Harmful effects of long-term HbA1c and fasting plasma glucose (FPG) variability on cardiovascular diseases (CVD) have not been causally examined. We employed a parametric g-formula to estimate the causal effect of HbA1C and fasting plasma glucose (FPG) variability on CVD.

This retrospective cohort study was conducted on 2078 patients with type 2 diabetes who were free of CVD and aged >18 years at the entrance to the clinic (2017-2022), with at least three HbA1c and FPG measurements. Variability was calculated using standard deviation (SD), and coefficient of variation (CV). We used the parametric g-formula to estimate the 5-year risk, risk ratio, and risk difference of CVD under different deciles of HbA1c-SD/CV, FPG-SD/CV, HbA1C levels (≤5%, 5 to ≤7%, and >7), and joint exposure to different deciles of HbA1c-SD and HbA1c values, adjusted for time-varying confounders that are affected by prior exposure.

The observed and simulated 5-year risk of CVD under no intervention were 11.6% (95% CI: 10.3, 13.1) and 11.03% (95% CI: 10.2, 12.6) for HbA1C-SD model. The estimated 5-year risk of CVD was increased from the 8.01% (95% CI: 7.5, 10.1%) in the first decile to 15.2% (95% CI: 14.1, 17.7%) in the tenth decile of HbA1c-SD. The results for FPG-SD were similar. Within the stable level of HbA1c (5 to ≤7%) the risk ratio increased from 1.37 (95% CI: 1.19, 1.48) in the first decile to 2.76 (95% CI: 2.06, 3.09) in the tenth decile of HbA1c-SD. Under a joint intervention of HbA1c <5% and the first decile of HbA1c-SD, CVD risk decreased by 6.4% (95%CI: 4.9, 7.3%) compared to the natural course.

Even within a stable HbA1c level, long-term glycemic variability may be a strong predictor of CVD.

Albuminuria within the normal range may predict an increased risk of subsequent nephropathy in type 1 diabetes (T1D). The role of sustained hyperglycaemia in the development of nephropathy is well-known. The relationship between albuminuria within the normal range and parameters of continuous glucose monitoring (CGM) in childhood has not yet been investigated. The aim of the present study was to analyze this relationship in young T1D patients.

A total of 54 normoalbuminuric, normotensive, real time CGM user pubertal children and adolescents with T1D were recruited for this study. Patients with medium to high normal (1.0-2.9 mg/mmol; n = 18) and those with low normal (< 1.0 mg/mmol; n = 36) urinary albumin-to-creatinin ratio (UACR) were compared regarding CGM metrics data. Relationships of UACR with clinical variables and CGM-derived metrics were analysed by multiple logistic regression.

Time in range (TIR) was lower in medium to high normal UACR patients than in low normal UACR patients (mean ± SD: 58.2 ± 8.4% vs. 64.5 ± 10.1%, p = 0.0199). Patients with medium to high normal UACR had a higher coefficient of variation for mean glucose (CV) than those with low normal UACR (42.4 ± 6.0% vs. 38.0 ± 6.1%, p = 0.0163). UACR was related to TIR (r=-0.55, p = 0.02), to CV (r=-0.51, p = 0.04) and to mean glucose (MG) (r=-0.48, p = 0.05). TIR, CV and puberty proved to be independently predictive for medium to high normal UACR [adjusted RR (95% CI): 0.70 (0.58-0.92), p = 0.0231; 1.28 (1.02-1.67), p = 0.0222; 1.19 (1.10-1.36), p = 0.0321, respectively].

The duration of the blood glucose level within the target range and the extent of its fluctuation may contribute to the early increase in albumin excretion within the normal range, which may play a role in the development of later complications of childhood T1D.

Cardiovascular disease (CVD) remains the leading cause of mortality among people with type 1 diabetes (T1D), with cardiovascular mortality rates 2-5 times higher than in the general population. A concerning sex disparity exists within this high-risk population, as the cardioprotective advantage typically observed in women without diabetes appears attenuated or eliminated in individuals with T1D. This disparity is evident across the CVD spectrum, including coronary artery disease, stroke, heart failure, and cardiovascular mortality, with women consistently experiencing an excess burden of disease. These differences are particularly pronounced in women with early-onset T1D, leading to a substantial loss of life-years-approximately 18 years for women compared to 14 for men. Several factors may contribute to this sex disparity. First, the effect of hyperglycemia on CVD appears to have a sex-based differential impact and women with T1D often demonstrate more difficulties to achieve optimal glycemic control. Second, although women with T1D generally exhibit a more favorable CVD risk factor profile than men with T1D, the presence of hypertension, smoking or diabetic kidney disease seem to have a strong impact on CVD in women. Diabetes also appears to diminish sex-based differences in lipid metabolism, and a trend towards increased obesity rates among women with T1D has been observed. Lastly, female-specific factors, which are more prevalent in T1D, exacerbate cardiovascular risk. These include premature menopause, pregnancy-related disorders (such as preeclampsia), polycystic ovary syndrome, and autoimmune diseases, which disproportionately affect women. This narrative review examines the epidemiological evidence highlighting the aspects regarding the excess risk of CVD in women with T1D and evaluates sex disparities in both traditional and female-specific risk factors. Finally, we include a sex-based analysis from the Catalan Registry, which highlights the critical need for greater awareness and enhanced early detection and management of CVD risk factors in this population.

People with type 1 diabetes (T1D) are usually considered to exclusively exhibit β-cell failure, but they frequently also feature insulin resistance. This review discusses the mechanisms, clinical features, and therapeutic relevance of insulin resistance by focusing mainly on human studies using gold-standard techniques (euglycemic-hyperinsulinemic clamp). In T1D, tissue-specific insulin resistance can develop early and sustain throughout disease progression. The underlying pathophysiology is complex, involving both metabolic- and autoimmune-related factors operating synergistically. Insulin treatment may play an important pathogenic role in predisposing individuals with T1D to insulin resistance. However, the established lifestyle-related risk factors and peripheral insulin administration inducing glucolipotoxicity, hyperinsulinemia, hyperglucagonemia, inflammation, mitochondrial abnormalities, and oxidative stress cannot always fully explain insulin resistance in T1D, suggesting a phenotype distinct from type 2 diabetes. The mutual interaction between insulin resistance and impaired endothelial function further contributes to diabetes-related complications. Insulin resistance should therefore be considered a treatment target in T1D. Aside from lifestyle modifications, continuous subcutaneous insulin infusion can ameliorate insulin resistance and hyperinsulinemia, thereby improving glucose toxicity compared with multiple injection insulin treatment. Among other concepts, metformin, pioglitazone, incretin-based drugs such as GLP-1 receptor agonists, sodium-glucose cotransporter inhibitors, and pramlintide can improve insulin resistance, either directly or indirectly. However, considering the current issues of high cost, side effects, limited efficacy, and their off-label status, these agents in people with T1D are not widely used in routine clinical care at present.

Stress hyperglycemia ratio (SHR) and glycemic variability (GV) reflect acute glucose elevation and fluctuations, which correlate with adverse outcomes in patients with atherosclerotic cardiovascular disease (ASCVD). However, the prognostic significance of combined SHR-GV evaluation for ASCVD mortality remains unclear. This study examines associations of SHR, GV, and their synergistic effects with mortality in patients with ASCVD across different glucose metabolic states, incorporating machine learning (ML) to identify critical risk factors influencing mortality.

Patients with ASCVD were screened in the Medical Information Mart for Intensive Care IV (MIMIC-IV) database and stratified into normal glucose regulation (NGR), pre-diabetes mellitus (Pre-DM), and diabetes mellitus (DM) groups based on glucose metabolic status. The primary endpoint was 28-day mortality, with 90-day mortality as the secondary outcome. SHR and GV levels were categorized into tertiles. Associations with mortality were analyzed using Kaplan-Meier(KM) curves, Cox proportional hazards models, restricted cubic splines (RCS), receiver operating characteristic (ROC) curves, landmark analyses, and subgroup analyses. Five ML algorithms were employed for mortality risk prediction, with SHapley Additive exPlanations (SHAP) applied to identify critical predictors.

A total of 2807 patients were included, with a median age of 71 years, and 58.78% were male. Overall, 483 (23.14%) and 608 (29.13%) patients died within 28 and 90 days of ICU admission, respectively. In NGR and Pre-DM subgroups, combined SHR-GV assessment demonstrated superior predictive performance for 28-day mortality versus SHR alone [NGR: AUC 0.688 (0.636-0.739) vs. 0.623 (0.568-0.679), P = 0.028; Pre-DM: 0.712 (0.659-0.764) vs. 0.639 (0.582-0.696), P = 0.102] and GV alone [NGR: 0.688 vs. 0.578 (0.524-0.633), P < 0.001; Pre-DM: 0.712 vs. 0.593 (0.524-0.652), P < 0.001]. Consistent findings were observed for 90-day mortality prediction. However, in the DM subgroup, combined assessment improved prediction only for 90-day mortality vs. SHR alone [AUC 0.578 (0.541-0.616) vs. 0.560 (0.520-0.599), P = 0.027], without significant advantages in other comparisons.

Combined SHR and GV assessment serves as a critical prognostic tool for ASCVD mortality, providing enhanced predictive accuracy compared to individual metrics, particularly in NGR and Pre-DM patients. This integrated approach could inform personalized glycemic management strategies, potentially improving clinical outcomes.

Continuous glucose monitoring (CGM) measures glucose levels every 1 to 15 minutes and is widely used in clinical and research contexts. Statistical packages and algorithms reduce the time-consuming and error-prone process of manually calculating CGM metrics and contribute to standardizing CGM metrics defined by international consensus. The aim of this systematic review is to summarize existing data on (1) statistical packages for retrospective CGM data analysis and (2) statistical algorithms for retrospective CGM analysis not available in these statistical packages.

A systematic literature search in PubMed and EMBASE was conducted on September 19, 2023. We also searched Google Scholar and Google Search until October 12, 2023 as sources of gray literature and performed reference checks of the included literature. Articles in English and Danish were included. This systematic review is registered with PROSPERO (CRD42022378163).

A total of 8731 references were screened and 46 references were included. We identified 23 statistical packages for the analysis of CGM data. The statistical packages could calculate many metrics of the 2022 CGM consensus and non-consensus CGM metrics, and 22/23 (96%) statistical packages were freely available. Also, 23 statistical algorithms were identified. The statistical algorithms could be divided into three groups based on content: (1) CGM data reduction (eg, clustering of CGM data), (2) composite CGM outcomes, and (3) other CGM metrics.

This systematic review provides detailed tabular and textual up-to-date descriptions of the contents of statistical packages and statistical algorithms for retrospective analysis of CGM data.

To examine the associations between antidiabetic medication type and a new episode of depression using 100% Texas Medicare database during 2009 and 2018.

A retrospective cohort study.

A population-based study using the Texas Medicare data.

11 common antihyperglycaemic medication types, alone and in combinations: metformin-only, five non-metformin-containing regimens (dipeptidyl peptidase-4 inhibitor (DPP4i) only, sulfonylureas (SU) only, thiazolidinediones (TZD) only, SU/DPP4i and SU/TZD) and five metformin-containing combination treatments (metformin/DPP4i, metformin/SU, metformin/TZD, metformin/SU/DPP4i and metformin/SU/TZD).

This study included 59 057 type 2 diabetes (T2D) patients from a cohort of Texas Medicare beneficiaries who were aged ≥66 years, had consistent diabetes medication intake, were not diagnosed with depression or prescribed antidepressants during the 2-year look-back period and received regular care from Medicare providers.

The main outcome was a new episode of depression, identified by a new depression diagnosis during the follow-up period.

A total of 59 057 T2D patients (mean (SD) age, 75.4 (6.4) years; 30 798 (52.1%) female) were followed up to 96 months. Of these, 22.5% patients had a new episode of depression at the 5-year follow-up. Compared with the metformin-only group, patients in the non-metformin-containing regimens had a higher risk of new episode depression (HR: 1.17, 95% CI 1.05 to 1.30 for DPP4i-only; HR: 1.06, 95% CI 1.01 to 1.12 for SU-only), but there was no significant difference among patients receiving metformin-containing combination therapy. Metformin/TZD and metformin/SU/DPP4i combination treatments had a lower risk of new episodes of depression than metformin-only (HR: 0.88, 95% CI 0.78 to 0.99 and HR: 0.83, 95% CI 0.71 to 0.98 separately). The same direction of association was observed in sensitivity analyses.

This retrospective cohort study found that T2D patients treated with metformin/TZD and metformin/SU/DPP4i had the lowest risk of new episodes of depression. These findings suggest that certain combinations of metformin with other antidiabetic medications may be associated with a reduced risk of new-onset depression. Therefore, it could be beneficial to incorporate depression risk evaluation into routine diabetes care and consider it in the decision-making process for diabetes medication types, especially when deprescribing metformin.

The study primarily aims to compare alterations in the daily patterns of glucose fluctuations across individuals with different kinds of diabetes in pregnancy and secondly investigate influencing factors that may react with glucose variations.

We conducted a retrospective cohort study of 776 pregnant women in Shanghai General Hospital. We grouped participants who were exposed to gestational hyperglycemia into 5 sub-groups [Type 1 diabetes (T1DM), Type 2 diabetes (T2DM), Overt diabetes, Gestational diabetes (GDMA1 and GDMA2). Demographic variables and GV parameters were compared among 5 groups through ANOVA-test and Chi-square test. We estimated odd ratios (ORs) for the association between glucose coefficient of variation (CV) and possible influencing variables.

A final total of 776 pregnant women were analyzed. The proportion of pregnant women with pre-gestational diabetes was 31.83% (T1DM: 3.35%,T2DM: 28.48%), ODM 26.68%, and GDM was 41.49% (GDMA1:18.04%, GDMA2: 23.45%). T1DM group performed greatest glucose fluctuations with a CV value 35.02% whereas the number in all the other groups was no more than 22.82% (ODM group). In terms of achieving glycemic control target, only 57.70% participants hit the goal while all the other groups achieved the standard with at least a percentage of 94.20% (ODM group). Other parameters (GMI < 6.0%, GA < 15.70% and HbA1c < 6.0%) showed similar trends in each group. On multivariate logistic regression analysis of possible factors influencing CV, only body mass index (BMI) (OR: 0.754, 95% CI: 0.585-0.971; P = 0.029), HOMA- β (OR:0.969, 95%CI: 0.959-0.976; P = 0.037) and fasting plasma glucose (FPG) (OR: 1.832, 95% CI: 1.170-2.870; P = 0.008) reached statistical significance.

Pregnant women with type 1 or type 2 diabetes exhibit significantly greater glycemic variability compared to those with gestational diabetes, with the ODM group showing intermediate variability, and BMI, HOMA-β, and FPG identified as independent risk factors for unstable glucose variability.

Elevated glycemic variability (GV) is commonly observed in intensive care unit (ICU) patients and has been associated with clinical outcomes. However, the relationship between GV and prognosis in ICU patients with hemorrhagic stroke (HS) remains unclear. This study aims to investigate the association between GV and short- and long-term all-cause mortality.

Clinical data for hemorrhagic stroke (HS) patients were obtained from the MIMIC-IV 3.1 database. GV was quantified using the coefficient of variation (CV), calculated as the ratio of the standard deviation to the mean blood glucose level. The association between GV and clinical outcomes was analyzed using Cox proportional hazards regression models. Additionally, restricted cubic spline (RCS) curves were employed to examine the nonlinear relationship between GV and short- and long-term all-cause mortality.

A total of 2,240 ICU patients with HS were included in this study. In fully adjusted models, RCS analyses revealed a U-shaped association between the CV and both short- and long-term all-cause mortality (P for nonlinearity < 0.001 for all outcomes). Two-piecewise Cox regression models were subsequently applied to identify CV thresholds. The thresholds for all-cause mortality in ICU, during hospitalization, and at 30, 90, and 180 days were determined to be 0.14, 0.16, 0.155, 0.14, and 0.14, respectively. These findings were consistent in sensitivity and subgroup analyses.

In HS patients, higher GV is associated with an increased risk of both short- and long-term all-cause mortality. Our findings suggest that stabilizing GV may improve the prognosis of HS patients.

Background/Objectives: Glycemic variability (GV) is a novel concept in the assessment of the quality of glycemic control in patients with diabetes, with its importance emphasized in patients with type 1 diabetes. Its adoption in clinical practice emerged with the increased availability of continuous glycemic monitoring systems. The aim of this study is to evaluate the GV in patients with type 1 diabetes mellitus (T1DM) and to assess its associations with other parameters used to evaluate the glycemic control. Methods: GV indexes and classical glycemic control markers were analyzed for 147 adult patients with T1DM in a multicentric cross-sectional study. Results: Stable glycemia was associated with a higher time in range (TIR) (78% vs. 63%; p < 0.001) and a lower HbA1c (6.8% vs. 7.1%; p = 0.006). The coefficient of variation (CV) was reversely correlated with TIR (Spearman's r = -0.513; p < 0.001) and positively correlated with hemoglobin A1c (HbA1c) (Spearman's r = 0.349; p < 0.001), while TIR was reversely correlated with HbA1c (Spearman's r = -0.637; p < 0.001). The composite GV and metabolic outcome was achieved by 28.6% of the patients. Conclusions: Stable glycemia was associated with a lower HbA1c, average and SD of blood glucose, and a higher TIR. A TIR higher than 70% was associated with a lower HbA1c, and SD and average blood glucose. Only 28.6% of the patients with T1DM achieved the composite GV and metabolic outcome, despite 53.7% of them achieving the HbA1c target, emphasizing thus the role of GV in the assessment of the glycemic control.

Individuals with diabetes frequently encounter sleep disturbances, which can detrimentally impact glycaemic management. We reviewed the relationship between sleep outcomes and glycaemic variability in adults with diabetes.

We systematically searched Medline, EMBASE and Cochrane Library (2002-March 2023) for studies evaluating sleep and glycaemic variability in adults with type 1 and type 2 diabetes. Among the 3049 records, 27 met the inclusion criteria (type 1 diabetes studies = 22). Due to methodological heterogeneity, a qualitative analysis was conducted.

Most studies measuring sleep quality (5 out 7; 71%) reported a significant association with glycaemic variability in type 1 and type 2 diabetes. Sleep duration was not significantly associated with glycaemic variability in type 1 diabetes, whereas other sleep metrics yielded inconclusive results. Hybrid closed-loop pump interventions (n = 12) demonstrated varying sleep outcomes with improved glycaemic variability. Similarly, sleep interventions (n = 3) consistently enhanced sleep but not glycaemic variability. Limitations included moderate to high risk of study bias, confounders, methodological heterogeneity and limited type 2 diabetes data.

A potential association between sleep quality and glycaemic variability exists. However, associations with other sleep metrics remain elusive, with no discernible association between sleep duration and glycaemic variability in type 1 diabetes. Despite advancements in continuous glucose monitoring and ambulatory sleep monitoring, standardised sleep assessment methodologies are lacking in real-world studies. Establishing standard protocols for sleep assessment and defining optimal sleep targets are crucial for meaningful comparisons between studies. Understanding the complex interplay between sleep and glycaemic variability holds promise in improving diabetes management and sleep health.

We aimed to investigate the predictive values of plasma C-peptide levels and the continuous glucose monitoring (CGM)-defined coefficient of variation (CV) in risk prediction for hypoglycemia in Korean people with diabetes with normal and impaired kidney function.

We analyzed data from 1,185 participants diagnosed with type 1 and type 2 diabetes who underwent blinded professional CGM between January 2009 and May 2021 at outpatient clinics. We explored correlations among CGM-defined CV, plasma C-peptide levels, and time below range at <70 and 54 mg/dL across different kidney function categories.

In patients with chronic kidney disease (CKD) stages 1-2 (n=934), 89.3% who had a random plasma C-peptide level higher than 600 pmol/L exhibited a CV of ≤36%. Among those in CKD stage 3 (n=161) with a random plasma C-peptide level exceeding 600 pmol/L, 66.7% showed a CV of ≤36%. In stages 4-5 of CKD (n=90), the correlation between random C-peptide levels and CV was not significant (r=-0.05, P=0.640), including cases with a CV greater than 36% despite very high random plasma C-peptide levels. Random plasma C-peptide levels and CGM-assessed CV significantly predicted hypoglycemia in CKD stages 1-2 and 1-5, respectively.

The established C-peptide criteria in Western populations are applicable to Korean people with diabetes for hypoglycemic risk prediction, unless kidney function is impaired equivalent to CKD stage 3-5. The CGM-defined CV is informative for hypoglycemic risk prediction regardless of kidney function.

Time-restricted eating (TRE) is a chrono-nutrition strategy where the daily 'eating window' is reduced to 8-10 h. We investigated the acute (14-h) effects of TRE, with and without post-meal exercise, on blood glucose and insulin concentrations in people with type 2 diabetes mellitus.

Fourteen participants (5 F, 9 M; HbA1c: 7.6 ± 1.0%) completed four conditions in this randomised crossover study: CON (eating window, 0800-2000 h), CON with exercise (CON + Ex; 0800-2000 h + 15 min walking at 60% VO2peak, 45 min post-meal), TRE (eating window 1000-1800 h), and TRE with exercise (TRE + Ex, 1000-1800 h + 15 min walking as per CON + Ex), with standardised meals. Venous blood samples were collected at 26-timepoints and analysed for glucose and insulin concentrations. Statistical analysis used linear mixed-effects models with P < 0.05.

Reducing the eating window had little effect on plasma glucose 14-h area under the curve (AUC). Exercise reduced insulin 14-h AUC (P=0.01) with no additive effect of TRE.

Post-meal exercise lowered 14-h insulin AUC, neither 8-h TRE nor post-meal exercise altered 14-h blood glucose compared with 12-h eating window. Future work should focus on long-term effects of TRE combined with exercise for enhancing blood glucose in people with type 2 diabetes mellitus.

Identifying high-risk acute pancreatitis (AP) patients in the ICU is vital for improving prognosis. Thus, this study aims to explore the relationship between the coefficient of variation (CV) of blood glucose and the all-cause mortality of patients with AP in the ICU.

A retrospective analysis was conducted on AP patients in the MIMIC-IV database. The CV was used to describe the glycemic variability (GV) and the optimal cut-off value was determined using the ROC curve. Subsequently, analyze the correlation between CV and all-cause mortality.

A total of 907 patients with AP in the ICU were included in this study. The ROC curve determined the optimal CV cut-off value as 0.25. The KM survival curves and univariate and multivariate logistics regression analyses all showed that CV was associated with the 30-day, 60-day, and 90-day all-cause mortality (P < 0.05). The RCS curves showed a nonlinear correlation (P < 0.05). When CV is less than 0.421, 0.449, and 0.428, respectively, the risk of death at 30-day, 60-day, and 90-day increases as the CV value rises. Subgroup analysis showed an interaction between congestive heart failure and CV in 30-day and 60-day all-cause mortality, between age and CV in 60-day and 90-day all-cause mortality, and between chronic pulmonary disease and CV in 30-day all-cause mortality (P all < 0.05).

The CV is associated with the all-cause mortality of AP patients in the ICU, especially when the CV value is between 0.25 and 0.45. When using CV, the effects of age, congestive heart failure, and chronic pulmonary disease should be considered.

Glycemic control is critical for managing transcatheter aortic valve replacement (TAVR) patients, especially those in intensive care units (ICUs). Emerging metrics such as the hemoglobin glycation index (HGI), stress hyperglycemia ratio (SHR), and glycemic variability (GV) offer advanced insights into glucose metabolism. However, their prognostic implications for short- and long-term outcomes post-TAVR remain underexplored.

This retrospective cohort study analyzed 3342 ICU-admitted TAVR patients via the MIMIC-IV database. Patients were stratified into tertiles for HGI, SHR, and GV levels. Survival analyses, including Kaplan‒Meier curves, Cox proportional hazards models and restricted cubic splines (RCSs), were used to assess associations between glycemic control metrics and 30-day and 365-day all-cause mortality in these patients. Sensitivity analyses, subgroup assessments, and external validation were also performed to verify the study findings.

During follow-up, 1.6% and 6.9% of patients experienced 30-day and 365-day mortality after TAVR, respectively. In the fully adjusted cox regression model, lower HGI (HR 1.48, 95% CI 1.05-2.09, P = 0.025) and higher SHR (HR 1.63, 95% CI 1.15-2.32, P = 0.006) were most significantly associated with an increased risk of 365-day mortality. Higher SHR was also significantly associated with an increased risk of 30-day mortality in patients (HR 2.92, 95% CI 1.32-6.45, P = 0.008). Both lower (HR 0.59, 95% CI 0.38-0.92, P = 0.019) and higher GV levels (HR 1.43, 95% CI 1.06-1.93, P = 0.020) were associated with the risk of 365-day mortality.

In critically ill TAVR patients, glycemic control metrics are closely associated with long-term all-cause mortality. The HGI, SHR, and GV provide prognostic insights into clinical outcomes that surpass conventional glucose measurements. These findings highlight the importance of personalized glycemic management strategies in improving TAVR patient outcomes.

Objective: Evaluate the adequacy of the coefficient of variation (CV) and standard deviation (SD) as metrics of glucose variability (GV) across mean glucose (MG) levels in individuals with type 1 diabetes. Methods: Data from the GOLD and SILVER trials were analyzed. Glucose metrics were derived from continuous glucose monitoring (CGM). Generalized estimating equations were used to assess the relationship between SD and MG, considering intraindividual correlations. Nonlinear associations were evaluated using restricted cubic splines, and glucose values outside the CGM detection range (<2.22 mmol/L and >22.2 mmol/L) were handled using a censored Gamma model. Results: In total, 158 individuals with an MG of 10.6 (SD 1.7) mmol/L were included. The SD of glucose values exhibited a nonlinear relationship with the MG during CGM and self-monitoring of blood glucose (SMBG) (both P < 0.001 vs. linear model). The lack of fit of the constant CV model was most distinct at high glucose levels >12 mmol/L. During SMBG, a 25% reduction in MG from 12 to 9 mmol/L was associated with a 16% (95% confidence interval [CI] 10%-21%) reduction in the SD of glucose values. Similar associations were observed during CGM. This deviation was attributed to the censoring of glucose values outside the detection range. After adjusting for censoring, the lack of fit was resolved. When transitioning from SMBG to CGM, the ordinary CV and SD underestimated the treatment effect on GV by 30% and 27%, respectively, compared to estimates adjusted for censoring. Similarly, ordinary CV underestimated the treatment effect by 11% compared with CV adjusted for the nonlinear SD-MG relationship in the GOLD study. Conclusion: The SD of glucose values does not increase linearly with the MG during glucose-lowering therapy, suggesting that CV is not an optimal measure of GV. After adjusting for censored glucose values, CV remains reliable. Alternatively, nonlinear SD adjustments relative to MG effectively evaluate glucose-lowering therapies' impact on GV.

Studies have shown a strong correlation between excess iron and the development of gestational diabetes mellitus (GDM), though iron is an essential trace element during pregnancy. This study aims to investigate the precise relationship between iron storage levels during late pregnancy and the development of GDM, trying to find out ways to meet pregnant iron storage requirements and reduce GDM risk simultaneously.

A non-anemic population consisting of 9,512 healthy singleton pregnant women were included in this study. Serum ferritin (SF) levels during the second and third trimesters and other clinical information were retrospectively collected. Restricted cubic splines (RCS) were performed to examined the non-linear associations between SF level and the GDM incidence as well as blood glucose related indicators during the second trimester. Moreover, the association between the variation of HbA1c levels and the fluctuation of SF levels throughout the third trimester was also explored with the method of RCS.

Overall, women with GDM had slightly higher median SF level than women without GDM 20.5 (13.3, 32.3) vs. 19.8 (12.9, 30.5), P = 0.017) in the second trimester. A U-shaped relationship between GDM risk and SF levels in the second trimester was established after accounting for other cofounding factors (P < 0.001 for nonlinearity). Both GDM and non-GMD women revealed a significant negative relationship between hemoglobin A1c (HbA1c) and SF levels (P < 0.001 for nonlinearity for both). The 1-hour post-glucose load plasma glucose showed a positive correlation tendency with SF levels (P = 0.748 for nonlinearity) in GDM women, while the relationship between these two variables was not obvious in non-GDM women (P = 0.045 for nonlinearity). Generally, the levels of HbA1c rose in the trimester, however, maintaining a high SF level throughout the third trimester would substantially increase the HbA1c level among GDM women with high SF levels (> 30ng/ml) in the second trimester (P < 0.001 for nonlinearity).

GDM might result from high or low SF levels during the second trimester. Iron supplementation during pregnancy should be administered judiciously based on blood glucose level and iron storage capacity to maintain the SF level within an appropriate range.

Comparing continuous glucose monitoring (CGM)-recorded metrics during treatment with insulin degludec (IDeg) versus insulin glargine U100 (IGlar-100) in people with type 1 diabetes (T1D) and recurrent nocturnal severe hypoglycemia.

This is a multicenter, two-year, randomized, crossover trial, including 149 adults with T1D and minimum one episode of nocturnal severe hypoglycemia within the last two years. Participants were randomized 1:1 to treatment with IDeg or IGlar-100 and given the option of six days of blinded CGM twice during each treatment. CGM traces were reviewed for the percentage of time-within-target glucose range (TIR), time-below-range (TBR), time-above-range (TAR), and coefficient of variation (CV).

Seventy-four participants were included in the analysis. Differences between treatments were greatest during the night (23:00-06:59). Treatment with IGlar-100 resulted in 54.0% vs 49.0% with IDeg TIR (70-180 mg/dL) (estimated treatment difference [ETD]: -4.6%, 95% confidence interval [CI]: -9.1, -0.0, P = .049). TBR was lower with IDeg at level 1 (54-69 mg/dL) (ETD: -1.7% [95% CI: -2.9, -0.5], P < .05) and level 2 (<54 mg/dL) (ETD: -1.3% [95% CI: -2.1, -0.5], P = .001). TAR was higher with IDeg compared with IGlar-100 at level 1 (181-250 mg/dL) (ETD: 4.0% [95% CI: 0.8, 7.3], P < .05) and level 2 (> 250 mg/dL) (ETD: 4.0% [95% CI: 0.8, 7.2], P < .05). The mean CV was lower with IDeg than that with IGlar-100 (ETD: -3.4% [95% CI: -5.6, -1.2], P < .05).

For people with T1D suffering from recurrent nocturnal severe hypoglycemia, treatment with IDeg, compared with IGlar-100, results in a lower TBR and CV during the night at the expense of more TAR.

Time in tight range (TITR) is a novel glycemic metric assessing normoglycemia in individuals with diabetes.

To assess the attainability of the TITR (70-140 mg/dL) target in youth with diabetes using different treatment strategies during Ramadan fasting.

This prospective study included 276 non-insulin-treated type 2 diabetes mellitus (T2DM) and 426 patients with type 1 diabetes mellitus (T1DM) who were categorized into: multiple daily injections [MDI] + intermittently scanned CGM (isCGM), sensor augmented pump (SAP) and advanced hybrid closed loop (AHCL).

At the end of Ramadan, the mean TITR was 42.3 ± 6.6 % for all T1DM patients and 63.5 ± 4.0 % in T2DM (p < 0.001). The highest TITR was in T2DM group together with T1DM on AHCL (62.3 ± 11.6 %), followed by SAP group (37.7 ± 5.7 %) and MDI + isCGM group (23.6 ± 5.9 %, p < 0.001). Hypoglycemic episodes as shown by time below range (TBR) < 70 mg/dL and TBR < 54 mg/dL were minimal during Ramadan in AHCL group in comparison to before Ramadan (2.6 ± 0.7 versus 2.9 ± 0.9 %; p = 0.061 and 0.4 ± 0.1 vs 0.5 ± 0.1 %, p = 0.561, respectively) with a lower coefficient of variation (CoV) (p < 0.001) than other T1DM participants.

At the end of Ramadan, TITR was decreased in patients with T1DM except those using AHCL who had similar levels to non-insulin-treated T2DM patients. Advanced technology has the potential for achieving tight glycemic targets, along with a reduction in CoV, without increasing hypoglycemic risk compared with other insulin treatment modalities.

To assess the effects of IDegLira on glucometric indices deriving from intermittently scanned Continuous Glucose Monitoring (isCGM) in patients with type 2 diabetes (T2D).

Retrospective, observational, cohort, multi-center, "pre - post" study. All adults consecutively identified in the medical records who started treatment with IDegLira, and for whom an isCGM report before and after the initiation of IDegLira was available were included in the study. Time in range (TIR) represented the primary endpoint. Additional glucometric indices, insulin doses and body weight were also assessed.

Overall, 87 patients were included by 5 diabetes centers [mean age 70.2 ± 11.0 years, mean duration of T2D 15.5 ± 9.6 years; BMI 29.4 ± 5.4 kg/m2, baseline HbA1c 9.1 ± 2.1%, 33% insulin naïve, 20.7% treated with basal-oral therapy (BOT), and 46% treated with multiple daily injections of insulin (MDI)]. After an average of 1.7 weeks from IDegLira initiation, TIR significantly increased from 56.8 ± 23.5% to 81.3 ± 13.5% (p < 0.0001), TAR decreased from 42.3 ± 24.2% to 17.1 ± 13.6% (p < 0.0001), while TBR remained steadily low (from 1.3 ± 2.3% to 1.4 ± 2.6%; p = 0.62). Estimated HbA1c decreased from 9.1 ± 2.1% to 6.7 ± 0.6% (p < 0.0001) and percentage of patients with a blood glucose coefficient of variation ≥ 36% dropped from 33.2 to 13.8% (p = 0.0005). In patients on MDI, the reduction in the total insulin dose was substantial (from 55.8 ± 31.2 IU to 27.2 ± 12.3 U).

In T2D patients with poor metabolic control, either insulin naïve or treated with BOT or MDI, the introduction of IDegLira produces a significant increase in the time spent in good metabolic control and a marked reduction in glycemic fluctuations.

While the relationship between glycemic variability (GV) and acute kidney injury (AKI) has been a subject of interest, the specific association of GV with persistent AKI beyond 48 hours postoperative after noncardiac surgery is not well-established.

This retrospective cohort study aimed to describe the patterns of different GV metrics in the immediate 48 hours after noncardiac surgery, evaluate the association between GV indices and persistent AKI within the 7-day postoperative window, and compare the risk identification capabilities of various GV for persistent AKI. A total of 10,937 patients who underwent major noncardiac surgery across 3 medical centers in eastern China between January 2015 and September 2023 were enrolled. GV was characterized using the coefficient of variations (CV), mean amplitude of glycemic excursions (MAGE), and the blood glucose risk index (BGRI). Multivariable logistic regression was used to examine the relationship between GV and AKI. Optimal cutoff values for GV metrics were calculated through the risk identification models, and an independent cohort from the INformative Surgical Patient dataset for Innovative Research Environment (INSPIRE) database with 7714 eligible cases served to externally validate the risk identification capability.

Overall, 274 (2.5%) of the 10,937 patients undergoing major noncardiac surgery met the criteria of persistent AKI. Higher GV was associated with an increased risk of persistent AKI (CV: odds ratio [OR] = 1.26, 95% confidence interval [CI], 1.08-1.46; MAGE: OR = 1.31, 95% CI, 1.15-1.49; BGRI: OR = 1.18, 95% CI, 1.08-1.29). Compared to models that did not consider glycemic factors, MAGE and BGRI independently contributed to predicting persistent AKI (MAGE: areas under the curve [AUC] = 0.768, P = .011; BGRI: AUC = 0.764, P = .014), with cutoff points of 3.78 for MAGE, and 3.02 for BGRI. The classification of both the internal and external validation cohorts using cutoffs demonstrated good performance, achieving the best AUC values of 0.768 for MAGE in the internal cohort and 0.777 for MAGE in the external cohort.

GV measured within 48 hours postoperative period is an independent risk factor for persistent AKI in patients undergoing noncardiac surgery. Specific cutoff points can be used to stratify at-risk patients. These findings indicate that stabilizing GV may potentially mitigate adverse kidney outcomes after noncardiac surgery, highlighting the importance of glycemic control in the perioperative period.

Intraindividual body weight variability (BWV), that is, the degree of weight fluctuations over time, is associated with an increased risk of cardiovascular diseases (CVDs) in multiple settings. The impact of BWV on cardiovascular risk in type 1 diabetes (T1D) remains unclear, despite the issues relative to weight management in individuals with this condition.

Using data from the Swedish National Diabetes Register, we identified individuals with T1D and without CVD at baseline with at least three measurements of body weight taken over three consecutive years. We estimated BWV as quartiles of the standard deviation of weight measures and explored its longitudinal association with the incidence of CVD during a 12.7 ± 4.6 year follow-up through adjusted Cox regression models. The primary endpoint was the composite of nonfatal myocardial infarction, nonfatal stroke and all-cause mortality. We modelled the function of risk in relation to the magnitude of BWV, testing also whether weight trends, that is, increasing, stable or decreasing, age, sex and glycaemic control modified the association between BWV and the outcome.

Among the 36 333 individuals with T1D in the register, we identified 19 373 individuals with at least three measures of body weight and without CVD at baseline. Participants with the highest BWV had a 42% increased risk of reaching the primary endpoint compared to those with the lowest BWV (hazard ratio [HR] = 1.42, 95% confidence interval [CI]: 1.24-1.62). In addition, high BWV was significantly associated with a 51% increased risk of all-cause mortality (HR = 1.51, 95% CI: 1.28-1.78), a 37% increased risk of peripheral artery disease (HR = 1.37, 95% CI: 1.06-1.77) and a 55% increased risk of hospitalization for heart failure (HR = 1.55, 95% CI: 1.20-2.01). BWV showed a quasi-linear association with the primary endpoint. No interaction was observed when comparing subgroups for weight trends, sex or degree of glycaemic control. In the subgroup of elderly individuals, the association of BWV with the primary endpoint was no longer significant.

High BWV is associated with an increased risk of CVD and all-cause mortality in individuals with T1D, independently of canonical risk factors. Weight trends, sex and glycaemic control do not modify such association while older age attenuates it.

Gestational diabetes mellitus (GDM) affects 14% of all pregnancies worldwide and is associated with cardiometabolic risk. We aimed to exploit high-resolution wearable device time-series data to create a fine-grained physiological characterisation of the postpartum GDM state in free-living conditions, including clinical variables, daily glucose dynamics, food and drink consumption, physical activity, sleep patterns and heart rate.

In a prospective observational study, we employed continuous glucose monitors (CGMs), a smartphone food diary, triaxial accelerometers and heart rate and heart rate variability monitors over a 2 week period to compare women who had GDM in the previous pregnancy (GDM group) and women who had a pregnancy with normal glucose metabolism (non-GDM group) at 1-2 months after delivery (baseline) and 6 months later (follow-up). We integrated CGM data with ingestion events recorded with the smartphone app MyFoodRepo to quantify the rapidity of returning to preprandial glucose levels after meal consumption. We inferred the properties of the underlying 24 h rhythm in the baseline glucose. Aggregating the baseline and follow-up data in a linear mixed model, we quantified the relationships between glycaemic variables and wearable device-derived markers of circadian timing.

Compared with the non-GDM group (n=15), the GDM group (n=22, including five with prediabetes defined based on fasting plasma glucose [5.6-6.9 mmol/l (100-125 mg/dl)] and/or HbA1c [39-47 mmol/mol (5.7-6.4%)]) had a higher BMI, HbA1c and mean amplitude of glycaemic excursion at baseline (all p≤0.05). Integrating CGM data and ingestion events showed that the GDM group had a slower postprandial glucose decrease (p=0.01) despite having a lower proportion of carbohydrate intake, similar mean glucose levels and a reduced amplitude of the underlying glucose 24 h rhythm (p=0.005). Differences in CGM-derived variables persisted when the five women with prediabetes were removed from the comparison. Longitudinal analysis from baseline to follow-up showed a significant increase in fasting plasma glucose across both groups. The CGM-derived metrics showed no differences from baseline to follow-up. Late circadian timing (i.e. sleep midpoint, eating midpoint and peak time of heart rate) was correlated with higher fasting plasma glucose and reduced amplitudes of the underlying glucose 24 h rhythm (all p≤0.05).

We reveal GDM-related postpartum differences in glucose variability and 24 h rhythms, even among women clinically considered to be normoglycaemic. Our results provide a rationale for future interventions aimed at improving glucose variability and encouraging earlier daily behavioural patterns to mitigate the long-term cardiometabolic risk of GDM.

ClinicalTrials.gov no. NCT04642534.

Background and Aims: Disordered eating behaviors (DEB) are common among individuals with type 1 diabetes (T1D). Glycemic variability, potentially harmful in T1D, may reveal distinct characteristics between those with higher versus lower variability, particularly concerning DEB. Our aim was to evaluate the prevalence of DEB and associated risk factors among adolescents and young adults with T1D and to investigate unique factors associated with DEB across different levels of glycemic variability. Methods: An observational, cross-sectional study was conducted with 147 individuals with T1D, aged 13-21 years. Data were collected from medical charts, personal technological devices for assessing glycemic variability, and self-reported questionnaires, including assessments of DEB. Results: DEB were found in 62 (42.1%) individuals, and 41.5% achieved the glycemic variability (% coefficient of variation) target ≤36%. Among individuals with low glycemic variability, DEB were positively associated with diabetes distress (odds ratio [OR]: 1.14 [95% confidence interval or CI: 1.05-1.22], P < 0.001), longer diabetes duration (OR: 1.34 [95% CI: 1.05-1.70], P = 0.016) and lower socioeconomic-status (OR: 0.53 [95% CI: 0.31-0.90], P = 0.019). Among those with high glycemic variability, body mass index Z score (OR: 3.82 [95% CI: 1.48-9.85], P = 0.005), HbA1c (OR: 4.12 [95% CI: 1.33-12.80], P = 0.014), disinhibited eating (OR: 1.57 [95% CI: 1.14-2.15], P = 0.005), and tendency to lower socioeconomic status (OR: 0.75 [95% CI: 0.56-1.01], P = 0.065). Discussion: DEB are prevalent among adolescents and young adults with T1D and are associated with various risk factors. Factors associated with DEB vary across different levels of glycemic variability. Both low and high glycemic variability are associated with specific risk factors for DEB. One notable risk factor is diabetes-specific disinhibited eating among individuals with high glycemic variability, in contrast to those with low glycemic variability. Given these different risk factors, it may be prudent to adjust intervention programs to reduce DEB among T1D adolescents according to their glycemic variability levels.

A growing body of evidence emphasizes the role of glycemic variability (GV) in the development of conventional diabetes-related complications. Furthermore, advancements in diabetes management and increased life expectancy have led to the emergence of new complications, such as cancer, liver disease, fractures, infections, and cognitive dysfunction. GV is considered to exacerbate oxidative stress and inflammation, acting as a major mechanism underlying these complications. However, few reviews have synthesized the association between GV and these emerging complications or examined their underlying mechanisms. Hence, this narrative review provides a comprehensive discussion of the burden, risks, and mechanisms of GV in these complications, offering further evidence supporting GV as a potential therapeutic target for diabetes management.

This study aimed to explore the association between glycemic variability (GV) and postoperative atrial fibrillation (POAF) incidence.

In this retrospective study, we included patients undergoing cardiac surgeries. GV was calculated as the coefficient of variation of blood glucose and categorized into tertiles based on its distribution. The primary endpoint was the incidence of POAF. Logistic regression and restricted cubic splines were used to assess the relationship between GV and POAF.

5365 patients were included, with a median age of 68.3 years, and 25.5 % were female. 1056 (19.7 %) patients developed new-onset POAF. Compared with the low GV group, moderate GV group (odds ratio [OR], 1.82; 95 % confidence interval [CI]: 1.49-2.22) and high GV group (OR, 2.25; 95 % CI, 1.80-2.82) were significantly associated with a higher incidence of POAF. The area under the receiver operating characteristic curve of GV in predicting POAF incidence was 0.77 (95 % CI: 0.76-0.79). There is a threshold-based nonlinear relationship between GV and the incidence of POAF: when GV was < 24 %, the likelihood of POAF increases with higher GV, whereas when GV ≥ 24 %, further increases did not significantly affect the risk of POAF.

Increased GV is associated with higher incidence of POAF.

Although newer technologies of insulin delivery in type 1 diabetes have facilitated an improvement in glycaemic control the risk of hypoglycaemia remains a threat. Therefore, it is important to define the thresholds of glycaemic variability below which the risk of hypoglycaemia can be eliminated or at least minimized.

Randomized controlled trials conducted from 2017 to 2023 comparing Sensor-Augmented-Pumps and Augmented Insulin Delivery Systems (n = 16 and 22 studies, respectively) were selected. A weighted linear model of regression was used to compute the relationship between glycaemic variability and times spent below glucose range. The intercepts of regression lines with the abscissa axis (time below range = 0 %) defined the glycaemic variability thresholds.

Positive relationships were observed between the 2 metrics. The scatter plots indicated that the times spent below range never reached the value of 0 % and that the glycaemic variability never fell below 28 %. By extrapolating the regression lines, the glycaemic variability at intercepts with time below range < 70 mg/dL of 0 % was 30.1 % with sensor augmented pumps and 18.9 % with automated insulin delivery. For a time below range < 54 mg/dL of 0 % the respective glycaemic variability values were 32.7 % and 19.9 % (with sensor augmented pumps and automated insulin delivery, respectively).

Importantly, glycaemic variability targets and ambient hyperglycaemia are interdependent. Users of automated insulin delivery need to reach a glycaemic variability of 18 % to 20 % to minimize or eradicate the risk of hypoglycaemia. Such values are those observed in healthy non-diabetic people.

Appropriate glycemic control is paramount for people with type 1 diabetes (PwT1D) by the effective delivery of exogenous insulin. However, glycemic variability and the risk of severe hypoglycemia must be reliably controlled.

COMET-T is a prospective, multicenter, observational study conducted in Germany, Austria, and Switzerland during 2021-2022 to assess the effectiveness and safety of insulin glargine 300 U/ml (Gla-300) after switching from other basal insulins. Out of 135 PwT1D, data of 94 patients were analyzed. The primary endpoint was the change in time in range (TIR) approximately 12 and 24 weeks after switching to Gla-300. Secondary endpoints were: change in HbA1c, fasting plasma glucose (FPG), coefficient of variation (CV%) of plasma glucose, body weight (BW) and insulin dose.

Patients had mean age of 48.6 ± 16.5 years, included 39.4% males and had 18.2 ± 15.5 years T1D duration. From baseline (54.3%), TIR changed at week 12 (mean change 0.3% [± 14.3]; p = 0.8383) and at week 24 (+ 4.5% [± 14.9], p = 0.078). At week 24, TIR significantly increased in patients with body mass index > 30 kg/m2 (8.4% [± 12.8] p = 0.0057) and patients who previously received insulin detemir (10.5%; [± 12.93]; p = 0.0005). At week 24, there was a significant reduction in the HbA1c value (8.1 ± 0.6% vs. 7.7 ± 0.9%; p < 0.001), a reduction in the CV% of plasma glucose (36.1 ± 12.4% vs. 32.8 ± 9.6%, p = 0.056), and increase in bolus insulin dose (26.5 ± 16.3 vs. 27.9 ± 16.6 U/day; p = 0.042). FPG, BW, and basal insulin doses were not significantly changed.

Although switching to Gla-300 in poorly controlled PwT1D did not significantly reduce TIR, it significantly decreased HbA1c values and glycemic variability without changes in BW and basal insulin dose.

Bakground: The mortality rate from community-acquired pneumonia (CAP) or coronavirus disease 19 (COVID-19) is high, especially in hospitalized patients. This study aimed to assess the disturbances of glucose and lipid metabolism with in-hospital complications and short-term outcomes for patients with pneumonia with different etiologies. Methods: This observational study comprised 398 patients divided as follows: 155 with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pneumonia, 129 participants with viral CAP, and 114 with bacterial pneumonia. Results: Fasting plasma glucose (FPG) at admission and glycemic variation during hospitalization was linked with acute kidney injury (AKI) in bacterial CAP. Compared with a value <110 mg/dL for FPG at admission, levels between 110 and 126 mg/dL are associated with mortality in both COVID-19 (OR = 3.462, 95% CI: 1.275-9.398, p = 0.015) and bacterial CAP participants (OR = 0.254; 95% CI: 0.069-0.935, p = 0.039), while a value ≥126 mg/dL was linked with mortality only in patients with SARS-CoV-2 (OR = 3.577, 95% CI: 1.166-10.976, p = 0.026). No relation between lipid biomarkers and complications or in-hospital outcomes was observed in all three participant groups. Conclusions: Patients with bacterial CAP are more prone to developing AKI due to increased FBG at admission and glycemic variations during hospitalization, while elevated FBG values at admission are associated with mortality in both COVID-19 and bacterial CAP.

In diabetic patients, repeated episodes of hypoglycemia can increase glucose variability (GV), which may lead to glutamate neurotoxicity in the brain and consequently affect cognitive functions. Astrocytes play a crucial role in regulating the balance of glutamate within the brain, and their function is influenced by the histamine receptor (HR) signaling pathway. However, the specific role of this mechanism under conditions of high GV is not yet clear. The results showed that increased GV resulted in decreased expression of HRs in mice hippocampus and astrocytes cultured in vitro. Additionally, a decrease in the expression of proteins related to glutamate metabolic clearance was observed, accompanied by a reduction in glutamate reuptake capacity. Notably, the intervention with histidine/histamine was able to reverse the above changes. Further mechanistic studies showed that inhibition of HRs that increased GV led to significant disturbances in astrocytic mitochondrial function. These abnormalities encompassed increased fragmentation morphology and the accumulation of reactive oxygen species, accompanied by decreased mitochondrial respiratory capacity and dysregulation of dynamics. Distinct HR subtypes exhibited variations in the modulation of mitochondrial function, with H3R demonstrating the most pronounced impact. The overexpression of H3R could enhance glutamate metabolic by reversing disturbances in mitochondrial dynamics. Therefore, this study suggests that H3R is able to maintain glutamate metabolic clearance capacity and exert neuroprotective effects in astrocytes that increased GV by regulating mitochondrial dynamic balance. This provides an important basis for potential therapeutic targets for diabetes-related cognitive dysfunction.

With the widespread use of continuous glucose monitoring (CGM), glycaemic variability (GV) is a glucose metric that has been gaining increasing attention. However, unlike other glucose metrics that are easily defined and have clear targets, GV has a large number of different measures given the complexity involved in assessment. While variabilities in HbA1c, fasting and postprandial glucose have been incorporated under the GV banner, short-term variability in glucose, within day and between days, is more in keeping with the correct definition of GV. This review is focused on short-term GV, as assessed by CGM data, although studies calculating GV from capillary glucose testing are also mentioned as appropriate. The different measures of GV are addressed, and their potential role in microvascular and macrovascular complications, as well as patient-related outcomes, discussed. It should be noted that the independent role of GV in vascular pathology is not always clear, given the inconsistent findings in different populations and the close association between GV and hypoglycaemia, itself an established risk factor for adverse outcomes. Therefore, this review attempts, where possible, to disentangle the contribution of GV to diabetes complications from other glycaemic parameters, particularly hypoglycaemia. Evidence to date strongly suggests an independent role for GV in vascular pathology but future large-scale outcome studies are required to fully understand the exact contribution of this metric to vascular complications. This can be followed by setting appropriate GV measures and targets in different diabetes subgroups, in order to optimise glycaemic management and limit the risk of complications.

To investigate possible associations of glucose patterns with outcomes of Corona Virus Disease 19 (COVID-19) using continuous glucose monitoring (CGM) in 43 patients hospitalized for COVID-19 mild-to-moderate pneumonia, regardless of diabetes.

Prospective observational study conducted during two pandemic waves in 2020-2021. Glucose sensor metrics of 7-day recording were obtained from blinded CGM. Respiratory function was evaluated as arterial partial pressure of oxygen (PaO2) to fraction of inspired oxygen (FiO2) ratio (PaO2:FiO2).

PaO2:FiO2 ratio was positively correlated with time in tight range (TITR) 70-140 (r = 0.49, p < 0.001) and time in range (TIR) 70-180 (r = 0.32, p < 0.05), and negatively correlated with average glucose (r =- 0.31, p < 0.05), coefficient of glucose variation (CV) (r =- 0.47, p < 0.01) and time above range (TAR) > 140 (r =- 0.49, p < 0.001). No relations were observed with HbA1c. Multivariate regression analysis showed that normal respiratory function at time of CGM removal correlated positively with TITR 70-140 mg/dL (p < 0.01), negatively with CV and TAR > 140 mg/dL (both p < 0.05) and not with TIR 70-180 and average glucose.

Lower glucose variability and optimal glucose control, expressed as CV and TITR, are CGM metrics predictive of a better prognosis in COVID-19 patients with pneumonia.

The burden of atrial fibrillation (AF) in the intensive care unit (ICU) remains heavy. Glycaemic control is important in the AF management. Glycaemic variability (GV), an emerging marker of glycaemic control, is associated with unfavourable prognosis, and abnormal GV is prevalent in ICUs. However, the impact of GV on the prognosis of AF patients in the ICU remains uncertain. This study aimed to evaluate the relationship between GV and all-cause mortality after ICU admission at short-, medium-, and long-term intervals in AF patients.

Data was obtained from the Medical Information Mart for Intensive Care IV 3.0 database, with admissions (2008-2019) as primary analysis cohort and admissions (2020-2022) as external validation cohort. Multivariate Cox proportional hazards models, and restricted cubic spline analyses were used to assess the associations between GV and mortality outcomes. Subsequently, GV and other clinical features were used to construct machine learning (ML) prediction models for 30-day all-cause mortality after ICU admission.

The primary analysis cohort included 8989 AF patients (age 76.5 [67.7-84.3] years; 57.8% male), while the external validation cohort included 837 AF patients (age 72.9 [65.3-80.2] years; 67.4% male). Multivariate Cox proportional hazards models revealed that higher GV quartiles were associated with higher risk of 30-day (Q3: HR 1.19, 95%CI 1.04-1.37; Q4: HR 1.33, 95%CI 1.16-1.52), 90-day (Q3: HR 1.25, 95%CI 1.11-1.40; Q4: HR 1.34, 95%CI 1.29-1.50), and 360-day (Q3: HR 1.21, 95%CI 1.09-1.33; Q4: HR 1.33, 95%CI 1.20-1.47) all-cause mortality, compared with lowest GV quartile. Moreover, our data suggests that GV needs to be contained within 20.0%. Among all ML models, light gradient boosting machine had the best performance (internal validation: AUC [0.780], G-mean [0.551], F1-score [0.533]; external validation: AUC [0.788], G-mean [0.578], F1-score [0.568]).

GV is a significant predictor of ICU short-term, mid-term, and long-term all-cause mortality in patients with AF (the potential risk stratification threshold is 20.0%). ML models incorporating GV demonstrated high efficiency in predicting short-term mortality and GV was ranked anterior in importance. These findings underscore the potential of GV as a valuable biomarker in guiding clinical decisions and improving patient outcomes in this high-risk population.

Diastolic heart failure (DHF) and type 2 diabetes mellitus (T2DM) often coexist, causing increased mortality rates. Glycaemic variability (GV) exacerbates cardiovascular complications, but its impact on outcomes in patients with DHF and T2DM remains unclear. This study examined the relationships between GV with mortality outcomes, and developed a machine learning (ML) model for long-term mortality in these patients.

Patients with DHF and T2DM were included from the Medical Information Mart for Intensive Care IV, with admissions (2008-2019) as primary analysis cohort and admissions (2020-2022) as external validation cohort. Multivariate Cox proportional hazards models and restricted cubic spline analyses were used to evaluate the associations of GV with 90-day, 1-year, and 3-year all-cause mortality. The primary analysis cohort was split into training and internal validation cohorts, then developing ML models for predicting 1-year all-cause mortality in training cohort, which were validated using the internal and external validation cohorts.

2,128 patients with DHF and T2DM were included in primary analysis cohort (meidian age 71.0years [IQR: 62.0-79.0]; 46.9% male), 498 patients with DHF and T2DM were included in the external validation cohort (meidian age 75.0years [IQR: 67.0-81.0]; 54.0% male). Multivariate Cox proportional hazards models showed that high GV tertiles were associated with higher risk of 90-day (T2: HR 1.45, 95%CI 1.09-1.93; T3: HR 1.96, 95%CI 1.48-2.60), 1-year (T2: HR 1.25, 95%CI 1.02-1.53; T3: HR 1.54, 95%CI 1.26-1.89), and 3-year (T2: HR 1.31, 95%CI: 1.10-1.56; T3: HR 1.48, 95%CI 1.23-1.77) all-cause mortality, compared with lowest GV tertile. Chronic kidney disease, creatinine, potassium, haemoglobin, and white blood cell were identified as mediators of GV and 1-year all-cause mortality. Additionally, GV and other clinical features were pre-selected to construct ML models. The random forest model performed best, with AUC (0.770) and G-mean (0.591) in internal validation, with AUC (0.753) and G-mean (0.599) in external validation.

GV was determined as an independent risk factor for short-term and long-term all-cause mortality in patients with DHF and T2DM, with a potential intervention threshold around 25.0%. The ML model incorporating GV demonstrated strong predictive performance for 1-year all-cause mortality, highlighting its importance in early risk stratification management of these patients.

We aimed to identify the normal range of glucose rates of change (RoC) observed in health and assess whether existing metrics of temporal glycemic variability (GV-timing), such as mean absolute glucose change (MAG) and continuous overlapping net glycemic action (CONGA), are predictive of abnormally rapid RoC in type 1 diabetes (T1D).

We identified the normal range of RoC over one-hour intervals from continuous glucose monitoring (CGM) data of healthy individuals. Rapidly rising glucose was defined as RoC values above percentiles 99% (level 1, L1) or 99.9% (level 2, L2), and rapidly falling glucose as below 1% (L1) or 0.1% (L2). The percentage of time these thresholds are exceeded in a given individual is referred to as time in fluctuation (TIF). In a separate CGM dataset of 736 T1D individuals, we calculated TIF-L1 and TIF-L2, and compared them against corresponding values of MAG and CONGA.

The extremum percentiles of RoC observed in health are 0.1%: -80 mg/dL/h, 1%: -50 mg/dL, 99%: +56 mg/dL/h, and 99.9%: +89 mg/dL/h. The T1D individuals spend significantly more TIF at rates exceeding these thresholds (TIF-L1: median, 16.7% [interquartile range, 12.7-21.5], TIF-L2: 5.0% [3.1-7.8]) than healthy individuals (TIF-L1: 1.4% [0.6-2.8], TIF-L2: 0.0% [0.0-0.2]). Both MAG and CONGA are highly correlated with TIF-L1 and TIF-L2 (r > .95 in each pairwise comparison).

Individuals with T1D spend significant time with glucose RoC exceeding those observed in health. Existing GV-timing metrics are strongly correlated with time with abnormal RoC. Incorporation of a GV-timing metric in clinical practice is recommended.

Diabetes mellitus (DM) is becoming increasingly prominent, posing a serious threat to human health. Its prevalence is rising every year, and often affects young people. In the past few decades, research on marine algae has been recognized as a major field of drug discovery. Seaweed active substances, including algal polysaccharides, algal polyphenols, algal unsaturated fatty acids, and algal dietary fiber, have unique biological activities. This article reviews the effects and mechanisms of the types, structures, and compositions of seaweed on inhibiting glucose and lipid metabolism disorders, with a focus on the inhibitory effect of active substances on blood glucose reduction. The aim is to provide a basis for the development of seaweed active substance hypoglycemic drugs.

Diabetic peripheral neuropathy (DPN) is a prevalent chronic complication of diabetes which is linked to chronic hyperglycemia and glycemic variability. This study aimed to investigate the association between the glycemia risk index (GRI) and DPN in patients with type 2 diabetes mellitus (T2DM) using continuous glucose monitoring (CGM) data.

From 2019 to 2023, 862 adults diagnosed with T2DM were enrolled at a tertiary care diabetes center in Ningbo, China. The medical history and laboratory parameters were recorded. Neurophysiological examinations were performed to evaluate DPN. The CGM data were recorded for 14 days, and the GRI was calculated based on these data. Multivariate logistic regression analyses were conducted to assess the odds ratio (OR) for DPN with an increased GRI.

The prevalence of DPN in the ascending GRI quartiles was 41.6%, 47.9%, 49.1%, and 59.5%, respectively (P for trend < 0.001). In the multivariable logistic analysis, the highest GRI quartile exhibited a 63% greater risk of DPN (OR 1.631, 95% CI: 1.071 to 2.484, P = 0.023) than the lowest quartile after adjusted for age, sex, body mass index, diabetes duration, blood pressure, creatinine, urinary albumin-to-creatinine ratio, lipid profile and glycated hemoglobin.

High GRI levels, as measured by CGM, were associated with a greater likelihood of DPN in T2DM patients.