We previously quantified frailty in aged mice with frailty index (FI) that used specialized equipment to measure health parameters. Here we developed a simplified, noninvasive method to quantify frailty through clinical assessment of C57BL/6J mice (5–28 months) and compared the relationship between FI scores and age in mice and humans. FIs calculated with the original performance-based eight-item FI increased from 0.06±0.01 at 5 months to 0.36±0.06 at 19 months and 0.38±0.04 at 28 months (n = 14). By contrast, the increase was graded with a 31-item clinical FI (0.02±0.005 at 5 months; 0.12±0.008 at 19 months; 0.33±0.02 at 28 months; n = 14). FI scores calculated from 70 self-report items from the first wave of the Survey of Health, Ageing and Retirement in Europe were plotted as function of age (n = 30,025 people). The exponential relationship between FI scores and age (normalized to 90% mortality) was similar in mice and humans for the clinical FI but not the eight-item FI. This noninvasive FI based on clinical measures can be used in longitudinal studies to quantify frailty in mice. Unlike the performance-based eight-item mouse FI, the clinical FI exhibits key features of the FI established for use in humans.

Background. Frailty and fitness are important attributes of older persons, but population samples of their prevalence, attributes, and outcomes are limited. Methods. The authors report data from the community-dwelling sample (n = 9008) of the Canadian Study of Health and Aging, a representative, 5-year prospective cohort study. Fitness and frailty were determined by self-reported exercise and function level and testing of cognition. Results. Among the community-dwelling elderly population, 171 per 1000 were very fit and 12 per 1000 were very frail. Frailty increased with age, so that by age 85 years and older, 44 per 1000 were very frail. The risk for adverse health outcomes increased markedly with frailty: Compared with older adults who exercise, those who were moderately or severely frail had a relative risk for institutionalization of 8.6 (95% confidence interval, 4.9 to 15.2) and for death of 7.3 (95% confidence interval, 4.7 to 11.4). These risks persist after adjustments for age, sex, comorbid conditions, and poor self-rated health. At all ages, men reported higher levels of exercise and less frailty compared with women. Decreased fitness and increased frailty were also associated with poor self-ratings of health (42% in the most frail vs 7% in the most fit), more comorbid illnesses (6 vs 3), and more social isolation (34% vs 29%). Conclusions. Fitness and frailty form a continuum and predict survival. Exercise influences survival, even in old age. Relative fitness and frailty can be determined quickly in a clinical setting, are potentially useful markers of the risk for adverse health outcomes, and add value to traditional medical assessments that focus on diagnoses.

The changes that occur in the microbiome of aging individuals are unclear, especially in light of the imperfect correlation of frailty with age. Studies in older human subjects have reported subtle effects, but these results may be confounded by other variables that often change with age such as diet and place of residence. To test these associations in a more controlled model system, we examined the relationship between age, frailty, and the gut microbiome of female C57BL/6 J mice. The frailty index, which is based on the evaluation of 31 clinical signs of deterioration in mice, showed a near-perfect correlation with age. We observed a statistically significant relationship between age and the taxonomic composition of the corresponding microbiome. Consistent with previous human studies, the Rikenellaceae family, which includes the Alistipes genus, was the most significantly overrepresented taxon within middle-aged and older mice. The functional profile of the mouse gut microbiome also varied with host age and frailty. Bacterial-encoded functions that were underrepresented in older mice included cobalamin (B12) and biotin (B7) biosynthesis, and bacterial SOS genes associated with DNA repair. Conversely, creatine degradation, associated with muscle wasting, was overrepresented within the gut microbiomes of the older mice, as were bacterial-encoded β-glucuronidases, which can influence drug-induced epithelial cell toxicity. Older mice also showed an overabundance of monosaccharide utilization genes relative to di-, oligo-, and polysaccharide utilization genes, which may have a substantial impact on gut homeostasis. We have identified taxonomic and functional patterns that correlate with age and frailty in the mouse microbiome. Differences in functions related to host nutrition and drug pharmacology vary in an age-dependent manner, suggesting that the availability and timing of essential functions may differ significantly with age and frailty. Future work with larger cohorts of mice will aim to separate the effects of age and frailty, and other factors.

Older adults are at an increased risk of death, but not all people of the same age have the same risk. Many methods identify frail people (that is, those at increased risk) but these often require time-consuming interactions with health care providers. We evaluated whether standard laboratory tests on their own, or added to a clinical frailty index (FI), could improve identification of older adults at increased risk of death. This is a secondary analysis of a prospective cohort study, where community dwelling and institutionalized participants in the Canadian Study of Health and Aging who also volunteered for blood collection (n = 1,013) were followed for up to six years. A standard FI (FI-CSHA) was constructed from data obtained during the clinical evaluation and a second, novel FI was constructed from laboratory data plus systolic and diastolic blood pressure measurements (FI-LAB). A combined FI included all items from each index. Predictive validity was tested using Cox proportional hazards analysis and discriminative ability by the area under receiver operating characteristic (ROC) curves. Of 1,013 participants, 51.3% had died by six years. The mean baseline value of the FI-LAB was 0.27 (standard deviation 0.11; range 0.05 to 0.63), the FI-CSHA was 0.25 (0.11; 0.02 to 0.72), and the combined FI was 0.26 (0.09; 0.06 to 0.59). In an age- and sex-adjusted model, with each increment in the FI-LAB, the hazard ratios increased by 2.8% (95% confidence interval 1.02 to 1.04). The hazard ratios for the FI-CSHA and the combined FI were 1.02 (1.01 to 1.03) and 1.04 (1.03 to 1.05), respectively. The FI-LAB and FI-CSHA remained independently associated with death in the face of the other. The areas under the ROC curves were 0.72 for FI-LAB, 0.73 for FI-CSHA and 0.74 for the combined FI. An FI based on routine laboratory data can identify older adults at increased risk of death. Additional evaluation of this approach in clinical settings is warranted.