ArticleCell reports. Medicine2023
A machine-learning algorithm integrating baseline serum proteomic signatures predicts exercise responsiveness in overweight males with prediabetes.
Article in Cell reports. Medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 24 citations in OpenAlex.
- Proteomic changes induced by acute exercise and high-intensity interval training during pregnancy: a pilot randomised controlled trial.Scientific reports · 2025Trial
- Trial
- Exerkines in precision management of metabolic diseases.Chinese medical journal · 2026Review
- Multi-omics signatures of circulating factors associated with cardiorespiratory fitness adaptations in individuals with prediabetes.Cardiovascular diabetology · 2026Article
- Influence of Fibroblast Growth Factor 21 on Delayed Glycemic Improvement Following Acute Exercise in Type 2 Diabetes Mellitus.Diabetes & metabolism journal · 2026Article
- A Five-Plasma Protein-Based Algorithm for Predicting Incident CKD in Type 2 Diabetes.Journal of the American Society of Nephrology : JASN · 2026Article
- Circulating protein biomarkers of physical fitness associated with cardiometabolic risk in women after gestational diabetes: a PONCH study.Cardiovascular diabetology · 2026Article
- Exercise-specific post-translational modification signatures: unveiling precise regulatory mechanisms of molecular exercise language and cellular adaptation.Frontiers in sports and active living · 2026Review
- Plasma inflammatory proteome profiles identify MASLD among children with overweight or obesity.Cardiovascular diabetology · 2025Article
- Association between plasma proteomic dynamic changes and metabolic outcomes in patients undergoing bariatric surgery.International journal of obesity (2005) · 2025Article
- Housing temperature influences exercise-induced glucose regulation and expression of exerkines in mice.Experimental physiology · 2025Article
- Applications of Artificial Intelligence and Machine Learning in Prediabetes: A Scoping Review.Journal of diabetes science and technology · 2025Review
- Mechanisms underlying alterations of the gut microbiota by exercise and their role in shaping ecological resilience.FEMS microbiology reviews · 2025Review
- A predictive system comprising serum microRNAs and radiomics for residual retroperitoneal masses in metastatic nonseminomatous germ cell tumors.Cell reports. Medicine · 2024Article
- Article
- Intra-Individual Variations in How Insulin Sensitivity Responds to Long-Term Exercise: Predictions by Machine Learning Based on Large-Scale Serum Proteomics.Metabolites · 2024Article
- Exerkines and cardiometabolic benefits of exercise: from bench to clinic.EMBO molecular medicine · 2024Review
- Identification and multicentric validation of soluble CDCP1 as a robust serological biomarker for risk stratification of NASH in obese Chinese.Cell reports. Medicine · 2023Article
Corrections and comments
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Authors and funding
10 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The molecular transducers conferring the benefits of chronic exercise in diabetes prevention remain to be comprehensively investigated. Herein, serum proteomic profiling of 688 inflammatory and metabolic biomarkers in 36 medication-naive overweight and obese men with prediabetes reveals hundreds of exercise-responsive proteins modulated by 12-week high-intensity interval exercise training, including regulators of metabolism, cardiovascular system, inflammation, and apoptosis. Strong associations are found between proteins involved in gastro-intestinal mucosal immunity and metabolic outcomes. Exercise-induced changes in trefoil factor 2 (TFF2) are associated with changes in insulin resistance and fasting insulin, whereas baseline levels of the pancreatic secretory granule membrane major glycoprotein GP2 are related to changes in fasting glucose and glucose tolerance. A hybrid set of 23 proteins including TFF2 are differentially altered in exercise responders and non-responders. Furthermore, a machine-learning algorithm integrating baseline proteomic signatures accurately predicts individualized metabolic responsiveness to exercise training.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.