Reviewnpj aging2025
Optimizing metabolic health with digital twins.
Review in npj aging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- DELTA: Strengthening human biological resilience with an N=1 digital health and dynamic biomarker protocol.PloS one · 2026Trial
- Exercise as a Programmable Regulator of Mitophagy Sensitivity in Aging Muscle and Age-Related Disease.IUBMB life · 2026Review
- Addressing antimicrobial resistance: Current challenges, emerging strategies, and an AI-powered, community-driven approach.PNAS nexus · 2026Review
- From linear methylation to spatial metabolic control: Rethinking DNMT1 targeting in MASLD: Letter to the editor on "DNMT1 facilitates the progression of MASLD by impeding transcription mediated by HNF4α and PPARα".Clinical and molecular hepatology · 2026Article
- Digital twin for Taekwondo athletes: integrating sports nutrition and psychological readiness using artificial intelligence.Frontiers in public health · 2026Review
- Personalized nutrition for healthy aging: precision diets based on genomic, metabolic, and microbiome profiles.Frontiers in nutrition · 2026Review
- Artificial intelligence in fundus photography for type 2 diabetes: a scoping review of systemic biomarkers and multi-organ risk prediction.Frontiers in digital health · 2026Review
- A multimodal, risk-stratified framework for AI-driven early risk prediction and personalised prevention in obesity.Frontiers in artificial intelligence · 2026Article
- WMRE2030: integrating wearable devices, multi-omics, and artificial intelligence-driven real-time feedback into a daily-scale closed-loop framework for a new era of precision exercise.Frontiers in physiology · 2026Review
- Nutrigenomics meets multi-omics: integrating genetic, metabolic, and microbiome data for personalized nutrition strategies.Genes & nutrition · 2025Review
- From omics to AI-mapping the pathogenic pathways in type 2 diabetes.FEBS letters · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
A hallmark of subclinical metabolic decline is impaired metabolic flexibility, which refers to the ability to switch fuel utilization between glucose and fat according to energy demand and substrate availability. Herein, we propose optimizing metabolic health with digital twins that model an individual's metabolic flexibility profile to gamify the process of health optimization and predict long-term health outcomes. We explore key characteristics of this approach from technological and socioeconomical perspectives, with the objective of reducing the burden from metabolic disorders through driving behavior change and early detection of metabolic decline.
Identifiers
What OpenQuestion holds
Registered trials
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.