Carbohydrate composition of infant formula and glycemic regulation in early infancy using continuous glucose monitoring: cross-sectional evidence of altered glucose patterns with corn syrup solid-based formulas.

Journal of Diabetes NEWS

699-P: Time-Restricted Eating Did Not Alter Glycemic Variability in Humans Who Are Overweight and without Diabetes

To compare glycemic variability (GV) indices between patients with fibrocalculous pancreatic diabetes (FCPD) and type 2 diabetes mellitus (T2D) using continuous glucose monitoring (CGM). We measured GV indices using CGM (iPro™2 Professional CGM, Medtronic, USA) data in 61 patients each with FCPD and T2D who were matched for glycated hemoglobin A1c (HbA1c) and duration of diabetes. GlyCulator2 software was used to estimate the CGM-derived measures of GV (SD, mean amplitude of glycemic excursion [MAGE], continuous overall net glycemic action [CONGA], absolute means of daily differences [MODD], M value, and coefficient of variance [%CV]), hypoglycemia (time spent below 70 mg/dL, AUC below 70 mg/dL, glycemic risk assessment diabetes equation hypoglycemia, Low Blood Glucose Index), and hyperglycemia (time spent above 180 mg/dL at night [TSA > 180], AUC above 180 mg/dL [AUC > 180], glycemic risk assessment diabetes equation hyperglycemia, High Blood Glucose Index [HBGI], and J index). The correlation of GV indices with HbA1c, duration of diabetes, and demographic and biochemical parameters were also assessed. All the CGM-derived measures of GV (SD, MAGE, CONGA, MODD, and %CV), except M value, were significantly higher in the FCPD group than in the T2D group (P < 0.05). Measures of hyperglycemia (TSA >180, AUC >180, HBGI, and J index) were significantly higher in the FCPD group than in the T2D group (P < 0.05). The measures of hypoglycemia were not significantly different between the two groups. All the hyperglycemia indices showed a positive correlation with HbA1c in both groups. FCPD is associated with higher GV than is T2D. The findings of higher postprandial glycemic excursions in patients with FCPD could have potential therapeutic implications.

To determine whether the alternate glycemic markers, fructosamine (FA), glycated albumin (GA), and 1,5-anhydroglucitol (1,5AG), predict glycemic variability captured by continuous glucose monitoring (CGM) in obese youth with prediabetes and type 2 diabetes (T2D). Youth with BMI ≥85th%ile, 10-18 years, had collection of fasting plasma glucose (FPG), hemoglobin A1c (HbA1c), FA, GA, and 1,5AG and 72 hours of CGM. Participants with HbA1c ≥5.7% were included. Relationships between glycemic markers and CGM variables were determined with Spearman correlation coefficients. Linear models were used to examine the association between alternate markers and CGM measures of glycemic variability-standard deviation (SD) and mean amplitude of glycemic excursions (MAGE)-after controlling for HbA1c. Total n = 56; Median (25th%ile, 75th%ile) age = 14.3 years (12.5, 15.9), 32% male, 64% Hispanic, 20% black, 13% white, HbA1c = 5.9% (5.8, 6.3), FA=211 mmol/L (200, 226), GA= 12% (11%, 12%), and 1,5AG = 22mcg/mL (19, 26). HbA1c correlated with average sensor glucose, AUC, SD, MAGE, and %time > 140 mg/dL. FA and GA correlated with average and peak sensor glucose, %time >140 and >200 mg/dL, and MAGE. GA also correlated with SD and AUC180. 1,5AG correlated with peak glucose, AUC180, SD, and MAGE. After adjusting for HbA1c, all 3 markers independently predicted MAGE; FA and GA independently predicted SD. Alternate glycemic markers predict glycemic variability as measured by CGM in youth with prediabetes and T2D. After adjusting for HbA1c, these alternate markers continued to predict components of glycemic variability detected by CGM.

Disclosure: A.J. Kretowski: None. P. Konopka: None. A. Janucik: None. A. Citko: None. A. Paszko: None. M. Klak: None. A. Golonko: None. A. Szklaruk: None. L. Szczerbinski: None. Background: The heterogeneity among individuals at elevated risk for Type 2 Diabetes (T2D) recently has led to the identification of six subphenotypes, each with distinct risks for developing T2D, its complications, and mortality. Our study leveraged Continuous Glucose Monitoring (CGM) to offer a detailed characterization of these subphenotypes, aiming to uncover differences in glycemic variability and control. Methods: CGM data from 616 individuals without diabetes, from the Polish Registry of Diabetes (PolReD) study, classified into six clusters, were analyzed. The PolReD used Freestyle Libre 2 (Abbott) as the CGM device. CGM-derived measures of glucose control and glucose variability were calculated using the iglu R package. We utilized Analysis of Variance (ANOVA) and Analysis of Covariance (ANCOVA), adjusting for age and sex, to identify significant differences in CGM parameters across the clusters. Results: Our analysis identified significant differences in several CGM-derived parameters among the clusters, notably in the Glycemic Risk Assessment Diabetes Equation (GRADE), Standard Deviation of weekly glucose values (SDw), Mean Amplitude of Glycemic Excursions (MAGE), Maximum glucose value, Glucose range, and Mean Absolute Glucose (MAG). Clusters 3 and 5, characterized by beta-cell dysfunction and high insulin resistance respectively, and associated with the highest risk of T2D, exhibited the greatest glucose variability, with elevated risk for glycemic excursions. In contrast, Cluster 4, indicative of a metabolically healthy obesity profile with a low risk of T2D, demonstrated the most stable glycemic control and the lowest variability among the clusters. Conclusions: This study shows that subtypes of patients at elevated risk for T2D exhibit distinct patterns in CGM-derived parameters, indicating variations in glucose control and stability that extend beyond traditional fasting and post-challenge glucose assessments. Notably, we found that subtypes facing the highest T2D risk display increased glycemic variability, despite their differing underlying mechanisms of dysglycemia. These findings highlight the utility of CGM as a tool for identifying individuals at an increased risk of T2D, reducing the need for extensive phenotyping typically required for cluster assignment. Presentation: 6/1/2024

Continuous Glucose Monitoring Glycemic Variability Profiles Are More Favorable for Dapagliflozin plus Saxagliptin Compared with Insulin Glargine in Type 2 Diabetes

Glycemic variability contributes to oxidative stress, which has been linked to the pathogenesis of the long-term complications of diabetes. Currently, the best metric for assessing glycemic variability is mean amplitude of glycemic excursion (MAGE); however, MAGE is not in routine clinical use. A glycemic variability metric in routine clinical use could potentially be an important measure of overall glucose control and a predictor of diabetes complication risk not detected by glycosylated hemoglobin (A1C) levels. This study aimed to develop and evaluate new automated metrics of glycemic variability that could be routinely applied to continuous glucose monitoring (CGM) data to assess and enhance glucose control. Individual 24 h CGM tracings from our clinical diabetes research database were scored for MAGE and two additional metrics designed to compensate for aspects of variability not captured by MAGE: (1) number of daily glucose fluctuations >75 mg/dl that leave the normal range (70-175 mg/dl), or excursion frequency, and (2) total daily fluctuation, or distance traveled. These scores were used to train machine learning algorithms to recognize excessive variability based on physician ratings of daily CGM charts, producing a third metric of glycemic variability: perceived variability. Finger stick A1C (average) and serum 1,5-anhydroglucitol (postprandial) levels were used as clinical markers of overall glucose control for comparison. Mean amplitude of glycemic excursion, excursion frequency, and distance traveled did not adequately quantify the glycemic variability visualized by physicians who evaluated the daily CGM plots. A naive Bayes classifier was developed that characterizes CGM tracings based on physician interpretations of tracings. Preliminary results suggest that the number of excessively variable days, as determined by this naive Bayes classifier, may be an effective way to automatically assess glycemic variability of CGM data. This metric more closely reflects 90-day changes in serum 1,5-anhydroglucitol levels than does MAGE. We have developed a new automated metric to assess overall glycemic variability in people with diabetes using CGM, which could easily be incorporated into commercially available CGM software. Additional work to validate and refine this metric is underway. Future studies are planned to correlate the metric with both urinary 8-iso-prostaglandin F2 alpha excretion and serum 1,5-anhydroglucitol levels to see how well it identifies patients with high glycemic variability and increased markers of oxidative stress to assess risk for long-term complications of diabetes.

Glycemic fluctuations around a mean glucose level, referred as glycemic variability and blood pressure variability (BPV) are considered as independent risk factors for cardiovascular diseases, all-cause mortality, and cardiovascular disease-mortality. With this background in mind, we aimed to investigate the association between glycemic variability and BPV and their association in normoglycemic and normotensive individuals. Twenty-seven normotensive normoglycemic individuals were recruited. Twenty-four hour Holter devices were utilized to measure ambulatory blood pressure (BP) while continuous glucose monitoring (CGM) devices were applied to measure glycemic variability simultaneously to the subjects. These devices were kept on for 48 h. For BP recordings, daytime, nighttime, and 24-h BP determinations, their mean and SD were calculated. From CGM measurements, mean blood glucose (MBG), SD of blood glucose, the mean amplitude of glycemic excursions (MAGE), the mean of daily differences (MODD), coefficient of variation (correction of variability for the MBG), and daytime and nighttime blood glucose were determined. The mean age of the subjects was 23.8 ± 2.7 years and 66% were women (18/27). In the correlation analysis between glycemic variability parameters and BPV parameters, SD of 24-h SBP was correlated with the SD of MBG (r = 0.52, P = 0.006), MAGE (r = 0.49, P = 0.009), and MODD (r = 0.46, P = 0.015). SD of daytime SBP was correlated with, MAGE (r = 0.42, P = 0.03) and MODD (r = 0.43, P = 0.02). We report correlation between glycemic variability and BPV variables in normoglycemic and normotensive healthy individuals.

Glycemic variability in diabetes secondary to pancreatic diseases (pancreatic diabetes) remains unclear. We compared glycemic control and variability in patients with pancreatic diabetes and a matched group of individuals with type 2 diabetes using continuous glucose monitoring (CGM). We included 30 patients with chronic pancreatitis and insulin-treated secondary diabetes and 30 individuals with insulin-treated type 2 diabetes (matched on HbA1c, age, and sex). Participants wore a blinded CGM for 20±2 days. Glycemic variability was assessed using coefficient of variation (CV), standard deviation (SD), mean amplitude of glycemic excursions (MAGE), and continuous overall net glycemic action (CONGA) at 1 and 2-hour intervals. Glycemic control was evaluated based on time spent in predefined glucose ranges: >250 mg/dL, 181 to 250 mg/dL, 70 to 180 mg/dL (target range), 54 to 69 mg/dL, and <54 mg/dL. CGM parameters were compared between groups. All CGM-derived measures of glycemic variability (CV, SD, MAGE, CONGA1, and CONGA2) were significantly higher in patients with chronic pancreatitis and diabetes compared to individuals with type 2 diabetes (P < 0.01). Patients with chronic pancreatitis spent more time with glucose >250 mg/dL (8.8% vs 3.1%, P = 0.008), less time in the target range (70-180 mg/dL; 56.7% vs 68.5%, P = 0.044), and more time at 54-69 mg/dL (0.2% vs 0.0%, P = 0.041). Their glycemia risk index for hyperglycemia was also higher (25.5 vs 16.5, P = 0.033). Patients with pancreatic diabetes have higher glycemic variability than individuals with type 2 diabetes despite comparable levels of HbA1c.

Objective To analyze the glycemic variability in patients with gestational diabetes mellitus (GDM) by using continuous glucose monitoring system (CGMS) and to explore the relationship between GDM glycemic variability and infant birth weight and maternal and neonatal adverse pregnancy outcomes. Methods From April 2011 to August 2012, 189 cases of patients with GDM who were ready to regularly receive prenatal examination in the Department of Obstetrics of Guangdong Women and Children Hospital were enrolled in this study. All the GDM patients in this research met the GDM diagnostic criteria recommended by American Diabetes Association. They were instructed to wear the CGMS for 72 consecutive hours in the first week of this study. After four weeks of individual glucose control following strictly with doctors' advice, all the patients wear the CGMS for 72 consecutive hours for the second time in the fifth week of this study The parameters of glycemic variability which included mean blood glucose (MBG), the standard deviation of blood glucose (SDBG), mean amplitude of glycemic excursions (MAGE), and the mean of daily differences (MODD) were measured in the first and fifth weeks of this study. The maternal and neonatal adverse pregnancy outcomes and infant birth weight were collected. The relationship between infant birth weight and GDM patients' age, height, pregnancy history, body weight before pregnancy, fasting blood glucose level, oral glucose tolerance test (OGTT) 1 h glucose (OGTT-GLU1h), OGTT 2 h glucose (OGTT-GLU2h) and level of glycosylated hemoglobin A1c (HbA1c) on OGTT day, and MBG, SDBG, MAGE, MODD in the first and fifth weeks of this study were analyzed by multiple linear regression analysis. The relationship between maternal and neonatal adverse pregnancy outcomes and those above indexes of GDM patients in the first and fifth weeks of this study were analyzed by multiple logistic regression analysis. The study protocol was approved by the Ethical Review Board of Investigation in Human Being of Guangdong Women and Children Hospital. Results ①The MBG, SDBG, MAGE, MODD, duration of hyperglycemia level (≥7.8 mmol/L) and duration of hypoglycemia level (≤3.3mmol/L) of GDM patients in the fifth week of this study were (5.7±0.7) mmol/L, (1.1±0.4) mmol/L, (2.4±0.9) mmol/L, (1.2±0.3) mmol/L, 60 min/d (0-380 min/d) and 0 min/d (0~270 min/d) respectively, which all were significantly lower than those (6.1±0.9) mmol/L, (1.3±0.4) mmol/L, (3.1±0.9) mmol/L, (1.5±0. 4) mmol/L, 100 min/d (0~760 min/d) and 0 min/d (0~470 min/d) in the first week of this study respectively, and all the differences were statistically significant (χ2=4.197, P<0.001; χ2=7.028, P<0.001; χ2=7.691, P<0.001; χ2=12.986, P<0.001; Z=-4.992, P<0.001; Z=-2.601, P=0.009). ②189 women with GDM delivered 186 living infants, as 1 woman miscarried and 2 women experienced intrauterine fetal death. The average birth weight of infants was (3 345±508) kg. The maternal adverse pregnancy outcomes included: 1(0.5%) miscarriage, 2 (1.1%) intrauterine fetal deaths, 19(10.1%) combined with preeclampsia, 88(46.6%) primary cesarean delivery, 36(19.0%) repeat cesarean delivery. The neonatal adverse pregnancy outcomes included: 1(0.5%) obstetric trauma, 20(10.8%) macrosomia, 48(25.8%) large for gestational age, 5(2.7%) small for gestational age, 26(14.0%) combined with hypoglycemia, 18(9.7%) combined with hyperbilirubinemia, 11(5.9%) combined with respiratory distress syndrome. In total, 92(49.5%) infants suffered adverse pregnancy outcomes. ③MBG in the first week and MAGE in the fifth week were strongly associated with infant birth weight (b=104.709, P=0.013; b=87.804, P=0.035). ④MBG in the first week of GDM patients and primipara were independent risk factors for primary cesarean delivery (OR=1.728, 95% CI: 1.160-2.573, P=0.007; OR=5.208, 95% CI: 2.677-10.133, P=0.000). MAGE and MBG in the first week of GDM patients were independent risk factors for large or small for gestational age (OR=1.632, 95% CI: 1.137-2.343, P=0.008; OR=1.992, 95% CI: 1.269-3.128, P=0.003). MAGE, MBG in the first week and MAGE in the fifth week of GDM patients were independent risk factors for macrosomia (OR=1.800, 95% CI: 1.107-2.925, P=0.018; OR=1.987, 95% CI: 1.038-3.803, P=0.038; OR=1.885, 95% CI: 1.063-3.341, P=0.030). MAGE in the first week of GDM patients was independent risk factor for neonatal adverse pregnancy outcome (OR=1.452, 95% CI: 1.050-2.008, P=0.024). Conclusions Abnormal glycemic variability in patients with GDM may lead to maternal and neonatal adverse pregnancy outcomes. Therefore, seven times of measurements for glucose levels per day may be not enough for GDM. Monitoring for glycemic variability is also recommended. Key words: Diabetes, gestational; Glycemic variability; Pregnancy outcome; Continuous glucose monitoring system

BackgroundLittle is known about whether the influence of glycemic variability on arrhythmia is related to age in type 2 diabetes mellitus (T2DM). Therefore, we aimed to compare the association between glycemic variability and arrhythmia in middle-aged and elderly T2DM patients.MethodsA total of 107 patients were divided into two groups: elderly diabetes mellitus group (EDM, n = 73) and middle-aged diabetes mellitus group (MDM, n = 34). The main clinical data, continuous glucose monitoring (CGM) and dynamic ECG reports were collected. The parameters including standard deviation of blood glucose (SDBG), largest amplitude of glycemic excursions (LAGE), mean amplitude of glycemic excursions (MAGE), absolute means of daily differences (MODD), time in range (TIR), time below range (TBR), time above range (TAR), coefficient of variation (CV) were tested for glycemic variability evaluation.ResultsIn terms of blood glucose fluctuations, MAGE (5.77 ± 2.16 mmol/L vs 4.63 ± 1.89 mmol/L, P = 0.026), SDBG (2.39 ± 1.00 mmol/L vs 2.00 ± 0.82 mmol/L, P = 0.048), LAGE (9.53 ± 3.37 mmol/L vs 7.84 ± 2.64 mmol/L, P = 0.011) was significantly higher in EDM group than those of MDM group. The incidences of atrial premature beat, couplets of atrial premature beat, atrial tachycardia and ventricular premature beat were significantly higher in EDM group compared with the MDM group (all P < 0.05). Among patients with hypoglycemia events, the incidences of atrial premature beat, couplets of atrial premature beat, atrial tachycardia and ventricular premature beat (all P < 0.05) were significantly higher in the EDM group than those in the MDM group. In EDM group, TIR was negatively correlated with atrial tachycardia in the MAGE1 layer and with atrial tachycardia and ventricular premature beat in the MAGE2 layer, TBR was significantly positively correlated with atrial tachycardia in the MAGE2 layer (all P < 0.05). In MDM group, TAR was positively correlated with ventricular premature beat and atrial tachycardia in the MAGE2 layer (all P < 0.05).ConclusionsThe study demonstrated the elderly patients had greater glycemic variability and were more prone to arrhythmias. Therefore, active control of blood glucose fluctuation in elderly patients will help to reduce the risk of severe arrhythmia.

INTRODUCTIONGlycemic variability increases the risk of the development of microvascular and macrovascular complications from diabetes mellitus. Currently, available metrics used to measure glycemic variability are derived from continuous glucose monitoring (CGM) data namely mean amplitude of glycemic excursion (MAGE), continuous overlapping net glycemic action at 1-hour intervals (CONGA-1), and mean of daily differences (MODD). Serum 1,5-anhydroglucitol (1,5-AG) as a biomarker of glucose fluctuations is a practical, cheaper, and surrogate measure of glycemic variability as compared to CGM. This study aims to determine the diagnostic accuracy of 1,5-AG in relation to the glycemic variability metrics derived from CGM as a surrogate measure of glycemic variability among adult Filipinos with type 2 diabetes mellitus (DM). METHODOLOGYRetrospective data analysis of 37 adult patients aged 20 years and above diagnosed with type 2 diabetes mellitus referred for CGM at the Diabetes, Endocrine, Metabolic, and Nutrition Center of Cardinal Santos Medical Center from January 2017 to October 2021 who underwent serum 1,5-AG level determination within 2 weeks of CGM were collected. Criteria for exclusion include (1) the presence of acute infection at the time of the study; (2) the presence of active malignancy or end-stage cardiac, pulmonary, hepatic, and renal diseases; (3) medications that could alter glomerular function (i.e., ACE inhibitor, SGLT-2 inhibitor). RESULTSThere was good diagnostic accuracy between serum 1,5-AG levels with the different measures of glycemic variability derived from CGM namely MAGE, CONGA-1, and MODD with significant correlation among patients with HbAlc level ≤7%. Subjects were on CGM for approximately 6 ± 1 day with statistical significance between the good glucose control (HbA1c ≤7%), acceptable glucose control (HbA1c 7.1-8%), and poor glucose control group (HbA1c &gt;8%) (p &lt;0.05). Determination of diagnostic accuracy between 1,5-AG and MAGE showed a good accuracy (Sensitivity 95.3%, specificity 100%, positive predictive value (PPV) 100%, negative predictive value (NPV) 75.43%, diagnostic accuracy 96%, and a Youden Index (YI) of 92.3) with a statistically significant correlation among subjects with HbA1c level ≤7% (p = 0.021). There is likewise good diagnostic accuracy between CONGA-1 and 1,5-AG level (Sensitivity 99%, specificity of 75.29%, PPV 89.1%, NPV 97%, Accuracy 89.50%, and YI of 58.41) with a statistically significant correlation among subjects with HbA1c ≤7% (p = 0.038). Comparison with interday glycemic variability showed fair diagnostic accuracy between MODD and 1,5- AG (Sensitivity 79.17%, specificity of 78%, PPV of 97%, NPV of 32%, Accuracy 76.89%, and YI of 49.07) and a statistically significant correlation among subjects with ≤7% (p = 0.009). CONCLUSIONThere is good diagnostic accuracy of serum 1,5-AG levels with the different measures of glycemic variability derived from CGM namely MAGE, CONGA-1, and MODD with significant correlation among patients with HbA1c level ≤7%. Among diabetics with HbA1c ≤7%, 1,5-AG could be used as a surrogate measure of glycemic variability and excursions.

There is some evidence that not only hemoglobin A1c (HbA1c) but also short-term variations in blood glucose could represent an independent risk factor for hypoglycemia.1 Normative ranges for measures of glycemic variability (GV) in normal subjects and in continuous glucose monitoring (CGM) data sets obtained from subjects with type 1 diabetes mellitus (T1DM) have been published.2 We sought to describe GV in T1DM patients with repeated episodes of severe hypoglycemia and non-severe hypoglycemia (SH/NSH). Thirty-six T1DM subjects underwent CGM for up to 72 h. Twenty-five of 36 were considered the hypoglycemic group [(H-group), presenting repeated SH/NSH], while the remaining 11 were evaluated as the control group (C-group). General characteristics, biochemical measurements, the number of hypoglycemic episodes, and the awareness of hypoglycemia (Clarke Sign Test) were evaluated. Glycemic variability was calculated using EasyGV© (by N. R. Hill, University of Oxford, Oxford, UK; available at www.easygv.co.uk) for the following: standard deviation (SD), M-value (mg glucose per kg body weight per min), mean amplitude of glucose excursions (MAGE), average daily risk ratio, lability index, J-Index, low blood glucose index (LBGI), high blood glucose index, continuous overlapping net glycemic action, mean of daily differences, glycemic risk assessment diabetes equation (GRADE), and mean absolute glucose. No differences were found in the entire set of clinical data or in HbA1c (H-group: age 34.5 ± 7.8 years, T1DM duration 16.0 ± 6.3 years, HbA1c 6.6 ± .0 %). Scores for the Clarke Sign Test differed for both groups, with higher values in the H-group. The H-group showed higher percentages of values and areas under the curve (AUC) <70 mg/dl with respect to the C-group (11.3 ± 8.4 vs 5.3 ± 5.3% <70 mg/dl; p < .05 and 2.5 ± 1.8 vs 0.8 ± 1.0 mg/dl AUC <70mg/dl; p < .005 for the H-group and the C-group, respectively). The H-group presented significantly higher scores than the T1DM C-group in LBGI (9.3 ± 5.0 vs 4.3 ± 2.4), GRADE (8.0 ± 3.4 vs 4.8 ± 3.5), GRADE-hypoglycemia (GRADE-hypo) (22.8 ± 22.0 vs 9.2 ± 8.9), and M-value (17.7 ± 6.5 vs 10.3 ± 6.0). Using CGM glucose profiles, we have shown that only the variability measures representing increased glycemic risk related to hypoglycemia and the quality of glycemic control differ significantly in T1DM subjects who are prone to repeated episodes of hypoglycemia. Almost all measurements (EasyGV software) from our entire group of subjects with T1DM were above the mean + 2 SD normative values that are described in a nondiabetic population. We could not find differences in the majority of measures, except for four (LBGI, GRADE, GRADE-hypo, and M-value), in our group of patients with repeated hypoglycemic episodes in comparison with the control group of T1DM patients without significant hypoglycemia and with good metabolic control. We found differences only in those glucose measures that mainly focused on increased glycemic risk coming from hypoglycemic episodes (LBGI and GRADE-hypo) and those mostly representing quality of glycemic control (GRADE and M-value). The only exception for a glucose measure specifically related to variability was MAGE, which was close to significantly higher in the group of patients with repeated hypoglycemia. As it has been claimed using self-monitoring blood glucose information,3 our data suggest that measures of glucose variability and quality of glucose control derived from CGM data could be used as instruments to estimate the risk of hypoglycemia and could help to prevent it.

BackgroundDiabetic peripheral neuropathy (DPN) is one of the most common microvascular complications of diabetes. Glycemic variability could be an independent risk factor for diabetes complications in addition to average glucose. Type 2 diabetes with well-controlled glycosylated hemoglobin A1c (HbA1c) may have different terms of glycemic variability and vascular complication consequences. The aim of the study is to investigate the relationship between glycemic variability and DPN in type 2 diabetes with well-controlled HbA1c (HbA1c < 7.0%).Methods45 type 2 diabetes with well-controlled HbA1c(HbA1c < 7.0%) and with DPN (DM/DPN group) were recruited in the study, and 45 type 2 diabetes with well-controlled HbA1c and without DPN (DM/–DPN group) were set as controls. The two groups were also matched for age and diabetic duration. Blood pressure, body mass index(BMI), insulin sensitivity index (Matsuda index, ISI), total cholesterol (TC), triglyceride (TG), high density lipoprotein cholesterol (HDLC), and low density lipoprotein cholesterol (LDLC) were tested in the two groups. And all patients were monitored using the continuous glucose monitoring (CGM) system for consecutive 72 hours. The multiple parameters of glycemic variability included the standard deviation of blood glucose (SDBG), mean of daily differences (MODD) and mean amplitude of glycemic excursions (MAGE).ResultsThe DM/DPN group had a greater SDBG, MODD and MAGE, when compared to the DM/–DPN group (p < 0.05). BMI, TC, and LDLC of DM/DPN group were lower than those of DM/–DPN group (p < 0.05). The patients with hypoglycemia were comparable between the two groups (p > 0.05). Univariate analysis showed DPN was closely associated with BMI (OR 0.82, CI 0.72–0.94, p = 0.005), TC (OR 0.63, CI 0.42–0.93, p = 0.02), LDLC (OR 0.4, CI 0.20–0.80, p = 0.009), SDBG (OR 2.95, CI 1.55–5.61, p = 0.001), MODD (OR 4.38, CI 1.48–12.93, p = 0.008), MAGE (OR 2.18, CI 1.47–3.24, p < 0.001). Multivariate logistic regression analysis showed that MAGE (OR 2.05, CI 1.36–3.09, p = 0.001) and BMI (OR 0.85, CI 0.73–0.99, p = 0.033) were significantly correlating with DPN. Glycemic variability, evaluated by MAGE, was the most significantly independent risk factor for DPN.ConclusionsThere was a close relationship between glycemic variability evaluated by MAGE and DPN in type 2 diabetes with well-controlled HbA1c.

Background: Among the various glycemic indices in current use, glycemic variability has the greatest contribution in the development of microvascular and macrovascular complications in Type 2 Diabetes mellitus (T2DM). Most metrics that are currently used to measure glycemic variability are derived from continuous glucose monitoring (CGM) data. However, CGM is burdensome to the patient due to its relatively high cost as well as the need for multiple visits with the health care provider. With the use of serum 1,5-anhydroglucitol (1,5-AG) as a biomarker of glucose fluctuations, physicians and patients alike could have an easier surrogate measure of glycemic variability thus aiding in achieving target glucose control. This study aims to determine the diagnostic accuracy of 1,5-AG as compared to the glycemic variability metrics derived from CGM as a surrogate measure of glycemic variability among adult Filipinos with T2DM. Methods: Retrospective analysis of data of adult patients aged 20 years old and above diagnosed with T2DM referred for CGM at the Diabetes, Endocrine, Metabolic, and Nutrition Center of Cardinal Santos Medical Center from January 2017 to October 2021 who underwent serum 1,5-AG level determination within 2 weeks of CGM were collected. Diagnostic accuracy was obtained by computing the sensitivity, specificity, positive (PPV) and negative predictive values (NPV), and Youden index. Pearson correlation coefficient was used to determine the correlation of 1,5-AG and the different metrics. Analysis of variance (ANOVA) was used to check for statistical significance with 99% confidence interval and a p &lt; 0.05 considered as statistically significant. Results: This study involving 37 subjects showed a good diagnostic accuracy of serum 1,5-AG levels with the different measures of glycemic variability derived from CGM namely mean amplitude of glycemic excursion (MAGE), continuous overlapping net glycemic action at 1-hour intervals (CONGA-1), and mean of daily differences (MODD) with significant correlation among patients with HbA1c ≤ 7%. Subjects were on CGM for approximately 6 ± 1 day with statistically significant difference between the good and poor glucose control group (p&lt;0.05). Determination of diagnostic accuracy between 1,5-AG and MAGE showed good accuracy (Sensitivity = 95.3%, Specificity = 100%, PPV = 100%, NPV = 75.43%, Diagnostic accuracy 96%, and a Youden Index of 92.3) with a statistically significant correlation among subjects with HbA1c level ≤ 7% (p=0.021). There is likewise good diagnostic accuracy between CONGA-1 and 1,5-AG level (Sensitivity = 99%, Specificity = 75.29%, PPV = 89.1%, NPV = 97%, Accuracy = 89.50% and Youden index of 58.41) with a statistically significant correlation among subjects with HbA1c ≤ 7% (p=0.038). Comparison with interday glycemic variability showed fair diagnostic accuracy between MODD and 1,5-AG (Sensitivity = 79.17%, Specificity = 78%, PPV = 97%, NPV = 32%, Accuracy = 76.89%, and Youden index of 49.07) and a statistically significant correlation among subjects with HbA1c ≤ 7% (p=0.009). Conclusion: There is good diagnostic accuracy of serum 1,5-AG levels with the different measures of glycemic variability derived from CGM namely MAGE, CONGA-1, and MODD with significant correlation among patients with HbA1c ≤ 7%. Among diabetics with HbA1c ≤7%, 1,5-AG could be used as a surrogate measure of glycemic variability and excursions. Keywords: serum 1,5-anhydroglucitol, continuous glucose monitoring, type 2 diabetes mellitus