Phenotype-specific omic expression patterns in people with frailty could provide invaluable insight into the underlying multi-systemic pathological processes and targets for intervention. Classical approaches to frailty have not considered the potential for different frailty phenotypes. We characterized associations between frailty (with/without disability) and sets of omic factors (genomic, proteomic, and metabolomic) plus markers measured in routine geriatric care. This study was a prevalent case control using stored biospecimens (urine, whole blood, cells, plasma, and serum) from 1522 individuals (identified as robust (R), pre-frail (P), or frail (F)] from the Toledo Study of Healthy Aging (R=178/P=184/F=109), 3 City Bordeaux (111/269/100), Aging Multidisciplinary Investigation (157/79/54) and InCHIANTI (106/98/77) cohorts. The analysis included over 35,000 omic and routine laboratory variables from robust and frail or pre-frail (with/without disability) individuals using a machine learning framework. We identified three protective biomarkers, vitamin D3 (OR: 0.81 [95% CI: 0.68-0.98]), lutein zeaxanthin (OR: 0.82 [95% CI: 0.70-0.97]), and miRNA125b-5p (OR: 0.73, [95% CI: 0.56-0.97]) and one risk biomarker, cardiac troponin T (OR: 1.25 [95% CI: 1.23-1.27]). Excluding individuals with a disability, one protective biomarker was identified, miR125b-5p (OR: 0.85, [95% CI: 0.81-0.88]). Three risks of frailty biomarkers were detected: pro-BNP (OR: 1.47 [95% CI: 1.27-1.7]), cardiac troponin T (OR: 1.29 [95% CI: 1.21-1.38]), and sRAGE (OR: 1.26 [95% CI: 1.01-1.57]). Three key frailty biomarkers demonstrated a statistical association with frailty (oxidative stress, vitamin D, and cardiovascular system) with relationship patterns differing depending on the presence or absence of a disability.

A robust machine learning framework to identify signatures for frailty: a nested case-control study in four aging {European} cohorts / Gomez-Cabrero, David; Walter, Stefan; Abugessaisa, Imad; Miñambres-Herraiz, Rebeca; Palomares Lucia, Bernad; Butcher, Lee; Erusalimsky Jorge, D.; Garcia-Garcia Francisco, Jose; Carnicero, José; Hardman Timothy, C.; Mischak, Harald; Zürbig, Petra; Hackl, Matthias; Grillari, Johannes; Fiorillo, Edoardo; Cucca, Francesco; Cesari, Matteo; Carrie, Isabelle; Colpo, Marco; Bandinelli, Stefania; Feart, Catherine; Peres, Karine; Dartigues, Jean-François; Helmer, Catherine; Viña, José; Olaso, Gloria; García-Palmero, Irene; Martínez Jorge, García; Jansen-Dürr, Pidder; Grune, Tilman; Weber, Daniela; Lippi, Giuseppe; Bonaguri, Chiara; Sinclair Alan, J.; Tegner, Jesper; Rodriguez-Mañas, Leocadio. - In: GEROSCIENCE. - ISSN 2509-2723. - (2021). [10.1007/s11357-021-00334-0]

A robust machine learning framework to identify signatures for frailty: a nested case-control study in four aging {European} cohorts

Carnicero José;Cucca Francesco;
2021-01-01

Abstract

Phenotype-specific omic expression patterns in people with frailty could provide invaluable insight into the underlying multi-systemic pathological processes and targets for intervention. Classical approaches to frailty have not considered the potential for different frailty phenotypes. We characterized associations between frailty (with/without disability) and sets of omic factors (genomic, proteomic, and metabolomic) plus markers measured in routine geriatric care. This study was a prevalent case control using stored biospecimens (urine, whole blood, cells, plasma, and serum) from 1522 individuals (identified as robust (R), pre-frail (P), or frail (F)] from the Toledo Study of Healthy Aging (R=178/P=184/F=109), 3 City Bordeaux (111/269/100), Aging Multidisciplinary Investigation (157/79/54) and InCHIANTI (106/98/77) cohorts. The analysis included over 35,000 omic and routine laboratory variables from robust and frail or pre-frail (with/without disability) individuals using a machine learning framework. We identified three protective biomarkers, vitamin D3 (OR: 0.81 [95% CI: 0.68-0.98]), lutein zeaxanthin (OR: 0.82 [95% CI: 0.70-0.97]), and miRNA125b-5p (OR: 0.73, [95% CI: 0.56-0.97]) and one risk biomarker, cardiac troponin T (OR: 1.25 [95% CI: 1.23-1.27]). Excluding individuals with a disability, one protective biomarker was identified, miR125b-5p (OR: 0.85, [95% CI: 0.81-0.88]). Three risks of frailty biomarkers were detected: pro-BNP (OR: 1.47 [95% CI: 1.27-1.7]), cardiac troponin T (OR: 1.29 [95% CI: 1.21-1.38]), and sRAGE (OR: 1.26 [95% CI: 1.01-1.57]). Three key frailty biomarkers demonstrated a statistical association with frailty (oxidative stress, vitamin D, and cardiovascular system) with relationship patterns differing depending on the presence or absence of a disability.
2021
A robust machine learning framework to identify signatures for frailty: a nested case-control study in four aging {European} cohorts / Gomez-Cabrero, David; Walter, Stefan; Abugessaisa, Imad; Miñambres-Herraiz, Rebeca; Palomares Lucia, Bernad; Butcher, Lee; Erusalimsky Jorge, D.; Garcia-Garcia Francisco, Jose; Carnicero, José; Hardman Timothy, C.; Mischak, Harald; Zürbig, Petra; Hackl, Matthias; Grillari, Johannes; Fiorillo, Edoardo; Cucca, Francesco; Cesari, Matteo; Carrie, Isabelle; Colpo, Marco; Bandinelli, Stefania; Feart, Catherine; Peres, Karine; Dartigues, Jean-François; Helmer, Catherine; Viña, José; Olaso, Gloria; García-Palmero, Irene; Martínez Jorge, García; Jansen-Dürr, Pidder; Grune, Tilman; Weber, Daniela; Lippi, Giuseppe; Bonaguri, Chiara; Sinclair Alan, J.; Tegner, Jesper; Rodriguez-Mañas, Leocadio. - In: GEROSCIENCE. - ISSN 2509-2723. - (2021). [10.1007/s11357-021-00334-0]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11388/245756
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