Cargando…

Assessing the added predictive ability of a metabolic syndrome severity score in predicting incident cardiovascular disease and type 2 diabetes: the Atherosclerosis Risk in Communities Study and Jackson Heart Study

BACKGROUND: The severity of the metabolic syndrome (MetS) predicts future coronary heart disease (CHD) and diabetes independent of the individual MetS components. Our aim was to evaluate whether MetS severity conferred additional discrimination to existing scoring systems for cardiovascular disease...

Descripción completa

Detalles Bibliográficos
Autores principales: Guo, Yi, Musani, Solomon K., Sims, Mario, Pearson, Thomas A., DeBoer, Mark D., Gurka, Matthew J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5956946/
https://www.ncbi.nlm.nih.gov/pubmed/29796112
http://dx.doi.org/10.1186/s13098-018-0344-3
_version_ 1783323980931792896
author Guo, Yi
Musani, Solomon K.
Sims, Mario
Pearson, Thomas A.
DeBoer, Mark D.
Gurka, Matthew J.
author_facet Guo, Yi
Musani, Solomon K.
Sims, Mario
Pearson, Thomas A.
DeBoer, Mark D.
Gurka, Matthew J.
author_sort Guo, Yi
collection PubMed
description BACKGROUND: The severity of the metabolic syndrome (MetS) predicts future coronary heart disease (CHD) and diabetes independent of the individual MetS components. Our aim was to evaluate whether MetS severity conferred additional discrimination to existing scoring systems for cardiovascular disease (CVD) and diabetes risk. METHODS: We assessed Cox proportional hazard models of CHD- and diabetes risk among 13,141 participants of the Atherosclerosis Risk in Communities Study and the Jackson Heart Study, using the Framingham Risk Calculator, the American Heart Association’s Atherosclerotic CVD calculator, the American Diabetes Association diabetes risk score and an additional diabetes risk score derived from ARIC data. We then added a MetS-severity Z-score to these models and assessed for added risk discrimination by assessing Akaike information criterion, c-statistic, integrated discrimination improvement (IDI) and continuous net reclassification improvement (NRI). RESULTS: The MetS severity score appears to add to the predictive ability of individual CHD and diabetes risk scores. Using the IDI, MetS improved risk prediction for diabetes but not CHD risk. In all 4 scoring systems, MetS severity had a significant non-event NRI, improving the ability to exclude individuals without events. Assessing interactions between risk scores and MetS severity revealed that MetS severity was more highly associated with disease risk among those in the lowest quintiles of risk score, suggesting that MetS was particularly able to identify risk among individuals judged to be of low risk by existing algorithms. CONCLUSIONS: Mets severity improved prediction of diabetes more so than CHD. Incorporation of multiple risk predictors into electronic health records may help in better identifying those at high disease risk, who can then be placed earlier on preventative therapy. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13098-018-0344-3) contains supplementary material, which is available to authorized users.
format Online
Article
Text
id pubmed-5956946
institution National Center for Biotechnology Information
language English
publishDate 2018
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-59569462018-05-24 Assessing the added predictive ability of a metabolic syndrome severity score in predicting incident cardiovascular disease and type 2 diabetes: the Atherosclerosis Risk in Communities Study and Jackson Heart Study Guo, Yi Musani, Solomon K. Sims, Mario Pearson, Thomas A. DeBoer, Mark D. Gurka, Matthew J. Diabetol Metab Syndr Research BACKGROUND: The severity of the metabolic syndrome (MetS) predicts future coronary heart disease (CHD) and diabetes independent of the individual MetS components. Our aim was to evaluate whether MetS severity conferred additional discrimination to existing scoring systems for cardiovascular disease (CVD) and diabetes risk. METHODS: We assessed Cox proportional hazard models of CHD- and diabetes risk among 13,141 participants of the Atherosclerosis Risk in Communities Study and the Jackson Heart Study, using the Framingham Risk Calculator, the American Heart Association’s Atherosclerotic CVD calculator, the American Diabetes Association diabetes risk score and an additional diabetes risk score derived from ARIC data. We then added a MetS-severity Z-score to these models and assessed for added risk discrimination by assessing Akaike information criterion, c-statistic, integrated discrimination improvement (IDI) and continuous net reclassification improvement (NRI). RESULTS: The MetS severity score appears to add to the predictive ability of individual CHD and diabetes risk scores. Using the IDI, MetS improved risk prediction for diabetes but not CHD risk. In all 4 scoring systems, MetS severity had a significant non-event NRI, improving the ability to exclude individuals without events. Assessing interactions between risk scores and MetS severity revealed that MetS severity was more highly associated with disease risk among those in the lowest quintiles of risk score, suggesting that MetS was particularly able to identify risk among individuals judged to be of low risk by existing algorithms. CONCLUSIONS: Mets severity improved prediction of diabetes more so than CHD. Incorporation of multiple risk predictors into electronic health records may help in better identifying those at high disease risk, who can then be placed earlier on preventative therapy. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13098-018-0344-3) contains supplementary material, which is available to authorized users. BioMed Central 2018-05-16 /pmc/articles/PMC5956946/ /pubmed/29796112 http://dx.doi.org/10.1186/s13098-018-0344-3 Text en © The Author(s) 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Guo, Yi
Musani, Solomon K.
Sims, Mario
Pearson, Thomas A.
DeBoer, Mark D.
Gurka, Matthew J.
Assessing the added predictive ability of a metabolic syndrome severity score in predicting incident cardiovascular disease and type 2 diabetes: the Atherosclerosis Risk in Communities Study and Jackson Heart Study
title Assessing the added predictive ability of a metabolic syndrome severity score in predicting incident cardiovascular disease and type 2 diabetes: the Atherosclerosis Risk in Communities Study and Jackson Heart Study
title_full Assessing the added predictive ability of a metabolic syndrome severity score in predicting incident cardiovascular disease and type 2 diabetes: the Atherosclerosis Risk in Communities Study and Jackson Heart Study
title_fullStr Assessing the added predictive ability of a metabolic syndrome severity score in predicting incident cardiovascular disease and type 2 diabetes: the Atherosclerosis Risk in Communities Study and Jackson Heart Study
title_full_unstemmed Assessing the added predictive ability of a metabolic syndrome severity score in predicting incident cardiovascular disease and type 2 diabetes: the Atherosclerosis Risk in Communities Study and Jackson Heart Study
title_short Assessing the added predictive ability of a metabolic syndrome severity score in predicting incident cardiovascular disease and type 2 diabetes: the Atherosclerosis Risk in Communities Study and Jackson Heart Study
title_sort assessing the added predictive ability of a metabolic syndrome severity score in predicting incident cardiovascular disease and type 2 diabetes: the atherosclerosis risk in communities study and jackson heart study
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5956946/
https://www.ncbi.nlm.nih.gov/pubmed/29796112
http://dx.doi.org/10.1186/s13098-018-0344-3
work_keys_str_mv AT guoyi assessingtheaddedpredictiveabilityofametabolicsyndromeseverityscoreinpredictingincidentcardiovasculardiseaseandtype2diabetestheatherosclerosisriskincommunitiesstudyandjacksonheartstudy
AT musanisolomonk assessingtheaddedpredictiveabilityofametabolicsyndromeseverityscoreinpredictingincidentcardiovasculardiseaseandtype2diabetestheatherosclerosisriskincommunitiesstudyandjacksonheartstudy
AT simsmario assessingtheaddedpredictiveabilityofametabolicsyndromeseverityscoreinpredictingincidentcardiovasculardiseaseandtype2diabetestheatherosclerosisriskincommunitiesstudyandjacksonheartstudy
AT pearsonthomasa assessingtheaddedpredictiveabilityofametabolicsyndromeseverityscoreinpredictingincidentcardiovasculardiseaseandtype2diabetestheatherosclerosisriskincommunitiesstudyandjacksonheartstudy
AT deboermarkd assessingtheaddedpredictiveabilityofametabolicsyndromeseverityscoreinpredictingincidentcardiovasculardiseaseandtype2diabetestheatherosclerosisriskincommunitiesstudyandjacksonheartstudy
AT gurkamatthewj assessingtheaddedpredictiveabilityofametabolicsyndromeseverityscoreinpredictingincidentcardiovasculardiseaseandtype2diabetestheatherosclerosisriskincommunitiesstudyandjacksonheartstudy