Diabetes associates with mortality in critically ill patients with SARS-CoV-2 pneumonia: No diabetes paradox in COVID-19 DOI Creative Commons
Priscila Bellaver, Larissa Schneider, Ariell Freires Schaeffer

и другие.

Heliyon, Год журнала: 2023, Номер 9(8), С. e18554 - e18554

Опубликована: Июль 22, 2023

Diabetes mellitus (DM) is not associated with increased mortality in critically ill patients, a phenomenon known as the "diabetes paradox". However, DM risk factor for patients COVID-19. This study aims to investigate association of and stress-induced hyperglycemia at intensive care unit (ICU) this population.This retrospective study. Electronic medical records from admitted March 2020 September were reviewed. Primary outcome was mortality. Secondary outcomes ICU hospital stay, need mechanical ventilation renal replacement therapy.187 included. Overall 43.2%, higher (55.7% vs. 34%; p = 0.007), even after adjustment age, hypertension, disease severity. When separated into groups, named normoglycemia (without glycemia ≤140 mg/dL), >140 (previous diagnosis or HbA1c ≥ 6.5%), rate 25.8%, 37.3%, 55.7%, respectively (p 0.021). Mortality glycemic variability. No statistical difference related secondary observed.DM, hyperglycemia, variability severe COVID-19, but did increase rates other clinical outcomes. More than

Язык: Английский

Metabolic health and cardiometabolic risk clusters: implications for prediction, prevention, and treatment DOI
Norbert Stefan, Matthias B. Schulze

The Lancet Diabetes & Endocrinology, Год журнала: 2023, Номер 11(6), С. 426 - 440

Опубликована: Май 5, 2023

Язык: Английский

Процитировано

111
A scoping review of artificial intelligence-based methods for diabetes risk prediction DOI Creative Commons
Farida Mohsen, Hamada R. H. Al-Absi, Noha A. Yousri

и другие.

npj Digital Medicine, Год журнала: 2023, Номер 6(1)

Опубликована: Окт. 25, 2023

Abstract The increasing prevalence of type 2 diabetes mellitus (T2DM) and its associated health complications highlight the need to develop predictive models for early diagnosis intervention. While many artificial intelligence (AI) T2DM risk prediction have emerged, a comprehensive review their advancements challenges is currently lacking. This scoping maps out existing literature on AI-based prediction, adhering PRISMA extension Scoping Reviews guidelines. A systematic search longitudinal studies was conducted across four databases, including PubMed, Scopus, IEEE-Xplore, Google Scholar. Forty that met our inclusion criteria were reviewed. Classical machine learning (ML) dominated these studies, with electronic records (EHR) being predominant data modality, followed by multi-omics, while medical imaging least utilized. Most employed unimodal AI models, only ten adopting multimodal approaches. Both showed promising results, latter superior. Almost all performed internal validation, but five external validation. utilized area under curve (AUC) discrimination measures. Notably, provided insights into calibration models. Half used interpretability methods identify key predictors revealed Although minority highlighted novel predictors, majority reported commonly known ones. Our provides valuable current state limitations highlights development clinical integration.

Язык: Английский

Процитировано

55
Diabetes and artificial intelligence beyond the closed loop: a review of the landscape, promise and challenges DOI Creative Commons
Scott C. Mackenzie, Christopher Sainsbury, Deborah J. Wake

и другие.

Diabetologia, Год журнала: 2023, Номер 67(2), С. 223 - 235

Опубликована: Ноя. 18, 2023

Abstract The discourse amongst diabetes specialists and academics regarding technology artificial intelligence (AI) typically centres around the 10% of people with who have type 1 diabetes, focusing on glucose sensors, insulin pumps and, increasingly, closed-loop systems. This focus is reflected in conference topics, strategy documents, appraisals funding streams. What often overlooked wider application data AI, as demonstrated through published literature emerging marketplace products, that offers promising avenues for enhanced clinical care, health-service efficiency cost-effectiveness. review provides an overview AI techniques explores use potential data-driven systems a broad context, covering all types, encompassing: (1) patient education self-management; (2) decision support predictive analytics, including diagnostic support, treatment screening advice, complications prediction; (3) multimodal data, such imaging or genetic data. perspective how data- AI-driven could transform care coming years they be integrated into daily practice. We discuss evidence benefits harms, consider existing barriers to scalable adoption, challenges related availability exchange, health inequality, clinician hesitancy regulation. Stakeholders, clinicians, academics, commissioners, policymakers those lived experience, must proactively collaborate realise AI-supported bring, whilst mitigating risk navigating along way. Graphical

Язык: Английский

Процитировано

29
Microenvironment-optimized GelMA microneedles for interstitial fluid extraction and real-time glucose detection DOI
Shixian Lin,

Yuehan Ouyang,

Wensheng Lin

и другие.

Surfaces and Interfaces, Год журнала: 2024, Номер 45, С. 103847 - 103847

Опубликована: Янв. 4, 2024

Язык: Английский

Процитировано

11
Targeting autophagy with natural products as a potential therapeutic approach for diabetic microangiopathy DOI Creative Commons

Fengzhao Liu,

Lijuan Zhao, Tao Wu

и другие.

Frontiers in Pharmacology, Год журнала: 2024, Номер 15

Опубликована: Апрель 10, 2024

As the quality of life improves, incidence diabetes mellitus and its microvascular complications (DMC) continues to increase, posing a threat people's health wellbeing. Given limitations existing treatment, there is an urgent need for novel approaches prevent treat DMC. Autophagy, pivotal mechanism governing metabolic regulation in organisms, facilitates removal dysfunctional proteins organelles, thereby sustaining cellular homeostasis energy generation. Anomalous states pancreatic β-cells, podocytes, Müller cells, cardiomyocytes, Schwann cells DMC are closely linked autophagic dysregulation. Natural products have property being multi-targeted can affect autophagy hence progression terms nutrient perception, oxidative stress, endoplasmic reticulum inflammation, apoptosis. This review consolidates recent advancements understanding pathogenesis via proposes perspectives on treating by either stimulating or inhibiting using natural products.

Язык: Английский

Процитировано

5
Causal Effects of Oxidative Stress on Diabetes Mellitus and Microvascular Complications: Insights Integrating Genome-Wide Mendelian Randomization, DNA Methylation, and Proteome DOI Creative Commons
Kang Liu,

Zitong Chen,

Lishan Liu

и другие.

Antioxidants, Год журнала: 2024, Номер 13(8), С. 903 - 903

Опубликована: Июль 26, 2024

Oxidative stress (OS) is involved in the development of diabetes, but genetic mechanisms are not completely understood. We integrated multi-omics data order to explore relations between OS-related genes, diabetes mellitus, and microvascular complications using Mendelian randomization colocalization analysis.

Язык: Английский

Процитировано

5
A mechanism linking ferroptosis and ferritinophagy in melatonin-related improvement of diabetic brain injury DOI Creative Commons

Jiaojiao Yu,

Yu Zhang,

Qin Zhu

и другие.

iScience, Год журнала: 2024, Номер 27(4), С. 109511 - 109511

Опубликована: Март 15, 2024

Ferroptosis and ferritinophagy play critical roles in various disease contexts. Herein, we observed that ferroptosis were induced both the brains of mice with diabetes mellitus (DM) neuronal cells after high glucose (HG) treatment, as evidenced by decreases GPX4, SLC7A11, ferritin levels, but increases NCOA4 levels. Interestingly, melatonin administration ameliorated damage inhibiting

Язык: Английский

Процитировано

4
Discovery of first-in-class highly selective TRPV1 antagonists with dual analgesic and hypoglycemic effects DOI
Chunxia Liu, Wenxin Wang, Shiyu Zhao

и другие.

Bioorganic & Medicinal Chemistry, Год журнала: 2024, Номер 107, С. 117750 - 117750

Опубликована: Май 17, 2024

Язык: Английский

Процитировано

3
Danggui Liuhuang Decoction Ameliorates Endothelial Dysfunction by Inhibiting the JAK2/STAT3 Mediated Inflammation DOI
Yuanying Xu, Wenjun Sha, Jun Lü

и другие.

Journal of Ethnopharmacology, Год журнала: 2024, Номер unknown, С. 119170 - 119170

Опубликована: Ноя. 1, 2024

Язык: Английский

Процитировано

3
Predictive ability of visit-to-visit glucose variability on diabetes complications DOI Creative Commons
Xin Rou Teh, Panu Looareesuwan, Oraluck Pattanaprateep

и другие.

BMC Medical Informatics and Decision Making, Год журнала: 2025, Номер 25(1)

Опубликована: Март 17, 2025

Abstract Background Identification of prognostic factors for diabetes complications are crucial. Glucose variability (GV) and its association with have been studied extensively but the inclusion measures glucose (GVs) in models is largely lacking. This study aims to assess which GVs (i.e., coefficient variation (CV), standard deviation (SD), time-varying) better predicting diabetic complications, including cardiovascular disease (CVD), retinopathy (DR), chronic kidney (CKD). The model performance between traditional statistical (adjusting covariates) machine learning (ML) were compared. Methods A retrospective cohort type 2 (T2D) patients 2010 2019 Ramathibodi Hospital was created. Complete case analyses used. Three using HbA1c fasting plasma (FPG) considered CV, SD, time-varying. Cox proportional hazard regression, ML random survival forest (RSF) left-truncated, right-censored (LTRC) compared two different data formats (baseline longitudinal datasets). Adjusted ratios 95% confidence intervals used report three complications. Model evaluated C-statistics along feature importance models. Results total 40,662 T2D patients, mostly female (61.7%), mean age 57.2 years included. After adjusting covariates, HbA1c-CV, HbA1c-SD, FPG-CV FPG-SD all associated CVD, DR CKD, whereas time-varying FPG CKD only. CPH RSF (C-indices: 0.748–0.758 0.774–0.787) 0.734–0.750 0.724–0.740) had modestly than CVD 0.703–0.730 0.698–0.727). Based on importance, GV ranked higher GV, both most important prediction. Both similar performance. Conclusions We found that based comparable Thus, may be as a potential monitoring parameter when unavailable or less accessible.

Язык: Английский

Процитировано

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