{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Xu Z"],"funding":["International Alliance for Cancer Early Detection","British Heart Foundation","British Heart Foundation-Turing Cardiovascular Data Science Award","National Institute for Health Research Cambridge Biomedical Research Centre","British Heart Foundation and Wellcome","Knight Cancer Institute, University College London","Oregon Health & Science University","BHF Cambridge Centre for Research Excellence","The Cardiovascular Epidemiology Unit","British Heart Foundation Programme","Oregon Health & Science University","School of Clinical Medicine at University of Cambridge","Canary Center at Stanford University","University of Manchester","Medical Research Council","British Heart Foundation Chair","National Institute for Health Research (NIHR)","AstraZeneca","Liverpool Centre for Cardiovascular Science, University of Liverpool, Liverpool","University of Cambridge","Cambridge Biomedical Research Centre","Innovative Medicines Initiative-2 Joint Undertaking"],"pagination":["1813-1823"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9749723"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["51(6)"],"pubmed_abstract":["Background
Cardiovascular disease (CVD) risk prediction models for individuals with type 2 diabetes are important tools to guide intensification of interventions for CVD prevention. We aimed to assess the added value of incorporating risk factors variability in CVD risk prediction for people with type 2 diabetes.Methods
We used electronic health records (EHRs) data from 83 910 adults with type 2 diabetes but without pre-existing CVD from the UK Clinical Practice Research Datalink for 2004-2017. Using a landmark-modelling approach, we developed and validated sex-specific Cox models, incorporating conventional predictors and trajectories plus variability of systolic blood pressure (SBP), total and high-density lipoprotein (HDL) cholesterol, and glycated haemoglobin (HbA1c). S"],"journal":["International journal of epidemiology"],"pubmed_title":["Incremental value of risk factor variability for cardiovascular risk prediction in individuals with type 2 diabetes: results from UK primary care electronic health records."],"pmcid":["PMC9749723"],"funding_grant_id":["MR/L003120/1","RG/13/13/30194","RE/13/6/30180","FS/18/56/34177","BRC-1215-2001","BRC-1215–2001","CH/12/2/29428","RG/18/13/33946","MC_UU_00002/5","BRC-1215–20014","BRC-1215-20014","C18081/A31373"],"pubmed_authors":["Sun L","Stevens D","Arnold M","Kaptoge S","Di Angelantonio E","Chung R","Ip S","Barrett J","Wood AM","Xu Z","Pennells L"],"additional_accession":[]},"is_claimable":false,"name":"Incremental value of risk factor variability for cardiovascular risk prediction in individuals with type 2 diabetes: results from UK primary care electronic health records.","description":"Background
Cardiovascular disease (CVD) risk prediction models for individuals with type 2 diabetes are important tools to guide intensification of interventions for CVD prevention. We aimed to assess the added value of incorporating risk factors variability in CVD risk prediction for people with type 2 diabetes.Methods
We used electronic health records (EHRs) data from 83 910 adults with type 2 diabetes but without pre-existing CVD from the UK Clinical Practice Research Datalink for 2004-2017. Using a landmark-modelling approach, we developed and validated sex-specific Cox models, incorporating conventional predictors and trajectories plus variability of systolic blood pressure (SBP), total and high-density lipoprotein (HDL) cholesterol, and glycated haemoglobin (HbA1c). S","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Dec","modification":"2025-04-18T16:31:27.026Z","creation":"2025-04-07T03:46:40.641Z"},"accession":"S-EPMC9749723","cross_references":{"pubmed":["35776101"],"doi":["10.1093/ije/dyac140"]}}