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Identifying and ranking novel independent features for cardiovascular disease prediction in people with type 2 diabetes
BACKGROUND: CVD prediction models do not perform well in people with diabetes. We therefore aimed to identify novel predictors for six facets of CVD, (including coronary heart disease (CHD), Ischemic stroke, heart failure (HF), and atrial fibrillation (AF)) in people with T2DM. METHODS: Analyses wer...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Cold Spring Harbor Laboratory
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10635178/ https://www.ncbi.nlm.nih.gov/pubmed/37961704 http://dx.doi.org/10.1101/2023.10.23.23297398 |
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author | Dziopa, K Chaturvedi, N Asselbergs, F W Schmidt, A F |
author_facet | Dziopa, K Chaturvedi, N Asselbergs, F W Schmidt, A F |
author_sort | Dziopa, K |
collection | PubMed |
description | BACKGROUND: CVD prediction models do not perform well in people with diabetes. We therefore aimed to identify novel predictors for six facets of CVD, (including coronary heart disease (CHD), Ischemic stroke, heart failure (HF), and atrial fibrillation (AF)) in people with T2DM. METHODS: Analyses were conducted using the UK biobank and were stratified on history of CVD and of T2DM: 459,142 participants without diabetes or a history of CVD, 14,610 with diabetes but without CVD, and 4,432 with diabetes and a history of CVD. Replication was performed using a 20% hold-out set, ranking features on their permuted c-statistic. RESULTS: Out of the 600+ candidate features, we identified a subset of replicated features, ranging between 32 for CHD in people with diabetes to 184 for CVD+HF+AF in people without diabetes. Classical CVD risk factors (e.g. parental or maternal history of heart disease, or blood pressure) were relatively highly ranked for people without diabetes. The top predictors in the people with diabetes without a CVD history included: cystatin C, self-reported health satisfaction, biochemical measures of ill health (e.g. plasma albumin). For people with diabetes and a history of CVD top features were: self-reported ill health, and blood cell counts measurements (e.g. red cell distribution width). We additionally identified risk factors unique to people with diabetes, consisting of information on dietary patterns, mental health and biochemistry measures. Consideration of these novel features improved risk classification, for example per 1000 people with diabetes 133 CVD and 165 HF cases appropriately received a higher risk. CONCLUSION: Through data-driven feature selection we identified a substantial number of features relevant for prediction of cardiovascular risk in people with diabetes, the majority of which related to non-classical risk factors such as mental health, general illness markers, and kidney disease. |
format | Online Article Text |
id | pubmed-10635178 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Cold Spring Harbor Laboratory |
record_format | MEDLINE/PubMed |
spelling | pubmed-106351782023-11-13 Identifying and ranking novel independent features for cardiovascular disease prediction in people with type 2 diabetes Dziopa, K Chaturvedi, N Asselbergs, F W Schmidt, A F medRxiv Article BACKGROUND: CVD prediction models do not perform well in people with diabetes. We therefore aimed to identify novel predictors for six facets of CVD, (including coronary heart disease (CHD), Ischemic stroke, heart failure (HF), and atrial fibrillation (AF)) in people with T2DM. METHODS: Analyses were conducted using the UK biobank and were stratified on history of CVD and of T2DM: 459,142 participants without diabetes or a history of CVD, 14,610 with diabetes but without CVD, and 4,432 with diabetes and a history of CVD. Replication was performed using a 20% hold-out set, ranking features on their permuted c-statistic. RESULTS: Out of the 600+ candidate features, we identified a subset of replicated features, ranging between 32 for CHD in people with diabetes to 184 for CVD+HF+AF in people without diabetes. Classical CVD risk factors (e.g. parental or maternal history of heart disease, or blood pressure) were relatively highly ranked for people without diabetes. The top predictors in the people with diabetes without a CVD history included: cystatin C, self-reported health satisfaction, biochemical measures of ill health (e.g. plasma albumin). For people with diabetes and a history of CVD top features were: self-reported ill health, and blood cell counts measurements (e.g. red cell distribution width). We additionally identified risk factors unique to people with diabetes, consisting of information on dietary patterns, mental health and biochemistry measures. Consideration of these novel features improved risk classification, for example per 1000 people with diabetes 133 CVD and 165 HF cases appropriately received a higher risk. CONCLUSION: Through data-driven feature selection we identified a substantial number of features relevant for prediction of cardiovascular risk in people with diabetes, the majority of which related to non-classical risk factors such as mental health, general illness markers, and kidney disease. Cold Spring Harbor Laboratory 2023-10-24 /pmc/articles/PMC10635178/ /pubmed/37961704 http://dx.doi.org/10.1101/2023.10.23.23297398 Text en https://creativecommons.org/licenses/by-nc-nd/4.0/This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (https://creativecommons.org/licenses/by-nc-nd/4.0/) , which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator. |
spellingShingle | Article Dziopa, K Chaturvedi, N Asselbergs, F W Schmidt, A F Identifying and ranking novel independent features for cardiovascular disease prediction in people with type 2 diabetes |
title | Identifying and ranking novel independent features for cardiovascular disease prediction in people with type 2 diabetes |
title_full | Identifying and ranking novel independent features for cardiovascular disease prediction in people with type 2 diabetes |
title_fullStr | Identifying and ranking novel independent features for cardiovascular disease prediction in people with type 2 diabetes |
title_full_unstemmed | Identifying and ranking novel independent features for cardiovascular disease prediction in people with type 2 diabetes |
title_short | Identifying and ranking novel independent features for cardiovascular disease prediction in people with type 2 diabetes |
title_sort | identifying and ranking novel independent features for cardiovascular disease prediction in people with type 2 diabetes |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10635178/ https://www.ncbi.nlm.nih.gov/pubmed/37961704 http://dx.doi.org/10.1101/2023.10.23.23297398 |
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