A retrospective evaluation of parental smoking and the risk of Type 1 diabetes in children.

Tob Induc Dis

Department of Public Health, Faculty of Medicine, Ege University, Izmir, Türkiye.

Published: November 2024

Introduction: The association between secondhand smoking (SHS) and the risk of Type 1 diabetes mellitus (DM) has garnered increasing interest. The aim of this study is to examine whether exposure to SHS is associated with an increased likelihood of Type 1 DM.

Methods: This study was designed as a case-control study. Children aged 4-14 years diagnosed with Type 1 DM who were followed in the Endocrine and Metabolic Diseases Outpatient Clinic were included as cases, and healthy children (without any chronic disease) in the same age range were included as the controls. A total of 248 children were included in the study, with two research arms. The structured questionnaire was applied face-to-face. Adjusted odds ratios (AOR) and 95% confidence intervals (CIs) of other risk factors were evaluated by multivariable regression analysis.

Results: No difference was found in the number of cigarettes mothers smoked daily and the duration of the smoking period during pregnancy and lactation, between the two groups. Among the cases, the daily number of cigarettes smoked by parents at home was 3.28 ± 4.90, higher than in the controls (p=0.039). Comparing the controls, children with Type 1 DM were more likely to be exposed to SHS at home by 1.08 (95% CI: 1.004-1.15, p=0.039) times in cases.

Conclusions: Children with Type 1 DM had higher odds of being exposed to SHS at home. These results suggest substantial health gains could be made by extending effective public health interventions to reduce exposure to SHS and prevent Type 1 DM in children.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11580006PMC
http://dx.doi.org/10.18332/tid/195228DOI Listing

Publication Analysis

Top Keywords

risk type
8
type diabetes
8
exposure shs
8
number cigarettes
8
children type
8
exposed shs
8
type
7
children
7
shs
5
retrospective evaluation
4

Similar Publications

Study Objective: Complex pharmacotherapy in cancer patients increases the likelihood of drug-drug interactions (DDIs). Pharmacists play a critical role in the identification and management of DDIs. The aim of present study was to evaluate the role of pharmacist in identifying antifungal drug interactions in cancer patients and providing relevant recommendations.

View Article and Find Full Text PDF

Background: Aortic dissection occurs rarely during pregnancy but carries a significantly high vital risk for both the mother and the fetus. Early diagnosis and treatment are critical for a successful outcome.

Case Presentation: A 32-year-old pregnant woman at 31 weeks of gestation began experiencing shortness of breath, chest pain, and palpitations, which were attributed to an anxiety disorder she had been previously diagnosed with.

View Article and Find Full Text PDF

Background: The triglyceride‒glucose index (TyG index) is a reliable surrogate for insulin resistance (IR) in individuals with type 2 diabetes mellitus and is associated with cardiovascular disease. Recent studies have reported that H-type hypertension is likewise a predictor of adverse events in patients with coronary heart disease (CHD). However, the relationship between the TyG index and prognosis in patients with H-type hypertension combined with CHD has not yet been reported.

View Article and Find Full Text PDF

Objective: To compare the sociodemographic and clinical profiles of patients with advanced cancer admitted to a tertiary palliative care unit before and during the COVID-19 pandemic.

Methods: This is an analysis of data from patients receiving care before (10/21/2019 to 03/16/2020) and during (09/23/2020 to 08/26/2021) the COVID-19 pandemic. Sociodemographic and clinical data were evaluated.

View Article and Find Full Text PDF

Background: Fetal growth restriction (FGR) is a leading risk factor for stillbirth, yet the diagnosis of FGR confers considerable prognostic uncertainty, as most infants with FGR do not experience any morbidity. Our objective was to use data from a large, deeply phenotyped observational obstetric cohort to develop a probabilistic graphical model (PGM), a type of "explainable artificial intelligence (AI)", as a potential framework to better understand how interrelated variables contribute to perinatal morbidity risk in FGR.

Methods: Using data from 9,558 pregnancies delivered at ≥ 20 weeks with available outcome data, we derived and validated a PGM using randomly selected sub-cohorts of 80% (n = 7645) and 20% (n = 1,912), respectively, to discriminate cases of FGR resulting in composite perinatal morbidity from those that did not.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!