Metabolic health and cardiometabolic risk clusters: implications for prediction, prevention, and treatment

The Lancet Diabetes & Endocrinology, Journal Year: 2023, Volume and Issue: 11(6), P. 426 - 440

Published: May 5, 2023

Language: Английский

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A scoping review of artificial intelligence-based methods for diabetes risk prediction

npj Digital Medicine, Journal Year: 2023, Volume and Issue: 6(1)

Published: Oct. 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.

Language: Английский

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Diabetes and artificial intelligence beyond the closed loop: a review of the landscape, promise and challenges

Diabetologia, Journal Year: 2023, Volume and Issue: 67(2), P. 223 - 235

Published: Nov. 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

Language: Английский

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Microenvironment-optimized GelMA microneedles for interstitial fluid extraction and real-time glucose detection

Surfaces and Interfaces, Journal Year: 2024, Volume and Issue: 45, P. 103847 - 103847

Published: Jan. 4, 2024

Language: Английский

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Targeting autophagy with natural products as a potential therapeutic approach for diabetic microangiopathy

Frontiers in Pharmacology, Journal Year: 2024, Volume and Issue: 15

Published: April 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.

Language: Английский

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A mechanism linking ferroptosis and ferritinophagy in melatonin-related improvement of diabetic brain injury

iScience, Journal Year: 2024, Volume and Issue: 27(4), P. 109511 - 109511

Published: March 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

Language: Английский

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Predictive ability of visit-to-visit glucose variability on diabetes complications

BMC Medical Informatics and Decision Making, Journal Year: 2025, Volume and Issue: 25(1)

Published: March 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.

Language: Английский

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Ratiometric Fluorescent Sensors for Real-Time Monitoring Cellular Autophagy Levels during Diabetic Wound Healing

ACS Sensors, Journal Year: 2025, Volume and Issue: unknown

Published: April 11, 2025

Hard-healing wounds are a serious issue faced by diabetic patients, and the cellular autophagy level is closely related to wound healing progress. However, it difficult monitor real-time levels in living organisms. In this work, we provided new fluorescent sensor IN-NH2 based on modification of 2-substituted quinoline, giving an excellent ability quantify pH fluctuation lysosomes during autophagy. This was successfully applied monitoring model rats, showing potential for exploring internal mechanism between disease progression.

Language: Английский

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Integration of network pharmacology, molecular docking and pharmacokinetic investigations of dandelion in rats for the treatment of diabetes mellitus

Journal of Chromatography B, Journal Year: 2025, Volume and Issue: 1258, P. 124610 - 124610

Published: April 18, 2025

Language: Английский

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External validation of a clinical risk score for the presence of cardiovascular autonomic neuropathy in type 1 diabetes

Journal of Diabetes and its Complications, Journal Year: 2025, Volume and Issue: 39(7), P. 109066 - 109066

Published: May 5, 2025

To validate in an independent external population a CAN Risk Score previously developed type 1 diabetes (T1D) and validated for cardiovascular autonomic neuropathy (CAN) with good diagnostic accuracy. Forty-seven participants T1D (age 47.7 ± 13.2 years, duration of 30.0 (19.0-40.5) 24 males) underwent 4 reflex tests (CARTs) to diagnose early confirmed (according or 2 abnormal results). was calculated from resting heart rate, HbA1c, retinopathy and/or nephropathy, disease, HDL cholesterol, systolic blood pressure smoking (range 0-10). Eleven (23.4 %) had CAN. The higher subjects overall (early confirmed) (P = 0.0498) 0.0142) compared those without, correlated CARTs severity (rho 0.32, P 0.026), Expiration/Inspiration ratio (r -0.33, 0.0258) Valsalva -0.47, 0.0015). A ≥ found 19 associated the presence 0.0129). showed area under ROC curve (AUC) 0.802 0.080 CAN, at cut-off 4, sensitivity, specificity negative predictive values 85.71 %, 67.50 % 96.43 %. This study value supports its inclusion algorithm identify candidates CARTs, thereby reducing universal screening. Using routinely available clinical data as categorical variables, score is easy calculate implement settings.

Language: Английский

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