Deciphering the complex interplay of risk factors in type 2 diabetes mellitus: A comprehensive review

Singh, Samradhi; Kriti, Mona; K.S., Anamika; Sarma, Devojit Kumar; Verma, Vinod; Nagpal, Ravinder; Mohania, Dheeraj; Tiwari, Rajnarayan; Kumar, Manoj

doi:10.1016/j.metop.2024.100287

Metabolism Open

2024

DOI: 10.1016/j.metop.2024.100287

|View full text |Cite

Deciphering the complex interplay of risk factors in type 2 diabetes mellitus: A comprehensive review

Samradhi Singh,

Mona Kriti,

Anamika K.S.

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article3

Relationship

Self Cite0

Independent3

Authors

Journals

Cited by 3 publications

References 201 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Utilizing machine learning to analyze trunk movement patterns in women with postpartum low back pain

A. Abdel Hady,

Abd El-Hafeez

2024

Sci Rep

View full text Add to dashboard Cite

This paper presents an analysis of trunk movement in women with postnatal low back pain using machine learning techniques. The study aims to identify the most important features related to low back pain and to develop accurate models for predicting low back pain. Machine learning approaches showed promise for analyzing biomechanical factors related to postnatal low back pain (LBP). This study applied regression and classification algorithms to the trunk movement proposed dataset from 100 postpartum women, 50 with LBP and 50 without. The Optimized optuna Regressor achieved the best regression performance with a mean squared error (MSE) of 0.000273, mean absolute error (MAE) of 0.0039, and R2 score of 0.9968. In classification, the Basic CNN and Random Forest Classifier both attained near-perfect accuracy of 1.0, the area under the receiver operating characteristic curve (AUC) of 1.0, precision of 1.0, recall of 1.0, and F1-score of 1.0, outperforming other models. Key predictive features included pain (correlation of -0.732 with flexion range of motion), range of motion measures (flexion and extension correlation of 0.662), and average movements (correlation of 0.957 with flexion). Feature selection consistently identified pain, flexion, extension, lateral flexion, and average movement as influential across methods. While limited to this initial dataset and constrained by generalizability, machine learning offered quantitative insight. Models accurately regressed (MSE < 0.01, R2 > 0.95) and classified (accuracy > 0.94) trunk biomechanics distinguishing LBP. Incorporating additional demographic, clinical, and patient-reported factors may enhance individualized risk prediction and treatment personalization. This preliminary application of advanced analytics supported machine learning's potential utility for both LBP risk determination and outcome improvement. This study provides valuable insights into the use of machine learning techniques for analyzing trunk movement in women with postnatal low back pain and can potentially inform the development of more effective treatments.Trial registration: The trial was designed as an observational and cross-section study. The study was approved by the Ethical Committee in Deraya University, Faculty of Pharmacy, (No: 10/2023). According to the ethical standards of the Declaration of Helsinki. This study complies with the principles of human research. Each patient signed a written consent form after being given a thorough description of the trial. The study was conducted at the outpatient clinic from February 2023 till June 30, 2023.

show abstract

Utilizing machine learning to analyze trunk movement patterns in women with postpartum low back pain

A. Abdel Hady,

Abd El-Hafeez

2024

Sci Rep

View full text Add to dashboard Cite

show abstract

Deciphering the Gut–Liver Axis: A Comprehensive Scientific Review of Non-Alcoholic Fatty Liver Disease

Singh,

Kriti,

Catanzaro

et al. 2024

Livers

View full text Add to dashboard Cite

Non-alcoholic fatty liver disease (NAFLD) has emerged as a significant global health issue. The condition is closely linked to metabolic dysfunctions such as obesity and type 2 diabetes. The gut–liver axis, a bidirectional communication pathway between the liver and the gut, plays a crucial role in the pathogenesis of NAFLD. This review delves into the mechanisms underlying the gut–liver axis, exploring the influence of gut microbiota, intestinal permeability, and inflammatory pathways. This review also explores the potential therapeutic strategies centered on modulating gut microbiota such as fecal microbiota transplantation; phage therapy; and the use of specific probiotics, prebiotics, and postbiotics in managing NAFLD. By understanding these interactions, we can better comprehend the development and advancement of NAFLD and identify potential therapeutic targets.

show abstract

Exploring the Antidiabetic Potential of Salvia officinalis Using Network Pharmacology, Molecular Docking and ADME/Drug-Likeness Predictions

Ononamadu,

Seidel

2024

Plants

View full text Add to dashboard Cite

A combination of network pharmacology, molecular docking and ADME/drug-likeness predictions was employed to explore the potential of Salvia officinalis compounds to interact with key targets involved in the pathogenesis of T2DM. These were predicted using the SwissTargetPrediction, Similarity Ensemble Approach and BindingDB databases. Networks were constructed using the STRING online tool and Cytoscape (v.3.9.1) software. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analysis and molecular docking were performed using DAVID, SHINEGO 0.77 and MOE suite, respectively. ADME/drug-likeness parameters were computed using SwissADME and Molsoft L.L.C. The top-ranking targets were CTNNB1, JUN, ESR1, RELA, NR3C1, CREB1, PPARG, PTGS2, CYP3A4, MMP9, UGT2B7, CYP2C19, SLCO1B1, AR, CYP19A1, PARP1, CYP1A2, CYP1B1, HSD17B1, and GSK3B. Apigenin, caffeic acid, oleanolic acid, rosmarinic acid, hispidulin, and salvianolic acid B showed the highest degree of connections in the compound-target network. Gene enrichment analysis identified pathways involved in insulin resistance, adherens junctions, metabolic processes, IL-17, TNF-α, cAMP, relaxin, and AGE-RAGE in diabetic complications. Rosmarinic acid, caffeic acid, and salvianolic acid B showed the most promising interactions with PTGS2, DPP4, AMY1A, PTB1B, PPARG, GSK3B and RELA. Overall, this study enhances understanding of the antidiabetic activity of S. officinalis and provides further insights for future drug discovery purposes.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Deciphering the complex interplay of risk factors in type 2 diabetes mellitus: A comprehensive review

Cited by 3 publications

References 201 publications

Utilizing machine learning to analyze trunk movement patterns in women with postpartum low back pain

Utilizing machine learning to analyze trunk movement patterns in women with postpartum low back pain

Deciphering the Gut–Liver Axis: A Comprehensive Scientific Review of Non-Alcoholic Fatty Liver Disease

Exploring the Antidiabetic Potential of Salvia officinalis Using Network Pharmacology, Molecular Docking and ADME/Drug-Likeness Predictions

Contact Info

Product

Resources

About