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A Murine Frailty Index Based on Clinical and Laboratory Measurements: Links Between Frailty and Pro-inflammatory Cytokines Differ in a Sex-Specific Manner

A Murine Frailty Index Based on Clinical and Laboratory Measurements: Links Between Frailty and... Abstract A frailty index (FI) based on clinical deficit accumulation (FI-Clinical) quantifies frailty in aging mice. We aimed to develop a laboratory test-based murine FI tool (FI-Lab) and to investigate the effects of age and sex on FI-Lab scores, FI-Clinical scores, and the combination (FI-Combined), as well as to explore links between frailty and inflammation. Studies used older (17 and 23 months) C57BL/6 mice of both sexes. We developed an FI-Lab (blood pressure, blood chemistry, echocardiography) based on deviation from reference values in younger adults (12 months), which showed similar characteristics to a human FI-Lab tool. Interestingly, while FI-Clinical scores were higher in females, the opposite was true for FI-Lab scores and there was no sex difference in FI-Combined scores. All three FI tools revealed a positive correlation between pro-inflammatory cytokine levels and frailty in aging mice that differed between the sexes. Elevated levels of the pro-inflammatory cytokines interleukin (IL)-6, IL-9, and interferon-γ were associated with higher FI scores in aging females, while levels of IL-12p40 rose as FI scores increased in older males. Thus, an FI tool based on common laboratory tests can quantify frailty in mice; the positive correlation between inflammation and frailty scores in naturally aging mice differs between the sexes. Deficit index, Deficit accumulation, Inflammation, Sex differences, Pro-inflammatory cytokines Frailty is considered a measure of unexplained heterogeneity in health status of people of the same chronological age, and as such can be considered a measure of a person’s biological age (1). One way that frailty is commonly assessed in humans is by quantifying the proportion of health-related deficits a person displays using a frailty index (FI) (2,3). An FI counts the number of deficits a person has accumulated, and divides this by the number of deficits measured to determine a score between 0 and 1, where the closer the score to 1, the more frail a person is (2,3). Increasing FI score is associated with a range of adverse health outcomes including hospitalization, institutionalization, and mortality (4,5). Clinically useful FIs are composed of a range of health-related deficits including symptoms, diseases, activities of daily living, and self-reported health status, that are often collected through clinical interviews/assessments (1,3,6). Recently, our group developed an FI for use in people that was based solely on deficits in standard laboratory blood tests (FI-Lab). We found that high FI-Lab scores predicted mortality risk (6,7). Deficits were counted if their values were outside the normal range for that measurement (6). The development of frailty measures that can be determined from routinely collected data in clinical care is a critically important step that may allow earlier and faster identification of those who are frail. Another important advance in frailty research has been the development of FIs to identify and quantify frailty in animal models (8–11). The first of these (8) was an invasive FI tool that included deficits across a variety of measurements (e.g. measures of hemodynamics, activity levels, blood chemistry, and body composition). Deficits were graded based on a measurements difference from mean reference values in healthy younger mice (8). Whitehead et al. (9) developed an FI based on noninvasive clinical assessment of mice (referred to as an FI-Clinical) and this tool has been widely used in animal frailty studies (12–20). An FI based on abnormalities in standard lab test results, analogous to the FI-Lab used in humans (6), has not yet been developed in mice. This ability to quantify frailty in animal models with tools developed for use in humans is an important advance. It facilitates the investigation of mechanisms that may contribute to the development of frailty in humans and provides a translational platform to test interventions to improve health span and attenuate frailty. In humans, there are clear sex differences in frailty, with females having higher frailty than males at almost all ages, despite having lower mortality risk (21–24). Proposed explanations for this paradox include differences in health-care seeking and reporting behaviors between sexes, different disease profiles, or perhaps underlying biological differences (23). Interestingly, women appear to have lower FI-Lab scores than FI-Clinical scores (6) although direct sex differences have not yet been explored. Some studies have explored sex differences in FI-Clinical scores in small numbers of mice, with two finding no clear sex differences (8,15) and two finding that female mice are more frail than male mice (9,11). Inflammation is proposed as an important pathophysiological mechanism of frailty in humans, and changes in inflammatory cytokines have been associated with increasing frailty in clinical studies (25–28). The IL-10 knockout (KO) mouse has been proposed as a mouse model of frailty, and displays characteristics of frailty such as chronic inflammation, sarcopenia, and reduced aerobic endurance (29,30). The association of frailty with inflammation in naturally aging rodents however, has not yet been explored. The aim of this study was thus to develop and characterize an FI-Lab tool in mice, and explore the effect of age and sex on FI-Lab scores alone, FI-Clinical scores alone, and the two combined (FI-Combined). We also aimed to explore the association of each of these FI scores with inflammatory cytokines in naturally aging mice of both sexes. Methods Animals C57BL/6 male and female mice were purchased from Charles River. All were retired breeders and were aged in the Carlton Animal Care Facility, which is a clean conventional multispecies facility which can house ~3000 cages of specific pathogen-free mice and rats. Mice were housed at 21°C with 35% humidity in Individually Ventilated Caging Systems (Allentown, Inc) with regular husbandry duties performed within Animal Transfer Stations or Biological Safety Cabinets. Animals were fed Standard Grain-Based Control Rodent Diet with bacon flavor (#F4059; Bio-Serv, Frenchtown, NJ) and maintained on a 12-h light–dark cycle with ad libitum access to food and water. Most mice were housed in groups of 2–5 per cage; one female and three males were housed separately. All experiments were approved by the Dalhousie University Committee on Laboratory Animals and performed in accordance with guidelines published by the Canadian Council on Animal Care. Assessments were completed in mice at two time points: 17.0 ± 0.1 months of age (n = 37 females, n = 34 males) and 22.5 ± 0.5 months of age (n = 17 females, n = 13 males). Measurements were repeated in mice at both ages, although the sample size is lower at 23 months of age, as a subset of the mice were started in a separate study at 17 months of age. The mean FI scores of the mice that left the study were not significantly different from the scores of the mice that remained (values were 0.19 ± 0.05 for females, and 0.17 ± 0.17 for males, versus 0.20 ± 0.06 for females and 0.17 ± 0.04 for males, for the mice that left versus the mice that remained, respectively; p = .63 for females and p = .93 for males). Reference values for the FI-Lab were determined from measurements in a group of adult mice (~1 year of age). Mouse Clinical Frailty Index (FI-Clinical) Assessment Frailty was assessed in mice using the mouse clinical FI (9) referred to in this article as FI-Clinical. This FI is made up of 31 clinically observed, noninvasive, health-related deficits. Most of the deficits are scored as either mild, receiving a score of 0.5; severe, receiving a score of 1; or absent, receiving a score of 0. Weight and temperature are scored based on the number of standard deviations (SDs) the value is from reference values, as previously described (9). Parameters Measured to Develop a Laboratory Frailty Index (FI-Lab) Assessment of blood pressure, basic metabolic status, and echocardiography was conducted to generate data for the individualized FI-Lab scores for each mouse in the study. These measures were also made in a group of 12-month-old male and female mice to generate reference values for adult animals, as described in detail in the next section. Blood pressure was assessed in mice using the IITC Life Sciences tail-cuff system (Woodland Hills, CA). After 2 days of acclimatization, blood pressure measurements were made on two consecutive days. Mice were allowed 15 min to acclimatize, then 3–5 rounds of five blood pressure measurements were made. A mean of all measurements over the 2 days was used for systolic blood pressure (SBP) and diastolic blood pressure (DBP) for each mouse. Mean arterial pressure (MAP) was calculated as MAP = [(2 × DBP) + SBP]/3 and pulse pressure (PP) was calculated as PP = SBP − DBP for each mouse. Metabolic status was evaluated from a blood sample collected from the mice. In most experiments, blood samples were collected from anesthetized (3% isoflurane for induction, maintained at 1–2%) mice via the facial vein at ~17 and 23 months of age. In some experiments in 23-month-old mice, mice were anesthetized (sodium pentobarbital; 200 mg/kg) and a blood sample was taken from the heart after it was removed for other studies at the end of the experiment. For each mouse, ~100 μL of whole blood was immediately tested in an iSTAT portable clinical analyzer (Abbott Diagnostics) with a CHEM8+ cartridge, to measure sodium (Na), potassium (K), chloride (Cl), ionized calcium (iCa), total bicarbonate (TCO2), glucose, urea nitrogen (BUN), creatinine (Crea), hematocrit (Hct), hemoglobin (Hb), and anion gap (AnGap). Echocardiographic analysis was completed for each mouse at 17 and 23 months of age using the Vevo 2100 imaging platform (VisualSonics). Briefly, mice were anesthetized with 3% isoflurane (maintained at 1–2%), and hair removed from their chest. A high-resolution linear transducer (18–38 MHz, MS400) was used to make short axis M-mode measurements, and four-chamber apical Doppler measurements. Mouse body temperature was monitored throughout and maintained, via a heated platform and heat lamp, at 35–36°C. Heart rate and respiration rate were monitored via the Vevo 2100 system. Images were analysed using Vevo LAB software, and data for left ventricle posterior wall (LVPW) thickness, intraventricular septum (IVS) thickness, ejection fraction (EF), fractional shortening (FS), stroke volume (SV), and left ventricle mass-corrected obtained from short-axis M-mode measurements in both systole and diastole. Mitral valve Doppler analysis was used to obtain E/A ratios. Mouse FI-Lab Scoring The measurements of blood pressure, basic metabolic status, and cardiac structure/function described above were used to create a 23-item FI-Lab. A full list of the individual items used to create the index can be found in Table 1. Normal reference values for each of these items were based on values measured in younger adult male and female C57BL/6 mice (12.0 ± 0.1 months of age; n = 4–9 per sex), as shown in Table 1. The reference values were obtained by calculating the mean and SD for each item separately for the 12-month-old male and female mice. Values that were within ± 1.5 SD of the mean were considered “normal” and received a score of 0, indicative of no deficit. Values that were above or below the cutoff (±1.5 SDs) were given a score of 1 and were considered a deficit. The mean values and the SDs obtained in the 12-month-old mice, as well as the high and low cutoff points, are illustrated in Table 1 separately for each sex. An FI-Lab score was determined for each of the older mice, at ~17 and 23 months of age, by summing the deficits a mouse displayed for each of the measured items, and dividing this by the total number of items measured. This gave an FI-Lab score between 0 and 1 for each mouse, at each time point. An FI-Combined score was also calculated for each mouse at both time points. This score was determined by adding the deficit scores for each mouse across both the FI-Clinical items (n = 31) and the FI-Lab items (n = 23), and dividing by the total number of items (n = 54). Cytokine Analysis Blood samples were collected from mice at 17 and 23 months of age, as described previously. Blood samples were stored on ice, then spun at 4°C at 9391 Gs, and serum collected and stored at −20°C. A Bio-Plex Pro Mouse cytokine assay was used to quantify serum levels of 23 cytokines from both time points. The assays were conducted as per manufacturer’s instructions and read using the MagPix (BioRad) Multiplex system. Cytokines measured included interleukin (IL)-6, IL-12p40, interferon gamma (IFN-γ), and IL-9; a complete list can be found in Supplementary Table 1. Sufficient serum to perform cytokine analysis was not available for all mice, so the final sample size was n = 18 males and n = 12 females. Cytokine analysis was performed on different mice at 17 and 23 months of age, so the data were not repeated measures. Statistics Data are expressed as mean ± SEM unless otherwise indicated. Normality tests were completed on all FI data (Supplementary Table 2). Two-sided t-tests were used to compare mean values for FI-Lab items between 12-month-old males and females. Scores for each FI (FI-Clinical, FI-Lab, and FI-Combined) were compared across sex and age groups using a two-way analysis of variance (ANOVA), with Bonferroni post hoc analysis. Two-sided t-tests were used to compare mean values for the FI-Clinical and FI-Lab for each age and sex group, and Mann–Whitney U tests were used to compare median values. Proportions of individual deficits observed in each sex, for each age group were compared with Chi-squared analysis. Spearman correlations were used to analyse the association between FI scores and cytokine levels. Data analysis was completed using the statistics program SPSS (Version 21.0, SPSS Inc., Chicago, IL) and SigmaPlot (Version 11.0, Systat Software, Germany). p values less than .05 were considered significant. Results Creation of an FI Based on Laboratory Items in Mice Reference values and cutoff points were determined from 12-month-old male and female mice (Table 1). There were significant differences in four reference values between males and females. TCO2 and heart rate were slightly higher in females than males, whilst glucose and IVSs were higher in males than females. These reference values were used to calculate FI-Lab scores in older mice (Figures 1 and 2) as discussed below. Table 1. Reference Values for the FI-Lab Obtained From Male and Female Mice ~12 Months of Age FI Item* Male Female Mean ± SD Low Cutoff High Cutoff Mean ± SD Low Cutoff High Cutoff Na (mmol/L) 144.4 ± 2.5 140.6 148.2 144.8 ± 1.9 141.9 147.6 K (mmol/L) 6.2 ± .5 5.4 7.0 6.9 ± 1.4 4.8 8.9 Cl (mmol/L) 112.2 ± 1.8 109.5 114.9 113.5 ± 2.9 109.2 117.8 iCa (mmol/L) 1.1 ± .1 1.0 1.2 1.0 ± 0.1 0.9 1.2 TCO2 (mmol/L) 26.4 ± 1.8 23.7 29.1 22.3 ± 1.5† 20.0 24.5 Glucose (mg/dL) 236.8 ± 25.2 199.0 274.6 180.5 ± 31.3† 133.5 227.5 BUN (mg/dL) 20.8 ± 2.5 17.1 24.5 23.5 ± 2.9 19.2 27.8 Crea (mg/dL) 0.6 ± 0.2 0.2 0.9 0.8 ± 0.1 0.6 1.0 Hematocrit (%PCV) 32.8 ± 4.9 25.4 40.2 30.5 ± 2.4 26.9 34.1 Hemoglobin (g/dL) 11.2 ± 1.7 8.6 13.7 10.4 ± 0.8 9.2 11.6 Anion gap (mmol/L) 12.8 ± 3.0 8.3 17.3 16.8 ± 3.3 11.8 21.7 Systolic BP (mmHg) 111.5 ± 8.0 99.5 123.5 110.0 ± 8.1 97.8 122.2 Diastolic BP (mmHg) 78.0 ± 6.4 68.4 87.5 78.5 ± 9.8 63.8 93.3 MAP (mmHg) 89.1 ± 6.7 79.1 99.2 89.0 ± 9.0 75.5 102.5 Pulse pressure (mmHg) 33.5 ± 3.9 27.7 39.4 31.4 ± 4.7 24.4 38.5 LVPWs (mm) 1.2 ± 0.1 1.0 1.3 1.2 ± 0.5 0.4 2.0 IVSs (mm) 1.3 ± 0.1 1.1 1.5 1.0 ± 0.2† 0.7 1.3 HR (bpm) 409.3 ± 22.9 374.9 443.7 452.2 ± 25.0† 414.7 489.6 MV E/A ratio 1.6 ± 0.2 1.4 1.9 1.8 ± 0.3 1.3 2.3 EF (%) 58.3 ± 11.9 40.4 76.1 49.3 ± 14.0 28.3 70.2 FS (%) 31.0 ± 8.3 18.5 43.4 25.2 ± 8.7 12.1 38.3 Stroke volume (uL) 42.2 ± 7.7 30.6 53.8 35.2 ± 9.2 21.5 48.9 LV mass corrected (mg) 112.6 ± 31.8 64.9 160.4 96.1 ± 48.3 23.7 168.6 FI Item* Male Female Mean ± SD Low Cutoff High Cutoff Mean ± SD Low Cutoff High Cutoff Na (mmol/L) 144.4 ± 2.5 140.6 148.2 144.8 ± 1.9 141.9 147.6 K (mmol/L) 6.2 ± .5 5.4 7.0 6.9 ± 1.4 4.8 8.9 Cl (mmol/L) 112.2 ± 1.8 109.5 114.9 113.5 ± 2.9 109.2 117.8 iCa (mmol/L) 1.1 ± .1 1.0 1.2 1.0 ± 0.1 0.9 1.2 TCO2 (mmol/L) 26.4 ± 1.8 23.7 29.1 22.3 ± 1.5† 20.0 24.5 Glucose (mg/dL) 236.8 ± 25.2 199.0 274.6 180.5 ± 31.3† 133.5 227.5 BUN (mg/dL) 20.8 ± 2.5 17.1 24.5 23.5 ± 2.9 19.2 27.8 Crea (mg/dL) 0.6 ± 0.2 0.2 0.9 0.8 ± 0.1 0.6 1.0 Hematocrit (%PCV) 32.8 ± 4.9 25.4 40.2 30.5 ± 2.4 26.9 34.1 Hemoglobin (g/dL) 11.2 ± 1.7 8.6 13.7 10.4 ± 0.8 9.2 11.6 Anion gap (mmol/L) 12.8 ± 3.0 8.3 17.3 16.8 ± 3.3 11.8 21.7 Systolic BP (mmHg) 111.5 ± 8.0 99.5 123.5 110.0 ± 8.1 97.8 122.2 Diastolic BP (mmHg) 78.0 ± 6.4 68.4 87.5 78.5 ± 9.8 63.8 93.3 MAP (mmHg) 89.1 ± 6.7 79.1 99.2 89.0 ± 9.0 75.5 102.5 Pulse pressure (mmHg) 33.5 ± 3.9 27.7 39.4 31.4 ± 4.7 24.4 38.5 LVPWs (mm) 1.2 ± 0.1 1.0 1.3 1.2 ± 0.5 0.4 2.0 IVSs (mm) 1.3 ± 0.1 1.1 1.5 1.0 ± 0.2† 0.7 1.3 HR (bpm) 409.3 ± 22.9 374.9 443.7 452.2 ± 25.0† 414.7 489.6 MV E/A ratio 1.6 ± 0.2 1.4 1.9 1.8 ± 0.3 1.3 2.3 EF (%) 58.3 ± 11.9 40.4 76.1 49.3 ± 14.0 28.3 70.2 FS (%) 31.0 ± 8.3 18.5 43.4 25.2 ± 8.7 12.1 38.3 Stroke volume (uL) 42.2 ± 7.7 30.6 53.8 35.2 ± 9.2 21.5 48.9 LV mass corrected (mg) 112.6 ± 31.8 64.9 160.4 96.1 ± 48.3 23.7 168.6 Notes: BP = Blood pressure; EF = Ejection fraction; FS = Fractional shortening; HR = Heart rate; IVSs = Intraventricular septum in systole; LVPWs = Left ventricle posterior wall in systole; LV = Left ventricle; MAP = Mean arterial pressure. *Reference values for the FI-Lab were obtained for 5–9 male and 4–9 female mice aged ~12 months. †Denotes significant difference between the sexes, p < .05. View Large Table 1. Reference Values for the FI-Lab Obtained From Male and Female Mice ~12 Months of Age FI Item* Male Female Mean ± SD Low Cutoff High Cutoff Mean ± SD Low Cutoff High Cutoff Na (mmol/L) 144.4 ± 2.5 140.6 148.2 144.8 ± 1.9 141.9 147.6 K (mmol/L) 6.2 ± .5 5.4 7.0 6.9 ± 1.4 4.8 8.9 Cl (mmol/L) 112.2 ± 1.8 109.5 114.9 113.5 ± 2.9 109.2 117.8 iCa (mmol/L) 1.1 ± .1 1.0 1.2 1.0 ± 0.1 0.9 1.2 TCO2 (mmol/L) 26.4 ± 1.8 23.7 29.1 22.3 ± 1.5† 20.0 24.5 Glucose (mg/dL) 236.8 ± 25.2 199.0 274.6 180.5 ± 31.3† 133.5 227.5 BUN (mg/dL) 20.8 ± 2.5 17.1 24.5 23.5 ± 2.9 19.2 27.8 Crea (mg/dL) 0.6 ± 0.2 0.2 0.9 0.8 ± 0.1 0.6 1.0 Hematocrit (%PCV) 32.8 ± 4.9 25.4 40.2 30.5 ± 2.4 26.9 34.1 Hemoglobin (g/dL) 11.2 ± 1.7 8.6 13.7 10.4 ± 0.8 9.2 11.6 Anion gap (mmol/L) 12.8 ± 3.0 8.3 17.3 16.8 ± 3.3 11.8 21.7 Systolic BP (mmHg) 111.5 ± 8.0 99.5 123.5 110.0 ± 8.1 97.8 122.2 Diastolic BP (mmHg) 78.0 ± 6.4 68.4 87.5 78.5 ± 9.8 63.8 93.3 MAP (mmHg) 89.1 ± 6.7 79.1 99.2 89.0 ± 9.0 75.5 102.5 Pulse pressure (mmHg) 33.5 ± 3.9 27.7 39.4 31.4 ± 4.7 24.4 38.5 LVPWs (mm) 1.2 ± 0.1 1.0 1.3 1.2 ± 0.5 0.4 2.0 IVSs (mm) 1.3 ± 0.1 1.1 1.5 1.0 ± 0.2† 0.7 1.3 HR (bpm) 409.3 ± 22.9 374.9 443.7 452.2 ± 25.0† 414.7 489.6 MV E/A ratio 1.6 ± 0.2 1.4 1.9 1.8 ± 0.3 1.3 2.3 EF (%) 58.3 ± 11.9 40.4 76.1 49.3 ± 14.0 28.3 70.2 FS (%) 31.0 ± 8.3 18.5 43.4 25.2 ± 8.7 12.1 38.3 Stroke volume (uL) 42.2 ± 7.7 30.6 53.8 35.2 ± 9.2 21.5 48.9 LV mass corrected (mg) 112.6 ± 31.8 64.9 160.4 96.1 ± 48.3 23.7 168.6 FI Item* Male Female Mean ± SD Low Cutoff High Cutoff Mean ± SD Low Cutoff High Cutoff Na (mmol/L) 144.4 ± 2.5 140.6 148.2 144.8 ± 1.9 141.9 147.6 K (mmol/L) 6.2 ± .5 5.4 7.0 6.9 ± 1.4 4.8 8.9 Cl (mmol/L) 112.2 ± 1.8 109.5 114.9 113.5 ± 2.9 109.2 117.8 iCa (mmol/L) 1.1 ± .1 1.0 1.2 1.0 ± 0.1 0.9 1.2 TCO2 (mmol/L) 26.4 ± 1.8 23.7 29.1 22.3 ± 1.5† 20.0 24.5 Glucose (mg/dL) 236.8 ± 25.2 199.0 274.6 180.5 ± 31.3† 133.5 227.5 BUN (mg/dL) 20.8 ± 2.5 17.1 24.5 23.5 ± 2.9 19.2 27.8 Crea (mg/dL) 0.6 ± 0.2 0.2 0.9 0.8 ± 0.1 0.6 1.0 Hematocrit (%PCV) 32.8 ± 4.9 25.4 40.2 30.5 ± 2.4 26.9 34.1 Hemoglobin (g/dL) 11.2 ± 1.7 8.6 13.7 10.4 ± 0.8 9.2 11.6 Anion gap (mmol/L) 12.8 ± 3.0 8.3 17.3 16.8 ± 3.3 11.8 21.7 Systolic BP (mmHg) 111.5 ± 8.0 99.5 123.5 110.0 ± 8.1 97.8 122.2 Diastolic BP (mmHg) 78.0 ± 6.4 68.4 87.5 78.5 ± 9.8 63.8 93.3 MAP (mmHg) 89.1 ± 6.7 79.1 99.2 89.0 ± 9.0 75.5 102.5 Pulse pressure (mmHg) 33.5 ± 3.9 27.7 39.4 31.4 ± 4.7 24.4 38.5 LVPWs (mm) 1.2 ± 0.1 1.0 1.3 1.2 ± 0.5 0.4 2.0 IVSs (mm) 1.3 ± 0.1 1.1 1.5 1.0 ± 0.2† 0.7 1.3 HR (bpm) 409.3 ± 22.9 374.9 443.7 452.2 ± 25.0† 414.7 489.6 MV E/A ratio 1.6 ± 0.2 1.4 1.9 1.8 ± 0.3 1.3 2.3 EF (%) 58.3 ± 11.9 40.4 76.1 49.3 ± 14.0 28.3 70.2 FS (%) 31.0 ± 8.3 18.5 43.4 25.2 ± 8.7 12.1 38.3 Stroke volume (uL) 42.2 ± 7.7 30.6 53.8 35.2 ± 9.2 21.5 48.9 LV mass corrected (mg) 112.6 ± 31.8 64.9 160.4 96.1 ± 48.3 23.7 168.6 Notes: BP = Blood pressure; EF = Ejection fraction; FS = Fractional shortening; HR = Heart rate; IVSs = Intraventricular septum in systole; LVPWs = Left ventricle posterior wall in systole; LV = Left ventricle; MAP = Mean arterial pressure. *Reference values for the FI-Lab were obtained for 5–9 male and 4–9 female mice aged ~12 months. †Denotes significant difference between the sexes, p < .05. View Large Figure 1. View largeDownload slide Impact of age and sex on FI-Clinical, FI-Lab, and FI-Combined. (A) Clinical frailty index (FI-Clinical) scores increased from 17 to 23 months of age in male and female mice. FI-Clinical scores for female mice were higher than male mice at 17 and 23 months of age. (B) Laboratory-based frailty index (FI-Lab) scores were higher in male than female mice at 17 and 23 months of age. (C) FI-Combined scores increased from 17 to 23 months in males and females, with no sex difference. The effect of age and sex was analyzed with two-way ANOVAs with Bonferroni post hoc tests (17-month males n = 34, females n = 37; 23-month males n = 13, females n = 13–17). *Indicates a significant difference p < .05. Figure 1. View largeDownload slide Impact of age and sex on FI-Clinical, FI-Lab, and FI-Combined. (A) Clinical frailty index (FI-Clinical) scores increased from 17 to 23 months of age in male and female mice. FI-Clinical scores for female mice were higher than male mice at 17 and 23 months of age. (B) Laboratory-based frailty index (FI-Lab) scores were higher in male than female mice at 17 and 23 months of age. (C) FI-Combined scores increased from 17 to 23 months in males and females, with no sex difference. The effect of age and sex was analyzed with two-way ANOVAs with Bonferroni post hoc tests (17-month males n = 34, females n = 37; 23-month males n = 13, females n = 13–17). *Indicates a significant difference p < .05. Figure 2. View largeDownload slide FI-Lab scores are higher than FI-Clinical scores at 17 and 23 months of age in mice. Mean and median scores for FI-Lab were higher than FI-Clinical scores in (A) 17-month-old females (FI-Lab median score 0.36, FI-Clinical median score 0.19), (B) 17-month-old males (FI-Lab median score 0.48, FI-Clinical median score 0.16), (C) 23-month-old females (FI-Lab median score 0.39, FI-Clinical median score 0.34), and (D) 23-month-old males (FI-Lab median score 0.55, FI-Clinical median score 0.27). Mean and median FI-Lab and FI-Clinical scores were compared with t-tests, and Mann–Whitney U tests (17-month males n = 34, females n = 37; 23-month males n = 13, females n = 13–17). Figure 2. View largeDownload slide FI-Lab scores are higher than FI-Clinical scores at 17 and 23 months of age in mice. Mean and median scores for FI-Lab were higher than FI-Clinical scores in (A) 17-month-old females (FI-Lab median score 0.36, FI-Clinical median score 0.19), (B) 17-month-old males (FI-Lab median score 0.48, FI-Clinical median score 0.16), (C) 23-month-old females (FI-Lab median score 0.39, FI-Clinical median score 0.34), and (D) 23-month-old males (FI-Lab median score 0.55, FI-Clinical median score 0.27). Mean and median FI-Lab and FI-Clinical scores were compared with t-tests, and Mann–Whitney U tests (17-month males n = 34, females n = 37; 23-month males n = 13, females n = 13–17). FI-Lab, FI-Clinical, and FI-Combined Scores in Older Male and Female Mice FI-Clinical scores were significantly affected by age, with increasing age associated with higher FI-Clinical scores in both sexes (Figure 1A). ANOVA analysis showed that sex also had a significant effect on FI-Clinical scores, and post hoc analysis showed that females had higher scores than males at 17 and 23 months (Figure 1A). FI-Lab scores were also calculated and compared in male and female mice at 17 and 23 months (Figure 1B). The mean values and SDs for each item of the FI-Lab are illustrated separately for each sex and age group in Supplementary Table 3. A two-way ANOVA showed that FI-Lab scores were significantly different between sexes, with higher scores in males than females at both ages. The overall effect of age on FI-Lab score was also significant (Figure 1B). A two-way ANOVA of FI-Combined scores showed a significant effect of age, but not sex, with scores at 23 months higher than at 17 months in both sexes (Figure 1C). Together these data suggest that, although FI scores generally increase with age, there are some clear sex differences in FI-Clinical and FI-Lab scores. To compare the distribution of FI scores for the FI-Lab and FI-Clinical in each age and sex group, frequency distributions were plotted (Figure 2). In all groups, mean and median FI-Lab scores were higher than FI-Clinical scores. For both 17-month males and females, the FI-Clinical score distribution was skewed slightly to the left with means of 0.17 ± 0.01 and 0.19 ± 0.01 and medians of 0.16 and 0.19, respectively (Figure 2A and B). For 17-month females, FI-Lab scores were higher than FI-Clinical scores with both a mean of 0.35 ± 0.02 and median of 0.36. Mean and median values for FI-Lab scores for 17-month males were 0.44 ± 0.02 and 0.48, respectively. For 23-month males and females, FI score distributions were shifted to the right, compared to 17 months, but FI-Lab scores remained higher than FI-Clinical scores (Figure 2C and D). The maximum FI-Clinical score in both females and males was 0.45, and the maximum FI-Lab scores were 0.67 for females and 0.75 for males. These observations support the idea of a submaximal limit to frailty of ~0.7 (1). We also investigated whether the FI-Lab and FI-Clinical scores were correlated. There was no significant correlation between these two measures for any group examined in the study (not shown). To determine whether the sex differences in FI-Lab, FI-Clinical, and FI-Combined scores were driven primarily by deficits in a single item, or a few items, the proportion of mice with each deficit were plotted for males and females for each age group (Figure 3). Mice displayed a range of deficits across all of the items in the FI-Lab and FI-Clinical, and there were a number of sex differences in the proportions with specific deficits at each age (Figure 3A and B). For example, at 17 months of age males showed more deficits than females in piloerection, stroke volume, and K, whilst females showed more deficits in Hb, EF, and menace reflex (Figure 3A). There are other sex-specific differences in individual deficits at both 17 (Figure 3A) and 23 (Figure 3B) months of age. This shows that the male–female difference in FI-Lab scores arises from deficits across a wide range of measures, rather than deficits in a specific deficit or group of deficits. Figure 3. View largeDownload slide Comparison of individual deficits from the FI-Lab and FI-Clinical in older male and female mice. Proportion of (A) 17-month-old and (B) 23-month-old male and female mice displaying deficits in each item that makes up the FI-Lab and FI-Clinical. Proportions of individual deficits observed in each sex, for each age group were compared with Chi-squared analysis. *Indicates significant difference between sexes, p < .05. Figure 3. View largeDownload slide Comparison of individual deficits from the FI-Lab and FI-Clinical in older male and female mice. Proportion of (A) 17-month-old and (B) 23-month-old male and female mice displaying deficits in each item that makes up the FI-Lab and FI-Clinical. Proportions of individual deficits observed in each sex, for each age group were compared with Chi-squared analysis. *Indicates significant difference between sexes, p < .05. Association of FI Scores With Inflammatory Cytokines The association between serum levels of cytokines and FI scores was explored in aging male and female mice (Figure 4; Supplementary Figures 1 and 2; Supplementary Table 1). FI-Combined scores in older female mice were significantly correlated with increasing levels of the pro-inflammatory cytokines IL-6, IFN-γ, and IL-9 (Figure 4A–D). By contrast in older males, higher FI-Combined scores were correlated with increasing levels of the pro-inflammatory cytokine IL-12p40 (Figure 4E–H). Additionally, increasing FI-Lab scores were associated with higher IFN-γ and IL-9 levels in females (Supplementary Figure 1), and increasing FI-Clinical scores were associated with higher IL-12p40 levels in males (Supplementary Figure 2). Other cytokines were not correlated with FI scores (Supplemental Table 1). These observations suggest that there are links between frailty and pro-inflammatory cytokines and that these differ in a sex-specific way. Figure 4. View largeDownload slide Inflammatory cytokines are correlated with increasing FI-Combined scores in mice in a sex-specific manner. Correlation of FI-Combined values in older female mice with (A) interleukin (IL)-6 (r = 0.66), (B) IL-12p40 (NS), (C) interferon (IFN)-γ (r = 0.77), and (D) IL-9 (r = 0.75). Correlation of FI-Combined values in older male mice with (E) IL-6 (NS), (F) IL-12p40 (r = 0.67), (G) IFN-γ (NS), and (H) IL-9 (NS). Data analyzed using Spearman correlations and lines indicate significant correlations (p < .05). All cytokine data for 17- and 23-month-old mice included (males n = 18, females n = 13). Figure 4. View largeDownload slide Inflammatory cytokines are correlated with increasing FI-Combined scores in mice in a sex-specific manner. Correlation of FI-Combined values in older female mice with (A) interleukin (IL)-6 (r = 0.66), (B) IL-12p40 (NS), (C) interferon (IFN)-γ (r = 0.77), and (D) IL-9 (r = 0.75). Correlation of FI-Combined values in older male mice with (E) IL-6 (NS), (F) IL-12p40 (r = 0.67), (G) IFN-γ (NS), and (H) IL-9 (NS). Data analyzed using Spearman correlations and lines indicate significant correlations (p < .05). All cytokine data for 17- and 23-month-old mice included (males n = 18, females n = 13). Discussion This study aimed to develop and characterize an FI-Lab tool for use in mice, examine the effects of sex and age on the FI-Lab, FI-Clinical, and FI-Combined scores and investigate links between frailty and inflammation in male and female mice. Our study showed, for the first time, an association between inflammation and frailty in naturally aging mice. Interestingly, clear sex differences in both frailty, inflammatory cytokines, and their interaction were observed. This study also demonstrated that it was possible to make an FI-Lab analogous to that used in humans (6), for use in mouse models. In this study, we made the novel observation that increasing levels of the pro-inflammatory cytokines IL-6, IL-9, and IFN-γ were associated with increasing FI scores in old female mice, whereas increasing levels of IL-12p40 were associated with frailty in old male mice. In terms of the cytokines associated with increasing frailty scores in females, IL-6 is a pro-inflammatory cytokine with a wide range of roles, including stimulating B-cell differentiation and activating macrophages (31). While IL-9 has not been studied as extensively as other cytokines, it is considered a pro-inflammatory cytokine with a role in stimulating cell proliferation (32). IFN-γ is considered the effector cytokine of Type 1 helper T (Th1) cells, and has important roles in macrophage activation and the production of other cytokines in particular TNF-α (33). These observations indicate that frailty is associated with an increase in several markers of inflammation in aging female mice. With respect to the cytokines linked to higher FI scores in aging males, IL-12p40 is a subunit of inflammatory cytokines IL-12 and IL-23. IL-12 is an important regulatory cytokine that activates Th1 cells (34), and IL-23 promotes the production of other pro-inflammatory cytokines including IL-6 (35). Although the role of IL-12p40 itself is unclear, it is known be an antagonist for the IL-12 receptor and to have macrophage chemoattractant properties (36). It also can be associated with a Th2-type response, and may have immunosuppressive roles (34). These data suggest that there is also a link between frailty and inflammation in aging males. Taken together, our results strongly suggest that frailty is associated with inflammation but that this differs importantly between the sexes. Increased levels of the cytokines IL-6 and IFN-γ have been observed in previous studies of IL-10 KO mice, which are proposed to be a mouse model of frailty (29). These mice were developed as a model of Crohn’s disease, but if maintained in pathogen-free conditions, they display some characteristics related to frailty such as sarcopenia, increased mortality risk, and chronic inflammation (29,30). Female IL-10 KO mice have been shown to have increased IL-6 and IFN-γ serum levels, compared to wild-type female mice (29,30,37). IL-6 levels are also higher in male IL-10 KO mice (38), but IFN-γ levels have not yet been measured in male KO mice. Our study, which was the first to explore the links between frailty and cytokines in naturally aging mice, also saw an increase in IL-6 with frailty, but interestingly, only in females. The present study also was the first to explore the association of cytokines IL-9 and IL-12p40 with frailty in mice of both sexes and our work suggests that increasing levels of these cytokines are also associated with frailty in mice from 17 to 23 months of age. It would be interesting to explore the relationship between markers of inflammation and frailty in an even older cohort of mice to determine if there is a stronger correlation between frailty and cytokines in very old animals. In humans, increasing levels of cytokines IL-6 and IFN-γ have been associated with both aging and frailty across a variety of populations (25–28,39–41). Although links between IL-9 and IL-12p40 levels and frailty have not yet been explored in humans, their association with aging has been studied. One study in older individuals reported that there was no association between IL-9 and aging (42), whereas another reported that IL-12p40 increases with age in humans (34). The mechanism responsible for levels of pro-inflammatory cytokines, particularly IL-6, in frailty may be related to muscle strength and sarcopenia (43,44) but this is not fully understood. The links between pro-inflammatory cytokines and FI scores reported in the present study provide further validation of the use of FI tools in mice to study frailty, and suggests that there may be similar underlying frailty mechanisms in both mice and humans. In the current study, we saw that the association between frailty and pro-inflammatory cytokines differs in a sex-specific manner. These results may imply a stronger relationship between frailty and inflammation in females than males. There are clear sex differences in cytokine profiles with aging in humans and mice (40,45,46). In most studies, females had greater levels of IL-6 and IFN-γ than males with aging (31,40,47). It may also be that there are different inflammatory cytokine profiles with frailty in males and females. In humans, the association of frailty and inflammation with sex has not been well explored. While many studies have found links between inflammation and frailty (25–28), they rarely consider sex as a factor. Recently, Gordon and Hubbard (48) highlighted the importance of considering sex in studies of inflammatory markers and frailty, and suggested that sex differences in inflammation may contribute to sex differences in frailty. Factors that may contribute to the higher inflammation in women when compared with men include prior pregnancy, higher levels of abdominal adiposity with age, and less effective anti-inflammatory responses (48). Further investigation of the mechanisms underlying the links between sex, frailty, and inflammation is now warranted. Along with sex differences in pro-inflammatory cytokines, we also saw clear differences in frailty between male and female mice. Female mice were frailer than males for FI-Clinical scores, but the opposite was seen for FI-Lab scores. One previous mouse study using the mouse FI-Clinical also saw higher scores in females than males (9), although two others saw no change with sex (8,15). In human studies, FI scores are almost always higher in females than males (21–24). FI-Lab values in males and females have not yet been compared, although one study did show that, for females, FI-Lab scores were lower than FI-Clinical scores (6). It is interesting, that for the FI-Combined, the index that includes the most items and across the most domains, there is no sex difference at either age. This implies that, although male and female mice may have different presentations of frailty, on average they accumulate deficits at the same rate. It has been well established in the clinical literature that an FI has the most predictive value when it contains many items across a variety of domains (1,6). This appears to be the case in the current study also, where the FI-Combined had better association with inflammatory cytokines, than the FI-Lab or FI-Clinical. In this study, an FI based solely on deficits in standard laboratory tests, analogous to that used in humans (6), was successfully developed and used in mice. It showed similar properties to both the human FI-Lab, and other FIs used in both humans and mice, with a submaximal limit of ~0.7, a normal distribution, and deficits seen across the full range of items in different mice (6,16). Interestingly, FI-Lab scores were always higher than FI-Clinical scores in both sexes. This supports the emerging concept that frailty results from the initial accumulation of subcellular and cellular deficits, which are first apparent in abnormal laboratory test results, and then become manifest as clinical deficits over time (6,49–51). Our results also showed that males were frailer than females when assessed with the FI-Lab (6). There are some differences between results obtained with the mouse FI-Lab tool and the human FI-Lab. Unlike the results of human studies (6), the FI-Clinical scores were not well correlated with the FI-Lab scores in the mouse model. This suggests that the FI-Clinical tool identifies different mice as frail than the FI-Lab tool used in this study. Refinement of the FI-Lab tool to sample a larger number of laboratory-based deficits over a wider range of physiological systems is now warranted. There are potential limitations to the results presented here. The sample size for the reference groups used to create FI-Lab values was relatively small and cytokine analysis was not available for all mice in the study. In addition, we used blood samples collected from either the facial vein or the heart; it is possible that these samples might not be identical. We also used retired breeders rather than virgin mice. This could, in theory, influence results although few studies have explored differences between retired breeders and virgin mice. There is an evidence that many behaviors, including open field, tightrope, and passive avoidance learning tests as well as voiding behavior, are similar in retired breeder and virgin mice (52,53). Retired breeders did fall from the rotorod and they showed less wheel running when compared with virgin mice (52), so more work in this area is warranted. We also used a few mice that were singly housed, although most were group housed. This could influence our results, potentially by increasing stress levels in singly housed mice. However, a recent study found that cortisol levels were actually higher in group-housed adult mice compared to singly housed animals (54), although this has not been seen in all studies (55). Group housing may also increase cortisol levels in aged mice (55). Additional work with a larger sample size that investigates the influence of environmental factors such as breeding history and housing conditions on frailty and inflammatory markers in the mouse model would be of interest. Overall, this study demonstrated a novel association between pro-inflammatory cytokines and frailty in aging C57BL/6 mice. This association was affected by sex, as were FI scores measured with the FI-Clinical and FI-Lab tools. This study represents the first to explore inflammation, frailty, and sex effects in naturally aging mice and to identify clear links between frailty and inflammation in males and females. These observations provide a translational platform to help identify the mechanisms involved in these associations and to develop novel strategies to attenuate frailty, potentially by targeting inflammation. Funding SEH is supported by Canadian Institute for Health Research grant (MOP 126018). AEK was supported by the Reynolds post-doctoral fellowship in Pharmacology at Dalhousie University. References 1. Rockwood K , Mitnitski A . Frailty in relation to the accumulation of deficits . J Gerontol A Biol Sci Med Sci . 2007 ; 62 : 722 – 727 . doi: 10.1093/gerona/62.7.722 Google Scholar Crossref Search ADS PubMed 2. Mitnitski AB , Mogilner AJ , Rockwood K . Accumulation of deficits as a proxy measure of aging . Sci World J . 2001 ; 1 : 323 – 336 . doi: 10.1100/tsw.2001.58 Google Scholar Crossref Search ADS 3. Searle SD , Mitnitski A , Gahbauer EA , Gill TM , Rockwood K . A standard procedure for creating a frailty index . BMC Geriatr . 2008 ; 8 : 24 . doi: 10.1186/1471-2318-8-24 Google Scholar Crossref Search ADS PubMed 4. Rockwood K , Song X , Mitnitski AB . Changes in relative fitness and frailty across the adult lifespan: evidence from the Canadian National Population Health Survey . Can Med Assoc J . 2011 ; 138 : 487 – 494 . doi: 10.1503/cmaj.110626 Google Scholar Crossref Search ADS 5. Collard RM , Boter H , Schoevers RA , Oude Voshaar RC . Prevalence of frailty in community-dwelling older persons: a systematic review . J Am Geriatr Soc . 2012 ; 60 : 1487 – 1492 . doi: 10.1111/j.1532-5415.2012.04054.x Google Scholar Crossref Search ADS PubMed 6. Howlett SE , Rockwood MR , Mitnitski A , Rockwood K . Standard laboratory tests to identify older adults at increased risk of death . BMC Med . 2014 ; 12 : 171 . doi: 10.1186/s12916-014-0171-9 Google Scholar Crossref Search ADS PubMed 7. Blodgett JM , Theou O , Howlett SE , Rockwood K . A frailty index from common clinical and laboratory tests predicts increased risk of death across the life course . GeroScience . 2017 ; 39 : 447 – 455 . doi: 10.1007/s11357-017-9993-7 Google Scholar Crossref Search ADS PubMed 8. Parks RJ , Fares E , Macdonald JK , et al. A procedure for creating a frailty index based on deficit accumulation in aging mice . J Gerontol A Biol Sci Med Sci . 2012 ; 67 : 217 – 227 . doi: 10.1093/gerona/glr193 Google Scholar Crossref Search ADS PubMed 9. Whitehead JC , Hildebrand BA , Sun M , et al. A clinical frailty index in aging mice: comparisons with frailty index data in humans . J Gerontol A Biol Sci Med Sci . 2014 ; 69 : 621 – 632 . doi: 10.1093/gerona/glt136 Google Scholar Crossref Search ADS PubMed 10. Yorke A , Kane AE , Hancock Friesen CL , Howlett SE , O’Blenes S . Development of a rat clinical frailty index . J Gerontol A Biol Sci Med Sci . 2017 ; 72 : 897 – 903 . doi: 10.1093/gerona/glw339 Google Scholar Crossref Search ADS PubMed 11. Antoch MP , Wrobel M , Kuropatwinski KK , et al. Physiological frailty index (PFI): quantitative in-life estimate of individual biological age in mice . Aging (Albany NY) . 2017 ; 9 : 615 – 626 . doi: 10.18632/aging.101206 Google Scholar Crossref Search ADS PubMed 12. Feridooni HA , Sun MH , Rockwood K , Howlett SE . Reliability of a frailty index based on the clinical assessment of health deficits in male C57BL/6J mice . J Gerontol A Biol Sci Med Sci . 2015 ; 70 : 686 – 693 . doi: 10.1093/gerona/glu161 Google Scholar Crossref Search ADS PubMed 13. Kane AAE , Mitchell SJSJ , Mach J , et al. Acetaminophen hepatotoxicity in mice: effect of age, frailty and exposure type . Exp Gerontol . 2016 ; 73 : 95 – 106 . doi: 10.1016/j.exger.2015.11.013 Google Scholar Crossref Search ADS PubMed 14. Huizer-Pajkos A , Kane AE , Howlett SE , et al. Adverse geriatric outcomes secondary to polypharmacy in a mouse model: the influence of aging . J Gerontol A Biol Sci Med Sci . 2016 ; 71 : 571 – 577 . doi: 10.1093/gerona/glv046 Google Scholar Crossref Search ADS PubMed 15. Kane AEA , Hilmer SSN , Boyer D , et al. Impact of longevity interventions on a validated mouse clinical frailty index . J Gerontol Ser A Biol Sci Med Sci . 2016 ; 71 : 333 – 339 . doi: 10.1093/gerona/glu315 Google Scholar Crossref Search ADS 16. Rockwood K , Blodgett JM , Theou O , et al. A frailty index based on deficit accumulation quantifies mortality risk in humans and in mice . Sci Rep . 2017 ; 7 : 43068 . doi: 10.1038/srep43068 Google Scholar Crossref Search ADS PubMed 17. Langille M , Meehan CJ , Koenig JE , et al. Microbial shifts in the aging mouse gut . Microbiome . 2016 ; 2 : 50 . doi: 10.1186/s40168-014-0050-9 Google Scholar Crossref Search ADS 18. Moghtadaei M , Jansen HJ , Mackasey M , et al. The impacts of age and frailty on heart rate and sinoatrial node function . J Physiol . 2016 ; 0 : 1 – 22 . doi: 10.1113/JP272979 19. Feridooni HAA , Kane AEE , Ayaz O , et al. The impact of age and frailty on ventricular structure and function in C57BL/6J mice . J Physiol . 2017 ; 595 : 3721 – 374212 . doi: 10.1113/JP274134 Google Scholar Crossref Search ADS PubMed 20. Jansen HJ , Moghtadaei M , Mackasey M , et al. Atrial structure, function and arrhythmogenesis in aged and frail mice . Sci Rep . 2017 ; 7 : 44336 . doi: 10.1038/srep44336 Google Scholar Crossref Search ADS PubMed 21. Mitnitski AB , Mogilner AJ , MacKnight C , Rockwood K . The mortality rate as a function of accumulated deficits in a frailty index . Mech Ageing Dev . 2002 ; 123 : 1457 – 1460 . doi: 10.1016/S0047-6374(02)00082-9 Google Scholar Crossref Search ADS PubMed 22. Yang Y , Lee LC . Dynamics and heterogeneity in the process of human frailty and aging: evidence from the U.S. older adult population . J Gerontol Ser B Psychol Sci Soc Sci . 2010 ; 65B : 246 – 255 . doi: 10.1093/geronb/gbp102 Google Scholar Crossref Search ADS 23. Gordon EH , Peel NM , Samanta M , Theou O , Howlett SE , Hubbard RE . Sex differences in frailty: a systematic review and meta-analysis . Exp Gerontol . 2017 ; 89 : 30 – 40 . doi: 10.1016/j.exger.2016.12.021 Google Scholar Crossref Search ADS PubMed 24. Puts MT , Lips P , Deeg DJ . Sex differences in the risk of frailty for mortality independent of disability and chronic diseases . J Am Geriatr Soc . 2005 ; 53 : 40 – 47 . doi: 10.1111/j.1532-5415.2005.53008.x Google Scholar Crossref Search ADS PubMed 25. Hubbard RE , Woodhouse KW . Frailty, inflammation and the elderly . Biogerontology . 2010 ; 11 : 635 – 641 . doi: 10.1007/s10522-010-9292-5 Google Scholar Crossref Search ADS PubMed 26. Hubbard RE , O’Mahony MS , Savva GM , Calver BL , Woodhouse KW . Inflammation and frailty measures in older people . J Cell Mol Med . 2009 ; 13 ( 9B ): 3103 – 3109 . doi: 10.1111/j.1582-4934.2009.00733.x Google Scholar Crossref Search ADS PubMed 27. Van Epps P , Oswald D , Higgins PA , et al. Frailty has a stronger association with inflammation than age in older veterans . Immun Ageing . 2016 ; 13 : 27 . doi: 10.1186/s12979-016-0082-z Google Scholar Crossref Search ADS PubMed 28. Soysal P , Stubbs B , Lucato P , et al. Inflammation and frailty in the elderly: a systematic review and meta-analysis . Ageing Res Rev . 2016 ; 31 : 1 – 8 . doi: 10.1016/j.arr.2016.08.006 Google Scholar Crossref Search ADS PubMed 29. Walston J , Fedarko N , Yang H , et al. The physical and biological characterization of a frail mouse model . J Gerontol A Biol Sci Med Sci . 2008 ; 63 : 391 – 398 . doi: 10.1093/gerona/63.4.391 Google Scholar Crossref Search ADS PubMed 30. Ko F , Yu Q , Xue QL , et al. Inflammation and mortality in a frail mouse model . Age (Dordr) . 2012 ; 34 : 705 – 715 . doi: 10.1007/s11357-011-9269-6 Google Scholar Crossref Search ADS PubMed 31. Bouman A , Heineman MJ , Faas MM . Sex hormones and the immune response in humans . Hum Reprod Update . 2005 ; 11 : 411 – 423 . doi: 10.1093/humupd/dmi008 Google Scholar Crossref Search ADS PubMed 32. Noelle RJ , Nowak EC . Cellular sources and immune functions of interleukin-9 . Nat Rev Immunol . 2010 ; 10 : 683 – 687 . doi: 10.1038/nri2848 Google Scholar Crossref Search ADS PubMed 33. Chomarat P , Rissoan MC , Banchereau J , Miossec P . Interferon gamma inhibits interleukin 10 production by monocytes . J Exp Med . 1993 ; 177 : 523 – 527 . doi: 10.1084/jem.177.2.523 Google Scholar Crossref Search ADS PubMed 34. Rea IM , McNerlan SE , Alexander HD . Total serum IL-12 and IL-12p40, but not IL-12p70, are increased in the serum of older subjects; relationship to CD3(+)and NK subsets . Cytokine . 2000 ; 12 : 156 – 159 . doi: 10.1006/cyto.1999.0537 Google Scholar Crossref Search ADS PubMed 35. El Mezayen R , El Gazzar M , Myer R , High KP . Aging-dependent upregulation of IL-23p19 gene expression in dendritic cells is associated with differential transcription factor binding and histone modifications . Aging Cell . 2009 ; 8 : 553 – 565 . doi: 10.1111/j.1474-9726.2009.00502.x Google Scholar Crossref Search ADS PubMed 36. Cooper AM , Khader SA . IL-12p40: an inherently agonistic cytokine . Trends Immunol . 2007 ; 28 : 33 – 38 . doi: 10.1016/j.it.2006.11.002 Google Scholar Crossref Search ADS PubMed 37. Ko F , Abadir P , Marx R , et al. Impaired mitochondrial degradation by autophagy in the skeletal muscle of the aged female interleukin 10 null mouse . Exp Gerontol . 2016 ; 73 : 23 – 27 . doi: 10.1016/j.exger.2015.11.010 Google Scholar Crossref Search ADS PubMed 38. Akki A , Yang H , Gupta A , et al. Skeletal muscle ATP kinetics are impaired in frail mice . Age (Dordr) . 2014 ; 36 : 21 – 30 . doi: 10.1007/s11357-013-9540-0 Google Scholar Crossref Search ADS PubMed 39. Li H , Manwani B , Leng SX . Frailty, inflammation, and immunity . Aging Dis . 2011 ; 2 : 466 – 473 . Google Scholar PubMed 40. Pietschmann P , Gollob E , Brosch S , et al. The effect of age and gender on cytokine production by human peripheral blood mononuclear cells and markers of bone metabolism . Exp Gerontol . 2003 ; 38 : 1119 – 1127 . doi: 10.1016/S0531-5565(03)00189-X Google Scholar Crossref Search ADS PubMed 41. Hsu B , Hirani V , CUmming R , et al. Cross-sectional and longitudinal relationships between inflammatory biomarkers and frailty in community-dwelling older men: the concord health and ageing in men project . J Gerontol A Biol Sci . 2017 . In press [Epub ahead of print]. doi:10.1093/gerona/glx142 42. Mansfield AS , Nevala WK , Dronca RS , Leontovich AA , Shuster L , Markovic SN . Normal ageing is associated with an increase in Th2 cells, MCP-1 (CCL1) and RANTES (CCL5), with differences in sCD40L and PDGF-AA between sexes . Clin Exp Immunol . 2012 ; 170 : 186 – 193 . doi: 10.1111/j.1365-2249.2012.04644.x Google Scholar Crossref Search ADS PubMed 43. Ferrucci L , Harris TB , Guralnik JM , et al. Serum IL-6 level and the development of disability in older persons . J Am Geriatr Soc . 1999 ; 47 : 639 – 646 . doi:10.1111/j.1532-5415.1999.tb01583.x Google Scholar Crossref Search ADS PubMed 44. Toth MJ , Matthews DE , Tracy RP , Previs MJ . Age-related differences in skeletal muscle protein synthesis: relation to markers of immune activation . Am J Physiol Endocrinol Metab . 2005 ; 288 : E883 – E891 . doi: 10.1152/ajpendo.00353.2004 Google Scholar Crossref Search ADS PubMed 45. Goetzl EJ , Huang MC , Kon J , et al. Gender specificity of altered human immune cytokine profiles in aging . FASEB J . 2010 ; 24 : 3580 – 3589 . doi: 10.1096/fj.10-160911 Google Scholar Crossref Search ADS PubMed 46. Alvarez-Rodríguez L , López-Hoyos M , Muñoz-Cacho P , Martínez-Taboada VM . Aging is associated with circulating cytokine dysregulation . Cell Immunol . 2012 ; 273 : 124 – 132 . doi: 10.1016/j.cellimm.2012.01.001 Google Scholar Crossref Search ADS PubMed 47. Barrat F , Lesourd B , Boulouis HJ , et al. Sex and parity modulate cytokine production during murine ageing . Clin Exp Immunol . 1997 ; 109 : 562 – 568 . doi: 10.1046/j.1365-2249.1997.4851387.x Google Scholar Crossref Search ADS PubMed 48. Gordon EH , Hubbard RE . The Pathophysiology of Frailty: why sex is so important . J Am Med Dir Assoc . 2018 ; 19 : 4 – 5 . doi: 10.1016/j.jamda.2017.10.009 Google Scholar Crossref Search ADS PubMed 49. Howlett SE , Rockwood K . New horizons in frailty: ageing and the deficit-scaling problem . Age Ageing . 2013 ; 42 : 416 – 423 . doi: 10.1093/ageing/aft059 Google Scholar Crossref Search ADS PubMed 50. Rockwood K , Mitnitski A , Howlett SE . Frailty: scaling from cellular deficit accumulation ? Interdiscip Top Gerontol Geriatr . 2015 ; 41 : 1 – 14 . doi: 10.1159/000381127 Google Scholar Crossref Search ADS PubMed 51. Mitnitski A , Song X , Rockwood K . Assessing biological aging: the origin of deficit accumulation . Biogerontology . 2013 ; 14 : 709 – 717 . doi: 10.1007/s10522-013-9446-3 Google Scholar Crossref Search ADS PubMed 52. Ingram DK , Spangler EL , Vincent GP . Behavioral comparison of aged virgin and retired breeder mice . Exp Aging Res . 1983 ; 9 : 111 – 113 . doi: 10.1080/03610738308258436 Google Scholar Crossref Search ADS PubMed 53. Keil KP , Abler LL , Altmann HM , et al. Influence of animal husbandry practices on void spot assay outcomes in C57BL/6J male mice . Neurourol Urodyn . 2016 ; 35 : 192 – 198 . doi: 10.1002/nau.22692 Google Scholar Crossref Search ADS PubMed 54. Kamakura R , Kovalainen M , Leppäluoto J , Herzig KH , Mäkelä KA . The effects of group and single housing and automated animal monitoring on urinary corticosterone levels in male C57BL/6 mice . Physiol Rep . 2016 ; 4 (3) :e12703 . doi: 10.14814/phy2.12703 Google Scholar Crossref Search ADS PubMed 55. Kannangara TS , Lucero MJ , Gil-Mohapel J , et al. Running reduces stress and enhances cell genesis in aged mice . Neurobiol Aging . 2011 ; 32 : 2279 – 2286 . doi: 10.1016/j.neurobiolaging.2009.12.025 Google Scholar Crossref Search ADS PubMed © The Author(s) 2018. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The Journals of Gerontology Series A: Biomedical Sciences and Medical Sciences Oxford University Press

A Murine Frailty Index Based on Clinical and Laboratory Measurements: Links Between Frailty and Pro-inflammatory Cytokines Differ in a Sex-Specific Manner

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Abstract

Abstract A frailty index (FI) based on clinical deficit accumulation (FI-Clinical) quantifies frailty in aging mice. We aimed to develop a laboratory test-based murine FI tool (FI-Lab) and to investigate the effects of age and sex on FI-Lab scores, FI-Clinical scores, and the combination (FI-Combined), as well as to explore links between frailty and inflammation. Studies used older (17 and 23 months) C57BL/6 mice of both sexes. We developed an FI-Lab (blood pressure, blood chemistry, echocardiography) based on deviation from reference values in younger adults (12 months), which showed similar characteristics to a human FI-Lab tool. Interestingly, while FI-Clinical scores were higher in females, the opposite was true for FI-Lab scores and there was no sex difference in FI-Combined scores. All three FI tools revealed a positive correlation between pro-inflammatory cytokine levels and frailty in aging mice that differed between the sexes. Elevated levels of the pro-inflammatory cytokines interleukin (IL)-6, IL-9, and interferon-γ were associated with higher FI scores in aging females, while levels of IL-12p40 rose as FI scores increased in older males. Thus, an FI tool based on common laboratory tests can quantify frailty in mice; the positive correlation between inflammation and frailty scores in naturally aging mice differs between the sexes. Deficit index, Deficit accumulation, Inflammation, Sex differences, Pro-inflammatory cytokines Frailty is considered a measure of unexplained heterogeneity in health status of people of the same chronological age, and as such can be considered a measure of a person’s biological age (1). One way that frailty is commonly assessed in humans is by quantifying the proportion of health-related deficits a person displays using a frailty index (FI) (2,3). An FI counts the number of deficits a person has accumulated, and divides this by the number of deficits measured to determine a score between 0 and 1, where the closer the score to 1, the more frail a person is (2,3). Increasing FI score is associated with a range of adverse health outcomes including hospitalization, institutionalization, and mortality (4,5). Clinically useful FIs are composed of a range of health-related deficits including symptoms, diseases, activities of daily living, and self-reported health status, that are often collected through clinical interviews/assessments (1,3,6). Recently, our group developed an FI for use in people that was based solely on deficits in standard laboratory blood tests (FI-Lab). We found that high FI-Lab scores predicted mortality risk (6,7). Deficits were counted if their values were outside the normal range for that measurement (6). The development of frailty measures that can be determined from routinely collected data in clinical care is a critically important step that may allow earlier and faster identification of those who are frail. Another important advance in frailty research has been the development of FIs to identify and quantify frailty in animal models (8–11). The first of these (8) was an invasive FI tool that included deficits across a variety of measurements (e.g. measures of hemodynamics, activity levels, blood chemistry, and body composition). Deficits were graded based on a measurements difference from mean reference values in healthy younger mice (8). Whitehead et al. (9) developed an FI based on noninvasive clinical assessment of mice (referred to as an FI-Clinical) and this tool has been widely used in animal frailty studies (12–20). An FI based on abnormalities in standard lab test results, analogous to the FI-Lab used in humans (6), has not yet been developed in mice. This ability to quantify frailty in animal models with tools developed for use in humans is an important advance. It facilitates the investigation of mechanisms that may contribute to the development of frailty in humans and provides a translational platform to test interventions to improve health span and attenuate frailty. In humans, there are clear sex differences in frailty, with females having higher frailty than males at almost all ages, despite having lower mortality risk (21–24). Proposed explanations for this paradox include differences in health-care seeking and reporting behaviors between sexes, different disease profiles, or perhaps underlying biological differences (23). Interestingly, women appear to have lower FI-Lab scores than FI-Clinical scores (6) although direct sex differences have not yet been explored. Some studies have explored sex differences in FI-Clinical scores in small numbers of mice, with two finding no clear sex differences (8,15) and two finding that female mice are more frail than male mice (9,11). Inflammation is proposed as an important pathophysiological mechanism of frailty in humans, and changes in inflammatory cytokines have been associated with increasing frailty in clinical studies (25–28). The IL-10 knockout (KO) mouse has been proposed as a mouse model of frailty, and displays characteristics of frailty such as chronic inflammation, sarcopenia, and reduced aerobic endurance (29,30). The association of frailty with inflammation in naturally aging rodents however, has not yet been explored. The aim of this study was thus to develop and characterize an FI-Lab tool in mice, and explore the effect of age and sex on FI-Lab scores alone, FI-Clinical scores alone, and the two combined (FI-Combined). We also aimed to explore the association of each of these FI scores with inflammatory cytokines in naturally aging mice of both sexes. Methods Animals C57BL/6 male and female mice were purchased from Charles River. All were retired breeders and were aged in the Carlton Animal Care Facility, which is a clean conventional multispecies facility which can house ~3000 cages of specific pathogen-free mice and rats. Mice were housed at 21°C with 35% humidity in Individually Ventilated Caging Systems (Allentown, Inc) with regular husbandry duties performed within Animal Transfer Stations or Biological Safety Cabinets. Animals were fed Standard Grain-Based Control Rodent Diet with bacon flavor (#F4059; Bio-Serv, Frenchtown, NJ) and maintained on a 12-h light–dark cycle with ad libitum access to food and water. Most mice were housed in groups of 2–5 per cage; one female and three males were housed separately. All experiments were approved by the Dalhousie University Committee on Laboratory Animals and performed in accordance with guidelines published by the Canadian Council on Animal Care. Assessments were completed in mice at two time points: 17.0 ± 0.1 months of age (n = 37 females, n = 34 males) and 22.5 ± 0.5 months of age (n = 17 females, n = 13 males). Measurements were repeated in mice at both ages, although the sample size is lower at 23 months of age, as a subset of the mice were started in a separate study at 17 months of age. The mean FI scores of the mice that left the study were not significantly different from the scores of the mice that remained (values were 0.19 ± 0.05 for females, and 0.17 ± 0.17 for males, versus 0.20 ± 0.06 for females and 0.17 ± 0.04 for males, for the mice that left versus the mice that remained, respectively; p = .63 for females and p = .93 for males). Reference values for the FI-Lab were determined from measurements in a group of adult mice (~1 year of age). Mouse Clinical Frailty Index (FI-Clinical) Assessment Frailty was assessed in mice using the mouse clinical FI (9) referred to in this article as FI-Clinical. This FI is made up of 31 clinically observed, noninvasive, health-related deficits. Most of the deficits are scored as either mild, receiving a score of 0.5; severe, receiving a score of 1; or absent, receiving a score of 0. Weight and temperature are scored based on the number of standard deviations (SDs) the value is from reference values, as previously described (9). Parameters Measured to Develop a Laboratory Frailty Index (FI-Lab) Assessment of blood pressure, basic metabolic status, and echocardiography was conducted to generate data for the individualized FI-Lab scores for each mouse in the study. These measures were also made in a group of 12-month-old male and female mice to generate reference values for adult animals, as described in detail in the next section. Blood pressure was assessed in mice using the IITC Life Sciences tail-cuff system (Woodland Hills, CA). After 2 days of acclimatization, blood pressure measurements were made on two consecutive days. Mice were allowed 15 min to acclimatize, then 3–5 rounds of five blood pressure measurements were made. A mean of all measurements over the 2 days was used for systolic blood pressure (SBP) and diastolic blood pressure (DBP) for each mouse. Mean arterial pressure (MAP) was calculated as MAP = [(2 × DBP) + SBP]/3 and pulse pressure (PP) was calculated as PP = SBP − DBP for each mouse. Metabolic status was evaluated from a blood sample collected from the mice. In most experiments, blood samples were collected from anesthetized (3% isoflurane for induction, maintained at 1–2%) mice via the facial vein at ~17 and 23 months of age. In some experiments in 23-month-old mice, mice were anesthetized (sodium pentobarbital; 200 mg/kg) and a blood sample was taken from the heart after it was removed for other studies at the end of the experiment. For each mouse, ~100 μL of whole blood was immediately tested in an iSTAT portable clinical analyzer (Abbott Diagnostics) with a CHEM8+ cartridge, to measure sodium (Na), potassium (K), chloride (Cl), ionized calcium (iCa), total bicarbonate (TCO2), glucose, urea nitrogen (BUN), creatinine (Crea), hematocrit (Hct), hemoglobin (Hb), and anion gap (AnGap). Echocardiographic analysis was completed for each mouse at 17 and 23 months of age using the Vevo 2100 imaging platform (VisualSonics). Briefly, mice were anesthetized with 3% isoflurane (maintained at 1–2%), and hair removed from their chest. A high-resolution linear transducer (18–38 MHz, MS400) was used to make short axis M-mode measurements, and four-chamber apical Doppler measurements. Mouse body temperature was monitored throughout and maintained, via a heated platform and heat lamp, at 35–36°C. Heart rate and respiration rate were monitored via the Vevo 2100 system. Images were analysed using Vevo LAB software, and data for left ventricle posterior wall (LVPW) thickness, intraventricular septum (IVS) thickness, ejection fraction (EF), fractional shortening (FS), stroke volume (SV), and left ventricle mass-corrected obtained from short-axis M-mode measurements in both systole and diastole. Mitral valve Doppler analysis was used to obtain E/A ratios. Mouse FI-Lab Scoring The measurements of blood pressure, basic metabolic status, and cardiac structure/function described above were used to create a 23-item FI-Lab. A full list of the individual items used to create the index can be found in Table 1. Normal reference values for each of these items were based on values measured in younger adult male and female C57BL/6 mice (12.0 ± 0.1 months of age; n = 4–9 per sex), as shown in Table 1. The reference values were obtained by calculating the mean and SD for each item separately for the 12-month-old male and female mice. Values that were within ± 1.5 SD of the mean were considered “normal” and received a score of 0, indicative of no deficit. Values that were above or below the cutoff (±1.5 SDs) were given a score of 1 and were considered a deficit. The mean values and the SDs obtained in the 12-month-old mice, as well as the high and low cutoff points, are illustrated in Table 1 separately for each sex. An FI-Lab score was determined for each of the older mice, at ~17 and 23 months of age, by summing the deficits a mouse displayed for each of the measured items, and dividing this by the total number of items measured. This gave an FI-Lab score between 0 and 1 for each mouse, at each time point. An FI-Combined score was also calculated for each mouse at both time points. This score was determined by adding the deficit scores for each mouse across both the FI-Clinical items (n = 31) and the FI-Lab items (n = 23), and dividing by the total number of items (n = 54). Cytokine Analysis Blood samples were collected from mice at 17 and 23 months of age, as described previously. Blood samples were stored on ice, then spun at 4°C at 9391 Gs, and serum collected and stored at −20°C. A Bio-Plex Pro Mouse cytokine assay was used to quantify serum levels of 23 cytokines from both time points. The assays were conducted as per manufacturer’s instructions and read using the MagPix (BioRad) Multiplex system. Cytokines measured included interleukin (IL)-6, IL-12p40, interferon gamma (IFN-γ), and IL-9; a complete list can be found in Supplementary Table 1. Sufficient serum to perform cytokine analysis was not available for all mice, so the final sample size was n = 18 males and n = 12 females. Cytokine analysis was performed on different mice at 17 and 23 months of age, so the data were not repeated measures. Statistics Data are expressed as mean ± SEM unless otherwise indicated. Normality tests were completed on all FI data (Supplementary Table 2). Two-sided t-tests were used to compare mean values for FI-Lab items between 12-month-old males and females. Scores for each FI (FI-Clinical, FI-Lab, and FI-Combined) were compared across sex and age groups using a two-way analysis of variance (ANOVA), with Bonferroni post hoc analysis. Two-sided t-tests were used to compare mean values for the FI-Clinical and FI-Lab for each age and sex group, and Mann–Whitney U tests were used to compare median values. Proportions of individual deficits observed in each sex, for each age group were compared with Chi-squared analysis. Spearman correlations were used to analyse the association between FI scores and cytokine levels. Data analysis was completed using the statistics program SPSS (Version 21.0, SPSS Inc., Chicago, IL) and SigmaPlot (Version 11.0, Systat Software, Germany). p values less than .05 were considered significant. Results Creation of an FI Based on Laboratory Items in Mice Reference values and cutoff points were determined from 12-month-old male and female mice (Table 1). There were significant differences in four reference values between males and females. TCO2 and heart rate were slightly higher in females than males, whilst glucose and IVSs were higher in males than females. These reference values were used to calculate FI-Lab scores in older mice (Figures 1 and 2) as discussed below. Table 1. Reference Values for the FI-Lab Obtained From Male and Female Mice ~12 Months of Age FI Item* Male Female Mean ± SD Low Cutoff High Cutoff Mean ± SD Low Cutoff High Cutoff Na (mmol/L) 144.4 ± 2.5 140.6 148.2 144.8 ± 1.9 141.9 147.6 K (mmol/L) 6.2 ± .5 5.4 7.0 6.9 ± 1.4 4.8 8.9 Cl (mmol/L) 112.2 ± 1.8 109.5 114.9 113.5 ± 2.9 109.2 117.8 iCa (mmol/L) 1.1 ± .1 1.0 1.2 1.0 ± 0.1 0.9 1.2 TCO2 (mmol/L) 26.4 ± 1.8 23.7 29.1 22.3 ± 1.5† 20.0 24.5 Glucose (mg/dL) 236.8 ± 25.2 199.0 274.6 180.5 ± 31.3† 133.5 227.5 BUN (mg/dL) 20.8 ± 2.5 17.1 24.5 23.5 ± 2.9 19.2 27.8 Crea (mg/dL) 0.6 ± 0.2 0.2 0.9 0.8 ± 0.1 0.6 1.0 Hematocrit (%PCV) 32.8 ± 4.9 25.4 40.2 30.5 ± 2.4 26.9 34.1 Hemoglobin (g/dL) 11.2 ± 1.7 8.6 13.7 10.4 ± 0.8 9.2 11.6 Anion gap (mmol/L) 12.8 ± 3.0 8.3 17.3 16.8 ± 3.3 11.8 21.7 Systolic BP (mmHg) 111.5 ± 8.0 99.5 123.5 110.0 ± 8.1 97.8 122.2 Diastolic BP (mmHg) 78.0 ± 6.4 68.4 87.5 78.5 ± 9.8 63.8 93.3 MAP (mmHg) 89.1 ± 6.7 79.1 99.2 89.0 ± 9.0 75.5 102.5 Pulse pressure (mmHg) 33.5 ± 3.9 27.7 39.4 31.4 ± 4.7 24.4 38.5 LVPWs (mm) 1.2 ± 0.1 1.0 1.3 1.2 ± 0.5 0.4 2.0 IVSs (mm) 1.3 ± 0.1 1.1 1.5 1.0 ± 0.2† 0.7 1.3 HR (bpm) 409.3 ± 22.9 374.9 443.7 452.2 ± 25.0† 414.7 489.6 MV E/A ratio 1.6 ± 0.2 1.4 1.9 1.8 ± 0.3 1.3 2.3 EF (%) 58.3 ± 11.9 40.4 76.1 49.3 ± 14.0 28.3 70.2 FS (%) 31.0 ± 8.3 18.5 43.4 25.2 ± 8.7 12.1 38.3 Stroke volume (uL) 42.2 ± 7.7 30.6 53.8 35.2 ± 9.2 21.5 48.9 LV mass corrected (mg) 112.6 ± 31.8 64.9 160.4 96.1 ± 48.3 23.7 168.6 FI Item* Male Female Mean ± SD Low Cutoff High Cutoff Mean ± SD Low Cutoff High Cutoff Na (mmol/L) 144.4 ± 2.5 140.6 148.2 144.8 ± 1.9 141.9 147.6 K (mmol/L) 6.2 ± .5 5.4 7.0 6.9 ± 1.4 4.8 8.9 Cl (mmol/L) 112.2 ± 1.8 109.5 114.9 113.5 ± 2.9 109.2 117.8 iCa (mmol/L) 1.1 ± .1 1.0 1.2 1.0 ± 0.1 0.9 1.2 TCO2 (mmol/L) 26.4 ± 1.8 23.7 29.1 22.3 ± 1.5† 20.0 24.5 Glucose (mg/dL) 236.8 ± 25.2 199.0 274.6 180.5 ± 31.3† 133.5 227.5 BUN (mg/dL) 20.8 ± 2.5 17.1 24.5 23.5 ± 2.9 19.2 27.8 Crea (mg/dL) 0.6 ± 0.2 0.2 0.9 0.8 ± 0.1 0.6 1.0 Hematocrit (%PCV) 32.8 ± 4.9 25.4 40.2 30.5 ± 2.4 26.9 34.1 Hemoglobin (g/dL) 11.2 ± 1.7 8.6 13.7 10.4 ± 0.8 9.2 11.6 Anion gap (mmol/L) 12.8 ± 3.0 8.3 17.3 16.8 ± 3.3 11.8 21.7 Systolic BP (mmHg) 111.5 ± 8.0 99.5 123.5 110.0 ± 8.1 97.8 122.2 Diastolic BP (mmHg) 78.0 ± 6.4 68.4 87.5 78.5 ± 9.8 63.8 93.3 MAP (mmHg) 89.1 ± 6.7 79.1 99.2 89.0 ± 9.0 75.5 102.5 Pulse pressure (mmHg) 33.5 ± 3.9 27.7 39.4 31.4 ± 4.7 24.4 38.5 LVPWs (mm) 1.2 ± 0.1 1.0 1.3 1.2 ± 0.5 0.4 2.0 IVSs (mm) 1.3 ± 0.1 1.1 1.5 1.0 ± 0.2† 0.7 1.3 HR (bpm) 409.3 ± 22.9 374.9 443.7 452.2 ± 25.0† 414.7 489.6 MV E/A ratio 1.6 ± 0.2 1.4 1.9 1.8 ± 0.3 1.3 2.3 EF (%) 58.3 ± 11.9 40.4 76.1 49.3 ± 14.0 28.3 70.2 FS (%) 31.0 ± 8.3 18.5 43.4 25.2 ± 8.7 12.1 38.3 Stroke volume (uL) 42.2 ± 7.7 30.6 53.8 35.2 ± 9.2 21.5 48.9 LV mass corrected (mg) 112.6 ± 31.8 64.9 160.4 96.1 ± 48.3 23.7 168.6 Notes: BP = Blood pressure; EF = Ejection fraction; FS = Fractional shortening; HR = Heart rate; IVSs = Intraventricular septum in systole; LVPWs = Left ventricle posterior wall in systole; LV = Left ventricle; MAP = Mean arterial pressure. *Reference values for the FI-Lab were obtained for 5–9 male and 4–9 female mice aged ~12 months. †Denotes significant difference between the sexes, p < .05. View Large Table 1. Reference Values for the FI-Lab Obtained From Male and Female Mice ~12 Months of Age FI Item* Male Female Mean ± SD Low Cutoff High Cutoff Mean ± SD Low Cutoff High Cutoff Na (mmol/L) 144.4 ± 2.5 140.6 148.2 144.8 ± 1.9 141.9 147.6 K (mmol/L) 6.2 ± .5 5.4 7.0 6.9 ± 1.4 4.8 8.9 Cl (mmol/L) 112.2 ± 1.8 109.5 114.9 113.5 ± 2.9 109.2 117.8 iCa (mmol/L) 1.1 ± .1 1.0 1.2 1.0 ± 0.1 0.9 1.2 TCO2 (mmol/L) 26.4 ± 1.8 23.7 29.1 22.3 ± 1.5† 20.0 24.5 Glucose (mg/dL) 236.8 ± 25.2 199.0 274.6 180.5 ± 31.3† 133.5 227.5 BUN (mg/dL) 20.8 ± 2.5 17.1 24.5 23.5 ± 2.9 19.2 27.8 Crea (mg/dL) 0.6 ± 0.2 0.2 0.9 0.8 ± 0.1 0.6 1.0 Hematocrit (%PCV) 32.8 ± 4.9 25.4 40.2 30.5 ± 2.4 26.9 34.1 Hemoglobin (g/dL) 11.2 ± 1.7 8.6 13.7 10.4 ± 0.8 9.2 11.6 Anion gap (mmol/L) 12.8 ± 3.0 8.3 17.3 16.8 ± 3.3 11.8 21.7 Systolic BP (mmHg) 111.5 ± 8.0 99.5 123.5 110.0 ± 8.1 97.8 122.2 Diastolic BP (mmHg) 78.0 ± 6.4 68.4 87.5 78.5 ± 9.8 63.8 93.3 MAP (mmHg) 89.1 ± 6.7 79.1 99.2 89.0 ± 9.0 75.5 102.5 Pulse pressure (mmHg) 33.5 ± 3.9 27.7 39.4 31.4 ± 4.7 24.4 38.5 LVPWs (mm) 1.2 ± 0.1 1.0 1.3 1.2 ± 0.5 0.4 2.0 IVSs (mm) 1.3 ± 0.1 1.1 1.5 1.0 ± 0.2† 0.7 1.3 HR (bpm) 409.3 ± 22.9 374.9 443.7 452.2 ± 25.0† 414.7 489.6 MV E/A ratio 1.6 ± 0.2 1.4 1.9 1.8 ± 0.3 1.3 2.3 EF (%) 58.3 ± 11.9 40.4 76.1 49.3 ± 14.0 28.3 70.2 FS (%) 31.0 ± 8.3 18.5 43.4 25.2 ± 8.7 12.1 38.3 Stroke volume (uL) 42.2 ± 7.7 30.6 53.8 35.2 ± 9.2 21.5 48.9 LV mass corrected (mg) 112.6 ± 31.8 64.9 160.4 96.1 ± 48.3 23.7 168.6 FI Item* Male Female Mean ± SD Low Cutoff High Cutoff Mean ± SD Low Cutoff High Cutoff Na (mmol/L) 144.4 ± 2.5 140.6 148.2 144.8 ± 1.9 141.9 147.6 K (mmol/L) 6.2 ± .5 5.4 7.0 6.9 ± 1.4 4.8 8.9 Cl (mmol/L) 112.2 ± 1.8 109.5 114.9 113.5 ± 2.9 109.2 117.8 iCa (mmol/L) 1.1 ± .1 1.0 1.2 1.0 ± 0.1 0.9 1.2 TCO2 (mmol/L) 26.4 ± 1.8 23.7 29.1 22.3 ± 1.5† 20.0 24.5 Glucose (mg/dL) 236.8 ± 25.2 199.0 274.6 180.5 ± 31.3† 133.5 227.5 BUN (mg/dL) 20.8 ± 2.5 17.1 24.5 23.5 ± 2.9 19.2 27.8 Crea (mg/dL) 0.6 ± 0.2 0.2 0.9 0.8 ± 0.1 0.6 1.0 Hematocrit (%PCV) 32.8 ± 4.9 25.4 40.2 30.5 ± 2.4 26.9 34.1 Hemoglobin (g/dL) 11.2 ± 1.7 8.6 13.7 10.4 ± 0.8 9.2 11.6 Anion gap (mmol/L) 12.8 ± 3.0 8.3 17.3 16.8 ± 3.3 11.8 21.7 Systolic BP (mmHg) 111.5 ± 8.0 99.5 123.5 110.0 ± 8.1 97.8 122.2 Diastolic BP (mmHg) 78.0 ± 6.4 68.4 87.5 78.5 ± 9.8 63.8 93.3 MAP (mmHg) 89.1 ± 6.7 79.1 99.2 89.0 ± 9.0 75.5 102.5 Pulse pressure (mmHg) 33.5 ± 3.9 27.7 39.4 31.4 ± 4.7 24.4 38.5 LVPWs (mm) 1.2 ± 0.1 1.0 1.3 1.2 ± 0.5 0.4 2.0 IVSs (mm) 1.3 ± 0.1 1.1 1.5 1.0 ± 0.2† 0.7 1.3 HR (bpm) 409.3 ± 22.9 374.9 443.7 452.2 ± 25.0† 414.7 489.6 MV E/A ratio 1.6 ± 0.2 1.4 1.9 1.8 ± 0.3 1.3 2.3 EF (%) 58.3 ± 11.9 40.4 76.1 49.3 ± 14.0 28.3 70.2 FS (%) 31.0 ± 8.3 18.5 43.4 25.2 ± 8.7 12.1 38.3 Stroke volume (uL) 42.2 ± 7.7 30.6 53.8 35.2 ± 9.2 21.5 48.9 LV mass corrected (mg) 112.6 ± 31.8 64.9 160.4 96.1 ± 48.3 23.7 168.6 Notes: BP = Blood pressure; EF = Ejection fraction; FS = Fractional shortening; HR = Heart rate; IVSs = Intraventricular septum in systole; LVPWs = Left ventricle posterior wall in systole; LV = Left ventricle; MAP = Mean arterial pressure. *Reference values for the FI-Lab were obtained for 5–9 male and 4–9 female mice aged ~12 months. †Denotes significant difference between the sexes, p < .05. View Large Figure 1. View largeDownload slide Impact of age and sex on FI-Clinical, FI-Lab, and FI-Combined. (A) Clinical frailty index (FI-Clinical) scores increased from 17 to 23 months of age in male and female mice. FI-Clinical scores for female mice were higher than male mice at 17 and 23 months of age. (B) Laboratory-based frailty index (FI-Lab) scores were higher in male than female mice at 17 and 23 months of age. (C) FI-Combined scores increased from 17 to 23 months in males and females, with no sex difference. The effect of age and sex was analyzed with two-way ANOVAs with Bonferroni post hoc tests (17-month males n = 34, females n = 37; 23-month males n = 13, females n = 13–17). *Indicates a significant difference p < .05. Figure 1. View largeDownload slide Impact of age and sex on FI-Clinical, FI-Lab, and FI-Combined. (A) Clinical frailty index (FI-Clinical) scores increased from 17 to 23 months of age in male and female mice. FI-Clinical scores for female mice were higher than male mice at 17 and 23 months of age. (B) Laboratory-based frailty index (FI-Lab) scores were higher in male than female mice at 17 and 23 months of age. (C) FI-Combined scores increased from 17 to 23 months in males and females, with no sex difference. The effect of age and sex was analyzed with two-way ANOVAs with Bonferroni post hoc tests (17-month males n = 34, females n = 37; 23-month males n = 13, females n = 13–17). *Indicates a significant difference p < .05. Figure 2. View largeDownload slide FI-Lab scores are higher than FI-Clinical scores at 17 and 23 months of age in mice. Mean and median scores for FI-Lab were higher than FI-Clinical scores in (A) 17-month-old females (FI-Lab median score 0.36, FI-Clinical median score 0.19), (B) 17-month-old males (FI-Lab median score 0.48, FI-Clinical median score 0.16), (C) 23-month-old females (FI-Lab median score 0.39, FI-Clinical median score 0.34), and (D) 23-month-old males (FI-Lab median score 0.55, FI-Clinical median score 0.27). Mean and median FI-Lab and FI-Clinical scores were compared with t-tests, and Mann–Whitney U tests (17-month males n = 34, females n = 37; 23-month males n = 13, females n = 13–17). Figure 2. View largeDownload slide FI-Lab scores are higher than FI-Clinical scores at 17 and 23 months of age in mice. Mean and median scores for FI-Lab were higher than FI-Clinical scores in (A) 17-month-old females (FI-Lab median score 0.36, FI-Clinical median score 0.19), (B) 17-month-old males (FI-Lab median score 0.48, FI-Clinical median score 0.16), (C) 23-month-old females (FI-Lab median score 0.39, FI-Clinical median score 0.34), and (D) 23-month-old males (FI-Lab median score 0.55, FI-Clinical median score 0.27). Mean and median FI-Lab and FI-Clinical scores were compared with t-tests, and Mann–Whitney U tests (17-month males n = 34, females n = 37; 23-month males n = 13, females n = 13–17). FI-Lab, FI-Clinical, and FI-Combined Scores in Older Male and Female Mice FI-Clinical scores were significantly affected by age, with increasing age associated with higher FI-Clinical scores in both sexes (Figure 1A). ANOVA analysis showed that sex also had a significant effect on FI-Clinical scores, and post hoc analysis showed that females had higher scores than males at 17 and 23 months (Figure 1A). FI-Lab scores were also calculated and compared in male and female mice at 17 and 23 months (Figure 1B). The mean values and SDs for each item of the FI-Lab are illustrated separately for each sex and age group in Supplementary Table 3. A two-way ANOVA showed that FI-Lab scores were significantly different between sexes, with higher scores in males than females at both ages. The overall effect of age on FI-Lab score was also significant (Figure 1B). A two-way ANOVA of FI-Combined scores showed a significant effect of age, but not sex, with scores at 23 months higher than at 17 months in both sexes (Figure 1C). Together these data suggest that, although FI scores generally increase with age, there are some clear sex differences in FI-Clinical and FI-Lab scores. To compare the distribution of FI scores for the FI-Lab and FI-Clinical in each age and sex group, frequency distributions were plotted (Figure 2). In all groups, mean and median FI-Lab scores were higher than FI-Clinical scores. For both 17-month males and females, the FI-Clinical score distribution was skewed slightly to the left with means of 0.17 ± 0.01 and 0.19 ± 0.01 and medians of 0.16 and 0.19, respectively (Figure 2A and B). For 17-month females, FI-Lab scores were higher than FI-Clinical scores with both a mean of 0.35 ± 0.02 and median of 0.36. Mean and median values for FI-Lab scores for 17-month males were 0.44 ± 0.02 and 0.48, respectively. For 23-month males and females, FI score distributions were shifted to the right, compared to 17 months, but FI-Lab scores remained higher than FI-Clinical scores (Figure 2C and D). The maximum FI-Clinical score in both females and males was 0.45, and the maximum FI-Lab scores were 0.67 for females and 0.75 for males. These observations support the idea of a submaximal limit to frailty of ~0.7 (1). We also investigated whether the FI-Lab and FI-Clinical scores were correlated. There was no significant correlation between these two measures for any group examined in the study (not shown). To determine whether the sex differences in FI-Lab, FI-Clinical, and FI-Combined scores were driven primarily by deficits in a single item, or a few items, the proportion of mice with each deficit were plotted for males and females for each age group (Figure 3). Mice displayed a range of deficits across all of the items in the FI-Lab and FI-Clinical, and there were a number of sex differences in the proportions with specific deficits at each age (Figure 3A and B). For example, at 17 months of age males showed more deficits than females in piloerection, stroke volume, and K, whilst females showed more deficits in Hb, EF, and menace reflex (Figure 3A). There are other sex-specific differences in individual deficits at both 17 (Figure 3A) and 23 (Figure 3B) months of age. This shows that the male–female difference in FI-Lab scores arises from deficits across a wide range of measures, rather than deficits in a specific deficit or group of deficits. Figure 3. View largeDownload slide Comparison of individual deficits from the FI-Lab and FI-Clinical in older male and female mice. Proportion of (A) 17-month-old and (B) 23-month-old male and female mice displaying deficits in each item that makes up the FI-Lab and FI-Clinical. Proportions of individual deficits observed in each sex, for each age group were compared with Chi-squared analysis. *Indicates significant difference between sexes, p < .05. Figure 3. View largeDownload slide Comparison of individual deficits from the FI-Lab and FI-Clinical in older male and female mice. Proportion of (A) 17-month-old and (B) 23-month-old male and female mice displaying deficits in each item that makes up the FI-Lab and FI-Clinical. Proportions of individual deficits observed in each sex, for each age group were compared with Chi-squared analysis. *Indicates significant difference between sexes, p < .05. Association of FI Scores With Inflammatory Cytokines The association between serum levels of cytokines and FI scores was explored in aging male and female mice (Figure 4; Supplementary Figures 1 and 2; Supplementary Table 1). FI-Combined scores in older female mice were significantly correlated with increasing levels of the pro-inflammatory cytokines IL-6, IFN-γ, and IL-9 (Figure 4A–D). By contrast in older males, higher FI-Combined scores were correlated with increasing levels of the pro-inflammatory cytokine IL-12p40 (Figure 4E–H). Additionally, increasing FI-Lab scores were associated with higher IFN-γ and IL-9 levels in females (Supplementary Figure 1), and increasing FI-Clinical scores were associated with higher IL-12p40 levels in males (Supplementary Figure 2). Other cytokines were not correlated with FI scores (Supplemental Table 1). These observations suggest that there are links between frailty and pro-inflammatory cytokines and that these differ in a sex-specific way. Figure 4. View largeDownload slide Inflammatory cytokines are correlated with increasing FI-Combined scores in mice in a sex-specific manner. Correlation of FI-Combined values in older female mice with (A) interleukin (IL)-6 (r = 0.66), (B) IL-12p40 (NS), (C) interferon (IFN)-γ (r = 0.77), and (D) IL-9 (r = 0.75). Correlation of FI-Combined values in older male mice with (E) IL-6 (NS), (F) IL-12p40 (r = 0.67), (G) IFN-γ (NS), and (H) IL-9 (NS). Data analyzed using Spearman correlations and lines indicate significant correlations (p < .05). All cytokine data for 17- and 23-month-old mice included (males n = 18, females n = 13). Figure 4. View largeDownload slide Inflammatory cytokines are correlated with increasing FI-Combined scores in mice in a sex-specific manner. Correlation of FI-Combined values in older female mice with (A) interleukin (IL)-6 (r = 0.66), (B) IL-12p40 (NS), (C) interferon (IFN)-γ (r = 0.77), and (D) IL-9 (r = 0.75). Correlation of FI-Combined values in older male mice with (E) IL-6 (NS), (F) IL-12p40 (r = 0.67), (G) IFN-γ (NS), and (H) IL-9 (NS). Data analyzed using Spearman correlations and lines indicate significant correlations (p < .05). All cytokine data for 17- and 23-month-old mice included (males n = 18, females n = 13). Discussion This study aimed to develop and characterize an FI-Lab tool for use in mice, examine the effects of sex and age on the FI-Lab, FI-Clinical, and FI-Combined scores and investigate links between frailty and inflammation in male and female mice. Our study showed, for the first time, an association between inflammation and frailty in naturally aging mice. Interestingly, clear sex differences in both frailty, inflammatory cytokines, and their interaction were observed. This study also demonstrated that it was possible to make an FI-Lab analogous to that used in humans (6), for use in mouse models. In this study, we made the novel observation that increasing levels of the pro-inflammatory cytokines IL-6, IL-9, and IFN-γ were associated with increasing FI scores in old female mice, whereas increasing levels of IL-12p40 were associated with frailty in old male mice. In terms of the cytokines associated with increasing frailty scores in females, IL-6 is a pro-inflammatory cytokine with a wide range of roles, including stimulating B-cell differentiation and activating macrophages (31). While IL-9 has not been studied as extensively as other cytokines, it is considered a pro-inflammatory cytokine with a role in stimulating cell proliferation (32). IFN-γ is considered the effector cytokine of Type 1 helper T (Th1) cells, and has important roles in macrophage activation and the production of other cytokines in particular TNF-α (33). These observations indicate that frailty is associated with an increase in several markers of inflammation in aging female mice. With respect to the cytokines linked to higher FI scores in aging males, IL-12p40 is a subunit of inflammatory cytokines IL-12 and IL-23. IL-12 is an important regulatory cytokine that activates Th1 cells (34), and IL-23 promotes the production of other pro-inflammatory cytokines including IL-6 (35). Although the role of IL-12p40 itself is unclear, it is known be an antagonist for the IL-12 receptor and to have macrophage chemoattractant properties (36). It also can be associated with a Th2-type response, and may have immunosuppressive roles (34). These data suggest that there is also a link between frailty and inflammation in aging males. Taken together, our results strongly suggest that frailty is associated with inflammation but that this differs importantly between the sexes. Increased levels of the cytokines IL-6 and IFN-γ have been observed in previous studies of IL-10 KO mice, which are proposed to be a mouse model of frailty (29). These mice were developed as a model of Crohn’s disease, but if maintained in pathogen-free conditions, they display some characteristics related to frailty such as sarcopenia, increased mortality risk, and chronic inflammation (29,30). Female IL-10 KO mice have been shown to have increased IL-6 and IFN-γ serum levels, compared to wild-type female mice (29,30,37). IL-6 levels are also higher in male IL-10 KO mice (38), but IFN-γ levels have not yet been measured in male KO mice. Our study, which was the first to explore the links between frailty and cytokines in naturally aging mice, also saw an increase in IL-6 with frailty, but interestingly, only in females. The present study also was the first to explore the association of cytokines IL-9 and IL-12p40 with frailty in mice of both sexes and our work suggests that increasing levels of these cytokines are also associated with frailty in mice from 17 to 23 months of age. It would be interesting to explore the relationship between markers of inflammation and frailty in an even older cohort of mice to determine if there is a stronger correlation between frailty and cytokines in very old animals. In humans, increasing levels of cytokines IL-6 and IFN-γ have been associated with both aging and frailty across a variety of populations (25–28,39–41). Although links between IL-9 and IL-12p40 levels and frailty have not yet been explored in humans, their association with aging has been studied. One study in older individuals reported that there was no association between IL-9 and aging (42), whereas another reported that IL-12p40 increases with age in humans (34). The mechanism responsible for levels of pro-inflammatory cytokines, particularly IL-6, in frailty may be related to muscle strength and sarcopenia (43,44) but this is not fully understood. The links between pro-inflammatory cytokines and FI scores reported in the present study provide further validation of the use of FI tools in mice to study frailty, and suggests that there may be similar underlying frailty mechanisms in both mice and humans. In the current study, we saw that the association between frailty and pro-inflammatory cytokines differs in a sex-specific manner. These results may imply a stronger relationship between frailty and inflammation in females than males. There are clear sex differences in cytokine profiles with aging in humans and mice (40,45,46). In most studies, females had greater levels of IL-6 and IFN-γ than males with aging (31,40,47). It may also be that there are different inflammatory cytokine profiles with frailty in males and females. In humans, the association of frailty and inflammation with sex has not been well explored. While many studies have found links between inflammation and frailty (25–28), they rarely consider sex as a factor. Recently, Gordon and Hubbard (48) highlighted the importance of considering sex in studies of inflammatory markers and frailty, and suggested that sex differences in inflammation may contribute to sex differences in frailty. Factors that may contribute to the higher inflammation in women when compared with men include prior pregnancy, higher levels of abdominal adiposity with age, and less effective anti-inflammatory responses (48). Further investigation of the mechanisms underlying the links between sex, frailty, and inflammation is now warranted. Along with sex differences in pro-inflammatory cytokines, we also saw clear differences in frailty between male and female mice. Female mice were frailer than males for FI-Clinical scores, but the opposite was seen for FI-Lab scores. One previous mouse study using the mouse FI-Clinical also saw higher scores in females than males (9), although two others saw no change with sex (8,15). In human studies, FI scores are almost always higher in females than males (21–24). FI-Lab values in males and females have not yet been compared, although one study did show that, for females, FI-Lab scores were lower than FI-Clinical scores (6). It is interesting, that for the FI-Combined, the index that includes the most items and across the most domains, there is no sex difference at either age. This implies that, although male and female mice may have different presentations of frailty, on average they accumulate deficits at the same rate. It has been well established in the clinical literature that an FI has the most predictive value when it contains many items across a variety of domains (1,6). This appears to be the case in the current study also, where the FI-Combined had better association with inflammatory cytokines, than the FI-Lab or FI-Clinical. In this study, an FI based solely on deficits in standard laboratory tests, analogous to that used in humans (6), was successfully developed and used in mice. It showed similar properties to both the human FI-Lab, and other FIs used in both humans and mice, with a submaximal limit of ~0.7, a normal distribution, and deficits seen across the full range of items in different mice (6,16). Interestingly, FI-Lab scores were always higher than FI-Clinical scores in both sexes. This supports the emerging concept that frailty results from the initial accumulation of subcellular and cellular deficits, which are first apparent in abnormal laboratory test results, and then become manifest as clinical deficits over time (6,49–51). Our results also showed that males were frailer than females when assessed with the FI-Lab (6). There are some differences between results obtained with the mouse FI-Lab tool and the human FI-Lab. Unlike the results of human studies (6), the FI-Clinical scores were not well correlated with the FI-Lab scores in the mouse model. This suggests that the FI-Clinical tool identifies different mice as frail than the FI-Lab tool used in this study. Refinement of the FI-Lab tool to sample a larger number of laboratory-based deficits over a wider range of physiological systems is now warranted. There are potential limitations to the results presented here. The sample size for the reference groups used to create FI-Lab values was relatively small and cytokine analysis was not available for all mice in the study. In addition, we used blood samples collected from either the facial vein or the heart; it is possible that these samples might not be identical. We also used retired breeders rather than virgin mice. This could, in theory, influence results although few studies have explored differences between retired breeders and virgin mice. There is an evidence that many behaviors, including open field, tightrope, and passive avoidance learning tests as well as voiding behavior, are similar in retired breeder and virgin mice (52,53). Retired breeders did fall from the rotorod and they showed less wheel running when compared with virgin mice (52), so more work in this area is warranted. We also used a few mice that were singly housed, although most were group housed. This could influence our results, potentially by increasing stress levels in singly housed mice. However, a recent study found that cortisol levels were actually higher in group-housed adult mice compared to singly housed animals (54), although this has not been seen in all studies (55). Group housing may also increase cortisol levels in aged mice (55). Additional work with a larger sample size that investigates the influence of environmental factors such as breeding history and housing conditions on frailty and inflammatory markers in the mouse model would be of interest. Overall, this study demonstrated a novel association between pro-inflammatory cytokines and frailty in aging C57BL/6 mice. This association was affected by sex, as were FI scores measured with the FI-Clinical and FI-Lab tools. This study represents the first to explore inflammation, frailty, and sex effects in naturally aging mice and to identify clear links between frailty and inflammation in males and females. These observations provide a translational platform to help identify the mechanisms involved in these associations and to develop novel strategies to attenuate frailty, potentially by targeting inflammation. Funding SEH is supported by Canadian Institute for Health Research grant (MOP 126018). AEK was supported by the Reynolds post-doctoral fellowship in Pharmacology at Dalhousie University. References 1. Rockwood K , Mitnitski A . Frailty in relation to the accumulation of deficits . J Gerontol A Biol Sci Med Sci . 2007 ; 62 : 722 – 727 . doi: 10.1093/gerona/62.7.722 Google Scholar Crossref Search ADS PubMed 2. Mitnitski AB , Mogilner AJ , Rockwood K . Accumulation of deficits as a proxy measure of aging . Sci World J . 2001 ; 1 : 323 – 336 . doi: 10.1100/tsw.2001.58 Google Scholar Crossref Search ADS 3. Searle SD , Mitnitski A , Gahbauer EA , Gill TM , Rockwood K . A standard procedure for creating a frailty index . BMC Geriatr . 2008 ; 8 : 24 . doi: 10.1186/1471-2318-8-24 Google Scholar Crossref Search ADS PubMed 4. Rockwood K , Song X , Mitnitski AB . Changes in relative fitness and frailty across the adult lifespan: evidence from the Canadian National Population Health Survey . Can Med Assoc J . 2011 ; 138 : 487 – 494 . doi: 10.1503/cmaj.110626 Google Scholar Crossref Search ADS 5. Collard RM , Boter H , Schoevers RA , Oude Voshaar RC . Prevalence of frailty in community-dwelling older persons: a systematic review . J Am Geriatr Soc . 2012 ; 60 : 1487 – 1492 . doi: 10.1111/j.1532-5415.2012.04054.x Google Scholar Crossref Search ADS PubMed 6. Howlett SE , Rockwood MR , Mitnitski A , Rockwood K . Standard laboratory tests to identify older adults at increased risk of death . BMC Med . 2014 ; 12 : 171 . doi: 10.1186/s12916-014-0171-9 Google Scholar Crossref Search ADS PubMed 7. Blodgett JM , Theou O , Howlett SE , Rockwood K . A frailty index from common clinical and laboratory tests predicts increased risk of death across the life course . GeroScience . 2017 ; 39 : 447 – 455 . doi: 10.1007/s11357-017-9993-7 Google Scholar Crossref Search ADS PubMed 8. Parks RJ , Fares E , Macdonald JK , et al. A procedure for creating a frailty index based on deficit accumulation in aging mice . J Gerontol A Biol Sci Med Sci . 2012 ; 67 : 217 – 227 . doi: 10.1093/gerona/glr193 Google Scholar Crossref Search ADS PubMed 9. Whitehead JC , Hildebrand BA , Sun M , et al. A clinical frailty index in aging mice: comparisons with frailty index data in humans . J Gerontol A Biol Sci Med Sci . 2014 ; 69 : 621 – 632 . doi: 10.1093/gerona/glt136 Google Scholar Crossref Search ADS PubMed 10. Yorke A , Kane AE , Hancock Friesen CL , Howlett SE , O’Blenes S . Development of a rat clinical frailty index . J Gerontol A Biol Sci Med Sci . 2017 ; 72 : 897 – 903 . doi: 10.1093/gerona/glw339 Google Scholar Crossref Search ADS PubMed 11. Antoch MP , Wrobel M , Kuropatwinski KK , et al. Physiological frailty index (PFI): quantitative in-life estimate of individual biological age in mice . Aging (Albany NY) . 2017 ; 9 : 615 – 626 . doi: 10.18632/aging.101206 Google Scholar Crossref Search ADS PubMed 12. Feridooni HA , Sun MH , Rockwood K , Howlett SE . Reliability of a frailty index based on the clinical assessment of health deficits in male C57BL/6J mice . J Gerontol A Biol Sci Med Sci . 2015 ; 70 : 686 – 693 . doi: 10.1093/gerona/glu161 Google Scholar Crossref Search ADS PubMed 13. Kane AAE , Mitchell SJSJ , Mach J , et al. Acetaminophen hepatotoxicity in mice: effect of age, frailty and exposure type . Exp Gerontol . 2016 ; 73 : 95 – 106 . doi: 10.1016/j.exger.2015.11.013 Google Scholar Crossref Search ADS PubMed 14. Huizer-Pajkos A , Kane AE , Howlett SE , et al. Adverse geriatric outcomes secondary to polypharmacy in a mouse model: the influence of aging . J Gerontol A Biol Sci Med Sci . 2016 ; 71 : 571 – 577 . doi: 10.1093/gerona/glv046 Google Scholar Crossref Search ADS PubMed 15. Kane AEA , Hilmer SSN , Boyer D , et al. Impact of longevity interventions on a validated mouse clinical frailty index . J Gerontol Ser A Biol Sci Med Sci . 2016 ; 71 : 333 – 339 . doi: 10.1093/gerona/glu315 Google Scholar Crossref Search ADS 16. Rockwood K , Blodgett JM , Theou O , et al. A frailty index based on deficit accumulation quantifies mortality risk in humans and in mice . Sci Rep . 2017 ; 7 : 43068 . doi: 10.1038/srep43068 Google Scholar Crossref Search ADS PubMed 17. Langille M , Meehan CJ , Koenig JE , et al. Microbial shifts in the aging mouse gut . Microbiome . 2016 ; 2 : 50 . doi: 10.1186/s40168-014-0050-9 Google Scholar Crossref Search ADS 18. Moghtadaei M , Jansen HJ , Mackasey M , et al. The impacts of age and frailty on heart rate and sinoatrial node function . J Physiol . 2016 ; 0 : 1 – 22 . doi: 10.1113/JP272979 19. Feridooni HAA , Kane AEE , Ayaz O , et al. The impact of age and frailty on ventricular structure and function in C57BL/6J mice . J Physiol . 2017 ; 595 : 3721 – 374212 . doi: 10.1113/JP274134 Google Scholar Crossref Search ADS PubMed 20. Jansen HJ , Moghtadaei M , Mackasey M , et al. Atrial structure, function and arrhythmogenesis in aged and frail mice . Sci Rep . 2017 ; 7 : 44336 . doi: 10.1038/srep44336 Google Scholar Crossref Search ADS PubMed 21. Mitnitski AB , Mogilner AJ , MacKnight C , Rockwood K . The mortality rate as a function of accumulated deficits in a frailty index . Mech Ageing Dev . 2002 ; 123 : 1457 – 1460 . doi: 10.1016/S0047-6374(02)00082-9 Google Scholar Crossref Search ADS PubMed 22. Yang Y , Lee LC . Dynamics and heterogeneity in the process of human frailty and aging: evidence from the U.S. older adult population . J Gerontol Ser B Psychol Sci Soc Sci . 2010 ; 65B : 246 – 255 . doi: 10.1093/geronb/gbp102 Google Scholar Crossref Search ADS 23. Gordon EH , Peel NM , Samanta M , Theou O , Howlett SE , Hubbard RE . Sex differences in frailty: a systematic review and meta-analysis . Exp Gerontol . 2017 ; 89 : 30 – 40 . doi: 10.1016/j.exger.2016.12.021 Google Scholar Crossref Search ADS PubMed 24. Puts MT , Lips P , Deeg DJ . Sex differences in the risk of frailty for mortality independent of disability and chronic diseases . J Am Geriatr Soc . 2005 ; 53 : 40 – 47 . doi: 10.1111/j.1532-5415.2005.53008.x Google Scholar Crossref Search ADS PubMed 25. Hubbard RE , Woodhouse KW . Frailty, inflammation and the elderly . Biogerontology . 2010 ; 11 : 635 – 641 . doi: 10.1007/s10522-010-9292-5 Google Scholar Crossref Search ADS PubMed 26. Hubbard RE , O’Mahony MS , Savva GM , Calver BL , Woodhouse KW . Inflammation and frailty measures in older people . J Cell Mol Med . 2009 ; 13 ( 9B ): 3103 – 3109 . doi: 10.1111/j.1582-4934.2009.00733.x Google Scholar Crossref Search ADS PubMed 27. Van Epps P , Oswald D , Higgins PA , et al. Frailty has a stronger association with inflammation than age in older veterans . Immun Ageing . 2016 ; 13 : 27 . doi: 10.1186/s12979-016-0082-z Google Scholar Crossref Search ADS PubMed 28. Soysal P , Stubbs B , Lucato P , et al. Inflammation and frailty in the elderly: a systematic review and meta-analysis . Ageing Res Rev . 2016 ; 31 : 1 – 8 . doi: 10.1016/j.arr.2016.08.006 Google Scholar Crossref Search ADS PubMed 29. Walston J , Fedarko N , Yang H , et al. The physical and biological characterization of a frail mouse model . J Gerontol A Biol Sci Med Sci . 2008 ; 63 : 391 – 398 . doi: 10.1093/gerona/63.4.391 Google Scholar Crossref Search ADS PubMed 30. Ko F , Yu Q , Xue QL , et al. Inflammation and mortality in a frail mouse model . Age (Dordr) . 2012 ; 34 : 705 – 715 . doi: 10.1007/s11357-011-9269-6 Google Scholar Crossref Search ADS PubMed 31. Bouman A , Heineman MJ , Faas MM . Sex hormones and the immune response in humans . Hum Reprod Update . 2005 ; 11 : 411 – 423 . doi: 10.1093/humupd/dmi008 Google Scholar Crossref Search ADS PubMed 32. Noelle RJ , Nowak EC . Cellular sources and immune functions of interleukin-9 . Nat Rev Immunol . 2010 ; 10 : 683 – 687 . doi: 10.1038/nri2848 Google Scholar Crossref Search ADS PubMed 33. Chomarat P , Rissoan MC , Banchereau J , Miossec P . Interferon gamma inhibits interleukin 10 production by monocytes . J Exp Med . 1993 ; 177 : 523 – 527 . doi: 10.1084/jem.177.2.523 Google Scholar Crossref Search ADS PubMed 34. Rea IM , McNerlan SE , Alexander HD . Total serum IL-12 and IL-12p40, but not IL-12p70, are increased in the serum of older subjects; relationship to CD3(+)and NK subsets . Cytokine . 2000 ; 12 : 156 – 159 . doi: 10.1006/cyto.1999.0537 Google Scholar Crossref Search ADS PubMed 35. El Mezayen R , El Gazzar M , Myer R , High KP . Aging-dependent upregulation of IL-23p19 gene expression in dendritic cells is associated with differential transcription factor binding and histone modifications . Aging Cell . 2009 ; 8 : 553 – 565 . doi: 10.1111/j.1474-9726.2009.00502.x Google Scholar Crossref Search ADS PubMed 36. Cooper AM , Khader SA . IL-12p40: an inherently agonistic cytokine . Trends Immunol . 2007 ; 28 : 33 – 38 . doi: 10.1016/j.it.2006.11.002 Google Scholar Crossref Search ADS PubMed 37. Ko F , Abadir P , Marx R , et al. Impaired mitochondrial degradation by autophagy in the skeletal muscle of the aged female interleukin 10 null mouse . Exp Gerontol . 2016 ; 73 : 23 – 27 . doi: 10.1016/j.exger.2015.11.010 Google Scholar Crossref Search ADS PubMed 38. Akki A , Yang H , Gupta A , et al. Skeletal muscle ATP kinetics are impaired in frail mice . Age (Dordr) . 2014 ; 36 : 21 – 30 . doi: 10.1007/s11357-013-9540-0 Google Scholar Crossref Search ADS PubMed 39. Li H , Manwani B , Leng SX . Frailty, inflammation, and immunity . Aging Dis . 2011 ; 2 : 466 – 473 . Google Scholar PubMed 40. Pietschmann P , Gollob E , Brosch S , et al. The effect of age and gender on cytokine production by human peripheral blood mononuclear cells and markers of bone metabolism . Exp Gerontol . 2003 ; 38 : 1119 – 1127 . doi: 10.1016/S0531-5565(03)00189-X Google Scholar Crossref Search ADS PubMed 41. Hsu B , Hirani V , CUmming R , et al. Cross-sectional and longitudinal relationships between inflammatory biomarkers and frailty in community-dwelling older men: the concord health and ageing in men project . J Gerontol A Biol Sci . 2017 . In press [Epub ahead of print]. doi:10.1093/gerona/glx142 42. Mansfield AS , Nevala WK , Dronca RS , Leontovich AA , Shuster L , Markovic SN . Normal ageing is associated with an increase in Th2 cells, MCP-1 (CCL1) and RANTES (CCL5), with differences in sCD40L and PDGF-AA between sexes . Clin Exp Immunol . 2012 ; 170 : 186 – 193 . doi: 10.1111/j.1365-2249.2012.04644.x Google Scholar Crossref Search ADS PubMed 43. Ferrucci L , Harris TB , Guralnik JM , et al. Serum IL-6 level and the development of disability in older persons . J Am Geriatr Soc . 1999 ; 47 : 639 – 646 . doi:10.1111/j.1532-5415.1999.tb01583.x Google Scholar Crossref Search ADS PubMed 44. Toth MJ , Matthews DE , Tracy RP , Previs MJ . Age-related differences in skeletal muscle protein synthesis: relation to markers of immune activation . Am J Physiol Endocrinol Metab . 2005 ; 288 : E883 – E891 . doi: 10.1152/ajpendo.00353.2004 Google Scholar Crossref Search ADS PubMed 45. Goetzl EJ , Huang MC , Kon J , et al. Gender specificity of altered human immune cytokine profiles in aging . FASEB J . 2010 ; 24 : 3580 – 3589 . doi: 10.1096/fj.10-160911 Google Scholar Crossref Search ADS PubMed 46. Alvarez-Rodríguez L , López-Hoyos M , Muñoz-Cacho P , Martínez-Taboada VM . Aging is associated with circulating cytokine dysregulation . Cell Immunol . 2012 ; 273 : 124 – 132 . doi: 10.1016/j.cellimm.2012.01.001 Google Scholar Crossref Search ADS PubMed 47. Barrat F , Lesourd B , Boulouis HJ , et al. Sex and parity modulate cytokine production during murine ageing . Clin Exp Immunol . 1997 ; 109 : 562 – 568 . doi: 10.1046/j.1365-2249.1997.4851387.x Google Scholar Crossref Search ADS PubMed 48. Gordon EH , Hubbard RE . The Pathophysiology of Frailty: why sex is so important . J Am Med Dir Assoc . 2018 ; 19 : 4 – 5 . doi: 10.1016/j.jamda.2017.10.009 Google Scholar Crossref Search ADS PubMed 49. Howlett SE , Rockwood K . New horizons in frailty: ageing and the deficit-scaling problem . Age Ageing . 2013 ; 42 : 416 – 423 . doi: 10.1093/ageing/aft059 Google Scholar Crossref Search ADS PubMed 50. Rockwood K , Mitnitski A , Howlett SE . Frailty: scaling from cellular deficit accumulation ? Interdiscip Top Gerontol Geriatr . 2015 ; 41 : 1 – 14 . doi: 10.1159/000381127 Google Scholar Crossref Search ADS PubMed 51. Mitnitski A , Song X , Rockwood K . Assessing biological aging: the origin of deficit accumulation . Biogerontology . 2013 ; 14 : 709 – 717 . doi: 10.1007/s10522-013-9446-3 Google Scholar Crossref Search ADS PubMed 52. Ingram DK , Spangler EL , Vincent GP . Behavioral comparison of aged virgin and retired breeder mice . Exp Aging Res . 1983 ; 9 : 111 – 113 . doi: 10.1080/03610738308258436 Google Scholar Crossref Search ADS PubMed 53. Keil KP , Abler LL , Altmann HM , et al. Influence of animal husbandry practices on void spot assay outcomes in C57BL/6J male mice . Neurourol Urodyn . 2016 ; 35 : 192 – 198 . doi: 10.1002/nau.22692 Google Scholar Crossref Search ADS PubMed 54. Kamakura R , Kovalainen M , Leppäluoto J , Herzig KH , Mäkelä KA . The effects of group and single housing and automated animal monitoring on urinary corticosterone levels in male C57BL/6 mice . Physiol Rep . 2016 ; 4 (3) :e12703 . doi: 10.14814/phy2.12703 Google Scholar Crossref Search ADS PubMed 55. Kannangara TS , Lucero MJ , Gil-Mohapel J , et al. Running reduces stress and enhances cell genesis in aged mice . Neurobiol Aging . 2011 ; 32 : 2279 – 2286 . doi: 10.1016/j.neurobiolaging.2009.12.025 Google Scholar Crossref Search ADS PubMed © The Author(s) 2018. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

Journal

The Journals of Gerontology Series A: Biomedical Sciences and Medical Sciences – Oxford University Press

Published: Feb 15, 2019

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