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. 2023 May 25;67(5):e000628.
doi: 10.20945/2359-3997000000628.

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

Angela J Reichelt  1 , Maria Amélia A de Campos  2 , Vânia N Hirakata  3 , Vanessa K Genro  4 , Maria Lúcia R Oppermann  4   5
Affiliations

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

Angela J Reichelt et al. Arch Endocrinol Metab. .

Abstract

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.

Keywords: Overt diabetes; machine learning technique; pregestational diabetes; risk factors.

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Conflict of interest statement

Disclosure: no potential conflict of interest relevant to this article was reported.

Figures

Figure 1
Figure 1. Machine learning analysis of risk factors for overt diabetes.
BMI cat: body mass index categories (normal BMI, overweight, obesity); CH: chronic hypertension; Fam DM: family history of diabetes; Pr GDM: previous gestational diabetes; Prv macro: previous macrosomia. 0 = no; 1 = yes; for BMI cat: 0 = normal; 1 = overweight; 2 = obesity. Each model corresponds to a model with the same number in Table 3.
See this image and copyright information in PMC

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