Glucose variability in subjects with normal glucose tolerance: Relations with body composition, insulin secretion and sensitivity

Glucose Variability in People with Type 1 Diabetes: Associations with Body Weight, Body Composition, and Insulin Sensitivity

The prevalence of overweight and obesity increases in people with type 1 diabetes (T1D). However, the impact of fat accumulation on glucose dynamics in T1D is poorly understood. We assessed continuous glucose monitoring (CGM) parameters in patients with T1D depending on their body weight, body composition, and insulin sensitivity. In 547 patients, including 238 overweight/obese individuals, CGM-derived time in range (TIR) and glucose variability (GV) were estimated. Body composition was assessed by DXA. Estimated glucose disposal rate (eGDR) was used as an indicator of insulin sensitivity. Overweight/obese patients, when compared to normal-weight ones, have a lower time below range (TBR) (<3 mmol/L), GV, and experienced fewer episodes of low glucose. In men, lower TIR, higher time above range (TAR), and GV reduction were associated with central adiposity assessed by total, trunk, and android fat mass. In women, gynoid fat mass only was associated with a lower TIR and higher TAR. The eGDR was a positive predictor of TIR and a negative predictor of TAR, TBR, and GV in men and women. In conclusion, adiposity in people with T1D is associated with a lower risk of CGM-confirmed hypoglycemia, higher TAR, and reduced GV. These features of daily glucose dynamics may be mediated by insulin resistance.

Higher adiposity predicts greater intra-individual inconsistencies in postprandial glycemic measurements-an analysis of three randomized controlled trials in Asian populations

BACKGROUND/OBJECTIVES

Acute glycemic responses offer important insights into glucose homeostasis although the repeatability of these measurements particularly in Asian populations remains unclear. This research aimed to critically investigate the inconsistencies of the postprandial glycemic profile within individuals, and identify potential variables predicting greater inconsistencies.

SUBJECTS/METHODS

This was a secondary analysis of three randomized controlled trials which fed subjects with glucose (and other carbohydrate-rich foods), and measured postprandial blood glucose at regular intervals. Intra-individual rank-order consistency in the glycemic profile between acute glucose treatments was evaluated and compared against demographic, anthropometric and cardio-metabolic health related indicators to delineate potential confounding variables. Correlations between the incremental area under curve at 120 min (iAUC120 min) for glucose and the carbohydrate-rich foods were further explored.

RESULTS

Rank-order consistency was identified to be moderate, with intra-individual inconsistencies marginally lower than inter-individual inconsistencies. Notably, greater inconsistencies within individuals were directly correlated with BMI and fat-mass index (P < 0.01) albeit non-significant for age, ethnicity, and other cardio-metabolic health-related risk indicators. Across the trials, there were positive monotonic correlations between the iAUC120 min for glucose and simple sugars (sucrose, isomaltulose), as well as different varieties of rice (jasmine white, Bapatla brown, Bapatla white; p < 0.05). However, there were a lack of associations between iAUC120 min for glucose with pastas (semolina and wholegrain penne, spaghetti) and mee pok noodles.

CONCLUSION

There are inherent inconsistencies in postprandial glycemic measurements within individuals, particularly among those with higher adiposity. These confounders need to be kept in mind for appropriate and meaningful interpretations of glycemia.

Continuous glucose monitoring as an objective measure of meal consumption in individuals with binge-spectrum eating disorders: A proof-of-concept study

OBJECTIVE

Going extended periods of time without eating increases risk for binge eating and is a primary target of leading interventions for binge-spectrum eating disorders (B-EDs). However, existing treatments for B-EDs yield insufficient improvements in regular eating and subsequently, binge eating. These unsatisfactory clinical outcomes may result from limitations in assessment and promotion of regular eating in therapy. Detecting the absence of eating using passive sensing may improve clinical outcomes by facilitating more accurate monitoring of eating behaviours and powering just-in-time adaptive interventions. We developed an algorithm for detecting meal consumption (and extended periods without eating) using continuous glucose monitor (CGM) data and machine learning.

METHOD

Adults with B-EDs (N = 22) wore CGMs and reported eating episodes on self-monitoring surveys for 2 weeks. Random forest models were run on CGM data to distinguish between eating and non-eating episodes.

RESULTS

The optimal model distinguished eating and non-eating episodes with high accuracy (0.82), sensitivity (0.71), and specificity (0.94).

CONCLUSIONS

These findings suggest that meal consumption and extended periods without eating can be detected from CGM data with high accuracy among individuals with B-EDs, which may improve clinical efforts to target dietary restriction and improve the field's understanding of its antecedents and consequences.

A low-glucose eating pattern is associated with improvements in glycemic variability among women at risk for postmenopausal breast cancer: an exploratory analysis

Background

High glycemic variability (GV) is a biomarker of cancer risk, even in the absence of diabetes. The emerging concept of chrononutrition suggests that modifying meal timing can favorably impact metabolic risk factors linked to diet-related chronic disease, including breast cancer. Here, we examined the potential of eating when glucose levels are near personalized fasting thresholds (low-glucose eating, LGE), a novel form of timed-eating, to reduce GV in women without diabetes, who are at risk for postmenopausal breast cancer.

Methods

In this exploratory analysis of our 16-week weight loss randomized controlled trial, we included 17 non-Hispanic, white, postmenopausal women (average age = 60.7 ± 5.8 years, BMI = 34.5 ± 6.1 kg/m2, HbA1c = 5.7 ± 0.3%). Participants were those who, as part of the parent study, provided 3-7 days of blinded, continuous glucose monitoring data and image-assisted, timestamped food records at weeks 0 and 16. Pearson's correlation and multivariate regression were used to assess associations between LGE and GV, controlling for concurrent weight changes.

Results

Increases in LGE were associated with multiple unfavorable measures of GV including reductions in CGM glucose mean, CONGA, LI, J-Index, HBGI, ADDR, and time spent in a severe GV pattern (r = -0.81 to -0.49; ps < 0.044) and with increases in favorable measures of GV including M-value and LBGI (r = 0.59, 0.62; ps < 0.013). These associations remained significant after adjusting for weight changes.

Conclusion

Low-glucose eating is associated with improvements in glycemic variability, independent of concurrent weight reductions, suggesting it may be beneficial for GV-related disease prevention. Further research in a larger, more diverse sample with poor metabolic health is warranted.Clinical trial registration: ClinicalTrials.gov, NCT03546972.

Glycemic variability assessed using continuous glucose monitoring in individuals without diabetes and associations with cardiometabolic risk markers: A systematic review and meta-analysis

BACKGROUND & AIMS

Continuous glucose monitoring (CGM) provides data on short-term glycemic variability (GV). GV is associated with adverse outcomes in individuals with diabetes. Whether GV is associated with cardiometabolic risk in individuals without diabetes is unclear. We systematically reviewed the literature to assess whether GV is associated with cardiometabolic risk markers or outcomes in individuals without diabetes.

METHODS

Searches were performed in PubMed/Medline, Embase and Cochrane from inception through April 2022. Two researchers were involved in study selection, data extraction and quality assessment. Studies evaluating GV using CGM for ≥24 h were included. Studies in populations with acute and/or critical illness were excluded. Both narrative synthesis and meta-analyzes were performed, depending on outcome.

RESULTS

Seventy-one studies were included; the majority were cross-sectional. Multiple measures of GV are higher in individuals with compared to without prediabetes and GV appears to be inversely associated with beta cell function. In contrast, GV is not clearly associated with insulin sensitivity, fatty liver disease, adiposity, blood lipids, blood pressure or oxidative stress. However, GV may be positively associated with the degree of atherosclerosis and cardiovascular events in individuals with coronary disease.

CONCLUSION

GV is elevated in prediabetes, potentially related to beta cell dysfunction, but less clearly associated with obesity or traditional risk factors. GV is associated with coronary atherosclerosis development and may predict cardiovascular events and type 2 diabetes. Prospective studies are warranted, investigating the predictive power of GV in relation to incident disease. GV may be an important risk measure also in individuals without diabetes.

The Effects of a High-Carbohydrate versus a High-Fat Shake on Biomarkers of Metabolism and Glycemic Control When Used to Interrupt a 38-h Fast: A Randomized Crossover Study

This study aimed to determine the impact of various fast-interrupting shakes on markers of glycemic control including glucose, β-hydroxybutyrate (BHB), insulin, glucagon, GLP-1, and GIP. Twenty-seven sedentary adults (twelve female, fifteen male) with overweight or obesity completed this study. One condition consisted of a 38-h water-only fast, and the other two conditions repeated this, but the fasts were interrupted at 24 h by either a high carbohydrate/low fat (HC/LF) shake or an isovolumetric and isocaloric low carbohydrate/high fat (LC/HF) shake. The water-only fast resulted in 135.3% more BHB compared to the HC/LF condition (p < 0.01) and 69.6% more compared to the LC/HF condition (p < 0.01). The LC/HF condition exhibited a 38.8% higher BHB level than the HC/LF condition (p < 0.01). The area under the curve for glucose was 14.2% higher in the HC/LF condition than in the water condition (p < 0.01) and 6.9% higher compared to the LC/HF condition (p < 0.01), with the LC/HF condition yielding 7.8% more glucose than the water condition (p < 0.01). At the 25-h mark, insulin and glucose-dependent insulinotropic polypeptide (GIP) were significantly elevated in the HC/LF condition compared to the LC/HF condition (p < 0.01 and p = 0.02, respectively) and compared to the water condition (p < 0.01). Furthermore, insulin, GLP-1, and GIP were increased in the LC/HF condition compared to the water condition at 25 h (p < 0.01, p = 0.015, and p < 0.01, respectively). By the 38-h time point, no differences were observed among the conditions for any of the analyzed hormones. While a LC/HF shake does not mimic a fast completely, it does preserve some of the metabolic changes including elevated BHB and glucagon, and decreased glucose and insulin compared to a HC/LF shake, implying a potential for improved metabolic health.

Relationship Between Time in Range and Dusk Phenomenon in Outpatients with Type 2 Diabetes Mellitus

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 (%TIR^3.9-10) 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.

Prediction of Hypoglycemia in Diabetic Patients During Colonoscopy Preparation

OBJECTIVE

To explore the clinical outcomes and establish a predictive model of hypoglycemia during colonoscopy preparation for diabetic patients.

METHODS

Three-hundred ninety-four patients with diabetes who received colonoscopy were retrospectively enrolled in this study and assigned to hypoglycemia or non-hypoglycemia groups. Information about clinical characteristics and outcomes during colonoscopy preparation was collected and compared between the two groups. Logistic regression analysis was applied to identify the risk factors of hypoglycemia. These risk factors were used to construct a hypoglycemia predictive model verified by the receiver operating characteristic (ROC) curve and Hosmer-Lemeshow goodness fit test.

RESULTS

Among 394 participants, 66 (16.8%) underwent a total of 88 hypoglycemia attacks during the bowel preparation. Grade 1 hypoglycemia (≤3.9 mmol/L) comprised 90.9% (80/88) of all hypoglycemia attacks and grade 2 hypoglycemia accounted for 9.1% (8/88), signifying that grade 1 hypoglycemia is the most common type. No severe hypoglycemia was identified. The incidence of nocturnal hypoglycemia was 15.9%. Logistic regression analyses revealed that the main risk factors of hypoglycemia during colonoscopy preparation were postprandial C-peptide, serum triglyceride, gender, type of diabetes mellitus, and insulin injection frequencies. The area under the ROC curve of the hypoglycemia prediction model was 0.777 (95% CI: 0.720-0.833).

CONCLUSION

Diabetic patients are prone to develop mild to moderate hypoglycemia during colonoscopy preparation. This study proposes a predictive model that could provide a reference for identifying patients with a high risk of hypoglycemia during colonoscopy preparation.

Glucose variability: A physiological correlate of eating disorder behaviors among individuals with binge-spectrum eating disorders

OBJECTIVES

Elevated glucose variability may be one mechanism that increases risk for significant psychological and physiological health conditions among individuals with binge-spectrum eating disorders (B-EDs), given the impact of eating disorder (ED) behaviors on blood glucose levels. This study aimed to characterize glucose variability among individuals with B-EDs compared with age-matched, sex-matched, and body mass index-matched controls, and investigate the association between frequency of ED behaviors and glucose variability.

METHODS

Participants were 52 individuals with B-EDs and 22 controls who wore continuous glucose monitors to measure blood glucose levels and completed ecological momentary assessment surveys to measure ED behaviors for 1 week. Independent samples t-tests compared individuals with B-EDs and controls and multiple linear regression models examined the association between ED behaviors and glucose variability.

RESULTS

Individuals with B-EDs demonstrated numerically higher glucose variability than controls (t = 1.42, p = .08, d = 0.43), although this difference was not statistically significant. When controlling for covariates, frequency of ED behaviors was significantly, positively associated with glucose variability (t = 3.17, p = .003) with medium effect size (f2  = 0.25). Post hoc analyses indicated that binge eating frequency was significantly associated with glucose variability, while episodes of 5+ hours without eating were not.

DISCUSSION

Glucose variability among individuals with B-EDs appears to be positively associated with engagement in ED behaviors, particularly binge eating. Glucose variability may be an important mechanism by which adverse health outcomes occur at elevated rates in B-EDs and warrants future study.

PUBLIC SIGNIFICANCE

This study suggests that some individuals with binge ED and bulimia nervosa may experience elevated glucose variability, a physiological symptom that is linked to a number of adverse health consequences. The degree of elevation in glucose variability is positive associated with frequency of eating disorder behaviors, especially binge eating.

A Retrospective Study on the Time in Range of Blood Glucose and Type 2 Diabetic Peripheral Neuropathy

Background

Time in range (TIR) is one of the basic indicators to assess glycemic control. In this study, the TIR of DPN patients was used as the observation index to further evaluate the correlation between TIR and DPN, so as to provide new ideas for preventing the occurrence of DPN and delaying its disease progression.

Methods

A total of 120 patients with T2DM (T2DM) who were hospitalized in the Endocrinology Department of our hospital from October 2018 to February 2020 were included and divided into two groups according to whether the nerve conduction velocity was normal or not, the diabetic peripheral neuropathy group (DPN) and the other groups. No diabetic peripheral neuropathy group (NDPN). According to the corresponding inclusion and exclusion criteria, the baseline data were recorded, and test indicators such as homocysteine and blood lipids were collected at the same time, and TIR was collected by a transient blood glucose meter. To explore the relationship between TIR and other indicators and peripheral neuropathy in T2DM.

Results

A total of 120 T2DM patients participated in the study, including 82 in the DPN group and 38 in the NDPN group. There were no statistically significant differences in basic indicators such as age, height, and weight between the two groups. Glycated hemoglobin (HbA1c) and homocysteine (Hcy) in DPN group were higher than those in NDPN group, while TIR and HDL-C were lower than those in NDPN group (P < 0.05). Logistic regression analysis showed that HbA1c and Hcy were risk factors for DPN, and TIR and HDL-C were protective factors for DPN, with statistical significance (P < 0.05). The prediction results of TIR, Hcy, HDL-C, and HbA1c on diabetic peripheral neuropathy were analyzed by ROC curve, and the prediction results of the five variables were all statistically significant (P < 0.05) and have a better prediction effect.

Conclusion

(1) The results of TIR level suggest that the longer the blood sugar is in the good control range, the more beneficial it is to reduce the occurrence of DPN. (2) TIR and HDL-C are protective factors for DPN, and HbA1c and Hcy are risk factors for DPN. (3) The results of ROC curve analysis showed that TIR, Hcy, HbA1c, and HDL-C had a good predictive effect on the occurrence of DPN.

The Link Between Obesity and the Skin

Obesity as a multi-organ disease that affects the entire human organism. Notably, the skin is no exclusion from this postulate. Skin changes in obese patients have been widely studied with regards to mechanical friction, skin infections, and skin hypertrophic conditions, such as acanthosis nigricans and, most commonly, fibromas (skin tags). Almost 60–70% of obese patients present with a variety of skin changes. Herein, we discuss our own experience and review the complex skin changes in obesity. The role of metabolic syndrome and obesity are responsible for the epidemiological prevalence and are involved in the pathogenesis of chronic inflammatory skin diseases, such as psoriasis, atopic dermatitis, and skin malignancies. Here, we comment on the role of nutritional interventions in these patients as it has been proven that low-calorie diet and weight loss is related to improvement of inflammatory skin diseases. The readership of this paper will receive up-to-date overview on the connection between obesity and the skin that is of a practical importance to any clinician working in the field.