ArticleJournal of diabetes science and technology2017
Digital Health Care by In Silico Glycation of HbA1 Blood Cells.
Article in Journal of diabetes science and technology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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.
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Who cites it
1 citing paper in PubMed.
- Evaluating Interventions and Titrations Using Fasting Blood Glucose.Journal of diabetes science and technology · 2018Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDiabetes health care relies on the HbA1c (A1c) assay and associated average glucose (AG) to evaluate and control chronic glycemia. However, the A1c assay is plagued with significant noise, lag time, and specificity issues. Current studies support the significant health care advantage of clinical action based on real-time blood glucose (BG) metrics. We seek to improve diabetes management by directly relating such metrics to AG levels as mediated by recently discovered recurrent endocrine cycles.
methodsSeveral studies collected multiple months of BG data on 111 subjects totaling 261 893 CGM measurements and 29 278 meter readings. These data are a rich source of multiday metrics in terms of the CGM and SMBG daily profiles. The recurrent endocrine patterns expose key metric relationships for monitoring AG related to A1c using CGM and SMBG data. Consequently, day-to-day tracking of AG is expressed as a simple two-parameter function of fasting BG for all studies.
resultsConsequently, when applied to 2518 qualified days of 64 subjects, the function predicts daily AG values with 2% relative standard error. All studies produced compatible results. By restricting one parameter to a constant, the error increased to 3%.
conclusionsThe recurrent endocrine patterns revealed a persistent structure hidden within the multiday fluctuations that becomes a simple meter-compatible equation that accurately measures real-time trending of AG using fasting BG values. This enables a digital health monitoring service and self-monitoring device that reveals immediate disease progression as well as the impact of interventions and medications better than possible with the A1c assay.
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