Frailty and Alzheimer’s Disease

We are writing regarding the recently published article, "Explore the Role of Frailty as a Contributor to the Association Between AT(N) Profiles and Cognition in Alzheimer’s Disease," by Han et al. [1]. The study addresses a critical and underexplored dimension of Alzheimer’s disease (AD) pathology, presenting frailty as a potential moderator of the relationship between AT(N) profiles and cognitive impairment. Furthermore, the use of multiple biomarkers within the AT(N) framework enhances the rigor and comprehensiveness of the findings, elucidating frailty's role across various stages of AD. Another notable strength is the interaction modeling, which identifies frailty as a significant modifier of cognitive outcomes. This advances our understanding of AD as a multifactorial condition rather than one solely determined by pathology.

While the study provides valuable insights, its limitations warrant attention. The use of the ADNI cohort, while beneficial for standardization, might introduce a selection bias. The exclusion of individuals with suspected non-Alzheimer’s pathologies (SNAP) also limits the generalizability to heterogeneous clinical populations. Moreover, the study acknowledges variability in AT(N) cutoff definitions, which may introduce classification bias. While efforts to validate thresholds within the cohort are commendable, future research should aim for standardization to improve comparability across studies.

Lastly, the use of the Modified Frailty Index-11 (mFI-11) in this study offers simplicity and clinical applicability but has several limitations compared to other more comprehensive and nuanced frailty indices. The mFI-11 primarily focuses on physical health deficits, omitting psychological, social, and environmental components crucial for a holistic assessment of frailty. Its binary scoring approach oversimplifies gradations of severity, while its static nature captures only a snapshot of frailty, limiting its ability to reflect dynamic changes. Originally validated for surgical populations, its generalizability to broader, community-dwelling cohorts is restricted. Furthermore, its reliance on comorbidities risks overlaps with disease burden indices and may fail to detect subtle functional declines. Comprehensive tools like the Frailty Phenotype [2], Rockwood Frailty Index [3], or Edmonton Frail Scale [4] provide a more multidimensional and sensitive evaluation, making them better suited for studies exploring complex conditions like Alzheimer’s disease [5,6].

Han et al. [1] have significantly advanced our understanding of frailty’s moderating role in AD-related cognitive impairment in their study. Addressing the highlighted limitations in future research could further solidify these findings and translate them into clinical strategies for managing frailty in AD patients.

Danoosh Esmaeili¹, Arian Faramarzinia², Hosseni Zare^3
1Hormozgan University of Medical Sciences, Bandar Abbas, IR, Iran
²Graduated in medicine, Tehran University of Medical Sciences, Tehran, Iran
³School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran  

References
[1] Han B-L, Ma L-Z, Han S-L, Alzheimer’s Disease Neuroimaging Initiative, Mi Y-C, Liu J-Y, Sheng Z-H, Wang H-F, Tan L (2024) Explore the role of frailty as a contributor to the association between AT (N) profiles and cognition in Alzheimer’s disease. J Alzheimers Dis 100, 1333–1343.
[2] Fried LP, Tangen CM, Walston J, Newman AB, Hirsch C, Gottdiener J, Seeman T, Tracy R, Kop WJ, Burke G (2001) Frailty in older adults: evidence for a phenotype. J Gerontol A Biol Sci Med Sci 56, M146–M157.
[3] Rockwood K, Song X, MacKnight C, Bergman H, Hogan DB, McDowell I, Mitnitski A (2005) A global clinical measure of fitness and frailty in elderly people. CMAJ 173, 489–495.
[4] Rolfson DB, Majumdar SR, Tsuyuki RT, Tahir A, Rockwood K (2006) Validity and reliability of the Edmonton Frail Scale. Age Ageing 35, 526–529.
[5] Clegg A, Young J, Iliffe S, Rikkert MO, Rockwood K (2013) Frailty in elderly people. Lancet 381, 752–762.
[6] Tracy BM, Adams MA, Schenker ML, Gelbard RB (2020) The 5 and 11 factor modified frailty indices are equally effective at outcome prediction using TQIP. J Surg Res 255, 456–462.