Biomarkers of Age-Related Frailty and Frailty Related to Diseases: An Exploratory, Cross-Sectional Analysis from the MAPT Study.

BACKGROUND: Frailty may in most cases result from two main causes: the aging process (age-related frailty) and diseases (evolving chronic conditions or acute medical illnesses - disease-related frailty). The biological determinants characterizing these two main causes of frailty may be different. OBJECTIVES: The aim of this study is to compare the biological and neuroimaging profile of people without frailty, those with age-related frailty, and subjects with disease-related frailty in community-dwelling older adults. MATERIAL AND METHODS: We performed a secondary, cross-sectional analysis from the Multidomain Alzheimer Preventive Trial (MAPT). We included 1199 subjects without frailty throughout the 5-year follow-up, 82 subjects with incident age-related frailty, and 53 with incident disease-related frailty. Available blood biomarkers involved nutritional (eg, vitamin D, omega-3 fatty acids), inflammatory-related (IL-6, TNFR1, GDF15), neurodegenerative (eg, beta-amyloid, neurofilament light chain) and neuroimaging markers (MRI, Amyloid-PET). RESULTS: Although not statistically significant, the results of the unadjusted model showed increasing gradients for inflammatory markers (GDF15, TNFR1) and decreasing gradients for nutritional and neuroimaging markers (omega 3 index, hippocampal volume) from age-related frailty participants to individuals with disease-related frailty. Considering the linear models we observed higher GDF15 values in disease-related frailty group compared to age-related frailty individuals [ß = 242.8 (49.5, 436.2)]. We did not find any significant difference between subjects without frailty and those with age-related frailty. Subjects with disease-related frailty compared to subjects without frailty had lower values of DHA [ß = -2.42 (-4.76, -0.08)], Omega 3 Index [ß = -0.50 (-0.95, -0.06)] and hippocampal volume [ß = -0.22 (-0.42,-0.02)]. They also had higher values of GDF15 [ß = 246.1 (88.9, 403.4)] and TNFR1 [ß = 157.5 (7.8, 307.2)]. CONCLUSION: Age-related frailty and disease-related frailty may represent different degrees of frailty severity on a biological level. Further research is needed to identify biomarkers potentially able to distinguish these classifications of frailty.

Age-Related Frailty: A Clinical Model for Geroscience?

In their everyday practice, geriatricians are confronted with the fact that older age and multimorbidity are associated to frailty. Indeed, if we take the example of a very old person with no diseases that progressively becomes frail with no other explanation, there is a natural temptation to link frailty to aging. On the other hand, when an old person with a medical history of diabetes, arthritis and congestive heart failure becomes frail there appears an obvious relationship between frailty and comorbidity. The unsolved question is: Considering that frailty is multifactorial and in the majority of cases comorbidity and aging are acting synergistically, can we disentangle the main contributor to the origin of frailty: disease or aging? We believe that it is important to be able to differentiate age-related frailty from frailty related to comorbidity. In fact, with the emergence of geroscience, the physiopathology, diagnosis, prognosis and treatment will probably have to be different in the future.

Can We Distinguish Age-Related Frailty from Frailty Related to Diseases ? Data from the MAPT Study.

BACKGROUND: No study has tried to distinguish subjects that become frail due to diseases (frailty related to diseases) or in the absence of specific medical events; in this latter case, it is possible that aging process would act as the main frailty driver (age-related frailty). OBJECTIVES: To classify subjects according to the origin of physical frailty: age-related frailty, frailty related to diseases, frailty of uncertain origin, and to compare their clinical characteristics. MATERIALS AND METHODS: We performed a secondary analysis of the Multidomain Alzheimer Preventive Trial (MAPT), including 195 subjects ≥70 years non-frail at baseline who became frail during a 5-year follow-up (mean age 77.8 years ± 4.7; 70% female). Physical frailty was defined as presenting ≥3 of the 5 Fried criteria: weight loss, exhaustion, weakness, slowness, low physical activity. Clinical files were independently reviewed by two different clinicians using a standardized assessment method in order to classify subjects as: "age-related frailty", "frailty related to diseases" or "frailty of uncertain origin". Inconsistencies among the two raters and cases of uncertain frailty were further assessed by two other experienced clinicians. RESULTS: From the 195 included subjects, 82 (42%) were classified as age-related frailty, 53 (27%) as frailty related to diseases, and 60 (31%) as frailty of uncertain origin. Patients who became frail due to diseases did not differ from the others groups in terms of functional, cognitive, psychological status and age at baseline, however they presented a higher burden of comorbidity as measured by the Cumulative Illness Rating Scale (CIRS) (8.20 ± 2.69; vs 6.22 ± 2.02 frailty of uncertain origin; vs. 3.25 ± 1.65 age-related frailty). Time to incident frailty (23.4 months ± 12.1 vs. 39.2 ± 19.3 months) and time spent in a pre-frailty condition (17.1 ± 11.4 vs 26.6 ± 16.6 months) were shorter in the group of frailty related to diseases compared to age-related frailty. Orthopedic diseases (n=14, 26%) were the most common pathologies leading to frailty related to diseases, followed by cardiovascular diseases (n=9, 17%) and neurological diseases (n = 8, 15%). CONCLUSION: People classified as age-related frailty and frailty related to diseases presented different frailty-associated indicators. Future research should target the underlying biological cascades leading to these two frailty classifications, since they could ask for distinct strategies of prevention and management.