Use of technology in prediabetes and precision prevention DOI Creative Commons
Jie He, Natural Chu, Heng Wan

et al.

Journal of Diabetes Investigation, Journal Year: 2025, Volume and Issue: unknown

Published: May 2, 2025

ABSTRACT Controlling the epidemic of diabetes is an urgent global healthcare challenge. The low uptake prevention programs highlights difficulties in scalability, partly due to need for intensive face‐to‐face contact and its impact on resource utilization. In this narrative review, we will summarize latest evidence technology‐assisted lifestyle interventions. We appraise digital that use internet platforms or text messaging tools support information delivery, coaching, peer support. also discuss wearables, including physical activity trackers continuous glucose monitoring (CGM) as part intervention. Experience from potential CGM a motivational tool promote change. integration data may facilitate earlier detection prediabetes, sub‐phenotyping, personalized nutritional predictions. highlight major gaps research rigorous clinical trials evaluate acceptability cost‐effectiveness integrating technologies multicomponent strategy prevention.

Language: Английский

Lifestyle Modification in Prediabetes and Diabetes: A Large Population Analysis DOI Open Access

Michael L. Dansinger,

Joi A. Gleason,

Julia Maddalena

et al.

Nutrients, Journal Year: 2025, Volume and Issue: 17(8), P. 1333 - 1333

Published: April 11, 2025

Background/Aims: Diabetes mellitus is a major cause of atherosclerotic cardiovascular disease (ASCVD). We examined large population and tested the efficacy voluntary lifestyle program in prediabetic diabetic subjects. Methods: Of 133,764 subjects, 56.3% were healthy, 36.2% prediabetic, 7.5% diabetic. Fasting serum measurements glucose, insulin, adiponectin, glycosylated hemoglobin (HbA1c), high-sensitivity C-reactive protein (hs-CRP), glycated (GSP), fibrinogen, myeloperoxidase (MPO), lipoprotein-associated phospholipase A2 (LpPLA2), as well standard lipids, direct low-density lipoprotein cholesterol (LDL-C), small dense LDL-C (sdLDL-C) performed using automated assays. Follow-up sampling at 6–12 months occurred 20.1% 22.2% subjects; these, 12.2% 9.7% subjects participated voluntary, real-world, digital dietitian-directed lifestyle-modification with 10-year diabetes risk being calculated biochemical model (Framingham). Results: Prediabetic had significantly elevated triglycerides, sdLDL-C, hs-CRP decreased HDL-C. They insulin resistant compared to healthy but only diabetics significant reductions production. Lifestyle modification reduced by 45.6% prediabetics increased (2.4-fold) percentage that remission follow-up (8.2% versus 3.4%) weight loss (6.5 2.0 pounds). intervention resulted favorable effects on many metabolic markers. Conclusions: The measurement fasting glucose essential for detection production diabetics. A can have ASCVD factors status.

Language: Английский

Citations

0

Use of technology in prediabetes and precision prevention DOI Creative Commons
Jie He, Natural Chu, Heng Wan

et al.

Journal of Diabetes Investigation, Journal Year: 2025, Volume and Issue: unknown

Published: May 2, 2025

ABSTRACT Controlling the epidemic of diabetes is an urgent global healthcare challenge. The low uptake prevention programs highlights difficulties in scalability, partly due to need for intensive face‐to‐face contact and its impact on resource utilization. In this narrative review, we will summarize latest evidence technology‐assisted lifestyle interventions. We appraise digital that use internet platforms or text messaging tools support information delivery, coaching, peer support. also discuss wearables, including physical activity trackers continuous glucose monitoring (CGM) as part intervention. Experience from potential CGM a motivational tool promote change. integration data may facilitate earlier detection prediabetes, sub‐phenotyping, personalized nutritional predictions. highlight major gaps research rigorous clinical trials evaluate acceptability cost‐effectiveness integrating technologies multicomponent strategy prevention.

Language: Английский

Citations

0