Publications by authors named "Oliwia Janota"

Background: People living with diabetes mellitus (DM) and chronic kidney disease (CKD) are at significantly high risk of cardiovascular events (CVEs), however the predictive performance of traditional risk prediction methods are limited.

Methods: We utilised machine learning (ML) model to predict CVEs in persons with DM and CKD from the Silesia Diabetes-Heart Project, a routine standard of care dataset. CVEs were defined as composite of nonfatal myocardial infarction, new onset heart failure, nonfatal stroke, incident atrial fibrillation, undergoing percutaneous coronary intervention or coronary artery bypass grafting, hospitalisation or death due to cardiovascular disease.

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Background: There is a growing burden of non-obese people with diabetes mellitus (DM). However, their cardiovascular risk (CV), especially in the presence of cardiovascular-kidney-metabolic (CKM) comorbidities is poorly characterised. The aim of this study was to analyse the risk of major CV adverse events in people with DM according to the presence of obesity and comorbidities (hypertension, chronic kidney disease, and dyslipidaemia).

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Introduction: From 2008 and following the withdrawal of rosiglitazone, obligatory cardiovascular outcomes trials are performed for glucose lowering drugs introduced to the market to ensure their cardiovascular (CV) safety. Paradoxically, these studies have demonstrated CV safety but also shown additional cardio-reno-vascular protection of some therapeutic agents. Additionally, nonsteroidal mineralocorticoid receptor antagonists (ns-MRA) have emerged as novel drugs for cardio - and renoprotection in type 2 diabetes (T2D) and chronic kidney disease (CKD).

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Article Synopsis
  • MASLD is often underdiagnosed in diabetes patients, increasing their cardiovascular disease risk, prompting the need for effective detection methods.
  • Researchers developed machine learning models to assess the risk of MASLD by analyzing 8 key patient parameters, achieving a high sensitivity and specificity in identifying affected individuals.
  • The study's findings indicate that this ML approach can improve risk stratification and prevention strategies for diabetes patients potentially facing MASLD.
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Background: Diabetes mellitus (DM), heart failure (HF) and metabolic dysfunction associated steatotic liver disease (MASLD) are overlapping diseases of increasing prevalence. Because there are still high numbers of patients with HF who are undiagnosed and untreated, there is a need for improving efforts to better identify HF in patients with DM with or without MASLD. This study aims to develop machine learning (ML) models for assessing the risk of the HF occurrence in patients with DM with and without MASLD.

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Article Synopsis
  • * Proposed advantages of SGLT2i include reduced blood pressure, increased production of urine (natriuresis), improved heart function by using fatty acids, and decreased inflammation and oxidative stress.
  • * The review focuses on how SGLT2i might influence oxidative stress in both animal and human studies, specifically regarding heart failure and chronic kidney disease in people with diabetes.
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We aimed to develop a machine learning (ML) model for predicting cardiovascular (CV) events in patients with diabetes (DM). This was a prospective, observational study where clinical data of patients with diabetes hospitalized in the diabetology center in Poland (years 2015-2020) were analyzed using ML. The occurrence of new CV events following discharge was collected in the follow-up time for up to 5 years and 9 months.

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Currently, there are about 150-200 million diabetic patients treated with insulin globally. The year 2021 is special because the 100th anniversary of the insulin discovery is being celebrated. It is a good occasion to sum up the insulin pen technology invention and improvement which are nowadays the leading mode of an insulin delivery.

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Introduction: This study presents a 10-year longitudinal assessment of bone status in adolescents and young adults with type 1 diabetes (T1D).

Material And Methods: Thirty-two patients (12 female, aged 20.5 ± 3.

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