A robust machine learning framework to identify signatures for frailty: a nested case-control study in four aging European cohorts
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 character...
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| Veröffentlicht in: | GeroScience 2021-06, Vol.43 (3), p.1317-1329 |
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| creator | 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 |
| description | 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. |
| doi_str_mv | 10.1007/s11357-021-00334-0 |
| format | Article |
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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.</description><identifier>ISSN: 2509-2715</identifier><identifier>ISSN: 2509-2723</identifier><identifier>EISSN: 2509-2723</identifier><identifier>DOI: 10.1007/s11357-021-00334-0</identifier><identifier>PMID: 33599920</identifier><language>eng</language><publisher>Cham: Springer International Publishing</publisher><subject>Advanced glycosylation end products ; Aging ; Biomarkers ; Biomedical and Life Sciences ; Calcium-binding protein ; Cardiovascular system ; Cell Biology ; Frailty ; Geriatrics & Gerontology ; Heart ; Learning algorithms ; Life Sciences ; Life Sciences & Biomedicine ; Machine learning ; Medicin och hälsovetenskap ; Metabolomics ; Molecular Medicine ; Original ; Original Article ; Oxidative stress ; Phenotypes ; Proteomics ; Science & Technology ; Troponin ; Troponin T ; Vitamin D ; Vitamin D3 ; Zeaxanthin</subject><ispartof>GeroScience, 2021-06, Vol.43 (3), p.1317-1329</ispartof><rights>American Aging Association 2021</rights><rights>American Aging Association 2021.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>true</woscitedreferencessubscribed><woscitedreferencescount>35</woscitedreferencescount><woscitedreferencesoriginalsourcerecordid>wos000619395300001</woscitedreferencesoriginalsourcerecordid><citedby>FETCH-LOGICAL-c524t-6ceb46c6b691a173f9632eaa79caf8f6c85e2c2f63d568a92c1c3359a3abe6233</citedby><cites>FETCH-LOGICAL-c524t-6ceb46c6b691a173f9632eaa79caf8f6c85e2c2f63d568a92c1c3359a3abe6233</cites><orcidid>0000-0002-4253-5789 ; 0000-0002-0348-3664 ; 0000-0001-6906-6268 ; 0000-0002-2054-6233 ; 0000-0003-3465-2173 ; 0000-0003-3690-6069 ; 0000-0002-8690-4988 ; 0000-0001-7458-801X ; 0000-0002-7414-1995 ; 0000-0002-1417-9019 ; 0000-0002-1527-7315 ; 0000-0001-5474-6332 ; 0000-0001-9709-0089 ; 0000-0001-9523-9054 ; 0000-0003-0323-0306 ; 0000-0002-6551-1333 ; 0000-0003-4186-3788 ; 0000-0002-9568-5588 ; 0000-0002-4136-7293 ; 0000-0002-6010-7550 ; 0000-0003-4839-6799 ; 0000-0002-6491-0850</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8190217/pdf/$$EPDF$$P50$$Gpubmedcentral$$H</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8190217/$$EHTML$$P50$$Gpubmedcentral$$H</linktohtml><link.rule.ids>230,315,554,729,782,786,887,27933,27934,39266,39267,41497,42566,51328,53800,53802</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/33599920$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink><backlink>$$Uhttp://kipublications.ki.se/Default.aspx?queryparsed=id:145945297$$DView record from Swedish Publication Index$$Hfree_for_read</backlink></links><search><creatorcontrib>Gomez-Cabrero, David</creatorcontrib><creatorcontrib>Walter, Stefan</creatorcontrib><creatorcontrib>Abugessaisa, Imad</creatorcontrib><creatorcontrib>Miñambres-Herraiz, Rebeca</creatorcontrib><creatorcontrib>Palomares, Lucia Bernad</creatorcontrib><creatorcontrib>Butcher, Lee</creatorcontrib><creatorcontrib>Erusalimsky, Jorge D.</creatorcontrib><creatorcontrib>Garcia-Garcia, Francisco Jose</creatorcontrib><creatorcontrib>Carnicero, José</creatorcontrib><creatorcontrib>Hardman, Timothy C.</creatorcontrib><creatorcontrib>Mischak, Harald</creatorcontrib><creatorcontrib>Zürbig, Petra</creatorcontrib><creatorcontrib>Hackl, Matthias</creatorcontrib><creatorcontrib>Grillari, Johannes</creatorcontrib><creatorcontrib>Fiorillo, Edoardo</creatorcontrib><creatorcontrib>Cucca, Francesco</creatorcontrib><creatorcontrib>Cesari, Matteo</creatorcontrib><creatorcontrib>Carrie, Isabelle</creatorcontrib><creatorcontrib>Colpo, Marco</creatorcontrib><creatorcontrib>Bandinelli, Stefania</creatorcontrib><creatorcontrib>Feart, Catherine</creatorcontrib><creatorcontrib>Peres, Karine</creatorcontrib><creatorcontrib>Dartigues, Jean-François</creatorcontrib><creatorcontrib>Helmer, Catherine</creatorcontrib><creatorcontrib>Viña, José</creatorcontrib><creatorcontrib>Olaso, Gloria</creatorcontrib><creatorcontrib>García-Palmero, Irene</creatorcontrib><creatorcontrib>Martínez, Jorge García</creatorcontrib><creatorcontrib>Jansen-Dürr, Pidder</creatorcontrib><creatorcontrib>Grune, Tilman</creatorcontrib><creatorcontrib>Weber, Daniela</creatorcontrib><creatorcontrib>Lippi, Giuseppe</creatorcontrib><creatorcontrib>Bonaguri, Chiara</creatorcontrib><creatorcontrib>Sinclair, Alan J</creatorcontrib><creatorcontrib>Tegner, Jesper</creatorcontrib><creatorcontrib>Rodriguez-Mañas, Leocadio</creatorcontrib><creatorcontrib>FRAILOMIC initiative</creatorcontrib><creatorcontrib>on behalf of the FRAILOMIC initiative</creatorcontrib><title>A robust machine learning framework to identify signatures for frailty: a nested case-control study in four aging European cohorts</title><title>GeroScience</title><addtitle>GeroScience</addtitle><addtitle>GEROSCIENCE</addtitle><addtitle>Geroscience</addtitle><description>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.</description><subject>Advanced glycosylation end products</subject><subject>Aging</subject><subject>Biomarkers</subject><subject>Biomedical and Life Sciences</subject><subject>Calcium-binding protein</subject><subject>Cardiovascular system</subject><subject>Cell Biology</subject><subject>Frailty</subject><subject>Geriatrics & Gerontology</subject><subject>Heart</subject><subject>Learning algorithms</subject><subject>Life Sciences</subject><subject>Life Sciences & Biomedicine</subject><subject>Machine learning</subject><subject>Medicin och hälsovetenskap</subject><subject>Metabolomics</subject><subject>Molecular Medicine</subject><subject>Original</subject><subject>Original Article</subject><subject>Oxidative stress</subject><subject>Phenotypes</subject><subject>Proteomics</subject><subject>Science & Technology</subject><subject>Troponin</subject><subject>Troponin T</subject><subject>Vitamin D</subject><subject>Vitamin 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robust machine learning framework to identify signatures for frailty: a nested case-control study in four aging European cohorts</title><author>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</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c524t-6ceb46c6b691a173f9632eaa79caf8f6c85e2c2f63d568a92c1c3359a3abe6233</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Advanced glycosylation end products</topic><topic>Aging</topic><topic>Biomarkers</topic><topic>Biomedical and Life Sciences</topic><topic>Calcium-binding protein</topic><topic>Cardiovascular system</topic><topic>Cell Biology</topic><topic>Frailty</topic><topic>Geriatrics & Gerontology</topic><topic>Heart</topic><topic>Learning algorithms</topic><topic>Life Sciences</topic><topic>Life Sciences & Biomedicine</topic><topic>Machine learning</topic><topic>Medicin och hälsovetenskap</topic><topic>Metabolomics</topic><topic>Molecular Medicine</topic><topic>Original</topic><topic>Original Article</topic><topic>Oxidative stress</topic><topic>Phenotypes</topic><topic>Proteomics</topic><topic>Science & Technology</topic><topic>Troponin</topic><topic>Troponin T</topic><topic>Vitamin D</topic><topic>Vitamin D3</topic><topic>Zeaxanthin</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Gomez-Cabrero, David</creatorcontrib><creatorcontrib>Walter, Stefan</creatorcontrib><creatorcontrib>Abugessaisa, Imad</creatorcontrib><creatorcontrib>Miñambres-Herraiz, Rebeca</creatorcontrib><creatorcontrib>Palomares, Lucia Bernad</creatorcontrib><creatorcontrib>Butcher, Lee</creatorcontrib><creatorcontrib>Erusalimsky, Jorge D.</creatorcontrib><creatorcontrib>Garcia-Garcia, Francisco Jose</creatorcontrib><creatorcontrib>Carnicero, José</creatorcontrib><creatorcontrib>Hardman, Timothy C.</creatorcontrib><creatorcontrib>Mischak, Harald</creatorcontrib><creatorcontrib>Zürbig, Petra</creatorcontrib><creatorcontrib>Hackl, Matthias</creatorcontrib><creatorcontrib>Grillari, Johannes</creatorcontrib><creatorcontrib>Fiorillo, Edoardo</creatorcontrib><creatorcontrib>Cucca, Francesco</creatorcontrib><creatorcontrib>Cesari, Matteo</creatorcontrib><creatorcontrib>Carrie, Isabelle</creatorcontrib><creatorcontrib>Colpo, Marco</creatorcontrib><creatorcontrib>Bandinelli, Stefania</creatorcontrib><creatorcontrib>Feart, Catherine</creatorcontrib><creatorcontrib>Peres, Karine</creatorcontrib><creatorcontrib>Dartigues, Jean-François</creatorcontrib><creatorcontrib>Helmer, Catherine</creatorcontrib><creatorcontrib>Viña, José</creatorcontrib><creatorcontrib>Olaso, Gloria</creatorcontrib><creatorcontrib>García-Palmero, Irene</creatorcontrib><creatorcontrib>Martínez, Jorge García</creatorcontrib><creatorcontrib>Jansen-Dürr, Pidder</creatorcontrib><creatorcontrib>Grune, Tilman</creatorcontrib><creatorcontrib>Weber, Daniela</creatorcontrib><creatorcontrib>Lippi, Giuseppe</creatorcontrib><creatorcontrib>Bonaguri, Chiara</creatorcontrib><creatorcontrib>Sinclair, Alan J</creatorcontrib><creatorcontrib>Tegner, Jesper</creatorcontrib><creatorcontrib>Rodriguez-Mañas, Leocadio</creatorcontrib><creatorcontrib>FRAILOMIC initiative</creatorcontrib><creatorcontrib>on behalf of the FRAILOMIC initiative</creatorcontrib><collection>Web of Knowledge</collection><collection>Web of Science Core Collection</collection><collection>Science Citation Index Expanded</collection><collection>Social Sciences Citation Index</collection><collection>Web of Science Primary (SCIE, SSCI & AHCI)</collection><collection>Web of Science - Social Sciences Citation Index – 2021</collection><collection>Web of Science - Science Citation Index Expanded - 2021</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Animal Behavior Abstracts</collection><collection>Immunology Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Premium</collection><collection>PubMed Central (Full Participant titles)</collection><collection>SwePub</collection><collection>SwePub Articles</collection><collection>SWEPUB Freely available online</collection><collection>SwePub Articles full text</collection><jtitle>GeroScience</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Gomez-Cabrero, David</au><au>Walter, Stefan</au><au>Abugessaisa, Imad</au><au>Miñambres-Herraiz, Rebeca</au><au>Palomares, Lucia Bernad</au><au>Butcher, Lee</au><au>Erusalimsky, Jorge D.</au><au>Garcia-Garcia, Francisco Jose</au><au>Carnicero, José</au><au>Hardman, Timothy C.</au><au>Mischak, Harald</au><au>Zürbig, Petra</au><au>Hackl, Matthias</au><au>Grillari, Johannes</au><au>Fiorillo, Edoardo</au><au>Cucca, Francesco</au><au>Cesari, Matteo</au><au>Carrie, Isabelle</au><au>Colpo, Marco</au><au>Bandinelli, Stefania</au><au>Feart, Catherine</au><au>Peres, Karine</au><au>Dartigues, Jean-François</au><au>Helmer, Catherine</au><au>Viña, José</au><au>Olaso, Gloria</au><au>García-Palmero, Irene</au><au>Martínez, Jorge García</au><au>Jansen-Dürr, Pidder</au><au>Grune, Tilman</au><au>Weber, Daniela</au><au>Lippi, Giuseppe</au><au>Bonaguri, Chiara</au><au>Sinclair, Alan J</au><au>Tegner, Jesper</au><au>Rodriguez-Mañas, Leocadio</au><aucorp>FRAILOMIC initiative</aucorp><aucorp>on behalf of the FRAILOMIC initiative</aucorp><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A robust machine learning framework to identify signatures for frailty: a nested case-control study in four aging European cohorts</atitle><jtitle>GeroScience</jtitle><stitle>GeroScience</stitle><stitle>GEROSCIENCE</stitle><addtitle>Geroscience</addtitle><date>2021-06-01</date><risdate>2021</risdate><volume>43</volume><issue>3</issue><spage>1317</spage><epage>1329</epage><pages>1317-1329</pages><issn>2509-2715</issn><issn>2509-2723</issn><eissn>2509-2723</eissn><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.</abstract><cop>Cham</cop><pub>Springer International Publishing</pub><pmid>33599920</pmid><doi>10.1007/s11357-021-00334-0</doi><tpages>13</tpages><orcidid>https://orcid.org/0000-0002-4253-5789</orcidid><orcidid>https://orcid.org/0000-0002-0348-3664</orcidid><orcidid>https://orcid.org/0000-0001-6906-6268</orcidid><orcidid>https://orcid.org/0000-0002-2054-6233</orcidid><orcidid>https://orcid.org/0000-0003-3465-2173</orcidid><orcidid>https://orcid.org/0000-0003-3690-6069</orcidid><orcidid>https://orcid.org/0000-0002-8690-4988</orcidid><orcidid>https://orcid.org/0000-0001-7458-801X</orcidid><orcidid>https://orcid.org/0000-0002-7414-1995</orcidid><orcidid>https://orcid.org/0000-0002-1417-9019</orcidid><orcidid>https://orcid.org/0000-0002-1527-7315</orcidid><orcidid>https://orcid.org/0000-0001-5474-6332</orcidid><orcidid>https://orcid.org/0000-0001-9709-0089</orcidid><orcidid>https://orcid.org/0000-0001-9523-9054</orcidid><orcidid>https://orcid.org/0000-0003-0323-0306</orcidid><orcidid>https://orcid.org/0000-0002-6551-1333</orcidid><orcidid>https://orcid.org/0000-0003-4186-3788</orcidid><orcidid>https://orcid.org/0000-0002-9568-5588</orcidid><orcidid>https://orcid.org/0000-0002-4136-7293</orcidid><orcidid>https://orcid.org/0000-0002-6010-7550</orcidid><orcidid>https://orcid.org/0000-0003-4839-6799</orcidid><orcidid>https://orcid.org/0000-0002-6491-0850</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 2509-2715 |
| ispartof | GeroScience, 2021-06, Vol.43 (3), p.1317-1329 |
| issn | 2509-2715 2509-2723 2509-2723 |
| language | eng |
| recordid | cdi_pubmed_primary_33599920 |
| source | Web of Science - Science Citation Index Expanded - 2021<img src="https://exlibris-pub.s3.amazonaws.com/fromwos-v2.jpg" />; SpringerNature Journals; PubMed Central; Web of Science - Social Sciences Citation Index – 2021<img src="https://exlibris-pub.s3.amazonaws.com/fromwos-v2.jpg" />; Alma/SFX Local Collection; SWEPUB Freely available online |
| subjects | Advanced glycosylation end products Aging Biomarkers Biomedical and Life Sciences Calcium-binding protein Cardiovascular system Cell Biology Frailty Geriatrics & Gerontology Heart Learning algorithms Life Sciences Life Sciences & Biomedicine Machine learning Medicin och hälsovetenskap Metabolomics Molecular Medicine Original Original Article Oxidative stress Phenotypes Proteomics Science & Technology Troponin Troponin T Vitamin D Vitamin D3 Zeaxanthin |
| title | A robust machine learning framework to identify signatures for frailty: a nested case-control study in four aging European cohorts |
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