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A Brazilian cohort of pregnant women with overt diabetes: analyses of risk factors using a machine learning technique

OBJECTIVE: Pregnancy complicated by type 2 diabetes is rising, while data on type 2 diabetes first diagnosed in pregnancy (overt diabetes) are scarce. We aimed to describe the frequency and characteristics of pregnant women with overt diabetes, compare them to those with known pregestational diabete...

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Autores principales: Reichelt, Angela J., de Campos, Maria Amélia A., Hirakata, Vânia N., Genro, Vanessa K., Oppermann, Maria Lúcia R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Sociedade Brasileira de Endocrinologia e Metabologia 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10665057/
https://www.ncbi.nlm.nih.gov/pubmed/37249459
http://dx.doi.org/10.20945/2359-3997000000628
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author Reichelt, Angela J.
de Campos, Maria Amélia A.
Hirakata, Vânia N.
Genro, Vanessa K.
Oppermann, Maria Lúcia R.
author_facet Reichelt, Angela J.
de Campos, Maria Amélia A.
Hirakata, Vânia N.
Genro, Vanessa K.
Oppermann, Maria Lúcia R.
author_sort Reichelt, Angela J.
collection PubMed
description OBJECTIVE: Pregnancy complicated by type 2 diabetes is rising, while data on type 2 diabetes first diagnosed in pregnancy (overt diabetes) are scarce. We aimed to describe the frequency and characteristics of pregnant women with overt diabetes, compare them to those with known pregestational diabetes, and evaluate the potential predictors for the diagnosis of overt diabetes. SUBJECTS AND METHODS: A retrospective cohort study including all pregnant women with type 2 diabetes evaluated in two public hospitals in Porto Alegre, Brazil, from May 20, 2005, to June 30, 2021. Classic and obstetric factors associated with type 2 diabetes risk were compared between the two groups, using machine learning techniques and multivariable analysis with Poisson regression. RESULTS: Overt diabetes occurred in 33% (95% confidence interval: 29%-37%) of 646 women. Characteristics of women with known or unknown type 2 diabetes were similar; excessive weight was the most common risk factor, affecting ~90% of women. Age >30 years and positive family history of diabetes were inversely related to a diagnosis of overt diabetes, while previous delivery of a macrosomic baby behaved as a risk factor in younger multiparous women; previous gestational diabetes and chronic hypertension were not relevant risk factors. CONCLUSION: Characteristics of women with overt diabetes are similar to those of women with pregestational diabetes. Classic risk factors for diabetes not included in current questionnaires can help identify women at risk of type 2 diabetes before they become pregnant.
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spelling pubmed-106650572023-05-29 A Brazilian cohort of pregnant women with overt diabetes: analyses of risk factors using a machine learning technique Reichelt, Angela J. de Campos, Maria Amélia A. Hirakata, Vânia N. Genro, Vanessa K. Oppermann, Maria Lúcia R. Arch Endocrinol Metab Original Article OBJECTIVE: Pregnancy complicated by type 2 diabetes is rising, while data on type 2 diabetes first diagnosed in pregnancy (overt diabetes) are scarce. We aimed to describe the frequency and characteristics of pregnant women with overt diabetes, compare them to those with known pregestational diabetes, and evaluate the potential predictors for the diagnosis of overt diabetes. SUBJECTS AND METHODS: A retrospective cohort study including all pregnant women with type 2 diabetes evaluated in two public hospitals in Porto Alegre, Brazil, from May 20, 2005, to June 30, 2021. Classic and obstetric factors associated with type 2 diabetes risk were compared between the two groups, using machine learning techniques and multivariable analysis with Poisson regression. RESULTS: Overt diabetes occurred in 33% (95% confidence interval: 29%-37%) of 646 women. Characteristics of women with known or unknown type 2 diabetes were similar; excessive weight was the most common risk factor, affecting ~90% of women. Age >30 years and positive family history of diabetes were inversely related to a diagnosis of overt diabetes, while previous delivery of a macrosomic baby behaved as a risk factor in younger multiparous women; previous gestational diabetes and chronic hypertension were not relevant risk factors. CONCLUSION: Characteristics of women with overt diabetes are similar to those of women with pregestational diabetes. Classic risk factors for diabetes not included in current questionnaires can help identify women at risk of type 2 diabetes before they become pregnant. Sociedade Brasileira de Endocrinologia e Metabologia 2023-05-29 /pmc/articles/PMC10665057/ /pubmed/37249459 http://dx.doi.org/10.20945/2359-3997000000628 Text en https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Article
Reichelt, Angela J.
de Campos, Maria Amélia A.
Hirakata, Vânia N.
Genro, Vanessa K.
Oppermann, Maria Lúcia R.
A Brazilian cohort of pregnant women with overt diabetes: analyses of risk factors using a machine learning technique
title A Brazilian cohort of pregnant women with overt diabetes: analyses of risk factors using a machine learning technique
title_full A Brazilian cohort of pregnant women with overt diabetes: analyses of risk factors using a machine learning technique
title_fullStr A Brazilian cohort of pregnant women with overt diabetes: analyses of risk factors using a machine learning technique
title_full_unstemmed A Brazilian cohort of pregnant women with overt diabetes: analyses of risk factors using a machine learning technique
title_short A Brazilian cohort of pregnant women with overt diabetes: analyses of risk factors using a machine learning technique
title_sort brazilian cohort of pregnant women with overt diabetes: analyses of risk factors using a machine learning technique
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10665057/
https://www.ncbi.nlm.nih.gov/pubmed/37249459
http://dx.doi.org/10.20945/2359-3997000000628
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