
was ensured via cervical dislocation, and organs were harvested and
weighed before storage.
## Spontaneous mobility and voluntary activity

Activity metrics were assessed through home-cage monitoring system Digital Ventilated Cage (DVC) manufactured by Tecniplast SpA (Buguggiate, Italy). The system measures spontaneous mobility using a sensing board with 12 electrodes connected to an integrated circuit, using capacitance sensing technology. These capacitance measurements enable accurate tracking of mouse activity over time (52). For voluntary activity, mice were given access to DVC Running Wheels installed within the cages (cages without voluntary activity were provided by blocked wheels), which afford automate data collection from specialized sensors capable of detecting rotations of the wheels. Both capacitance and rotation data were automatically registered remotely through the DVC Analytics platform.

## Body weight

One month preceding behavioral tests, mice were weighed at 8 a.m. ±30 min to mitigate variations caused by food intake and activity. The same protocol was performed the day before behavioral tests. Body weight change over this period was then calculated and was expressed in percentage.

## Y-maze test

The Y-maze spontaneous alternation test was used to assess spatial working memory (53). In brief, mice were placed in one of the three arms of a Y-shaped maze (Bioseb LE847) and allowed 8 min for exploration. The percentage of alternation, derived from the ratio of alternations to total arm entries, served as the measure for memory function.

## Grip strength test

The grip strength test (Bioseb BIO-GS3) was used to measure the force exerted by the mouse forelimbs (54). Mice were positioned above a horizontally-placed grid and drawn along a straight line leading away from the sensor. The peak force was identified as the highest value obtained from five trials for each attempt, with each attempt conducted three times at hourly intervals. Values were automatically registered by the apparatus. The test was considered invalid if the mouse released one of its forelimbs or added another limb before completion.

## Tightrope test

The tightrope test was used to assess motor coordination in mice as previously reported (41). Mice were positioned in the center of a 60-cm-long, 2-mm-diameter tightrope, suspended at a height of 70 cm from the floor. Success in the test was defined by mice reaching one of the cable ends or maintaining balance on the tightrope for the maximum test duration of 240 s. The mean fall latency was calculated based on three repeated measures.

## Open field test

The open field test was used to study anxiety-like behavior and locomotor activity (55). Mice were positioned in the central area of a polyvinyl chloride cylinder with a 40 cm diameter for a duration of 10 min. Percentage of time spent in the center as well as the walking speed were recorded and computed by the EthoVision software.

## Incremental treadmill test

To evaluate endurance, mice were placed on a treadmill (Bioseb BX-TM5) as previously described (56). The test protocol included a warm-up phase at 6 m/min for 6 min, followed by a continuous increment of 1 m/min every minute.

Critical points for test termination were defined as experiencing three shocks within a 10-s period or remaining on the shock grid for more than 2 s. Net running time was then calculated by subtracting the cumulative shock duration from the total duration of the test.

## Echocardiography and hemodynamic measurements

Mice were anesthetized with 2% isoflurane and imaged in the supine position using a Vevo 2100 Imaging System (Visualsonics). Core temperature was maintained at 37°C. Standard two-dimensional echocardiographic examination was conducted in the parasternal long-axis view to evaluate left ventricular dimensions and systolic function and short-axis view to assess chamber dimensions, wall thickness, and fractional shortening. To assess diastolic dysfunction, transmitral pulsed-wave Doppler echocardiography was performed. Then, ratio of peak velocity of blood flow from left ventricular relaxation in early diastole (E wave) to peak velocity flow in late diastole caused by atrial contraction (A wave) was calculated (E/A ratio). In addition, isovolumetric relaxation time (IVRT), indicating the time between aortic valve closure and mitral valve opening, was measured as another indicator of diastolic dysfunction.

## Void spot assay

Void spot assay was performed to assess urinary function in mice (57). In brief, individual mice were placed in a standard cage with floor covered with absorbent paper for 2 hours. Following the mice's return to their home cage, the filter paper was dried, before exposure to ultraviolet light to visualize urine spots. The test was repeated on three consecutive days. Then, number of urine spots, total urine area, and voiding pattern (edge voiding or center voiding) were analyzed using Fiji software.

## Complete blood count

Complete blood count was determined with ProCyte Dx analyzer (Idexx Laboratories). The analysis included a detailed examination of erythrocyte parameters (RBC, HCT, HGB, MCV, MCH, MCHC, and RDW), leukocyte parameters (WBC, NEU, LYM, MONO, EO, and BASO), and platelet parameters (PLT, PDW, MPV, and PCT).

## Blood glucose measurement

Blood glucose concentrations were measured from the tail vein in 2-hour fasted mice with the Accu-Chek Guide glucometer (Roche). This fasting period was applied to minimize metabolic stress (58).

## Physical performance score

Physical performance score was adapted from existing frailty scores (30, 41) to mirror human clinical parameters described by Fried et al. (29). The term “physical performance” was chosen instead of “frailty” because this score was also applied to young mice, while frailty typically pertains to older individuals. The score was based on five criteria including body weight change over 1 month, weakness, poor endurance, slowness, and low activity level. Thresholds were set as follows: A 5% loss or a 10% gain in body weight over 1 month was deemed indicative for this criterion. For the remaining parameters, any animal ranking below the 20th percentile, calculated within the control group for each age and sex separately, was considered to have failed the test. Subsequently, a scoring system was developed, wherein a mouse failing three or more criteria out of the five was classified as having “low performance,” while those failing one
or two criteria were categorized as having “intermediate performance.” Mice that did not fail any criteria were classified as having “high performance.”

## Topological data analysis

Building topological models as networks, TDA allows complex processes to be inspected in a continuous space, by the identification of specific connected clusters (called “communities”) in the network, sharing similar features (59). We preprocessed the original dataset of 709 mice for TDA as follows: (i) features with more than 25% missing values were removed, (ii) samples with more than 10 missing values were discarded, and (iii) data imputation was performed by “missForest” R package (60) on the remaining dataset, composed of 520 mice and 104 features. The first two projections, extracted from the t-distributed stochastic neighbor embedding with perplexity = 15, have been chosen as lenses, while the number of TDA bins and the bin overlapping ratio have been set to 8 and 0.4, respectively. TDA was performed in R using the “PIUMA” package (https://bioconductor.org/packages/release/bioc/html/PIUMA.html). Networks have been plotted by Cytoscape (61).

## Statistical analysis

Continuous variables are presented as mean ± SEM, while categorical data are reported as frequencies and percentages. Group comparisons for continuous and categorical variables were performed by Student *t* test for independent samples and by χ² test, respectively. Correlation analysis was performed by computing the Pearson's correlation coefficient (R). *P* values adjusted for multiple testing, using Benjamini and Hochberg's method to control the false discovery rate, has been implemented, when appropriate. Comparison of survival curves was performed with log-rank (Mantel-Cox) test. Comparison of slopes of linear regressions was performed with the following method: $t = (b1 - b2) / s_{b1,b2}$ where b1 and b2 are the two slope coefficients and $s_{b1,b2}$ the pooled standard error of the slope, exploiting the “lm” native R function (62). A *P* value < 0.05 was considered statistically significant.

## Supplementary Materials

### The PDF file includes:

Figs. S1 to S7

Legends for tables S1 to S4

### Other Supplementary Material for this manuscript includes the following:

Tables S1 to S4

## REFERENCES AND NOTES

1. A. Ibanez, M. Maito, F. Botero-Rodríguez, S. Fittipaldi, C. Coronel, J. Migeot, A. Lacroix, B. Lawlor, C. Duran-Aniotz, S. Baez, H. Santamaria-Garcia, Healthy aging meta-analyses and scoping review of risk factors across Latin America reveal large heterogeneity and weak predictive models. *Nat. Aging* **4**, 1153–1165 (2024).

2. M. L. Elliott, A. Caspi, R. M. Houts, A. Ambler, J. M. Broadbent, R. J. Hancox, H. Harrington, S. Hogan, R. Keenan, A. Knodt, J. H. Leung, T. R. Melzer, S. C. Purdy, S. Ramrakha, L. S. Richmond-Rakerd, A. Righarts, K. Sugden, W. M. Thomson, P. R. Thorne, B. S. Williams, G. Wilson, A. R. Hariri, R. Poulton, T. E. Moffitt, Disparities in the pace of biological aging among midlife adults of the same chronological age have implications for future frailty risk and policy. *Nat. Aging* **1**, 295–308 (2021).

3. Y. E. Tian, V. Cropley, A. B. Maier, N. T. Lautenschlager, M. Breakspear, A. Zalesky, Heterogeneous aging across multiple organ systems and prediction of chronic disease and mortality. *Nat. Med.* **29**, 1221–1231 (2023).

4. N. Sayed, Y. Huang, K. Nguyen, Z. Krejciova-Rajaniemi, A. P. Grawe, T. Gao, R. Tibshirani, T. Hastie, A. Alpert, L. Cui, T. Kuznetsova, Y. Rosenberg-Hasson, R. Ostan, D. Monti, B. Lehallier, S. S. Shen-Orr, H. T. Maecker, C. L. Dekker, T. Wyss-Coray, C. Franceschi, V. Jojic, F. Haddad, J. G. Montoya, J. C. Wu, M. M. Davis, D. Furman, An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging. *Nat. Aging* **1**, 598–615 (2021).

5. J. N. Farr, M. Xu, M. M. Weivoda, D. G. Monroe, D. G. Fraser, J. L. Onken, B. A. Negley, J. G. Sfeir, M. B. Ogrodnik, C. M. Hachfeld, N. K. LeBrasseur, M. T. Drake, R. J. Pignolo, T. Pirtskhalava, T. Tchonia, M. J. Oursler, J. L. Kirkland, S. Khosla, Targeting cellular senescence prevents age-related bone loss in mice. *Nat. Med.* **23**, 1072–1079 (2017).

6. M. Xu, T. Pirtskhalava, J. N. Farr, B. M. Weigand, A. K. Palmer, M. M. Weivoda, C. L. Inman, M. B. Ogrodnik, C. M. Hachfeld, D. G. Fraser, J. L. Onken, K. O. Johnson, G. C. Verzosa, L. G. P. Langhi, M. Weigl, N. Giorgadze, N. K. LeBrasseur, J. D. Miller, D. Jurk, R. J. Singh, D. B. Allison, K. Ejima, G. B. Hubbard, Y. Ikeno, H. Cubro, V. D. Garovic, X. Hou, S. J. Weroha, P. D. Robbins, L. J. Niedernhofer, S. Khosla, T. Tchonia, J. L. Kirkland, Senolytics improve physical function and increase lifespan in old age. *Nat. Med.* **24**, 1246–1256 (2018).

7. A. E. Tarkhov, T. Lindstrom-Vautrin, S. Zhang, K. Ying, M. Moqri, B. Zhang, A. Tyshkovskiy, O. Levy, V. N. Gladyshev, Nature of epigenetic aging from a single-cell perspective. *Nat. Aging* **4**, 854–870 (2024).

8. A. K. Palmer, M. D. Jensen, Metabolic changes in aging humans: Current evidence and therapeutic strategies. *J. Clin. Invest.* **132**, e158451 (2022).

9. J. A. Amorim, G. Coppotelli, A. P. Rolo, C. M. Palmeira, J. M. Ross, D. A. Sinclair, Mitochondrial and metabolic dysfunction in ageing and age-related diseases. *Nat. Rev. Endocrinol.* **18**, 243–258 (2022).

10. Y. Cai, M. Xiong, Z. Xin, C. Liu, J. Ren, X. Yang, J. Lei, W. Li, F. Liu, Q. Chu, Y. Zhang, J. Yin, Y. Ye, D. Liu, Y. Fan, S. Sun, Y. Jing, Q. Zhao, L. Zhao, S. Che, Y. Zheng, H. Yan, S. Ma, S. Wang, J. C. Izpisua Belmonte, J. Qu, W. Zhang, G.-H. Liu, Decoding aging-dependent regenerative decline across tissues at single-cell resolution. *Cell Stem Cell* **30**, 1674–1691. e8 (2023).

11. S. H. D. Jackson, M. R. Weale, R. A. Weale, Biological age—What is it and can it be measured? *Arch. Gerontol. Geriatr.* **36**, 103–115 (2003).

12. S. N. Austad, K. E. Fischer, Sex differences in lifespan. *Cell Metab.* **23**, 1022–1033 (2016).

13. M. P. Farina, J. K. Kim, E. M. Crimmins, Racial/ethnic differences in biological aging and their life course socioeconomic determinants: The 2016 health and retirement study. *J. Aging Health* **35**, 209–220 (2023).

14. K. N. Fitzgerald, R. Hodges, D. Hanes, E. Stack, D. Cheishvili, M. Szyf, J. Henkel, M. W. Twedt, D. Giannopoulou, J. Herdell, S. Logan, R. Bradley, Potential reversal of epigenetic age using a diet and lifestyle intervention: A pilot randomized clinical trial. *Aging* **13**, 9419–9432 (2021).

15. J. Jylhävä, N. L. Pedersen, S. Hägg, Biological age predictors. *EBioMedicine* **21**, 29–36 (2017).

16. S. Hägg, J. Jylhävä, Sex differences in biological aging with a focus on human studies. *eLife* **10**, e63425 (2021).

17. R. Waziry, C. P. Ryan, D. L. Corcoran, K. M. Huffman, M. S. Kobor, M. Kothari, G. H. Graf, V. B. Kraus, W. E. Kraus, D. T. S. Lin, C. F. Pieper, M. E. Ramaker, M. Bhapkar, S. K. Das, L. Ferrucci, W. J. Hastings, M. Kebbe, D. C. Parker, S. B. Racette, I. Shalev, B. Schilling, D. W. Belsky, Effect of long-term caloric restriction on DNA methylation measures of biological aging in healthy adults from the CALERIE trial. *Nat. Aging* **3**, 248–257 (2023).

18. M. Ogrodnik, Y. Zhu, L. G. P. Langhi, T. Tchonia, P. Krüger, E. Fielder, S. Victorelli, R. A. Ruswhandi, N. Giorgadze, T. Pirtskhalava, O. Podgorni, G. Enikolopov, K. O. Johnson, M. Xu, C. Inman, A. K. Palmer, M. Schafer, M. Weigl, Y. Ikeno, T. C. Burns, J. F. Passos, T. von Zglinicki, J. L. Kirkland, D. Jurk, Obesity-induced cellular senescence drives anxiety and impairs neurogenesis. *Cell Metab.* **29**, 1061–1077.e8 (2019).

19. R. R. Kalyani, M. Corriere, L. Ferrucci, Age-related and disease-related muscle loss: The effect of diabetes, obesity, and other diseases. *Lancet Diabetes Endocrinol.* **2**, 819–829 (2014).

20. N. Schaum, B. Lehallier, O. Hahn, R. Pálovics, S. Hosseinzadeh, S. E. Lee, R. Sit, D. P. Lee, P. M. Losada, M. E. Zardeneta, T. Fehlmann, J. T. Webber, A. McGeever, K. Calcuttawala, H. Zhang, D. Berdnik, V. Mathur, W. Tan, A. Zee, M. Tan, A. O. Pisco, J. Karkanias, N. F. Neff, A. Keller, S. Darmanis, S. R. Quake, T. Wyss-Coray, Ageing hallmarks exhibit organ-specific temporal signatures. *Nature* **583**, 596–602 (2020).

21. Tabula Muris Consortium, A single-cell transcriptomic atlas characterizes ageing tissues in the mouse. *Nature* **583**, 590–595 (2020).

22. H. S.-H. Oh, J. Rutledge, D. Nachun, R. Pálovics, O. Abiose, P. Moran-Losada, D. Channappa, D. Y. Urey, K. Kim, Y. J. Sung, L. Wang, J. Timsina, D. Western, M. Liu, P. Kohlfeld, J. Budde, E. N. Wilson, Y. Guen, T. M. Maurer, M. Haney, A. C. Yang, Z. He, M. D. Greicius, K. I. Andreasson, S. Sathyan, E. F. Weiss, S. Milman, N. Barzilai, C. Cruchaga, A. D. Wagner, E. Mirmo, B. Lehallier, V. W. Henderson, F. M. Longo, S. B. Montgomery, T. Wyss-Coray, Organ aging signatures in the plasma proteome track health and disease. *Nature* **624**, 164–172 (2023).

23. L. Ferrucci, R. Cooper, M. Shardell, E. M. Simonsick, J. A. Schrack, D. Kuh, Age-related change in mobility: Perspectives from life course epidemiology and geroscience. *J. Gerontol. A Biol. Sci. Med. Sci.* **71**, 1184–1194 (2016).

24. J. Raffin, P. de Souto Barreto, A. P. Le Traon, B. Vellas, M. Aubertin-Leheudre, Y. Rolland, Sedentary behavior and the biological hallmarks of aging. *Ageing Res. Rev.* **83**, 101807 (2023).
25. L. Etzel, W. J. Hastings, M. A. Hall, C. M. Heim, M. J. Meaney, J. G. Noll, K. J. O'Donnell, I. Pokhvisneva, E. J. Rose, H. M. C. Schreier, C. E. Shenk, I. Shalev, Obesity and accelerated epigenetic aging in a high-risk cohort of children. *Sci. Rep.* **12**, 8328 (2022).

26. D. Frasca, Several areas of overlap between obesity and aging indicate obesity as a biomarker of accelerated aging of human B cell function and antibody responses. *Immun. Ageing* **19**, 48 (2022).

27. D. Frasca, B. B. Blomberg, Obesity accelerates age defects in mouse and human B cells. *Front. Immunol.* **11**, 2060 (2020).

28. A. Sizemore Blevins, D. S. Bassett, Reorderability of node-filtered order complexes. *Phys. Rev. E* **101**, 052311 (2020).

29. L. P. Fried, C. M. Tangen, J. Walston, A. B. Newman, C. Hirsch, J. Gottdiener, T. Seeman, R. Tracy, W. J. Kop, G. Burke, M. A. McBurnie, Frailty in older adults: Evidence for a phenotype. *J. Gerontol. A Biol. Sci. Med. Sci.* **56**, M146–M157 (2001).

30. M. C. Gomez-Cabrera, R. Garcia-Valles, L. Rodriguez-Mañas, F. J. Garcia-Garcia, G. Olaso-Gonzalez, A. Salvador-Pascual, F. J. Tarazona-Santabalbina, J. Viña, A new frailty score for experimental animals based on the clinical phenotype: Inactivity as a model of frailty. *J. Gerontol. A Biol. Sci. Med. Sci.* **72**, 885–891 (2017).

31. Y. Santin, S. Lopez, I. Ader, S. Andrieu, N. Blanchard, A. Carrière, L. Casteilla, B. Cousin, N. Davezac, P. de Souto Barreto, C. Dray, N. Fazilleau, D. Gonzalez-Dunia, P. Gourdy, S. Guyonnet, N. Jabbrane-Ferrat, O. Kunduzova, F. Lezoualc'h, R. Liblau, L. O. Martinez, C. Moro, P. Payoux, L. Pénicaud, V. Planat-Bénard, C. Rampon, Y. Rolland, J.-P. Schanstra, F. Sierra, P. Valet, A. Varin, N. Vergnolle, B. Vellas, J. Viña, B. P. Guardi, A. Parini, Towards a large-scale assessment of the relationship between biological and chronological aging: The INSPIRE Mouse cohort. *J. Frailty Aging* **10**, 121–131 (2021).

32. C. J. Cheng, J. A. L. Gelfond, R. Strong, J. F. Nelson, Genetically heterogeneous mice exhibit a female survival advantage that is age- and site-specific: Results from a large multi-site study. *Aging Cell* **18**, e12905 (2019).

33. E. N. Allen, J. E. Cavanaugh, Loss of motor coordination in an aging mouse model. *Behav. Brain Res.* **267**, 119–125 (2014).

34. T. P. O'Leary, H. M. Mantolino, K. R. Stover, R. E. Brown, Age-related deterioration of motor function in male and female 5xFAD mice from 3 to 16 months of age. *Genes Brain Behav.* **19**, e12538 (2020).

35. S. Dutta, P. Sengupta, Men and mice: Relating their ages. *Life Sci.* **152**, 244–248 (2016).

36. C. Liberios, S. H. Liew, P. Zareie, N. L. La Gruta, A. Mansell, K. Hutt, Evaluation of inflammation and follicle depletion during ovarian ageing in mice. *Sci. Rep.* **11**, 278 (2021).

37. I. Winkler, A. Goncalves, Do mammals have menopause? *Cell* **186**, 4729–4733 (2023).

38. S. M. Temkin, E. Barr, H. Moore, J. P. Caviston, J. G. Regensteiner, J. A. Clayton, Chronic conditions in women: The development of a National Institutes of health framework. *BMC Womens Health* **23**, 162 (2023).

39. K. Auro, A. Joensuu, K. Fischer, J. Kettunen, P. Salo, H. Mattsson, M. Niironen, J. Kaprio, J. G. Eriksson, T. Lehtimäki, O. Raitakari, A. Jula, A. Tiitinen, M. Jauhiainen, P. Soininen, A. J. Kangas, M. Kähönen, A. S. Havulinna, M. Ala-Korpela, V. Salomaa, A. Metspalu, M. Perola, A metabolic view on menopause and ageing. *Nat. Commun.* **5**, 4708 (2014).

40. K. Rockwood, X. Song, C. MacKnight, H. Bergman, D. B. Hogan, I. McDowell, A. Mitnitski, A global clinical measure of fitness and frailty in elderly people. *CMAJ* **173**, 489–495 (2005).

41. I. Martinez de Toda, A. Garrido, C. Vida, M. C. Gomez-Cabrera, J. Viña, M. De la Fuente, Frailty quantified by the "Valencia Score" as a potential predictor of lifespan in mice. *J. Gerontol. A Biol. Sci. Med. Sci.* **73**, 1323–1329 (2018).

42. T. Strain, S. Flaxman, R. Guthold, E. Semenova, M. Cowan, L. M. Riley, F. C. Bull, G. A. Stevens, Country Data Author Group, National, regional, and global trends in insufficient physical activity among adults from 2000 to 2022: A pooled analysis of 507 population-based surveys with 5-7 million participants. *Lancet Glob. Health* **12**, e1232–e1243 (2024).

43. W. Li, E. Procter-Gray, L. Churchill, S. E. Crouter, K. Kane, J. Cheng, F. Rui, J. Tian, P. D. Franklin, J. K. Ockene, J. Gurwitz, Gender and age differences in levels, types and locations of physical activity among older adults living in car-dependent neighborhoods. *J. Frailty Aging* **6**, 129 (2017).

44. S. C. Shaw, E. M. Dennison, C. Cooper, Epidemiology of sarcopenia: Determinants throughout the lifecourse. *Calcif. Tissue Int.* **101**, 229–247 (2017).

45. S. Guyonnet, Y. Rolland, C. Takeda, P.-J. Ousset, I. Ader, N. Davezac, C. Dray, N. Fazilleau, P. Gourdy, R. Liblau, A. Parini, P. Payoux, L. Pénicaud, C. Rampon, P. Valet, N. Vergnolle, S. Andrieu, P. de Souto Barreto, L. Casteilla, B. Vellas. The INSPIRE bio-resource research platform for healthy aging and geroscience: Focus on the human translational research cohort (The INSPIRE-T Cohort). *J. Frailty Aging* **10**, 110–120 (2021).

46. J. Raffin, Y. Rolland, M. Aubertin-Leheudre, J. Aragoni da Silva, S. Guyonnet, F. Pillard, B. Vellas, P. de Souto Barreto, INSPIRE group, Cross-sectional interactive associations of physical activity and sedentary behaviour with physical capacity across adulthood. *J. Cachexia. Sarcopenia Muscle* **15**, 1134–1145 (2024).

47. N. Tavassoli, P. de Souto Barreto, C. Berbon, C. Mathieu, J. de Kerimel, C. Lafont, C. Takeda, I. Carrie, A. Piau, T. Jouffrey, S. Andrieu, F. Nourhashemi, J. R. Beard, M. E. Soto Martin,

B. Vellas, Implementation of the WHO integrated care for older people (ICOPE) programme in clinical practice: A prospective study. *Lancet Healthy Longev.* **3**, e394–e404 (2022).

48. P. de Souto Barreto, E. Gonzalez-Bautista, H. A. Bischoff-Ferrari, V. Pelegrim de Oliveira, R. Gorga Bandeira de Mello, S. Andrieu, C. Berbon, N. Tavassoli, J. R. Beard, Y. Rolland, M. E. S. Martín, B. Vellas, Real-life intrinsic capacity screening data from the ICOPE-Care program. *Nat. Aging* **4**, 1279–1289 (2024).

49. D. A. Levine, A. L. Gross, E. M. Briceño, N. Tilton, B. J. Giordani, J. B. Sussman, R. A. Hayward, J. F. Burke, S. Hingtgen, M. S. V. Elkind, J. J. Manly, R. F. Gottesman, D. J. Gaskin, S. Sidney, R. L. Sacco, S. E. Tom, C. B. Wright, K. Yaffe, A. T. Galecki, Sex differences in cognitive decline among US adults. *JAMA Netw. Open* **4**, e210169 (2021).

50. J. R. Beard, A. Officer, I. A. de Carvalho, R. Sadana, A. M. Pot, J.-P. Michel, P. Lloyd-Sherlock, J. E. Epping-Jordan, G. M. E. E. G. Peeters, W. R. Mahanani, J. A. Thiyagarajan, S. Chatterji, The World report on ageing and health: A policy framework for healthy ageing. *Lancet* **387**, 2145–2154 (2016).

51. W.-H. Lu, Y. Rolland, S. Guyonnet, P. de Souto Barreto, B. Vellas, Reference centiles for intrinsic capacity throughout adulthood and their association with clinical outcomes: A cross-sectional analysis from the INSPIRE-T cohort. *Nat. Aging* **3**, 1521–1528 (2023).

52. J. Iannello, Non-intrusive high throughput automated data collection from the home cage. *Heliyon* **5**, e01454 (2019).

53. A.-K. Kraeuter, P. C. Guest, Z. Sarnyai, The Y-maze for assessment of spatial working and reference memory in mice, in *Pre-Clinical Models: Techniques and Protocols*, P. C. Guest, Ed. (Springer, New York, NY, 2019); https://doi.org/10.1007/978-1-4939-8994-2_10, pp. 105–111.

54. H. Takeshita, K. Yamamoto, S. Nozato, T. Inagaki, H. Tsuchimochi, M. Shirai, R. Yamamoto, Y. Imaizumi, K. Hongyo, S. Yokoyama, M. Takeda, R. Oguro, Y. Takami, N. Itoh, Y. Takeya, K. Sugimoto, S. Fukada, H. Rakugi, Modified forelimb grip strength test detects aging-associated physiological decline in skeletal muscle function in male mice. *Sci. Rep.* **7**, 42323 (2017).

55. D. J. David, B. A. Samuels, Q. Rainer, J.-W. Wang, D. Marsteller, I. Mendez, M. Drew, D. A. Craig, B. P. Guardi, J.-P. Guilloux, R. P. Artymyshyn, A. M. Gardier, C. Gerald, I. A. Antonijevic, E. D. Leonardo, R. Hen, Neurogenesis-dependent and -independent effects of fluoxetine in an animal model of anxiety/depression. *Neuron* **62**, 479–493 (2009).

56. C. Viguier, S. Bullich, M. Botella, L. Fasseu, A. Alfonso, K. Rekik, S. Gauzin, B. P. Guardi, N. Davezac, Impact of physical activity on brain oxidative metabolism and intrinsic capacities in young Swiss mice fed a high fat diet. *Neuropharmacology* **241**, 109730 (2023).

57. W. G. Hill, M. L. Zeidel, D. E. Bjorling, C. M. Vezina, Void spot assay: Recommendations on the use of a simple micturition assay for mice. *Am. J. Physiol. Renal Physiol.* **315**, F1422–F1429 (2018).

58. D. Carper, M. Coué, C. Laurens, D. Langin, C. Moro, Reappraisal of the optimal fasting time for insulin tolerance tests in mice. *Mol. Metab.* **42**, 101058 (2020).

59. V. A. Myasoedova, M. Chiesa, N. Cosentino, A. Bonomi, M. Ludergnani, M. Bozzi, V. Valerio, D. Moschetta, I. Massaui, V. Mantegazza, G. Marenzi, P. Poggio, Non-stenotic fibro-calcific aortic valve as a predictor of myocardial infarction recurrence. *Eur. J. Prev. Cardiol.*, zwae062 (2024).

60. D. J. Stekhoven, P. Bühlmann, MissForest—Non-parametric missing value imputation for mixed-type data. *Bioinformatics* **28**, 112–118 (2012).

61. P. Shannon, A. Markiel, O. Ozier, N. S. Baliga, J. T. Wang, D. Ramage, N. Amin, B. Schwikowski, T. Ideker, Cytoscape: A software environment for integrated models of biomolecular interaction networks. *Genome Res.* **13**, 2498–2504 (2003).

62. E. Sommariva, I. Stadiotti, M. Casella, V. Catto, A. Dello Russo, C. Carbucicchio, L. Arnaboldi, S. De Metrio, G. Milano, A. Scopece, M. Casaburo, D. Andreini, S. Mushtaq, E. Conte, M. Chiesa, W. Birchmeier, E. Cogliati, A. Paolin, E. König, V. Meraviglia, M. De Musso, C. Volani, G. Cattelan, W. Rauhe, L. Turnu, B. Porro, M. Pedrazzini, M. Camera, A. Corsini, C. Tondo, A. Rossini, G. Pompilio, Oxidized LDL-dependent pathway as new pathogenic trigger in arrhythmogenic cardiomyopathy. *EMBO Mol. Med.* **13**, e14365 (2021).

**Acknowledgments:** We thank the CREFRE core facility (UMS 006) for hosting and taking care of the animals used for this project. We also thank the Anatomopathology service from Rangueil Hospital for sample management and transfer and the Center of Biological Resources-Cancer (CRB) of the Toulouse Hospital for samples preparation and storage. We also thank Toulouse Veterinary School for Complete Blood Count analysis, together with A. Moreau and A. Le Mouel for technical support. We particularly thank all the members of the INSPIRE platform, including Inspire-T human cohort group: Coordinators: S.Gu. and B.V.; project managers: L. Brigitte and A. Milhet; clinical research assistants: E. Paez, E. Muller, and S. Le Floch; investigators: C. Takeda, C. Faisant, F. Lala, G. A. Van Kan, Z. Steinmeyer, A. Piau, T. Macaron, D. Angioni, and P.-J. Ousset; nurses: M. Comté, N. Daniaud, F. Boissou-Parachaud; methodology, statistical analysis, and data management subgroup: S. Andrieu and C. Cantet; body composition, VO₂max, isocinetism subgroup: Y.R., P.d.S.B., and F. Pillard; MRI subgroup: M. Faruch and P. Payoux;
COPE subgroup: C. Takeda and N. Tavassoli; biological sample collection subgroup: M. Dorard, B. Razat, C. Champigny, and S. Guyonnet; IHU HealthAge Open Science group: N. Coley, S.Gu., and S. Andrieu; INSPIRE animal cohort groups: C. Dray and J.-P. Pradère (fish colony); A.P. and Y.S. (murine cohort); associated research teams: D. Langin, P. Gourdy, L. Martinez, A. Bouloumié, and A.P. (I2MC lab); N. Fazilleau, R. Liblau, J.-C. Guéry, M. Simon, N. Gaudenzio, L. Bostan, H. El Costa, and N. J. Ferrat (Infinity lab); P. Valet, C. Dray, I. Ader, and V. Planat (Restore); P. Payoux and P. Peran (Tonic lab); C. Delpierre and S. Andrieu (CERPOP lab); C. Rampon, N. Davezac, and B.G. (CRCA/CBI lab); N. Vergnolles, J.-P. Motta, S. Djelabi, and P. Floch (IRSD lab); and J.-E. Sarry (CRCT lab).

**Funding:** This work was supported by grants from the Region Occitanie/Pyrénées-Méditerranée (reference number: 1901175), the European Regional Development Fund (ERDF) (project number: MP0022856), and the Inspire Chairs of Excellence funded by: Alzheimer Prevention in Occitania and Catalonia (APOC), EDENIS, KORIAN, Pfizer, Pierre-Fabre to S.Gu., Y.R., P.d.S.B., B.V., and A.P. The IHU HealthAge was supported by the French National Research Agency (ANR) as part of the France 2030 program (reference number: ANR 23 IAHU 0011), to S.Gu., Y.R., P.d.S.B., B.V., and A.P. Y.S. has been partially supported by funds from the Institute of Aging, Toulouse University Hospital. **Author contributions:** Conceptualization: Y.S., A.A., C.F., S.Ga., S.Gu., Y.R., P.d.S.B., B.V., B.G., and A.P. Methodology: Y.S., M.C., A.A., P.O.-F., C.F., S.Ga., S.Gu., Y.R., B.V., B.G., and A.P. Investigation: Y.S., A.A., Y.D., R.K., F.H., P.O.-F., O.F., R.Z., M.G., L.L., C.F., S.Ga., and B.G.

Resources: Y.S., P.O.-F., S.Gu., B.V., and B.G. Data curation: Y.S., M.C., F.H., and P.O.-F. Formal analysis: Y.S., M.C., Y.D., P.O.-F., O.F., R.Z., M.G., L.L., M.R., and G.R. Validation: Y.S., M.C., C.F., S.Ga., S.Gu., B.V., B.G., A.P., and G.R. Visualization: Y.S., M.C., M.R., S.Gu., Y.R., P.d.S.B., and A.P. Software: M.C. Project administration: Y.S., C.F., S.Gu., B.V., and A.P. Supervision: Y.S., S.Gu., B.V., B.G., and A.P. Funding acquisition: S.Gu., Y.R., P.d.S.B., B.V., and A.P. Writing—original draft: Y.S. and A.P. Writing—review and editing: Y.S., M.C., R.Z., M.G., L.L., M.R., S.Gu., Y.R., P.d.S.B., B.V., and A.P. **Competing interests:** M.R. and G.R. are employed by Tecniplast SpA. Tecniplast SpA did not have any role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The other authors declare that they have no competing interests. **Data and materials availability:** All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials.

Submitted 14 September 2024

Accepted 8 November 2024

Published 13 December 2024

10.1126/sciadv.adt1670