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GLU: a software package for analysing continuously measured glucose levels in epidemiology
被引:18
作者:
Millard, Louise A. C.
[1
,2
,3
]
Patel, Nashita
[4
]
Tilling, Kate
[1
,3
]
Lewcock, Melanie
[3
]
Flach, Peter A.
[2
]
Lawlor, Debbie A.
[1
,3
,5
]
机构:
[1] Univ Bristol, MRC Integrat Epidemiol Unit, Room OS1,Oakfield House, Bristol BS8 2BN, Avon, England
[2] Univ Bristol, Intelligent Syst Lab, Dept Comp Sci, Bristol, Avon, England
[3] Univ Bristol, Populat Hlth Sci, Bristol Med Sch, Bristol, Avon, England
[4] Kings Coll London, Dept Women & Childrens Hlth, Sch Life Course Sci, London, England
[5] Bristol NIHR Biomed Res Ctr, Bristol, Avon, England
基金:
英国惠康基金;
欧洲研究理事会;
英国医学研究理事会;
关键词:
Glucose;
continuous glucose monitoring;
CGM;
BMI;
pregnancy;
AMERICAN-DIABETES-ASSOCIATION;
GLYCEMIC VARIABILITY;
PREGNANT-WOMEN;
EUROPEAN ASSOCIATION;
CLINICAL-TRIALS;
RECOMMENDATIONS;
HYPERGLYCEMIA;
PREVALENCE;
STATEMENT;
PROFILES;
D O I:
10.1093/ije/dyaa004
中图分类号:
R1 [预防医学、卫生学];
学科分类号:
1004 ;
120402 ;
摘要:
Continuous glucose monitors (CGM) record interstitial glucose levels 'continuously', producing a sequence of measurements for each participant (e.g. the average glucose level every 5 min over several days, both day and night). To analyse these data, researchers tend to derive summary variables such as the area under the curve (AUC), to then use in subsequent analyses. To date, a lack of consistency and transparency of precise definitions used for these summary variables has hindered interpretation, replication and comparison of results across studies. We present GLU, an open-source software package for deriving a consistent set of summary variables from CGM data. GLU performs quality control of each CGM sample (e.g. addressing missing data), derives a diverse set of summary variables (e.g. AUC and proportion of time spent in hypo-, normo- and hyperglycaemic levels) covering six broad domains, and outputs these (with quality control information) to the user.
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页码:744 / 757
页数:14
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