[en] Frailty is a geriatric condition with multidimensional consequences that strongly affect older adults' quality of life. The lack of a universal standard to describe, diagnose, and treat frailty further complicates this situation. Nowadays, multitudinous frailty assessment tools are applied depending on the regional and clinical context, adding complexity by increasing heterogeneity in the definition and characterization of frailty. Better insights into the causes and pathophysiology of frailty and its early stages are required to establish strong and accurately tailored treatment rationales for frail patients. We analysed participants aged 60 and above using cross-sectional biochemical and survey data from the Berlin Aging Study II (BASE-II, N=1512, pre-frail=470, frail=14), applying machine-learning techniques to investigate determinants of physical frailty measured by Fried et al.'s 5-item frailty phenotype. Our findings highlight new prognostic sex-specific biomarkers of pre-frailty (the early stage of frailty) with possible clinical applications, enriching the current sex-agnostic diagnostic scores with easy monitorable physical and physiological characteristics. Low appendicular lean mass and high fat composition in men, or vitamin D deficiency and high white blood cell counts in women, emerged as strong indicators of the respective pre-frailty profiles. Because the number of fully frail individuals was extremely small (n = 14, <1%), our findings should be interpreted as reflecting predictors of pre-frailty, not of frailty itself. We conclude that understanding the development of frailty remains a complex challenge, and that sex-specific differences must be considered by clinical geriatricians and researchers.
Disciplines :
Geriatrics
Author, co-author :
DIDIER, Jeff ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Life Sciences and Medicine (DLSM)
Landtsheer, Sébastien De; Systems Biology & Epigenetics Group, Department of Life Sciences and Medicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg. Electronic address: sebastien.delandtsheer@uni.lu
Pacheco, Maria Pires ; Systems Biology & Epigenetics Group, Department of Life Sciences and Medicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg. Electronic address: maria.pacheco@uni.lu
KISHK, Ali ; University of Luxembourg > Faculty of Science, Technology and Medicine > Department of Life Sciences and Medicine > Team Thomas SAUTER
SCHNEIDER, Jochen ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Medical Translational Research
Goldeck, David ; Department of Internal Medicine 2, University of Tübingen, Tübingen, Germany. Electronic address: dgoldeck@yahoo.de
Pawelec, Graham ; Department of Immunology, University of Tübingen, Tübingen, Germany. Electronic address: graham.pawelec@uni-tuebingen.de
Spira, Dominik ; Division of Lipid Metabolism, Department of Endocrinology and Metabolic Diseases, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Charité - Universitätsmedizin Berlin, Berlin, Germany. Electronic address: dominik.spira@charite.de
Demuth, Ilja ; Division of Lipid Metabolism, Department of Endocrinology and Metabolic Diseases, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Charité - Universitätsmedizin Berlin, Berlin, Germany, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, BCRT-Berlin Institute of Health Center for Regenerative Therapies, Berlin, Germany. Electronic address: ilja.demuth@charite.de
SAUTER, Thomas ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Life Sciences and Medicine (DLSM)
External co-authors :
yes
Language :
English
Title :
Clinical Data-Driven Classification of Pre-Frailty Reveals Sex-Specific Patterns - Data from the Berlin Aging Study II (BASE-II).
National Research Fund Federal Ministry of Education and Research Bonn Office Université du Luxembourg
Funding text :
The Doctoral Training Unit Data-driven computational modelling and applications (DRIVEN) is funded by the Luxembourg National Research Fund under the PRIDE program ( PRIDE17/12252781 ). This article uses data from the Berlin Aging Study II (BASE-II) which was supported by the German Federal Ministry of Education and Research under grant numbers #01UW0808 ; #16SV5536K , #16SV5537 , #16SV5538 , #16SV5837 , #01GL1716A and #01GL1716B .The experiments presented in this paper were carried out using the High-Performance Computing (HPC) facilities of the University of Luxembourg (see https://hpc.uni.lu/, and reference (Varrette et al. 2022)). The authors would like to thank the steering committee of BASE-II and the BASE-II participants. The Doctoral Training Unit Data-driven computational modelling and applications (DRIVEN) is funded by the Luxembourg National Research Fund under the PRIDE program (PRIDE17/12252781). This article uses data from the Berlin Aging Study II (BASE-II) which was supported by the German Federal Ministry of Education and Research under grant numbers #01UW0808; #16SV5536K, #16SV5537, #16SV5538, #16SV5837, #01GL1716A and #01GL1716B.
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