A machine-learning algorithm integrating baseline serum proteomic signatures predicts exercise responsiveness in overweight males with prediabetes

Intra-Individual Variations in How Insulin Sensitivity Responds to Long-Term Exercise: Predictions by Machine Learning Based on Large-Scale Serum Proteomics

Physical activity is effective for preventing and treating type 2 diabetes, but some individuals do not achieve metabolic benefits from exercise ("non-responders"). We investigated non-responders in terms of insulin sensitivity changes following a 12-week supervised strength and endurance exercise program. We used a hyperinsulinaemic euglycaemic clamp to measure insulin sensitivity among 26 men aged 40-65, categorizing them into non-responders or responders based on their insulin sensitivity change scores. The exercise regimen included VO2max, muscle strength, whole-body MRI scans, muscle and fat biopsies, and serum samples. mRNA sequencing was performed on biopsies and Olink proteomics on serum samples. Non-responders showed more visceral and intramuscular fat and signs of dyslipidaemia and low-grade inflammation at baseline and did not improve in insulin sensitivity following exercise, although they showed gains in VO2max and muscle strength. Impaired IL6-JAK-STAT3 signalling in non-responders was suggested by serum proteomics analysis, and a baseline serum proteomic machine learning (ML) algorithm predicted insulin sensitivity responses with high accuracy, validated across two independent exercise cohorts. The ML model identified 30 serum proteins that could forecast exercise-induced insulin sensitivity changes.

Exerkines and cardiometabolic benefits of exercise: from bench to clinic

Regular exercise has both immediate and long-lasting benefits on cardiometabolic health, and has been recommended as a cornerstone of treatment in the management of diabetes and cardiovascular conditions. Exerkines, which are defined as humoral factors responsive to acute or chronic exercise, have emerged as important players conferring some of the multiple cardiometabolic benefits of exercise. Over the past decades, hundreds of exerkines released from skeletal muscle, heart, liver, adipose tissue, brain, and gut have been identified, and several exerkines (such as FGF21, IL-6, and adiponectin) have been exploited therapeutically as exercise mimetics for the treatment of various metabolic and cardiovascular diseases. Recent advances in metagenomics have led to the identification of gut microbiota, a so-called "hidden" metabolic organ, as an additional class of exerkines determining the efficacy of exercise in diabetes prevention, cardiac protection, and exercise performance. Furthermore, multiomics-based studies have shown the feasibility of using baseline exerkine signatures to predict individual responses to exercise with respect to metabolic and cardiorespiratory health. This review aims to explore the molecular pathways whereby exerkine networks mediate the cardiometabolic adaptations to exercise by fine-tuning inter-organ crosstalk, and discuss the roadmaps for translating exerkine-based discovery into the therapeutic application and personalized medicine in the management of the cardiometabolic disease.