Investigating the value of glucodensity analysis of continuous glucose monitoring data in type 1 diabetes : an exploratory analysis
Copyright © 2023 Cui, Goldfine, Quinlan, James and Sverdlov..
Introduction: Continuous glucose monitoring (CGM) devices capture longitudinal data on interstitial glucose levels and are increasingly used to show the dynamics of diabetes metabolism. Given the complexity of CGM data, it is crucial to extract important patterns hidden in these data through efficient visualization and statistical analysis techniques.
Methods: In this paper, we adopted the concept of glucodensity, and using a subset of data from an ongoing clinical trial in pediatric individuals and young adults with new-onset type 1 diabetes, we performed a cluster analysis of glucodensities. We assessed the differences among the identified clusters using analysis of variance (ANOVA) with respect to residual pancreatic beta-cell function and some standard CGM-derived parameters such as time in range, time above range, and time below range.
Results: Distinct CGM data patterns were identified using cluster analysis based on glucodensities. Statistically significant differences were shown among the clusters with respect to baseline levels of pancreatic beta-cell function surrogate (C-peptide) and with respect to time in range and time above range.
Discussion: Our findings provide supportive evidence for the value of glucodensity in the analysis of CGM data. Some challenges in the modeling of CGM data include unbalanced data structure, missing observations, and many known and unknown confounders, which speaks to the importance of--and provides opportunities for--taking an approach integrating clinical, statistical, and data science expertise in the analysis of these data.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2023 |
|---|---|
| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:4 |
|---|---|
| Enthalten in: |
Frontiers in clinical diabetes and healthcare - 4(2023) vom: 15., Seite 1244613 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Cui, Elvis Han [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
CGM |
|---|
| Anmerkungen: |
Date Revised 28.09.2023 published: Electronic-eCollection Citation Status Publisher |
|---|
| doi: |
10.3389/fcdhc.2023.1244613 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM362496374 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM362496374 | ||
| 003 | DE-627 | ||
| 005 | 20231226091350.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231226s2023 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.3389/fcdhc.2023.1244613 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1208.xml |
| 035 | |a (DE-627)NLM362496374 | ||
| 035 | |a (NLM)37753312 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Cui, Elvis Han |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Investigating the value of glucodensity analysis of continuous glucose monitoring data in type 1 diabetes |b an exploratory analysis |
| 264 | 1 | |c 2023 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Revised 28.09.2023 | ||
| 500 | |a published: Electronic-eCollection | ||
| 500 | |a Citation Status Publisher | ||
| 520 | |a Copyright © 2023 Cui, Goldfine, Quinlan, James and Sverdlov. | ||
| 520 | |a Introduction: Continuous glucose monitoring (CGM) devices capture longitudinal data on interstitial glucose levels and are increasingly used to show the dynamics of diabetes metabolism. Given the complexity of CGM data, it is crucial to extract important patterns hidden in these data through efficient visualization and statistical analysis techniques | ||
| 520 | |a Methods: In this paper, we adopted the concept of glucodensity, and using a subset of data from an ongoing clinical trial in pediatric individuals and young adults with new-onset type 1 diabetes, we performed a cluster analysis of glucodensities. We assessed the differences among the identified clusters using analysis of variance (ANOVA) with respect to residual pancreatic beta-cell function and some standard CGM-derived parameters such as time in range, time above range, and time below range | ||
| 520 | |a Results: Distinct CGM data patterns were identified using cluster analysis based on glucodensities. Statistically significant differences were shown among the clusters with respect to baseline levels of pancreatic beta-cell function surrogate (C-peptide) and with respect to time in range and time above range | ||
| 520 | |a Discussion: Our findings provide supportive evidence for the value of glucodensity in the analysis of CGM data. Some challenges in the modeling of CGM data include unbalanced data structure, missing observations, and many known and unknown confounders, which speaks to the importance of--and provides opportunities for--taking an approach integrating clinical, statistical, and data science expertise in the analysis of these data | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a CGM | |
| 650 | 4 | |a functional data analysis | |
| 650 | 4 | |a glucodensity | |
| 650 | 4 | |a pharmacodynamics | |
| 650 | 4 | |a visualization | |
| 700 | 1 | |a Goldfine, Allison B |e verfasserin |4 aut | |
| 700 | 1 | |a Quinlan, Michelle |e verfasserin |4 aut | |
| 700 | 1 | |a James, David A |e verfasserin |4 aut | |
| 700 | 1 | |a Sverdlov, Oleksandr |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Frontiers in clinical diabetes and healthcare |d 2020 |g 4(2023) vom: 15., Seite 1244613 |w (DE-627)NLM332515869 |x 2673-6616 |7 nnns |
| 773 | 1 | 8 | |g volume:4 |g year:2023 |g day:15 |g pages:1244613 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.3389/fcdhc.2023.1244613 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 4 |j 2023 |b 15 |h 1244613 | ||