Evidence map›Paper›PMID 30694076›Full record

ReviewJournal of diabetes science and technology2019

Glycemic Variability: Risk Factors, Assessment, and Control.

Boris Kovatchev

Open access · bronzeAbstract readReview
In one paragraph

Review in Journal of diabetes science and technology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
52citing papers in PubMed, 3 pooled it
8.9field-weighted citation impact, top 1% of its field
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

52 citing papers in PubMed, 3 syntheses or guidelines pooled it, 109 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author at 1 institution in 1 country.

Boris Kovatchev1 University of Virginia School of Medicine and School of Engineering and Applied Sciences, UVA Center for Diabetes Technology, Charlottesville, VA, USA.ORCID 0000-0003-0495-3901
University of Virginia · US

Funding

Clinical Acceptance of the Artificial Pancreas: the International Diabetes Closed Loop (iDCL) TrialUC4DK108483 · NIDDK · UNIVERSITY OF VIRGINIA · PI ANDERSON, STACEY, DOYLE, FRANCIS J · 2016 to 2016
$12.7M
Personalized Fully-Automated Regulation and Co-Regulation of Type 1 Diabetes: A Foundation Biobehavioral ApproachR01DK085623 · NIDDK · UNIVERSITY OF VIRGINIA · PI SUE A BROWN, BORIS P KOVATCHEV · 2009 to 2026
$10.6M
Improving intensive insulin therapy through the personalization of data-driven decision support system to patients’ goals and preferences and its adaptation to long term health needsR01DK051562 · NIDDK · UNIVERSITY OF VIRGINIA CHARLOTTESVILLE · PI MARC D BRETON, Chiara Fabris · 1996 to 2026
$9.4M
NIDDK NIH HHS R01 DK051562NIDDK NIH HHS R01 DK085623NIDDK NIH HHS UC4 DK108483
6 · The paper itself

Abstract

Glycemic variability (GV) a well-established risk factor for hypoglycemia and a suspected risk factor for diabetes complications. GV is also a marker of the instability of a person's metabolic system, expressed as frequent high and low glucose excursions and overall volatile glycemic control. In this review, the author discusses topics related to the assessment, quantification, and optimal control of diabetes, including (1) the notion that optimal control of diabetes, that is, lowering of HbA1c-the commonly accepted gold-standard outcome-can be achieved only if accompanied by simultaneous reduction of GV; (2) assessment and visualization of the two principal dimensions of GV, amplitude and time, which is now possible via continuous glucose monitoring (CGM) and various metrics quantifying GV and the risks associated with hypo- and hyperglycemic excursions; and (3) the evolution of diabetes science and technology beyond quantifying GV and into the realm of GV control via pharmacological agents, for example, GLP-1 receptor agonists and DPP-4 inhibitors, which have pronounced variability-reducing effect, or real-time automated closed-loop systems commonly referred to as the "artificial pancreas." The author concludes that CGM allows close tracking over time, and therefore precise quantification, of glycemic variability in diabetes. The next step-optimal control of glucose fluctuations-is also taken by medications with pronounced GV-lowering effect primarily in type 2 diabetes, and by automated insulin delivery in type 1 diabetes. Contemporary CGM-based artificial pancreas systems use specific GV representations as input signals, and thus their main objective is to minimize GV and, from there, optimize glycemic control.

Indexed as

Blood GlucoseBlood Glucose Self-MonitoringDiabetes MellitusHumansRisk FactorsBlood Glucoseartificial pancreasclosed-loop controlcontinuous glucose monitoringglycemic variabilityhyperglycemiahypoglycemia

Identifiers

PMID30694076
PMCPMC6610616
OpenAlexW2913349237

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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 Socratic graph.