1
|
Satuluri VKRR, Ponnusamy V. Enhancement of Ambulatory Glucose Profile for Decision Assistance and Treatment Adjustments. Diagnostics (Basel) 2024; 14:436. [PMID: 38396474 PMCID: PMC10888350 DOI: 10.3390/diagnostics14040436] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 01/23/2024] [Accepted: 01/30/2024] [Indexed: 02/25/2024] Open
Abstract
The ambulatory glucose profile (AGP) lacks sufficient statistical metrics and insightful graphs; indeed, it is missing important information on the temporal patterns of glucose variations. The AGP graph is difficult to interpret due to the overlapping metrics and fluctuations in glucose levels over 14 days. The objective of this proposed work is to overcome these challenges, specifically the lack of insightful information and difficulty in interpreting AGP graphs, to create a platform for decision assistance. The present work proposes 20 findings built from decision rules that were developed from a combination of AGP metrics and additional statistical metrics, which have the potential to identify patterns and insightful information on hyperglycemia and hypoglycemia. The "CGM Trace" webpage was developed, in which insightful metrics and graphical representations can be used to make inferences regarding the glucose data of any user. However, doctors (endocrinologists) can access the "Findings" tab for a summarized presentation of their patients' glycemic control. The findings were implemented for 67 patients' data, in which the data of 15 patients were collected from a clinical study and the data of 52 patients were gathered from a public dataset. The findings were validated by means of MANOVA (multivariate analysis of variance), wherein a p value of < 0.05 was obtained, depicting a strong significant correlation between the findings and the metrics. The proposed work from "CGM Trace" offers a deeper understanding of the CGM data, enhancing AGP reports for doctors to make treatment adjustments based on insightful information and hidden patterns for better diabetic management.
Collapse
Affiliation(s)
| | - Vijayakumar Ponnusamy
- Department of ECE, SRM Institute of Science and Technology, Kattankulathur 603203, Tamil Nadu, India;
| |
Collapse
|
2
|
Gao X, Li H, Yu Y, Huai X, Feng B, Song J. Relationship Between Time in Range and Dusk Phenomenon in Outpatients with Type 2 Diabetes Mellitus. Diabetes Metab Syndr Obes 2023; 16:1637-1646. [PMID: 37304668 PMCID: PMC10257429 DOI: 10.2147/dmso.s410761] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 05/25/2023] [Indexed: 06/13/2023] Open
Abstract
Purpose The dusk phenomenon refers to a spontaneous and transient pre-dinner hyperglycemia that affects glucose fluctuation and glycemic control, and the increasing use of continuous glucose monitoring (CGM) has facilitated its diagnosis. We investigated the frequency of the dusk phenomenon and its relationship with the time in range (TIR) in patients with type 2 diabetes mellitus (T2DM). Patients and Methods This study involved 102 patients with T2DM who underwent CGM for 14 days. CGM-derived metrics and clinical characteristics were evaluated. A consecutive dusk blood glucose difference (pre-dinner glucose minus 2-hour post-lunch glucose) of ≥ 0 or once-only dusk blood glucose difference of < 0 was diagnosed as the clinical dusk phenomenon (CLDP). Results We found that the percentage of CLDP was 11.76% (10.34% in men, 13.64% in women). Compared with the non-CLDP group, the CLDP group tended to be younger and have a lower percentage of TIR (%TIR3.9-10) and higher percentage of time above range (%TAR>10 and %TAR>13.9) (P ≤ 0.05). Adjusted for confounding factors, the binary logistic regression analysis showed a negative association of CLDP with %TIR (odds ratio < 1, P < 0.05). We repeated the correlation analysis based on 70%TIR and found significant differences in hemoglobin A1c, fasting blood glucose, mean blood glucose, standard deviation of the sensor glucose values, glucose coefficient of variation, largest amplitude of glycemic excursions, mean amplitude of glycemic excursions, glucose management indicator, and percentage of CLDP between the two subgroups of TIR ≤ 70% and TIR > 70% (P < 0.05). The negative association between TIR and CLDP still remained after adjustment by the binary logistic regression analysis. Conclusion The CLDP was frequently present in patients with T2DM. The TIR was significantly correlated with the CLDP and could serve as an independent negative predictor.
Collapse
Affiliation(s)
- Xiangyu Gao
- Department of Endocrinology, East Hospital, Tongji University School of Medicine, Shanghai, 200120, People’s Republic of China
| | - Hongmei Li
- Department of Endocrinology, East Hospital, Tongji University School of Medicine, Shanghai, 200120, People’s Republic of China
| | - Yuan Yu
- Department of Endocrinology, East Hospital, Tongji University School of Medicine, Shanghai, 200120, People’s Republic of China
| | - Xiaoyuan Huai
- Department of Endocrinology, East Hospital, Tongji University School of Medicine, Shanghai, 200120, People’s Republic of China
| | - Bo Feng
- Department of Endocrinology, East Hospital, Tongji University School of Medicine, Shanghai, 200120, People’s Republic of China
| | - Jun Song
- Department of Endocrinology, East Hospital, Tongji University School of Medicine, Shanghai, 200120, People’s Republic of China
| |
Collapse
|
3
|
Tokutsu A, Okada Y, Mita T, Torimoto K, Wakasugi S, Katakami N, Yoshii H, Uryu K, Nishida K, Arao T, Tanaka Y, Gosho M, Shimomura I, Watada H. Relationship between blood glucose variability in ambulatory glucose profile and standardized continuous glucose monitoring metrics: Subanalysis of a prospective cohort study. Diabetes Obes Metab 2022; 24:82-93. [PMID: 34498346 DOI: 10.1111/dom.14550] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 09/01/2021] [Accepted: 09/07/2021] [Indexed: 12/22/2022]
Abstract
AIM To clarify the relationship between ambulatory glucose profile (AGP) indexes and standardized continuous glucose monitoring (CGM) metrics in patients with type 2 diabetes (T2D). METHODS This is an exploratory, cross-sectional analysis of baseline data collected from a prospective, multicentre, 5-year follow-up observational study conducted and published previously by our group. The study participants were 999 outpatients with T2D who used CGM at baseline, and had no apparent history of cardiovascular disease. We investigated the relationship between average interquartile range (IQR) and time in range (TIR). We also calculated, for the first time, the cutoff values to achieve the TIR target values. RESULTS In both the TIR more than 70% and TIR more than 90% achievement groups, the average IQR was notably small compared with the non-achievement groups. Particularly in comparison of the TIR quartiles, the average IQR became significantly smaller as the TIR became larger. The average IQR correlated negatively with TIR, and the cutoff values for TIR of more than 70% achievement and TIR of more than 90% achievement were an average IQR (>70%/>90%) of 2.13/1.85 mmol/L. CONCLUSION Our results showed a negative correlation between TIR and the range of blood glucose variations visually represented in AGP. The results also showed that the range of blood glucose variations in AGP is associated with indices of intraday and interday blood glucose variations and also with hypoglycaemia. Our results may provide new perspectives in the assessment and application of AGP in the clinical setting.
Collapse
Affiliation(s)
- Akemi Tokutsu
- First Department of Internal Medicine, School of Medicine, University of Occupational and Environmental Health, Japan, Kitakyushu, Japan
| | - Yosuke Okada
- First Department of Internal Medicine, School of Medicine, University of Occupational and Environmental Health, Japan, Kitakyushu, Japan
| | - Tomoya Mita
- Department of Metabolism & Endocrinology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Keiichi Torimoto
- First Department of Internal Medicine, School of Medicine, University of Occupational and Environmental Health, Japan, Kitakyushu, Japan
| | - Satomi Wakasugi
- Department of Metabolism & Endocrinology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Naoto Katakami
- Department of Metabolic Medicine, Osaka University Graduate School of Medicine, Osaka, Japan
- Department of Metabolism and Atherosclerosis, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Hidenori Yoshii
- Department of Medicine, Diabetology & Endocrinology, Juntendo Tokyo Koto Geriatric Medical Center, Tokyo, Japan
| | - Kohei Uryu
- Department of Internal Medicine, Ashiya Central Hospital, Ongagun, Fukuoka, Japan
| | | | - Tadashi Arao
- Department of Internal Medicine, Division of Diabetes, Metabolism and Endocrinology, Japan Labour Health and Safety Organization Kyushu Rosai Hospital, Moji Medical Center, Kitakyushu, Japan
| | - Yoshiya Tanaka
- First Department of Internal Medicine, School of Medicine, University of Occupational and Environmental Health, Japan, Kitakyushu, Japan
| | - Masahiko Gosho
- Department of Biostatistics, Faculty of Medicine, University of Tsukuba, Tsukuba, Ibaraki, Japan
| | - Iichiro Shimomura
- Department of Metabolic Medicine, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Hirotaka Watada
- Department of Metabolism & Endocrinology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| |
Collapse
|