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. Author manuscript; available in PMC: 2026 May 26.
Published in final edited form as: J Gerontol A Biol Sci Med Sci. 2026 Mar 10;81(4):glag045. doi: 10.1093/gerona/glag045

Social and physical frailty indices in aging mice: A comparative analysis of longitudinal and cross-sectional designs

Maria Razzoli 1,*, Charles W Collinge 2,*, Monica Luciana 2, Alessandro Bartolomucci 1
PMCID: PMC13200211  NIHMSID: NIHMS2169778  PMID: 41692986

Abstract

Aging is a heterogeneous phenomenon provoked by biological processes that still need to be fully understood. Frailty is a relevant outcome of the aging process that reflects biological decline and that can be quantified through indices measuring the accumulation of functional deficits. How different indices assessing different functional outcomes might inform on natural aging, and how different study designs might impact the measurement of frailty is largely unknown. Here, the Clinical Frailty Index (CFI) and the Mouse Social Frailty Index (mSFI) were applied to male and female mice both longitudinally and cross-sectionally over the lifespan. An overall similar association with aging was apparent: within each cohort, both CFI and mSFI were strongly positively associated with age. The utility of the CFI and mSFI as age predictors within the longitudinal study was also confirmed. Critically, a model developed within the longitudinal study based on CFI scores, mSFI scores, and sex was able to predict age better than alternative models using only one of the frailty indices. This result suggests that the CFI and the mSFI capture intrinsically different elements of deficit accumulation with age. The same model also showed a good performance in predicting the age of mice in the cross-sectional study. Overall, these results demonstrate that the information captured by both frailty indices is relevant to aging, that both frailty domains are needed to produce better age predictions, and that despite item content differences, both are valid in longitudinal and cross-sectional study designs.

Keywords: Geroscience, Biomarkers of aging, Animal model, Linear mixed-effects models

Introduction

Frailty is an established outcome of aging-related decline used from the bench to the clinic. Defined as a state of increased vulnerability to adverse outcomes resulting from cumulative biological decline, frailty reflects heterogeneity in the biological aging process1–4. Unlike other validated measures of biological aging based on omics data5,6 or brain structure7, frailty measurements are non-invasive, inexpensive, and relatively simple, thus positioning frailty as an excellent candidate outcome measure relevant for geroscience8. Frailty indices based on an accumulation of health deficits have been reverse-translated for use in laboratory mice9–11. The Clinical Frailty Index (CFI)9 is now a widely used instrument, demonstrating high reliability and high evolutionarily-conserved predictive validity for age-related health outcomes8,12–14.

Nonetheless, despite enduring recognition that frailty is a multidimensional construct15–17, extant frailty measures predominantly include items that are physical in nature18. To address this fundamental gap, several cognitive19,20 and social frailty indices have been developed21–24 for use in both humans and animal models, including the deficit accumulation-based Mouse Social Frailty Index (mSFI)10,25. In application, the mSFI shows good reliability and high construct validity as a measure of biological aging, exhibiting sensitivity to the natural aging process, social manipulations that have been documented to reduce lifespan, and mouse models of accelerated biological aging25,26.

Although social and physical frailty are different manifestations of the same latent construct, their reciprocal lifelong dynamics remain largely unknown. Our previous application of the mSFI revealed a strong, positive relationship between frailty scores on the mSFI and CFI measured in mice of different ages across the lifespan, with stark differences between sexes25. However, this was a cross-sectional analysis, which precluded the ability to examine changes in indices within individuals over time. Further, human studies have provided ambiguous results, with some finding that physical frailty precedes social frailty27, and others reporting the opposite22. These scarce studies have a limitation in that they have not comprehensively followed individuals longitudinally, only conducting one follow-up assessment over a brief period of the life course. Frailty has been reported to be dynamic and reversible throughout the lifespan28–30, and frailty index values generally increase with age on a population level31–33. Repeated measures designs involve potential carryover effects on behavioral outcomes within individuals, which are not present in cross-sectional designs. However, repeated measures data are better positioned to distinguish between age and cohort effects and can be better leveraged to establish causal relationships. Therefore, longitudinal repeated measurements of frailty in the same individuals over time may capture a more comprehensive picture of i) the nature of social and physical frailty manifestation over time, ii) the relationship between social and physical frailty through the lifespan, and iii) the utility of social and physical frailty indices as proxies of the aging process and their predictive validity for age-related outcomes.

In the present study, we sought to examine how social and physical frailty manifest longitudinally over the lifespan of naturally aging mice of both sexes and characterize the extent to which this differs from social and physical frailty measured cross-sectionally in age-matched mice. Overall, results demonstrate that both the CFI and mSFI capture facets of aging in male and female mice, but with some distinctions. The CFI tracks age more accurately than the mSFI longitudinally, whereas both indices associate to the same extent with age cross-sectionally. Critically, a model developed within the longitudinal study based on CFI scores, mSFI scores, and sex was able to predict age at the majority of the timepoints tested in the cross-sectional study, affirming the importance of both physical and social frailty measures in characterizing aspects of the aging process.

Results

Cohort description

Data from two separate cohorts of group-housed C57BL/6 mice was used in the present study, one evaluated longitudinally and one evaluated cross-sectionally, during six timepoints throughout the mouse lifespan chosen to capture the natural progression of frailty through the aging process. The longitudinal cohort was composed of n = 15 mice per sex, in which both the CFI and mSFI were measured at each timepoint up to ~31 months of age (Suppl. Table 1). The cross-sectional cohort included subsets of mice in different age groups, totaling n = 57 females and n = 49 males, in which the CFI and mSFI data were measured at each of the six timepoints (Suppl. Table 1).

Associations between physical and social frailty

To explore the associations between social (mSFI) and physical (CFI) frailty as well as age, we computed correlations between each variable within each cohort (Figure 1). Associations between mSFI, CFI, and age measured in the longitudinal cohort were analyzed with repeated measures correlations to account for non-independence of observed data collected repeatedly from the same individuals (Figure 1A, C). Within the longitudinal cohort, all variables were significantly positively correlated with one another. The largest correlation was observed between CFI-based physical frailty and age (r = 0.80, p <0.001), while mSFI-based social frailty and age exhibited a lesser, albeit still significant, positive correlation (r = 0.42, p <0.001). A significant positive correlation was also observed between scores on the mSFI and CFI (r = 0.39, p <0.001). When examined in the cross-sectional cohort, similar significant positive Pearson product-moment correlations were found between pairs of the same variables (Figure 1B, D). Notably, in the longitudinal cohort, a greater correlation was observed between CFI scores and age, doubling the value of the correlation coefficient between mSFI scores and age, while in the cross-sectional cohort, both CFI and mSFI scores showed a similar magnitude correlation coefficient with the chronological age of the tested animals (CFI: r = 0.66, p <0.001; mSFI: r = 0.67, p <0.001). These data indicate that while the two cohorts had comparable features, the relationship between the frailty indices in each weren’t identical.

Figure 1.

Figure 1.

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Correlations between the clinical frailty index (CFI), mouse social frailty index (mSFI), and age. (A) Correlation matrix between the variables in the longitudinal and (B) cross-sectional cohort. The areas of circles show the absolute value of the corresponding correlation coefficients. Color intensity of the circle is proportional to the correlation coefficients. Correlation coefficient values are displayed in the lower triangle. C-D) Visualization of individual bivariate relationships between pairs of variables within the longitudinal (C) and cross-sectional (D) cohort. CI=confidence interval.

Variations in physical and social frailty scores across study designs

Linear mixed-effects models were employed to analyze systematic differences between mSFI and CFI scores across study designs, as a function of sex and age of testing (Figure 2, Suppl. Table 2). CFI scores were significantly higher in the longitudinal cohort compared to the cross-sectional cohort. No differences between sexes were present, but a significant expected increase due to age of testing was observed (Figure 2A). On the other hand, mSFI scores were significantly lower in the longitudinal compared to the cross-sectional cohort. Significantly greater values in males were observed compared to females, and, as expected, with higher mSFI values due to age of testing (Figure 2B). Overall, these results illustrate systematic commonalities as well as differences between the indices depending on the longitudinal vs cross-sectional study design.

Figure 2.

Figure 2.

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Comparison of the mean values of Clinical Frailty Index (CFI, A) and mouse social frailty index (mSFI, B) values between the longitudinal and cross-sectional cohorts. The graphs reflect the results of the linear mixed models utilized, showing the general effects, as well as the main effects of study, sex and time.

Evaluation of frailty indices as aging indicators

To further characterize the relationship between each frailty index and age, and evaluate the extent to which each measure proxies the aging process, we performed linear mixed-effect model analysis in the longitudinal cohort to estimate both between- and within-subject associations between frailty index scores and chronological age as measured across time, controlling for the effects of sex.

Several techniques were employed to evaluate the performance of the chosen model, which includes fixed effects of CFI scores, mSFI scores, and sex (Suppl. Figure 1–2, Suppl. Table 3). Although the distribution of the residuals was not normal, there was low collinearity among variables in the model, no outliers were detected, and the error variance was found to be homoscedastic (p = 0.401). Additionally, the Bland-Altman method was employed at each of the tested time-points to quantify the level of agreement between CFI and mSFI scores and detect the presence of systematic differences between the indices (Suppl. Fig. 2). Overall, this analysis showed good concordance between the two frailty indices for most of the tested time points, with the only indications of systematic bias at the two earliest time points. Alternative models composed of the various possible combinations of CFI, mSFI, and sex led to poorer model performances than the model including the three variables (Figure 3A, Suppl. Table 4).

Figure 3.

Figure 3.

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Modeling age with clinical frailty index (CFI), mouse Social frailty index (mSFI) and sex in the longitudinal cohort. A) “Spiderweb” plot for model indices comparison. The different indices are normalized and larger values indicate better model performance, therefore points closer to the center indicate worse fit indices. The following models were compared: Model 1 fixed effects: CFI, mSFI, sex; Model 2 fixed effects: CFI, sex; Model 3 fixed effects: mSFI, sex; Model 4 fixed effects: sex. Abbreviation list: AIC_wt = Akaike Information Criterion weight. BIC_wt = Bayesian Information Criterion weight. ICC_wt = Intraclass correlation coefficient weight. R2_marginal = variance of the fixed effects. RMSE = Root Mean Standard Error. Sigma = residual standard deviation. B-D) Main effect graphs on age prediction of the three different fixed effects tested in the model on the longitudinal data: Clinical Frailty index (A), mouse Social Frailty index (B), and sex (C). Age is expressed in months.

As shown in Figure 3 and Supplementary Table 5, for the chosen model’s main effects, age was significantly explained by each included variable. Increases in both CFI and mSFI scores corresponded to significant increases in predicted age, while being male significantly corresponded to younger age (p <0.001, Supplementary Table 5). Despite its limitations, the model explained ~60% of the dataset variance (conditional R2=0.61, marginal R2=0.59), supporting the validity of the combination of both frailty indices in describing age progression in the longitudinal cohort.

Evaluation of frailty indices in age prediction

Based on the assumption that both the CFI and mSFI reflect the biological aging process, and having shown that they are related while also capturing unique aspects of the aging process, we sought to evaluate whether they could synergistically predict chronological age. To do so, we applied the model previously fit in the longitudinal cohort to mice in the cross-sectional cohort from all six timepoints.

To evaluate potential initial inherent biases affecting the predictive performance of the longitudinal model on age of the cross-sectional cohort, as well as to evaluate possible discrepancies due to inherent differences between the different cohorts of mice, we compared body weight data from both cohorts. Body weight was chosen due to its inclusion in the CFI, as well as its documented relationship with longevity in mice34,35. Throughout the experimental timepoints, body weight was significantly greater as a function of being male, later timepoints, and inclusion in the longitudinal cohort (Suppl. Fig.3A, Supplementary Table 6). On the other hand, at the baseline experimental timepoint, only sex was linked to a significantly different body weight, higher as expected in males, while no differences were present between mice tested longitudinally or cross-sectionally (Suppl. Fig. 3B, Supplementary Table 6). This finding provides plausible evidence that there was an initial absence of bias between the two cohorts we compared, while reinforcing the notion that the two cohorts diverged over time.

Interestingly, the model based on CFI, mSFI, and sex and used to predict the associated age of the cross-sectional mice, resulted in a greater predictive accuracy for females, for which predicted ages were close to their actual ages for 5 out of 6 timepoints, while for males only two predicted ages closely approximated their true age (4.6 and 10.6 months of age; Figure 4, Supplementary Table 7). The model performance, as indicated by RMSE values, became worse at older ages, when both males and females were available in smaller sample sizes, and individual age-related variability is expected to increase36,37.

Figure 4.

Figure 4.

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Predicted age for the cross-sectional cohort as estimated by the predictions from the model developed on the longitudinal for each of the tested timepoints. Data are shown as group average +/− standard error. The number reported illustrates the group average. RMSE = root mean square error.

Discussion

Our analysis revealed that indices of social and physical frailty are highly correlated with age when measured in two cohorts of mice, one tested cross-sectionally and the other tested longitudinally. Yet, the two cohorts demonstrated distinct features, pertaining to associations between the indices and their change over the age of the experimental subjects. In our longitudinal cohort, we observed higher CFI-based physical frailty scores and lower mSFI-based social frailty scores compared to age-matched mice in the cross-sectional cohort. The longitudinal cohort was then utilized to build a model to best describe the relationship between frailty indices and sex as proxies of age, accounting for more than half the observed variability in age. This model was then utilized to predict age in the cross-sectional cohort, leading to varying degrees of prediction accuracy across age groups.

Interestingly, in the longitudinal cohort, the CFI had a greater correlation with age than observed between the mSFI and age, while in the cross-sectional cohort both indices were equally associated with age. In both cohorts the CFI and mSFI were significantly positively correlated with each other to a similar degree. These results suggest the validity of both physical and social frailty as independent predictors of age, while at the same time supporting the existence of a fundamental distinction between the aspects of aging they capture. The longitudinal cohort exhibited higher CFI scores, without the emergence of sex differences, while the cross-sectional cohort was characterized by higher mSFI scores that were also higher in male vs. female mice. Non-mortality aging-related outcomes such as frailty are considered to better capture individual differences in rates of functional impairment during aging when measured longitudinally, thus illustrating individual trajectories over time and capturing dynamic changes. Longitudinal data are generally recommended in geroscience to capture the pace of aging38,39. Nevertheless, repeated measurements involve the possible burden of carryover effects on behavioral outcomes on the same individual40,41, or of habituation and memory phenomena, as was suggested by the flattening of the mSFI trajectory in the longitudinal cohort. This phenomenon may represent one of the advantages of cross-sectional studies, in which direct associations can be drawn between chronological age/health status and biomarkers. In fact, such studies were the main source for the development of early biomarkers of aging42.

We utilized the longitudinal cohort to assess the predictive validity of the CFI and mSFI as indicators of age. The model thus constructed, indicated that both indices were significant predictors of age, and that, accounting for the effect of sex, a majority of the variation in the distribution of age values could be explained in the longitudinal dataset. This result is in line with physical frailty increasing with age and being predictive of adverse health outcomes including mortality both in mouse and human data43,44. At the same time, the contribution of mSFI scores was notable and not redundant with the CFI, as suggested by the worse fit of a model including CFI and sex only. This finding provides support for the validity of the mSFI and resonates with the well-known multifactoriality of health deterioration during aging, requiring the consideration of multiple scoring systems for the loss of physiological functioning38.

It is recommended that biomarkers of aging should be evaluated across different populations, to account for differences due to factors such as genetics, environmental factors, life stages, etc., and in light of the near universality of the aging phenomenon38. To test the generalizability of the CFI and mSFI as aging predictors, we generated age predictions for the cross-sectional cohort based on the model developed on the longitudinal cohort. Interestingly, the prediction proved accurate only on half the tested timepoints and mostly in females. There are several explanations for this outcome. First, the predictive model was developed in the longitudinal cohort with a relatively small sample size. This may limit the predictive accuracy of our model as it has been shown across other validated biomarkers of aging that chronological age prediction is enhanced as the size of the sample used to develop predictive models increases45. Our modeling strategy is compatible with many existing models assuming a linear relation between biomarkers of aging, age, and the likelihood of age-related outcomes throughout the lifespan46,47. Furthermore overfitting, interpretability, and accuracy were best targeted with linear models for our small size datasets. Nevertheless, data heterogeneity which is a fundamental feature of the aging process also poses a challenge to the extent to which relationships between variables can be captured linearly, especially with the emergence of recent studies discovering multiple examples of non-linearity46,47, and more in the later phases of life where our prediction proved less accurate. Additionally, we couldn’t dismiss the possibility that inherent heterogeneity between cohorts could have limited the translatability of the longitudinal model predictions. We attempted to control for this factor by comparing body weight values between cohorts. The initial body weight values were not different between the two cohorts. This is particularly relevant, since body weight in early adulthood has been shown to be more predictive of longevity than other body weight related metrics35. Furthermore, the longitudinal and cross-sectional cohorts diverged over time, possibly reflecting a response to repeated testing, to environmental differences (i.e., different testing rooms) and/or provenance (i.e., NIA colony vs breeding in house). Notwithstanding these limitations, our results provide a robust demonstration of the validity of the model developed.

In conclusion, our data emphasizes similarities and differences between frailty measures in naturally aging mice across longitudinal and cross-sectional sampling designs and provides support for the synergistic use of social and physical frailty measures in studies of aging. Future studies should add other frailty measurements48 and frailty domains, such as cognitive frailty19, to be able to capture all the facets of frailty and evaluate their role as biomarkers of aging.

Methods

Mice

The longitudinal cohort was composed of group-housed male and female C57BL/6 mice (n=15/sex) which were bred from breeders purchased from Jackson Labs (JAX™ stock number #000664) and maintained in same-sex sibling groups since weaning, to be tested repeatedly as described. The cross-sectional cohort was composed of group-housed male and female C57BL/6 mice ages 4–36 months (n = 3–15 per sex/age group). All mice in the cross-sectional cohort were obtained from the NIA Aged Rodent Colony, housed in same-sex groups of three to five mice and allowed to acclimatize to our animal facility for 10–14 days before testing. The 21-month-old experimental time point refers to group-housed mice of an age ranging from 20 to 22 months of age, 27 months represents mice aged 26–28 months, and 34 months represents mice aged 32–36 months.

All mice were housed in static cages with standard bedding and nesting material on a 12:12-h light/dark cycle at 21 ± 2°C and had access to standard diet (2018 Teklad; Inotiv) and water ad libitum. All mice were cared for and maintained according to ethical guidelines set by the US National Institutes of Health. Experiments were approved by the Institutional Animal Care and Use Committee (IACUC) at the University of Minnesota-Twin Cities (Minneapolis, MN).

Mouse Social Frailty index (mSFI)

The Mouse Social Frailty Index (mSFI) was used to quantify social frailty across cohorts. The protocol for the mSFI has been described extensively elsewhere25,26. Briefly, the mSFI consists of seven behavioral assays that quantify distinct aspects of mouse social behavior: 1) olfactory test; 2) urine marking; 3) urine countermarking; 4) juvenile social interaction in home environment; 5) juvenile social interaction in a novel environment; 6) social/novel object preference test; 7) nest building. Performance in each assay was assigned an index score based on the extent of deviation from a reference sample of sex- and strain-matched young adult mice. For the longitudinal cohort, the reference sample used consisted of n = 45 3 month-old sex-matched mice, which included the 3 month-old mice included herein. For the cross-sectional cohort, the n = 10 4 month-old mice were used as sex-matched reference samples. Index scores were assigned as follows: > ±1 standard deviation (SD) of reference sample means = 0, ≤ 2 SD = 0.25, ≤ 3 SD = 0.5, ≤ 4 SD = 0.75, > 4 SD = 1. The nest building assessment is the exception. It is scored on a 1–5 scale, with higher scores indicating better performance. Index scores were assigned as: 5 = 0, 4 = 0.25, 3 = 0.5, 2 = 0.75, 1 = 1. Index scores for each mouse over all items were summed and divided by the total number of items (7) to calculate a final mSFI score for each mouse ranging from 0 to 1, with higher scores indicating greater levels of social frailty.

The Clinical Frailty Index (CFI)

The 31-item Clinical Frailty Index (CFI) was used to quantify physical frailty across cohorts43. Briefly, trained evaluators observed the integument, musculoskeletal, vestibulocochlear/auditory, ocular, nasal, digestive, urogenital, and respiratory systems of each mouse. Each observational item was scored 0, 0.5, or 1 based on previously published criteria to reflect the severity of deficit present. Grip strength was evaluated with a grip strength meter (Columbus Instruments, Columbus, OH), and internal body temperature was taken with an anal temperature probe (Cole-Parmer, Vernon Hills, IL). Deficits in grip strength, internal body temperature, and body weight were assigned values from 0, 0.25, 0.5, 0.75, to 1 based on extent of deviation from the same reference samples used to calculate the mSFI, in each respective cohort. Deficit scores for all items were summed and divided by the total number of items (31) to calculate a final CFI score for each mouse ranging from 0 to 1, with higher scores indicating greater levels of physical frailty. In all comparisons, CFI assessments occurred within 24 h of mSFI testing.

Statistical analysis

All statistical analyses were conducted using R (v4.5.0) in a RStudio environment (v2025.09.2). Pearson product-moment correlations were performed with the corrplot package (v0.95) and repeated measures correlations were performed using the rmcorr package (v0.7.0). A linear mixed-effects model was fit to model age as a function of CFI, SFI and sex specified as fixed effects and individual mice as random effects with the nlme package (v3.1–168). The model main effects were visualized with the package effects (v4.2–2). The model performance was checked with the packages performance (v0.15.2), lattice (v0.22–7), and randomForest (v4.7–1.2). All significance testing was conducted at the α = 0.05 level of significance.

Supplementary Material

Supplementary Tables and Figures

Acknowledgements

The authors wish to thank M. Abdi, C. Anderson, A. Bermudez, J. Fournier, R. Lawabni, R. Mansk, S. McGonigle, J.P. Pallais, N. Rabeaa, P. Rodriguez, and A. Undavia for their assistance in the conduct of these experiments. Patricia Smith, Diana Creswell, and the staff of the Research Animal Resources Department at the University of Minnesota are also acknowledged for their instrumental role in animal care.

Funding

Supported by NIH/NIA R61/R33 AG078520, MN Partnership for Biotechnology and Molecular Genomics #18.4 and #24.01, and IBP Grant Accelerator Program to A.B.

Footnotes

Conflict of Interest

Authors declare no conflict of interest.

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