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Hyperglycemia screening based on survey data: an international instrument based on WHO STEPs dataset

BACKGROUND: Hyperglycemia is rising globally and its associated complications impose heavy health and economic burden on the countries. Developing effective survey-based screening tools for hyperglycemia using reliable surveillance data, such as the WHO STEPs surveys, would be of great importance in...

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Autores principales: Moradifar, Pooyan, Amini, Hossein, Amiri, Mohammad Meskarpour
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9749216/
https://www.ncbi.nlm.nih.gov/pubmed/36514025
http://dx.doi.org/10.1186/s12902-022-01222-0
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author Moradifar, Pooyan
Amini, Hossein
Amiri, Mohammad Meskarpour
author_facet Moradifar, Pooyan
Amini, Hossein
Amiri, Mohammad Meskarpour
author_sort Moradifar, Pooyan
collection PubMed
description BACKGROUND: Hyperglycemia is rising globally and its associated complications impose heavy health and economic burden on the countries. Developing effective survey-based screening tools for hyperglycemia using reliable surveillance data, such as the WHO STEPs surveys, would be of great importance in early detection and/or prevention of hyperglycemia, especially in low or middle-income regions. METHODS: In this study, data from the nationwide 2016 STEPs study in Iran were used to identify socioeconomic, lifestyle, and metabolic factors associated with hyperglycemia. Furthermore, the ability of five commonly used machine learning algorithms (random forest; gradient boosting; support vector machine; logistic regression; artificial neural network) in the prediction of hyperglycemia on STEPs dataset were compared via tenfold cross validation in terms of specificity, sensitivity, and the area under the receiver operating characteristic curve. RESULTS: A total of 17,705 individuals were included in this study, of those 29.624% (n = 5245) had (undiagnosed) hyperglycemia. Multivariate logistic regression analysis showed that older age (for the elderly group: OR = 5.096; for the middle-aged group: OR = 2.784), high BMI status (morbidly obese: OR = 3.465; obese: OR = 1.992), having hypertension (OR = 1.647), consuming fish more than twice per week (OR = 1.496), and abdominal obesity (OR = 1.464) were the five most important risk factors for hyperglycemia. Furthermore, all the five hyperglycemia prediction models achieved AUC around 0.70, and logistic regression (specificity = 70.22%; sensitivity = 70.2%) and random forest (specificity = 70.75%; sensitivity = 69.78%) had the optimal performance. CONCLUSIONS: This study shows that it is possible to develop survey-based screening tools for early detection of hyperglycemia using data from nationwide surveys, such as WHO STEPs surveys, and machine learning techniques, such as random forest and logistic regression, without using blood tests. Such screening tools can potentially improve hyperglycemia control, especially in low or middle-income countries.
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spelling pubmed-97492162022-12-15 Hyperglycemia screening based on survey data: an international instrument based on WHO STEPs dataset Moradifar, Pooyan Amini, Hossein Amiri, Mohammad Meskarpour BMC Endocr Disord Research BACKGROUND: Hyperglycemia is rising globally and its associated complications impose heavy health and economic burden on the countries. Developing effective survey-based screening tools for hyperglycemia using reliable surveillance data, such as the WHO STEPs surveys, would be of great importance in early detection and/or prevention of hyperglycemia, especially in low or middle-income regions. METHODS: In this study, data from the nationwide 2016 STEPs study in Iran were used to identify socioeconomic, lifestyle, and metabolic factors associated with hyperglycemia. Furthermore, the ability of five commonly used machine learning algorithms (random forest; gradient boosting; support vector machine; logistic regression; artificial neural network) in the prediction of hyperglycemia on STEPs dataset were compared via tenfold cross validation in terms of specificity, sensitivity, and the area under the receiver operating characteristic curve. RESULTS: A total of 17,705 individuals were included in this study, of those 29.624% (n = 5245) had (undiagnosed) hyperglycemia. Multivariate logistic regression analysis showed that older age (for the elderly group: OR = 5.096; for the middle-aged group: OR = 2.784), high BMI status (morbidly obese: OR = 3.465; obese: OR = 1.992), having hypertension (OR = 1.647), consuming fish more than twice per week (OR = 1.496), and abdominal obesity (OR = 1.464) were the five most important risk factors for hyperglycemia. Furthermore, all the five hyperglycemia prediction models achieved AUC around 0.70, and logistic regression (specificity = 70.22%; sensitivity = 70.2%) and random forest (specificity = 70.75%; sensitivity = 69.78%) had the optimal performance. CONCLUSIONS: This study shows that it is possible to develop survey-based screening tools for early detection of hyperglycemia using data from nationwide surveys, such as WHO STEPs surveys, and machine learning techniques, such as random forest and logistic regression, without using blood tests. Such screening tools can potentially improve hyperglycemia control, especially in low or middle-income countries. BioMed Central 2022-12-14 /pmc/articles/PMC9749216/ /pubmed/36514025 http://dx.doi.org/10.1186/s12902-022-01222-0 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Moradifar, Pooyan
Amini, Hossein
Amiri, Mohammad Meskarpour
Hyperglycemia screening based on survey data: an international instrument based on WHO STEPs dataset
title Hyperglycemia screening based on survey data: an international instrument based on WHO STEPs dataset
title_full Hyperglycemia screening based on survey data: an international instrument based on WHO STEPs dataset
title_fullStr Hyperglycemia screening based on survey data: an international instrument based on WHO STEPs dataset
title_full_unstemmed Hyperglycemia screening based on survey data: an international instrument based on WHO STEPs dataset
title_short Hyperglycemia screening based on survey data: an international instrument based on WHO STEPs dataset
title_sort hyperglycemia screening based on survey data: an international instrument based on who steps dataset
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9749216/
https://www.ncbi.nlm.nih.gov/pubmed/36514025
http://dx.doi.org/10.1186/s12902-022-01222-0
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