Observational studyNutrients2022
Personalised Nutritional Recommendations Based on Individual Post-Prandial Glycaemic Responses Improve Glycaemic Metrics and PROMs in Patients with Type 2 Diabetes: A Real-World Assessment.
Observational study in Nutrients, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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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
7 citing papers in PubMed, 13 citations in OpenAlex.
- Effects of basic carbohydrate counting versus standard dietary care for glycaemic control in type 2 diabetes (The BCC Study): a randomised, controlled trial.Nutrition & diabetes · 2024Trial
- Age-Dependent Serum Volatilomics of Milk and Yogurt Intake: A Randomized Crossover Study in Healthy Young and Older Men.Journal of proteome research · 2023Trial
- Application study of an artificial intelligence and big data-based personalized chronic disease management model for diabetes patients.Frontiers in public health · 2026Article
- Multiple additive moderation effects of insulin resistance and dietary inflammatory index on the relationship between body mass index and postprandial glucose levels in Korea: a retrospective analysis.Journal of Korean biological nursing science · 2025Article
- From meal to malfunction: exploring molecular pathways, biomarkers and interventions in postprandial cardiometabolic health.Frontiers in cardiovascular medicine · 2025Review
- A Tailored and Engaging mHealth Gamified Framework for Nutritional Behaviour Change.Nutrients · 2023Article
- Effects of low-GI biscuits as pre-loads or mid-meal snacks on post-prandial glycemic excursions in women with recent gestational diabetes: A protocol for a randomized crossover trial and an extended tailored intervention.Frontiers in nutrition · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
The recommended first-line therapy in type 2 diabetes (T2D) is lifestyle modification. In many patients, such interventions fail, and disease progresses inexorably to medication requirement. A potential reason for the failure of standard nutritional interventions is the use of generic dietary advice, with no personalisation to account for differences in the effect of food on blood glucose between different individuals. Another is the lack of instant feedback on the impact of dietary modification on glycaemic control, which supports sustained behaviour change. The use of continuous glucose monitoring (CGM) may help address both these shortcomings. We conducted an observational study to explore how personalised nutritional information impacts glycaemic control and patient-reported outcome measures (PROMs) of well-being. Free-living people with T2D eating their normal diet were provided with personalised nutritional recommendations by state-registered nutritionists based on the CGM-enabled analysis of individual post-prandial glycaemic responses (PPGRs). Participants demonstrated considerable inter-individual differences in PPGRs, reductions in post-prandial incremental area under the curve (iAUC) and daytime AUC, and improvements in energy levels, ability to concentrate, and other PROMs. These results suggest a role for personalised nutritional recommendations based on individual-level understanding of PPGRs in the non-pharmaceutical management of T2D.
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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.