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Data-driven phenomapping for novel classification for cardiovascular outcomes compared with traditional obesity index: Tehran Lipid and Glucose Study
OBJECTIVE: This study aimed to propose a data-driven framework for classification of at-risk people for cardiovascular outcomes regarding obesity and metabolic syndrome. DESIGN: A population-based prospective cohort study with a long-term follow-up. SETTING: Data from the Tehran Lipid and Glucose St...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BMJ Publishing Group
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10277045/ https://www.ncbi.nlm.nih.gov/pubmed/37316323 http://dx.doi.org/10.1136/bmjopen-2022-071011 |
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author | Zare Borzeshi, Elahe Valizadeh, Majid Panahi, Mohammad Hossein Khalili, Davood Mousavizadeh, Mostafa Mehrabi, Yadollah |
author_facet | Zare Borzeshi, Elahe Valizadeh, Majid Panahi, Mohammad Hossein Khalili, Davood Mousavizadeh, Mostafa Mehrabi, Yadollah |
author_sort | Zare Borzeshi, Elahe |
collection | PubMed |
description | OBJECTIVE: This study aimed to propose a data-driven framework for classification of at-risk people for cardiovascular outcomes regarding obesity and metabolic syndrome. DESIGN: A population-based prospective cohort study with a long-term follow-up. SETTING: Data from the Tehran Lipid and Glucose Study (TLGS) were interrogated. PARTICIPANTS: 12 808 participants of the TLGS cohort, aged ≥20 years who have followed for over 15 years were assessed. MAIN OUTCOME MEASURES: Data for 12 808 participants, aged ≥20 years who have followed for over 15 years, collected through TLGS as a prospective, population-based cohort study, were analysed. Feature engineering followed by hierarchical clustering was used to determine meaningful clusters and novel endophenotypes. Cox regression was used to demonstrate the clinical validity of phenomapping. The performance of endophenotype compared with traditional classifications was evaluated by the value of Akaike information criterion/Bayesian information criterion. R software V.4.2 was employed. RESULTS: The mean age was 42.1±14.9 years, 56.2% were female, 13.1%, 2.8% and 6.2% had experienced cardiovascular disease (CVD), CVD mortality and hard CVD, respectively. Low-risk cluster compared with the high risk had significant difference in age, body mass index, waist-to-hip ratio, 2 hours post load plasma glucose, triglyceride, triglycerides to high density lipoprotein ratio, education, marital status, smoking and the presence of metabolic syndrome. Eight distinct endophenotypes were detected with significantly different clinical characteristics and outcomes. CONCLUSION: Phenomapping resulted in a novel classification of population with cardiovascular outcomes, which can, better, stratify individuals into homogeneous subclasses for prevention and intervention as an alternative of traditional methods solely based on either obesity or metabolic status. These findings have important clinical implications for a particular part of the Middle Eastern population for which it is a common practice to use tools/evidence derived from western populations with substantially different backgrounds and risk profiles. |
format | Online Article Text |
id | pubmed-10277045 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BMJ Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-102770452023-06-19 Data-driven phenomapping for novel classification for cardiovascular outcomes compared with traditional obesity index: Tehran Lipid and Glucose Study Zare Borzeshi, Elahe Valizadeh, Majid Panahi, Mohammad Hossein Khalili, Davood Mousavizadeh, Mostafa Mehrabi, Yadollah BMJ Open Epidemiology OBJECTIVE: This study aimed to propose a data-driven framework for classification of at-risk people for cardiovascular outcomes regarding obesity and metabolic syndrome. DESIGN: A population-based prospective cohort study with a long-term follow-up. SETTING: Data from the Tehran Lipid and Glucose Study (TLGS) were interrogated. PARTICIPANTS: 12 808 participants of the TLGS cohort, aged ≥20 years who have followed for over 15 years were assessed. MAIN OUTCOME MEASURES: Data for 12 808 participants, aged ≥20 years who have followed for over 15 years, collected through TLGS as a prospective, population-based cohort study, were analysed. Feature engineering followed by hierarchical clustering was used to determine meaningful clusters and novel endophenotypes. Cox regression was used to demonstrate the clinical validity of phenomapping. The performance of endophenotype compared with traditional classifications was evaluated by the value of Akaike information criterion/Bayesian information criterion. R software V.4.2 was employed. RESULTS: The mean age was 42.1±14.9 years, 56.2% were female, 13.1%, 2.8% and 6.2% had experienced cardiovascular disease (CVD), CVD mortality and hard CVD, respectively. Low-risk cluster compared with the high risk had significant difference in age, body mass index, waist-to-hip ratio, 2 hours post load plasma glucose, triglyceride, triglycerides to high density lipoprotein ratio, education, marital status, smoking and the presence of metabolic syndrome. Eight distinct endophenotypes were detected with significantly different clinical characteristics and outcomes. CONCLUSION: Phenomapping resulted in a novel classification of population with cardiovascular outcomes, which can, better, stratify individuals into homogeneous subclasses for prevention and intervention as an alternative of traditional methods solely based on either obesity or metabolic status. These findings have important clinical implications for a particular part of the Middle Eastern population for which it is a common practice to use tools/evidence derived from western populations with substantially different backgrounds and risk profiles. BMJ Publishing Group 2023-06-13 /pmc/articles/PMC10277045/ /pubmed/37316323 http://dx.doi.org/10.1136/bmjopen-2022-071011 Text en © Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) . |
spellingShingle | Epidemiology Zare Borzeshi, Elahe Valizadeh, Majid Panahi, Mohammad Hossein Khalili, Davood Mousavizadeh, Mostafa Mehrabi, Yadollah Data-driven phenomapping for novel classification for cardiovascular outcomes compared with traditional obesity index: Tehran Lipid and Glucose Study |
title | Data-driven phenomapping for novel classification for cardiovascular outcomes compared with traditional obesity index: Tehran Lipid and Glucose Study |
title_full | Data-driven phenomapping for novel classification for cardiovascular outcomes compared with traditional obesity index: Tehran Lipid and Glucose Study |
title_fullStr | Data-driven phenomapping for novel classification for cardiovascular outcomes compared with traditional obesity index: Tehran Lipid and Glucose Study |
title_full_unstemmed | Data-driven phenomapping for novel classification for cardiovascular outcomes compared with traditional obesity index: Tehran Lipid and Glucose Study |
title_short | Data-driven phenomapping for novel classification for cardiovascular outcomes compared with traditional obesity index: Tehran Lipid and Glucose Study |
title_sort | data-driven phenomapping for novel classification for cardiovascular outcomes compared with traditional obesity index: tehran lipid and glucose study |
topic | Epidemiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10277045/ https://www.ncbi.nlm.nih.gov/pubmed/37316323 http://dx.doi.org/10.1136/bmjopen-2022-071011 |
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