Impact of visit-to-visit glycemic variability on the risks of macrovascular and microvascular events and all-cause mortality in type 2 diabetes: the ADVANCE trial

Blood pressure variability: a review

Blood pressure variability (BPV) predicts cardiovascular events independent of mean blood pressure. BPV is defined as short-term (24-h), medium or long- term (weeks, months or years). Standard deviation, coefficient of variation and variation independent of the mean have been used to quantify BPV. High BPV is associated with increasing age, diabetes, smoking and vascular disease and is a consequence of premature ageing of the vasculature. Long-term BPV has been incorporated into cardiovascular risk models (QRISK) and elevated BPV confers an increased risk of cardiovascular outcomes even in subjects with controlled blood pressure. Long-acting dihydropyridine calcium channel blockers and thiazide diuretics are the only drugs that reduce BPV and for the former explains their beneficial effects on cardiovascular outcomes. We believe that BPV should be incorporated into blood pressure management guidelines and based on current evidence, long-acting dihydropyridines should be preferred drugs in subjects with elevated BPV.

HbA1c variability and all-cause mortality in type 1 and type 2 diabetes: A population-based cohort study using electronic health records

AIMS

To investigate associations between HbA1c variability and all-cause mortality in individuals with diabetes, accounting for average HbA1c level METHODS: Mean HbA1c and variability score (HVS) were estimated for people aged 31-90 with diabetes (type 1 = 20,347, type 2 = 409,821) with 4 + HbA1c measurements recorded in the Clinical Practice Research Datalink in 2011-14 and alive on 1/1/2015. Cox models estimated hazard ratios (HR) for all-cause mortality, ascertained from national linked mortality data during 2015-17. HbA1c level and variability were mutually adjusted for each other and other measured confounders.

RESULTS

Greater HbA1c variability was associated with younger age, non-white ethnicities (type 1 only), obesity, co-morbidities, and living in deprived areas. During follow-up, 1,043 (5.1 %) individuals with type 1 diabetes and 40,723 (9.9 %) individuals with type 2 diabetes died. In those with the most HbA1c variability compared to the least (HVS = 80-100 vs 0-20), the estimated adjusted HRs for mortality were 2.78(95 %CI 2.15, 3.60) in type 1 diabetes and 1.91(1.83, 1.99) in type 2 diabetes.

CONCLUSIONS

Variability in HbA1c was associated with greater subsequent mortality among people living with diabetes, independent from average HbA1c. Future research should investigate whether reducing HbA1c variability over time in selected patients lowers mortality risk independent of HbA1c level improvements.

AI4Food, a feasibility study for the implementation of automated devices in the nutritional advice and follow up within a weight loss intervention

BACKGROUND & AIMS

The widespread prevalence of NCDs calls for an improvement in their prevention and treatment. Wearable technologies can be an important asset in the development of precision nutrition strategies, for both health professionals and patients. However, their clinical use is hindered by a lack of validation against current methodologies or appropriate tools to deliver nutritional strategies based on their data. Our study includes manual and automatic data capture methods within a weight loss intervention with the aim to create an essential asset for the implementation, validation, and benchmarking of AI-based tools in nutritional clinical practice.

METHODS

This is a feasibility prospective and crossover controlled trial for weight loss in overweight and obese participants, randomized into two groups: Group 1 used manual data collection methods based on validated questionnaires for the first two weeks; while Group 2 started with automatic data collection methods consisting of wearable sensors. After two weeks, the two groups switched data collection methods. Lifestyle data, anthropometric measurements and biological samples were collected from all participants.

RESULTS

A total of 93 participants completed the nutritional intervention designed for weight loss, achieving a mean reduction of 2 kg (V1: 84.99 SD ± 13.69, V3: 82.72 SD ± 13.32, p < 0.001). Significant reductions were observed in body mass index, visceral fat, waist circumference, total cholesterol, and HbA1c levels. The use of electronic devices proved satisfactory among the participants (System Usability Scale score 78.27 ± 12.86). We also report the presence of distinct patient groups based on continuous glucose measurements.

CONCLUSION

This study has yielded a large amount of data and has showcased how automatic data collection devices can be employed to gather data in the context of a nutritional intervention. This will enable the implementation of AI-based tools in nutritional clinical practice.

CLINICAL TRIAL REGISTRATION

ClinicalTrials.gov, NCT05807243.

Predict and prevent microvascular complications of type 2 diabetes: a cross-sectional and longitudinal study in Chinese communities

Purpose

This study investigates the incidence, predictors, and preventive strategies for microvascular complications in type 2 diabetes patients in community settings.

Methods

Data were collected from 3,008 type 2 diabetes patients enrolled across 31 clinics in Beijing and Hebei. Prevalence and incidence of diabetic kidney disease (DKD), diabetic retinopathy (DR), and diabetic peripheral neuropathy (DPN) were assessed. Predictors were identified using XGBoost and Cox regression, and the impact of lifestyle and multifactorial interventions (MFI) was analyzed.

Results

The prevalence of DKD, DR, and DPN were 39.5%, 26.2%, and 27.1%, respectively, with incidences of 74, 21, and 28 per 1000-person year. XGBoost identified that diabetes duration, age, HbA1c, FBG, triglyceride, BP, serum creatinine, proteinuria, aspirin and statin use were associated with those microvascular complications. The risk of DKD increased more rapidly as HbA1c exceeded 7.5% and decreased as blood pressure was maintained below 120/70 mmHg. Cox regression models showed that community-based intervention, including lifestyle modifications, were associated with a lower risk of DR and DPN. The study also found that higher variability in HbA1c and albumin-to-creatinine ratio (ACR) was associated with an increased risk of microvascular complications.

Conclusions

Community-based interventions significantly reduce the of DR and DPN, highlighting the need for individualized glycemic and BP management in primary care. The findings emphasize the importance of comprehensive management strategies to prevent the development and progression of microvascular complications in type 2 diabetes patients.

Clinical trial registration

http://www.chictr.org.cn/, identifier ChiCTR-TRC-13003222.

Acute effects of a single bout structured resistance and combined exercise on blood glucose profile during exercise in patients with type 2 diabetes and healthy adults: A randomized crossover study

AIMS

The effects of postprandial resistance and combined exercise on blood glucose profiles, particularly in patients with type 2 diabetes, remain unclear. Comprehending these responses may aid in diabetes management.

METHODS

Three trials were conducted: trial A examined aerobic, resistance, and combined exercise; trials B and C focused on three intensities of resistance and combined exercise. Participants including patients with type 2 diabetes and healthy adults completed a randomized crossover experiment with two arrangements of three interventions and continuous glucose monitoring. Blood glucose iAUC and slope were analyzed via repeated measures two-way ANOVA.

RESULTS

A total of 21 patients with type 2 diabetes (47.81±11.88 years) and 26 healthy adults (31.77±6.66 years) were assigned. In trials A-C, the main effect of subject group on iAUC/min was significant (p＜0.001, p = 0.003, and p＜0.001). The exercise in trial A (p = 0.006) and subject group in trial C (p = 0.005) significantly impacted the blood glucose slope.

CONCLUSIONS

Resistance and combined exercise reduce postprandial hyperglycemia in type 2 diabetes patients. Monitoring glucose before exercise may help prevent extreme events.

The triglyceride-glucose index as a new predictor of coronary artery complexity in elderly patients with non-ST-segment elevation acute coronary syndrome

BACKGROUND

Non-ST-segment elevation acute coronary syndrome (NSTE-ACS) has a significant impact on cardiovascular mortality in elderly patients. Identification of high-risk patients is essential to optimize clinical management. This study investigates the relationship between the TyG index and CAD complexity, as measured by the SYNTAX score, in elderly patients with NSTE-ACS.

METHODS

We performed a retrospective analysis of 216 patients aged >65 years undergoing coronary angiography stratified according to tertiles of the TyG index and the SYNTAX score (SYNTAX score ≤ 22 versus SYNTAX score > 22).

RESULTS

After adjustment for confounders, the TyG index was identified as an independent predictor of moderate/high scores (SYNTAX score > 22). As a continuous variable, it was significantly associated with moderate/high SYNTAX scores in both diabetic ( P  = 0.003) and nondiabetic groups ( P  = 0.004). When presented as a categorical variable, the TyG index remained a significant predictor after adjustment for sex, hypertension, BMI, and glomerular filtration rate. Compared with the T1 group, the risk of a moderate/high SYNTAX score was 5.410-fold (95% CI: 1.034-28.315; P  = 0.046) and 7.774-fold (95% CI: 1.836-32.916; P  = 0.005) higher in the T2 and T3 groups, respectively. The TyG index showed superior predictive ability for CAD complexity with an AUC of 0.747 compared with the AUC of 0.624 for HbA1c ( P  = 0.005).

CONCLUSION

This study showed that the TyG index is an independent predictor of moderate/high SYNTAX scores in elderly patients with NSTE-ACS, demonstrating superior predictive performance compared to HgA1c and highlighting its potential as a valuable tool for assessing CAD severity in this population.

Associations of metabolic variabilities and cardiovascular outcomes according to estimated glomerular filtration rate in chronic kidney disease: a nationwide observational cohort study

BACKGROUND

The impact of baseline estimated glomerular filtration rate (eGFR) on the risk of adverse outcomes according to metabolic parameter variabilities in chronic kidney disease has rarely been investigated.

METHODS

We conducted a retrospective nationwide cohort study using the National Health Insurance System data in Korea from 2007 to 2013 to identify individuals with three or more health screenings. The metabolic components variability was defined as intraindividual variability between measurements using the variability independent of the mean. The metabolic variability score was defined as the total number of high-variability metabolic components. Multivariable-adjusted Cox regression analysis was conducted to evaluate the risks of all-cause mortality, myocardial infarction, and ischemic stroke.

RESULTS

During a mean follow-up of 6.0 ± 0.7 years, 223,531 deaths, 107,140 myocardial infarctions, and 116,182 ischemic strokes were identified in 9,971,562 patients. Low eGFR categories and higher metabolic variability scores were associated with a higher risk of adverse outcomes. The degree of association between metabolic variability and adverse outcomes was significantly larger in those with low eGFR categories than in those with preserved eGFR (p for interaction < 0.001). Representatively, those with high metabolic variability in the eGFR of <15 mL/min/1.73 m2 group showed a prominently higher risk for all-cause mortality (adjusted hazard ratio [aHR], 5.28; 95% confidence interval [CI], 4.02-6.94) when the degree was compared to the findings in those with preserved (eGFR of ≥60 mL/min/1.73 m2) kidney function (aHR, 2.55; 95% CI, 2.41-2.69).

CONCLUSION

The degree of adverse association between metabolic variability and poor prognosis is accentuated in patients with impaired kidney function.

Association of glycemic variability with short and long-term mortality among critically ill patients with heart failure: Analysis of the MIMIC-IV database

OBJECTIVE

High glycemic variability (GV) often indicates a poor prognosis. Our aim is to investigate the relationship between GV and short and long-term mortality in critically ill heart failure (HF) patients.

METHODS

We extracted data from the Medical Information Mart for Intensive Care IV database. The risks of in-hospital and 1-year mortality were calculated using Logistic and COX regression. In addition, mediation analysis was used to investigate the indirect effect of ventricular arrhythmias (VA) on in-hospital mortality.

RESULTS

Among 8,980 critically ill HF patients, the multifactorial regression analysis showed that high GV was associated with an increased risk of in-hospital and 1-year mortality (OR 1.69, 95 % CI 1.47-1.93; HR 1.12, 95 % CI 1.02-1.22). The Kaplan-Meier survival curve and restricted cubic spline plot also emphasized this association. Furthermore, the impact of GV on in-hospital mortality was partially mediated by VA (4.98%). And the increased risk of 1-year mortality associated with high GV was more significant in person with diabetes (p for interaction =0.018).

CONCLUSION

Our Study indicates that high GV may be an independent risk factor for short and long-term mortality in critically ill HF patients. Maintaining the stability of blood glucose can reduce adverse outcomes in critically ill HF patients.

Type 2 diabetes mellitus: A cross-sectional analysis of glycemic controls and brain health outcomes

In this cross-sectional analysis, we explored how fluctuations in glycemic levels impact executive functions and psychosocial outcomes in patients with type 2 diabetes mellitus (T2DM). The goal was to understand the relationship between glycemic control and both neuropsychological and psychosocial health. We stratified participants into well-controlled and poorly controlled groups based on glycated hemoglobin (HbA1c) levels and variability, including a healthy control group for comparison. The study consisted of neuropsychological tests and psychosocial assessments. Results indicated that the poorly controlled T2DM group experienced significant executive dysfunction and scored lower on the Tower of London, Wisconsin Card Sorting, and Digit Span Tests, reflecting a broader impact on quality of life and resilience. These findings support the importance of maintaining stable glycemic levels for better executive and psychosocial outcomes and highlight the need for regular neuropsychological and psychosocial assessments in diabetes care.

Acute kidney disease and postoperative glycemia variability in patients undergoing cardiac surgery: A multicenter cohort analysis of 8,090 patients

BACKGROUND

No previous research has specifically investigated the relationship between postoperative glycemic variability (GV) and acute kidney disease (AKD) in patients undergoing cardiac surgery. In this study, several methods of modelling postoperative GV were used to examine the association between GV and AKD risk and subtypes of AKD.

METHODS

We undertook a retrospective study involving a total of 8,090 adult patients from three academic medical centers in Eastern China who underwent cardiac surgery with cardiopulmonary bypass between 2015 and 2023. Seven-day postoperative GV was calculated using the standard deviation (SD), coefficient of variation (CV), mean amplitude of glycemic excursion (MAGE), average daily risk range (ADRR), and time out of target range (TOR). The primary focus was on the occurrence of AKD between 8 and 90 days post-surgery, which was further categorized into persistent AKD and delayed AKD depending on the acute kidney injury (AKI) status in the first 7 days.

RESULTS

During the 8-90 days postoperative period, AKD occurred in 522 out of 8,090 patients (6.5%). Seven-day postoperative GV was significantly and consistently higher in the AKD group (p<0.001 for each metric). After adjusting for relevant covariates, 7-day GV metrics were significantly associated with elevated AKD risk (standardized hazard ratio (SHR):1.20 (95% confidence interval (CI): (1.12 - 1.27) for SD; SHR: 1.30 (95% CI: 1.20 - 1.40) for TOR). GV was correlated with persistent AKD, while no statistically significant association was observed between GV and delayed AKD. Unique cutoff thresholds were calculated for each GV metric to provide a quantitative indicator of high GV, enhancing its practical utility.

CONCLUSIONS

Our study highlights the association between postoperative GV and increased AKD risk, and identifies specific GV thresholds in adults undergoing cardiac surgery.

"Hyperglycemic Memory": Observational Evidence to Experimental Inference

Several epidemiological studies have appreciated the impact of "duration" and "level" of hyperglycemia on the initiation and development of chronic complications of diabetes. However, glycemic profiles could not fully explain the presence/absence and severity of diabetic complications. Genetic issues and concepts of "hyperglycemic memory" have been introduced as additional influential factors involved in the pathobiology of late complications of diabetes. In the extended phase of significant diabetes randomized, controlled clinical trials, including DCCT/EDIC and UKPDS, studies have concluded that the quality of glycemic or metabolic control at the early time around the diabetes onset could maintain its protective or detrimental impact throughout the following diabetes course. There is no reliable indication of the mechanism by which the transient exposure to a given glucose concentration level could evoke a consistent cellular response at target tissues at the molecular levels. Some biological phenomena, such as the production and the concentration of advanced glycation end products (AGEs), reactive oxygen species (ROS) and protein kinase C (PKC) pathway activations, epigenetic changes, and finally, the miRNAs-mediated pathways, may be accountable for the development of hyperglycemic memory. This work summarizes evidence from previous experiments that may substantiate the hyperglycemic memory soundness by its justification in molecular terms.

Non-recovery acute kidney injury and additional risk factors for short-term and long-term hypoglycemia: A multi-institutional cohort study

BACKGROUND

Acute kidney injury (AKI) is associated with increased in-hospital and short-term hypoglycemia risk. However, the impact of non-recovery of renal function before discharge on hypoglycemia risk in AKI survivors after discharge have not been well studied. We aim to analyze the association between non-recovery of AKI and hypoglycemia, hyperglycemia crisis risk, to identify additional risk factors for hypoglycemia, and to examine the impact of AKI non-recovery on HbA1c variability.

METHODS

This multi-institutional retrospective cohort study based on Chang Gung Research Database included type 2 diabetes patients with first admission for AKI. Participants were categorized into renal recovery or non-recovery groups. Hypoglycemia, hyperglycemia crisis, new insulin use, re-AKI admission, all-cause mortality and HbA1c variability within 2 years after discharge were assessed. IPTW was used for covariate balance.

RESULTS

A total of 16,796 participants were enrolled. AKI non-recovery was associated with increased higher hypoglycemia risk (HR: 1.19; 95% CI: 1.11-1.28), increased new insulin prescriptions (HR: 1.21; 95% CI: 1.14-1.27), and lower mean HbA1c levels compared to the recovery group. Subgroup analysis showed higher hypoglycemia risk in the non-recovery group with post-AKI anemia/hypoalbuminemia status (HR: 1.39; 95% CI: 1.23-1.55). De novo CKD development in AKI non-recovery group was a protective factor for hyperglycemia crisis (HR: 0.77; 95% CI: 0.69-0.87).

CONCLUSIONS

Non-recovery AKI is associated with an increased risk of hypoglycemia, regardless of whether de novo CKD develops, and is linked to lower mean A1c level within two-years post-discharge. Identifying high-risk individuals for post-AKI hypoglycemia is vital for optimal patient care.

Glycemic Risk Index in a Cohort of Patients with Type 1 Diabetes Mellitus Stratified by the Coefficient of Variation: A Real-Life Study

Objective: To analyze the Glycemic Risk Index (GRI) and assess their possible differences according to coefficient of variation (CV) in a cohort of real-life type 1 diabetes mellitus (DM) patient users of intermittently scanned continuous glucose monitoring (isCGM). Patients and Methods: In total, 447 adult users of isCGM with an adherence ≥70% were included in a cross-sectional study. GRI was calculated with its hypoglycemia (CHypo) and hyperglycemia (CHyper) components. Multivariate linear regression analysis was performed to evaluate the factors associated with GRI. Results: Mean age was 44.6 years (standard deviation [SD] 13.7), 57.7% being male; age of DM onset was 24.5 years (SD 14.3) and time of evolution was 20.6 years (SD 12.3). In patients with CV >36% (52.8%) versus CV ≤36% (47.2%), differences were observed in relation to GRI (18.8% [SD 1.9]; P < 0.001), CHypo (2.9% [SD 0.3]; P < 0.001), CHyper (6.3% [SD 1.4]; P < 0.001), and all classical glucometric parameters except time above range level 1. The variables that were independently associated with GRI in patient with CV >36% were time in range (TIR) (β = -1.49; confidence interval [CI:] 95% -1.63 to -1.37; P < 0.001), glucose management indicator (GMI) (β = -7.22; CI: 95% -9.53 to -4.91; P < 0.001), and CV (β = 0.85; CI: 95% 0.69 to 1.02; P < 0.001). However, in patients with CV ≤36%, the variables were age (β = 0.15; CI: 95% 0.03 to 0.28; P = 0.019), age of onset (β = -0.15; CI: 95% -0.28 to -0.02; P = 0.023), TIR (β = -1.35; CI: 95% -1.46 to -1.23; P < 0.001), GMI (β = -6.67; CI: 95% -9.18 to -4.15; P < 0.001), and CV (β = 0.33; CI: 95% 0.11 to 0.56; P = 0.004). Conclusions: In this study, the factors independently associated with metabolic control according to GRI are modified by glycemic variability.

The clinical importance of measuring glycaemic variability: Utilising new metrics to optimise glycaemic control

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.

Continuous Glucose Monitoring Profiles and Health Outcomes After Dapagliflozin Plus Saxagliptin vs Insulin Glargine

CONTEXT

Glycemic variability and hypoglycemia during diabetes treatment may impact therapeutic effectiveness and safety, even when glycated hemoglobin (HbA1c) reduction is comparable between therapies.

OBJECTIVE

We employed masked continuous glucose monitoring (CGM) during a randomized trial of dapagliflozin plus saxagliptin (DAPA + SAXA) vs insulin glargine (INS) to compare glucose variability and patient-reported outcomes (PROs).

DESIGN

24-week substudy of a randomized, open-label, 2-arm, parallel-group, phase 3b study.

SETTING

Multicenter study (112 centers in 11 countries).

PATIENTS

283 adults with type 2 diabetes (T2D) inadequately controlled with metformin ± sulfonylurea.

INTERVENTIONS

DAPA + SAXA vs INS.

MAIN OUTCOME MEASURES

Changes in CGM profiles, HbA1c, and PROs.

RESULTS

Changes from baseline in HbA1c with DAPA + SAXA were similar to those observed with INS, with mean difference [95% confidence interval] between decreases of -0.12% [-0.37 to 0.12%], P = .33. CGM analytics were more favorable for DAPA + SAXA, including greater percent time in range (> 3.9 and ≤ 10 mmol/L; 34.3 ± 1.9 vs 28.5 ± 1.9%, P = .033), lower percent time with nocturnal hypoglycemia (area under the curve ≤ 3.9 mmol/L; 0.6 ± 0.5 vs 2.7 ± 0.5%, P = .007), and smaller mean amplitude of glycemic excursions (-0.7 ± 0.1 vs -0.3 ± 0.1 mmol/L, P = .017). Improvements in CGM were associated with greater satisfaction, better body weight image, less weight interference, and improved mental and emotional well-being.

CONCLUSION

DAPA + SAXA and INS were equally effective in reducing HbA1c at 24 weeks, but people with T2D treated with DAPA + SAXA achieved greater time in range, greater reductions in glycemic excursions and variability, less time with hypoglycemia, and improved patient-reported health outcomes.

Glycemic profile variability as an independent predictor of diabetic retinopathy in patients with type 2 diabetes: a prospective cohort study

Background

Glycemic variability is a novel predictor for diabetic complications. Different studies have demonstrated contradictory results for the association between HbA1c variability and diabetic retinopathy. We aimed to assess the relationship between visit-to-visit variability in glycemic profile (HbA1c, 2hPP, and FBS) and diabetic retinopathy.

Methods

Patients with type 2 diabetes were monitored for the development of retinopathy for 10 years. The association between the incidence of retinopathy and glycemic variability was assessed via Cox regression analysis, and coefficient of variation for glycemic indices was compared using independent sample t-test.

Results

Patients with diabetic retinopathy had significantly higher glycemic profile variability. The incidence of retinopathy was positively correlated with cv-FBS10% (10% of coefficient of variance), cv-FBS20%, cv-2hpp10%, and cv-HbA1c5%. Our analysis revealed that the higher variability of FBS increased the incidence and progression of retinopathy (HR: 12.29, p-value = 0.003).

Conclusion

Our findings demonstrated glycemic profile variability as an independent risk factor for diabetic retinopathy in patients with type 2 diabetes and support glycemic profile variability measurement in addition to common glycemic parameters to improve risk stratification in patients with type 2 diabetes. Further investigation is required to demonstrate the long-term effects of alleviating glycemic variability on the prognosis of patients with type 2 diabetes.

Increased FT3/FT4 ratio in a certain range is associated with decreased glycemic variability in patients with type 2 diabetes

Thyroid hormone (TH) plays a crucial role in regulating glucose metabolism. However, the potential impact of the FT3/FT4 ratio, which reflects peripheral sensitivity to thyroid hormones, on glycemic variability in patients with type 2 diabetes (T2DM), has not been previously reported. To investigate the correlation between the FT3/FT4 ratio and glycemic variability in individuals with T2DM. In this retrospective analysis, a total of 468 inpatients with T2DM underwent continuous glucose monitoring (CGM) systems for a period of 6-14 days. Baseline clinical characteristics, laboratory tests, and CGM parameters were documented to investigate the correlation between FT3/FT4 ratio and CGM parameters. The levels of HBA, MG, SD, CV, LAGE, MODD and TAR2Scale were all higher in FT3/FT4Q1 compared with FT3/FT4Q2, FT3/FT4Q3 and FT3/FT4Q4 (all P < 0.01). Additionally, TIR was lower in FT3/FT4Q1 compared with the other quartiles (P < 0.01). Smooth curve fitting and saturation effect analysis revealed that there are curve-like relationships between the FT3/FT4 ratio and SD, MAGE, MODD and TAR2Scale. The inflection points of the fitted curves were found to be at FT3/FT4 = 0.279, 0.237, 0.253 and 0.282 respectively (all P < 0.05). Prior to the inflection point, the FT3/FT4 ratio was negatively related to SD, MAGE, MODD and TAR2Scale (all P < 0.05). Furthermore, The FT3/FT4 ratio exhibits a negative linear correlation with HBA and MG, while demonstrating a positive linear relationship with TIR (all P < 0.05). Binary logistic regression demonstrated that the FT3/FT4 ratio was independently related to HBA (P = 0.001), MG (P = 0.01), TAR2Scale (P = 0.003), LAGE (P = 0.014) and MAGE (P < 0.001). The increased FT3/FT4 ratio within a certain range (FT3/FT4 ≤ 0.282) is associated with decreased blood glucose variability and increased TIR. The FT3/FT4 ratio may act as a potential independent protective factor for glycemic fluctuation and glycemic control in patients with T2DM when it increases within a specific range.

Clinical implications and pharmacological considerations of glycemic variability in patients with type 2 diabetes mellitus

Glycemic variability (GV), independently of glycemic control, has emerged as a prognostic marker in patients with type 2 diabetes mellitus (DM). In this study, we assessed the prognostic value of long-term GV for predicting major adverse cardiovascular events (MACE) in our local population. We also assessed its prognostic value for diabetic microvascular complications (DMC) and its relationships with antidiabetic medications. This was a retrospective cohort study that recruited 680 patients with type 2 DM across 2015-2017. MACE were defined as: the composite of; total death, myocardial infarction (MI), stroke, hospitalization due to heart failure, and revascularization. GV was calculated for two glycemic control markers: glycated hemoglobin (G-Hb) and fasting blood sugar (FBS); via three metrics- standard deviation (SD), coefficient of variation (CV), and variability independent from the mean (VIM). Cox proportional hazard models and Kruskal-Wallis tests were used in the statistical analysis. 105 events classified as MACE were identified in 86 patients and 104 DMC in 98 patients in an average follow-up period of 78.43 months. Long-term GV was found to be an independent predictor of MACE, particularly for FBS-CV but not a predictor of DMC. FBS-CV ≥ 17.51% as compared with < 17.51% was a significant and independent predictor of MACE, with HR 1.589 (95% CI; 1.022, 2.472) (P = 0.040). DMC were predicted mainly by the duration of type 2 DM, and by the glycemic control; similarly represented by G-Hb and FBS. Patients on metformin, and dipeptidyl peptidase (DPP) 4 inhibitors, had the lowest GV, as compared with patients whose treatments included insulin/sulphonylureas (P < 0.001). In our population, long-term GV predicted MACE: with FBS-CV superior to the "gold standard" glycemic control marker G-Hb. Further, GV may be explained, partially at least, by the choice of antidiabetic medications: this finding might contribute to the cardiovascular protection attributed to one class rather than another.

A comparative study of the relationship between time in range assessed by self-monitoring of blood glucose and continuous glucose monitoring with microalbuminuria outcome, HOMA-IR and HOMA-β test

AIMS

To compare the time in range (TIR) obtained from self-monitoring of blood glucose (SMBG) with that obtained from continuous glucose monitoring (CGM), and explore the relationship of TIR with microalbuminuria outcome, HOMA-IR and HOMA-β test.

METHODS

We recruited 400 patients with type 2 diabetes to carry out blood glucose monitoring by both SMBG and CGM for 3 consecutive days. TIR, TAR, TBR and other blood glucose variation indices were calculated respectively through the glucose data achieved from SMBG and CGM. The HOMA-IR and HOMA-β test was evaluated by an oral glucose tolerance test. Urinary microalbumin-to-creatinine ratio completed in the laboratory.

RESULTS

The median (25 %, 75 % quartile) of TIRCGM and TIRSMBG were 74.94(44.90, 88.04) and 70.83(46.88, 87.50) respectively, and there was no significant difference, p = 0.489; For every 1 % increase in TIRCGM, the risk of microalbuminuria decreased by 1.6 % (95%CI:0.973, 0.995, p = 0.006) and for every 1 % increase in TIRSMBG, the risk of microalbuminuria decreased by 1.3 % (95%CI:0.975, 0.999, p = 0.033). Stepwise multiple linear regression analysis showed an independent positive correlation between TIR (including TIRCGM and TIRSBMG) and LnDI30 and LnDI120 levels (p = 0.000).

CONCLUSIONS

The TIR calculated by SMBG was highly consistent with that reported by CGM and was significantly associated with the risk of microalbuminuria and the HOMA-β. Higher TIR quartiles were associated with lower incidence of microalbuminuria as well as higher lever of HOMA-β. For patients with limited CGM application, SMBG-derived TIR may be an alternative to CGM-derived TIR, to assess blood glucose control.

Risk prediction of diabetic retinopathy based on visit-to-visit fasting blood glucose indices

Objective

The long-term glucose monitoring is essential to the risk assessment of diabetic retinopathy (DR), the aim of this study was to investigate the predictive ability of visit-to-visit fasting blood glucose (FBG) indices on the risk of DR.

Methods

This was a community-based, cohort study conducted from 2013 to 2021. DR was diagnosed by digital fundus photography. The FPG indices included FBG, var. Associations of each FBG indices and DR were estimated using multinomial logistic regression models adjusting for confounders, and discrimination was determined by area under the curve (AUC). Predictive utility of different models was compared by changes in AUC, integrated discrimination improvement (IDI), and net reclassification index (NRI).

Results

This study analyzed 5054 participants, the mean age was 46.26 ± 11.44 years, and 2620 (51.84%) were women. After adjustment for confounders, the adjusted odds ratios (ORs) with 95% confidence intervals (CIs) for FBG, SD, CV, VIM, ARV, M-FBG, and cumulative FBG load were 1.62 (1.52-1.73), 2.74 (2.38-3.16), 1.78 (1.62-1.95), 1.11 (0.95-1.29), 1.72 (1.56-1.91), 2.15 (1.96-2.36), and 2.57 (2.31-2.85), respectively. The AUC of the model with separate cumulative FBG load and classical risk factors was 0.9135 (95%CI 0.8890-0.9380), and no substantive improvement in discrimination was achieved with the addition of other FBG indices once cumulative FBG load was in the model.

Conclusions

Cumulative FBG load is adequate for capturing the glucose-related DR risk, and the predictive utility of cumulative FBG load is not significantly improved by adding or replacing other FBG indices in the assessment of DR risk.

Potential Benefits of Continuous Glucose Monitoring for Predicting Vascular Outcomes in Type 2 Diabetes: A Rapid Review of Primary Research

(1) Background: Chronic hyperglycaemia is a cause of vascular damage and other adverse clinical outcomes in type 2 diabetes mellitus (T2DM). Emerging evidence suggests a significant and independent role for glycaemic variability (GV) in contributing to those outcomes. Continuous glucose monitoring (CGM) provides valuable insights into GV. Unlike in type 1 diabetes mellitus, the use of CGM-derived GV indices has not been widely adopted in the management of T2DM due to the limited evidence of their effectiveness in predicting clinical outcomes. This study aimed to explore the associations between GV metrics and short- or long-term vascular and clinical complications in T2DM. (2) Methods: A rapid literature review was conducted using the Cochrane Library, MEDLINE, and Scopus databases to seek high-level evidence. Lower-quality studies such as cross-sectional studies were excluded, but their content was reviewed. (3) Results: Six studies (five prospective cohort studies and one clinical trial) reported associations between GV indices (coefficient of variation (CV), standard deviation (SD), Mean Amplitude of Glycaemic Excursions (MAGE), Time in Range (TIR), Time Above Range (TAR), and Time Below Range (TBR)), and clinical complications. However, since most evidence came from moderate to low-quality studies, the results should be interpreted with caution. (4) Conclusions: Limited but significant evidence suggests that GV indices may predict clinical compilations in T2DM both in the short term and long term. There is a need for longitudinal studies in larger and more diverse populations, longer follow-ups, and the use of numerous CGM-derived GV indices while collecting information about all microvascular and macrovascular complications.

Glycemic Variability as a Predictor of Graft Failure Following Infrainguinal Bypass for Peripheral Arterial Disease: A Retrospective Cohort Study

BACKGROUND

Glycemic variability (GV), measured as the change in visit-to-visit glycated hemoglobin (HbA1c), increases the risk of multiple adverse outcomes. However, the impact of GV on graft patency following infrainguinal bypass (IIB) is unknown. A retrospective cohort study was undertaken to assess the impact of GV on graft patency.

METHODS

A 3-year single-center retrospective case notes analysis of all people undergoing IIB between 2017 and 2019. Rutherford stage, graft conduit, level of bypass, procedure details, baseline demographics, comorbidities, and GV were assessed. Time to reintervention, ipsilateral amputation, or death was recorded to determine primary patency (PP).

RESULTS

One hundred six IIB outcomes were analyzed: mean (± standard deviation) age 68.0 (9.2) years; 69 (65.1%) male, 37 (33.9%), 75 (70.8%) had diabetes mellitus; and 46 (43.4%) underwent elective procedures. GV > 9.1% was associated with significantly lower median PP than GV < 9.1%, 198 (97-753.5) vs. 713 (166.5-1,044.5) days (P = 0.045). On univariate analysis, GV > 9.1% vs. < 9.1% was significantly associated with PP (hazard ratio [HR] 1.85 [confidence interval {CI} 1.091-3.136], P = 0.022). Bypass level was also a univariate predictor, with below knee bypasses (HR 2.31 [CI 1.164-4.564], P = 0.017), and tibial (HR 2.00 [CI 1.022-3.090], P < 0.043) having lower PP than above knee bypasses. On multivariate adjustment, GV > 9.1% and level of bypass remained independent predictors of PP, HR 1.96 (95% CI: 1.12-3.42, P = 0.018) and HR 2.54 (95% CI: 1.24-5.22, P = 0.011), respectively.

CONCLUSIONS

GV is an independent predictor of PP following infrainguinal bypass, thus optimizing GV should be a therapeutic target.

Relationship Between Glycosylated Hemoglobin Variability and the Severity of Coronary Artery Disease in Patients With Type 2 Diabetes Mellitus

Background: Glycosylated hemoglobin (HbA1c) variability is a risk factor for cardiovascular complications in patients with Type 2 diabetes mellitus (T2DM), but its relationship with the severity of coronary artery disease (CAD) is unclear. Methods: Patients with T2DM who underwent coronary angiography due to angina were enrolled. HbA1c variability was expressed as coefficient of variation (CV), standard deviation (SD), variability independent of mean (VIM), and time in range (TIR). The severity of CAD was expressed by the number of involved vessels and Gensini score. Multivariate regression models were constructed to test the relationship between HbA1c variability, number of involved vessels, and the Gensini score, followed by linear regression analysis. Results: A total of 147 patients were included. In multivariate analysis, VIM-HbA1c (OR = 2.604; IQR: 1.15, 5.90; r = 0.026) and HbA1cTIR (OR = 0.13; IQR: 0.04, 0.41; r < 0.001) were independent risk factors for the number of involved vessels. After adjustment, HbA1cTIR (OR = 0.01; IQR: 0.002, 0.04; r < 0.001), SD-HbA1c (OR = 4.12, IQR: 1.64, 10.35; r = 0.001), CV-HbA1c (OR = 1.41, IQR: 1.04, 1.92; r = 0.007), and VIM-HbA1c (OR = 3.26; IQR: 1.43, 7.47; r = 0.003) were independent risk factors for the Gensini score. In the linear analysis, the Gensini score was negatively correlated with HbA1cTIR (β = -0.629; r < 0.001) and positively correlated with SD-HbA1c (β = 0.271; r = 0.001) and CV-HbA1c (β = 0.176; r = 0.033). After subgroup analysis, HbA1cTIR was a risk factor for the number of involved vessels. The Gensini score was negatively correlated with HbA1cTIR and positively correlated with SD-HbA1c at subgroups of subjects with a mean HbA1c ≤ 7%. Conclusions: Our analysis indicates that HbA1c variability, especially HbA1cTIR, plays a role for the severity of CAD in patients with T2DM. HbA1c variability may provide additional information and require management even at the glycemic target. Translational Aspects: Studies have shown that HbA1c variability is related to cardiovascular complications. Further, we explore the correlation between HbA1c variability and the severity of CAD. HbA1c variability is a risk factor for coronary stenosis in T2DM. It may be a potential indicator reflecting glycemic control for the prevention and treatment of cardiovascular complications.

What is the best predictor of mortality in patients with type 2 diabetes and chronic kidney disease: mean, variability of HbA1c or HbA1c-Hemoglobin ratio?

AIM

Limitations in the measurement of glycated hemoglobin (HbA1c) in patients with type 2 diabetes (T2D) and chronic kidney disease (CKD) result in uncertainty about the best predictor of mortality among these patients. Our study aimed to determine the association between the mean and average real variability (ARV) of HbA1c, as well as HbA1c-hemoglobin (HH) ratio with mortality among patients with T2D and CKD.

MATERIALS AND METHODS

We identified 16,868 T2D patients with stage 3 or above CKD from outpatient visits during 2003-2018. We ascertained all-cause and cardiovascular mortality through linkage to Taiwan's National Death Registry. Mortality rates were estimated using the Poisson distribution, and we conducted Cox proportional hazards regressions to assess relative risks of mortality corresponding to the mean HbA1c, ARV of HbA1c and HH ratio.

RESULTS

Compared to patients with a mean HbA1c of 7.0-7.9%, a mean HbA1c < 7.0% was persistently associated with highest risk of all-cause but not cardiovascular mortality after adjusting for confounders. On the contrary, patients with HbA1c-ARV in the second to fourth quartiles and HH ratios in the higher quartiles showed increased risk of all-cause and cardiovascular mortality compared to those in the first quartiles.

CONCLUSIONS

HbA1c-ARV was more effective than mean HbA1c or HH ratio in predicting mortality in T2D patients with CKD. Apart from optimal glucose control, multidisciplinary care focusing on glycemic variability is essential for reducing mortality in these patients.

Research progress on the association between glycemic variability index derived from CGM and cardiovascular disease complications

Currently, glycated hemoglobin A1c (HbA1c) has been widely used to assess the glycemic control of patients with diabetes. However, HbA1c has certain limitations in describing both short-term and long-term glycemic control. To more accurately evaluate the glycemic control of diabetes patients, the continuous glucose monitoring (CGM) technology has emerged. CGM technology can provide robust data on short-term glycemic control and introduce new monitoring parameters such as time in range, time above range, and time below range as indicators of glycemic fluctuation. These indicators are used to describe the changes in glycemic control after interventions in clinical research or treatment modifications in diabetes patient care. Recent studies both domestically and internationally have shown that these indicators are not only associated with microvascular complications of diabetes mellitus but also closely related to cardiovascular disease complications and prognosis. Therefore, this article aims to comprehensively review the association between CGM-based glycemic parameters and cardiovascular disease complications by analyzing a large number of domestic and international literature. The purpose is to provide scientific evidence and guidance for the standardized application of these indicators in clinical practice, in order to better evaluate the glycemic control of diabetes patients and prevent the occurrence of cardiovascular disease complications. This research will contribute to improving the quality of life for diabetes patients and provide important references for clinical decision-making.

Glycated Hemoglobin Variability Is Associated With Adverse Outcomes in Patients With Heart Failure Irrespective of Diabetic Status

BACKGROUND

The effect of glycated hemoglobin (HbA1c) variability on adverse outcomes in patients with heart failure (HF) is unclear. We aim to investigate the predictive value of HbA1c variability on the risks of all-cause death and HF rehospitalization in patients with HF irrespective of their diabetic status.

METHODS AND RESULTS

Using a previously validated territory-wide clinical data registry, HbA1c variability was assessed by average successive variability (ASV) or SD of all HbA1c measurements after HF diagnosis. Multivariable Cox proportional hazards models were used to estimate the adjusted hazard ratio (HR) and its corresponding 95% CI. A total of 65 950 patients with HF were included in the study. Over a median follow-up of 6.7 (interquartile range, 4.0-10.6) years, 34 508 patients died and 52 446 required HF rehospitalization. Every unit increment of variability in HbA1c was significantly associated with higher HF rehospitalization (HR ASV, 1.20 [95% CI, 1.18-1.23]) and all-cause death (HR ASV, 1.50 [95% CI, 1.47-1.53]). Diabetes significantly modified the association between HbA1c variability and outcomes (Pinteraction<0.001). HbA1c variability in patients with HF without diabetes conferred a higher risk of rehospitalization (HR ASV, 1.92 [95% CI, 1.70-2.17] versus HR ASV, 1.19 [95% CI, 1.17-1.21]), and all-cause death (HR ASV, 3.90 [95% CI, 3.31-4.61] versus HR ASV, 1.47 [95% CI, 1.43-1.50] compared with patients with diabetes).

CONCLUSIONS

HbA1c variability is significantly associated with greater risk of rehospitalization and all-cause death in patients with HF, irrespective of their diabetic status. These observations were more pronounced in patients with HF without diabetes.

Effects of long-term HbA1c variability on serious infection risks in patients with type 2 diabetes and the influence of age, sex and ethnicity: A cohort study of primary care data

AIMS

Long-term HbA1c (glycated haemoglobin) variability is associated with micro- and macrovascular complications in Type 2 diabetes (T2D). We explored prospective associations between HbA1c variability and serious infections, and how these vary by HbA1c level, age, sex and ethnicity.

METHODS

411,963 T2D patients in England, aged 18-90, alive on 01/01/2015 in the Clinical Practice Research Datalink with ≥ 4 HbA1c measurements during 2011-14. Poisson regression estimated incidence rate ratios (IRRs) for infections requiring hospitalisation during 2015-19 by HbA1c variability score (HVS) and average level, adjusting for confounders, and stratified by age, sex, ethnicity and average level. Attributable risk fractions (AF) were calculated using reference categories for variability (HVS < 20) and average level (42-48 mmol/mol).

RESULTS

An increased infection risk (IRR > 1.2) was seen with even modest variability (HVS ≥ 20, 73 % of T2D patients), but only at higher average levels (≥64 mmol/mol, 27 % patients). Estimated AFs were markedly greater for variability than average level (17.1 % vs. 4.1 %). Associations with variability were greater among older patients, and those with lower HbA1c levels, but not observed among Black ethnicities.

CONCLUSIONS

HbA1c variability between T2D patients' primary care visits appears to be associated with more serious infections than average level overall. Well-designed trials could test whether these associations are causal.

Prevalence of nonalcoholic fatty liver disease and liver cirrhosis in Chinese adults with type 2 diabetes mellitus

BACKGROUND

Nonalcoholic fatty liver disease (NAFLD) and liver cirrhosis are significant clinical concerns, especially among individuals with type 2 diabetes mellitus (T2DM). However, in China, there is a paucity of reliable evidence detailing the characteristics of NAFLD and liver cirrhosis in T2DM. Furthermore, the relationship between blood glucose levels and NAFLD prevalence remains unclear.

METHODS

Data from the Shanghai Suburban Adult Cohort and Biobank were analyzed, including 6621 participants with T2DM. NAFLD was diagnosed by ultrasonography and liver cirrhosis was performed according to the health information systems. Logistic regression and restricted cubic spline analysis were used to explore the potential risk factors for NAFLD and liver cirrhosis.

RESULTS

The prevalence of NAFLD was 59.36%, and liver cirrhosis was 1.43% among T2DM patients. In these patients, factors like age, being female, marital status, and obesity significantly increased the risk of NAFLD. Specifically, obesity had a strong positive association with NAFLD (odds ratio [OR] = 4.70, 95% confidence interval [CI]: 4.13-5.34). The higher glycated hemoglobin (HbA1c) quartile was associated with a heightened NAFLD risk compared to the lowest quartile (all p < .001). The HbA1c-NAFLD relationship displayed a linear that mimicked an inverted L-shaped pattern. A significant positive association existed between HbA1c levels and NAFLD for HbA1c <8.00% (OR = 1.59, 95% CI: 1.44-1.75), but this was not observed for HbA1c >8.00% (OR = 1.03, 95% CI: 0.92-1.15).

CONCLUSION

Systematic screening for NAFLD is essential in T2DM patients, especially with poor glucose control and obesity in female.

Assessing the temporal within-day glycemic variability during hospitalization in patients with type 2 diabetes patients using continuous glucose monitoring: a retrospective observational study

AIMS

Frequent and extensive within-day glycemic variability (GV) in blood glucose levels may increase the risk of hypoglycemia and long-term mortality in hospitalized patients with diabetes. We aimed to assess the amplitude and frequency of within-day GV in inpatients with type 2 diabetes and to explore the factors influencing within-day GV.

METHODS

We conducted a single-center, retrospective observational study by analyzing hospital records and 10-day real-time continuous glucose monitoring data. Within-day GV was assessed using the coefficient of variation (%CV). The primary outcome was the amplitude and frequency of within-day GV. The frequency of within-day GV was assessed by the consecutive days (CD) of maintaining within the target %CV range after first reaching it (CD after first reaching the target) and the maximum consecutive days of maintaining within the target %CV range (Max-CD). The target %CV range was less than 24.4%. We evaluated the factors influencing within-day GV using COX regression and Poisson regression models.

RESULTS

A total of 1050 cases were analyzed, of whom 86.57% reduced the amplitude of within-day GV before the sixth day of hospitalization. Of the 1050 hospitalized patients, 66.57% stayed within the target %CV range for less than two days after first reaching the target and 69.71% experienced a Max-CD of fewer than four days. Reducing the average postprandial glucose excursion (hazard ratio [HR]: 0.81, 95% confidence interval [CI]: 0.77-0.85; incidence rate ratios [IRR]: 0.72, 95% CI: 0.69-0.74) and the use of α-glucosidase inhibitors (IRR: 1.1, 95% CI: 1.01-1.18) and glucagon-like peptide-1 agonist (IRR: 1.30, 95% CI: 1.02-1.65) contributed to reducing the amplitude and decreasing the frequency of within-day GV. However, the use of insulin (HR: 0.64, 95% CI: 0.55-0.75; IRR: 0.86, 95% CI: 0.79-0.93) and glinide (HR: 0.47, 95% CI: 0.31-0.73; IRR: 0.84, 95% CI: 0.73-0.97) may lead to an increased frequency of within-day GV.

CONCLUSIONS

An increasing frequency of within-day GV was observed during the hospitalization in patients with type 2 diabetes, despite the effective reduction in the amplitude of within-day GV. Using medications designed to lower postprandial blood glucose could contribute to minimize the risk of frequent within-day GV.

Effect of Intensive Lifestyle Intervention on Cardiovascular Risk Factors: Analysis From the Perspective of Long-Term Variability

BACKGROUND

An association between variability of cardiovascular risk factors and cardiovascular events has been reported. We examined whether intensive lifestyle intervention (ILI) for weight loss decreased variability of cardiovascular risk factors with a view to additional cardiometabolic benefits.

METHODS AND RESULTS

This study was a post hoc secondary analysis of the Look AHEAD (Action for Health in Diabetes) study. Cardiovascular risk factors were measured at 1-year intervals for 4 years in 4249 adults with overweight or obesity and type 2 diabetes who were randomly assigned to ILI or diabetes support and education. Long-term variability was defined as the SD of cardiovascular risk factors during 4-year follow-up. At multiple linear regression analysis, compared with the diabetes support and education group, the ILI group was associated with reduced variability of fasting blood glucose (β=-1.49 [95% CI, -2.39 to -0.59]), total cholesterol (β=-1.12 [95% CI, -1.75 to -0.48]), and low-density lipoprotein cholesterol (β=-1.04 [95% CI, -1.59 to -0.49]), as well as increased variability of systolic blood pressure (β=0.27 [95% CI, 0.00-0.54]). No significant effect of ILI was found on the variability of diastolic blood pressure (β=-0.08 [95% CI, -0.22 to 0.05]).

CONCLUSIONS

Among adults with overweight or obesity and type 2 diabetes, ILI may reduce long-term variability of fasting blood glucose, total cholesterol, and low-density lipoprotein cholesterol. Our results support that ILI should be recommended to individuals with diabetes as part of management of long-term glycemic and blood lipid control.

Effect of delay in treatment intensification in people with type 2 diabetes and suboptimal glycaemia after basal insulin initiation: A real-world observational study

AIM

Despite global recommendations for type 2 diabetes mellitus treatment to maintain optimal glycaemic targets, a significant proportion of people remain in suboptimal glycaemic control. Our objective was to investigate the impact of intensification delay after basal insulin (BI) initiation on long-term complications in people with suboptimal glycaemia.

MATERIALS AND METHODS

We conducted a retrospective cohort study in individuals with type 2 diabetes mellitus initiated on BI. Those with suboptimal glycaemia (glycated haemoglobin ≥7% or ≥53 mmol/mol) within 12 months of BI initiation were divided into early (treatment intensified within 5 years), or late (≥5 years) intensification groups. We estimated the age-stratified risks of micro- and macrovascular complications among these groups compared with those with optimal glycaemia (glycated haemoglobin <7%).

RESULTS

Of the 13 916 people with suboptimal glycaemia, 52.5% (n = 7304) did not receive any treatment intensification. In those aged <65 years, compared with the optimal glycaemia group late intensification was associated with a 56% higher risk of macrovascular complications (adjusted hazard ratio 1.56; 95% confidence intervals 1.08, 2.26). In elderly people (≥65 years), late intensification was associated with a higher risk of cardiovascular-related death (1.62; 1.03, 2.54) and a lower risk of microvascular complications (0.26; 0.08, 0.83).

CONCLUSIONS

Those who had late intensification were at an increased risk of cardiovascular death if they were ≥65 years and an increased risk of macrovascular complications if they were <65 years. These findings highlight the critical need for earlier intensification of treatment and adopting personalized treatment strategies to improve patient outcomes.

Glycemic variability leads to higher levels of auto-oxidized oxysterol species in patients with type 1 diabetes mellitus

PURPOSE

Hyperglycemia and glycemic variability (GV) are associated with oxidative stress in patients with diabetes mellitus (DM). Oxysterol species, produced by the non-enzymatic oxidation of cholesterol, are potential biomarkers of oxidative stress. This study examined the relationship between auto-oxidized oxysterols and GV in patients with type 1 DM.

METHODS

Thirty patients with type 1 DM using a continuous subcutaneous insulin infusion pump therapy and a healthy control group (n = 30) were included in this prospective study. A Continuous Glucose Monitoring System device was applied for 72 h. Blood samples were taken for oxysterols produced by non-enzymatic oxidation [7-ketocholesterol (7-KC) and cholestane-3β, 5α, 6β-triol (Chol-Triol)] levels at 72 h. Short-term glycemic variability parameters, mean amplitude of glycemic excursions (MAGE), the standard deviation of glucose measurements (Glucose-SD), and mean of daily differences (MODD) were calculated with continuous glucose monitoring data. HbA1c was used to evaluate glycemic control and HbA1c-SD (the SD of HbA1c over the past year) for long-term glycemic variability.

RESULTS

7-KC and Chol-triol levels were significantly higher in the study group than in the control group. Strong positive correlations were found between 7-KC with MAGE(24-48 h) and Glucose-SD(24-48 h). 7-KC was positively correlated with MAGE(0-72 h) and Glucose-SD(0-72 h). No significant correlation was found between HbA1c and HbA1c -SD with oxysterol levels. The regression models showed that SD(24-48 h) and MAGE(24-48 h) predicted 7-KC levels while HbA1c did not.

CONCLUSIONS

Glycemic variability leads to higher levels of auto-oxidized oxysterol species in patients with type 1 DM independent of long-term glycemic control.

The effect of sodium-glucose cotransporter 2 inhibitors as an adjunct to insulin in patients with type 1 diabetes assessed by continuous glucose monitoring: A systematic review and meta-analysis

AIMS

Patients undergoing insulin-based therapy for type 1 diabetes often experience poor glycemic control characterized by significant fluctuations. This study was undertaken to analyze the effect of sodium-glucose cotransporter 2 inhibitors (SGLT2Is), as an adjunct to insulin, on time in range (TIR) and glycemic variability in patients with type 1 diabetes, using continuous glucose monitoring (CGM). In addition, we examined which type of SGLT2I yielded a superior effect compared to others.

METHODS

We conducted a comprehensive search of PubMed, EMBASE, the Cochrane Library, Web of Science, and clinical trial registry websites, retrieving all eligible randomized clinical trials (RCTs) published up until February 2023. We analyzed the mean TIR, mean amplitude of glucose excursions (MAGE), mean daily glucose (MDG), diabetic ketoacidosis (DKA), standard deviation (SD), total insulin dose, and severe hypoglycemia to evaluate the efficacy and safety of SGLT2Is. A random-effects model was also employed.

RESULTS

This study encompassed 15 RCTs. The meta-analysis revealed that the use of SGLT2Is as an adjuvant therapy to insulin led to a significant increase in TIR (MD = 10.78, 95%CI = 9.33-12.23, I2 = 42 %, P < 0.00001) and a decrease in SD (MD = -0.38, 95%CI = -0.50 to -0.26, I2 = 0 %, P < 0.00001), MAGE (MD = -0.92, 95%CI = -1.17 to -0.67, I2 = 19 %, P < 0.00001), MDG(MD = -1.01, 95%CI = -1.32 to -0.70, I2 = 48 %, P < 0.00001), and total insulin dose (MD = -5.81, 95%CI = -7.81 to -3.82, I2 = 32 %, P < 0.00001). No significant increase was observed in the rate of severe hypoglycemia (RR = 1.04, 95 % CI = 0.76-1.43, P = 0.80). However, SGLT2I therapy was associated with increased DKA occurrence (RR = 2.79, 95 % CI = 1.42-5.48; P = 0.003, I2 = 16 %). In addition, the subgroup analyses based on the type of SGLT2Is revealed that dapagliflozin might exhibit greater efficacy compared to other SGLT2Is across most outcomes.

CONCLUSIONS

SGLT2Is exhibited a positive effect on improving blood glucose level fluctuations. Subgroup analysis showed that dapagliflozin appeared to have more advantages. However, giving due consideration to preventing adverse effects, particularly DKA, is paramount.

REGISTRATION

Prospero CRD42023408276.

Gut Microbiota Composition and Functionality Are Associated With REM Sleep Duration and Continuous Glucose Levels

CONTEXT

Sleep disruption is associated with worse glucose metabolic control and altered gut microbiota in animal models.

OBJECTIVE

We aimed to evaluate the possible links among rapid eye movement (REM) sleep duration, continuous glucose levels, and gut microbiota composition.

METHODS

This observational, prospective, real-life, cross-sectional case-control study included 118 (60 with obesity), middle-aged (39.1-54.8 years) healthy volunteers recruited at a tertiary hospital. Glucose variability and REM sleep duration were assessed by 10-day continuous glucose monitoring (CGM) (Dexcom G6) and wrist actigraphy (Fitbit Charge 3), respectively. The coefficient of variation (CV), interquartile range (IQR), and SD of glucose variability was assessed and the percentage of time in range (% TIR), at 126-139 mg/dL (TIR2), and 140-199 mg/dL (TIR3) were calculated. Shotgun metagenomics sequencing was applied to study gut microbiota taxonomy and functionality.

RESULTS

Increased glycemic variability (SD, CV, and IQR) was observed among subjects with obesity in parallel to increased % TIR2 and % TIR3. REM sleep duration was independently associated with % TIR3 (β = -.339; P < .001) and glucose variability (SD, β = -.350; P < .001). Microbial taxa from the Christensenellaceae family (Firmicutes phylum) were positively associated with REM sleep and negatively with CGM levels, while bacteria from Enterobacteriacea family and bacterial functions involved in iron metabolism showed opposite associations.

CONCLUSION

Decreased REM sleep duration was independently associated with a worse glucose profile. The associations of species from Christensenellaceae and Enterobacteriaceae families with REM sleep duration and continuous glucose values suggest an integrated picture of metabolic health.

Glucose variability: a new risk factor for cardiovascular disease

AIMS AND DATA SYNTHESIS

Glucose variability (GV) is increasingly considered an additional index of glycemic control. Growing evidence indicates that GV is associated with diabetic vascular complications, thus being a relevant point to address in diabetes management. GV can be measured using various parameters, but to date, a gold standard has not been identified. This underscores the need for further studies in this field also to identify the optimal treatment.

CONCLUSIONS

We reviewed the definition of GV, the pathogenetic mechanisms of atherosclerosis, and its relationship with diabetic complications.

Particularities of Urinary Tract Infections in Diabetic Patients: A Concise Review

Diabetes mellitus is a chronic disease that, untreated or poorly controlled, can lead to serious complications, reducing life expectancy and quality. Diabetic patients are more likely to develop infections, including many common infections, but also pathognomonic ones such as emphysematous pyelonephritis, malignant otitis externa, mucormycosis and Fournier's gangrene. Considering the fact that diabetic patients experience more frequently urinary tract infections (UTIs) with a worse prognosis than non-diabetic people, we conducted a review study based on data in the literature, following the particularities of UTIs in this group of patients, the risk factors, the mechanisms involved and the challenges in their management. The findings highlight that UTI in diabetic patients have some particularities, including a more frequent evolution to bacteremia, increased hospitalizations, and elevated rates of recurrence and mortality than non-diabetic patients. The possible risk factors identified seem to be female gender, pregnancy, older age, UTI in the previous six months, poor glycemic control and duration of diabetes. The mechanisms involved are related to glucosuria and bladder dysfunction, factors related to bacterial strains and host response. The bacterial strains involved in UTIs in diabetic patients and their antibiotic susceptibility profile are, with some exceptions, similar to those in non-diabetic people; however, the antimicrobial agents should be carefully chosen and the duration of the treatment should be as those required for a complicated UTI. The data related to the risk of developing UTIs in patients treated with SGLT-2 inhibitors, a new class of oral hypoglycaemic agents with cardiovascular and renal benefits, are controversial; overall, it was evidenced that UTIs occurred at the initiation of the treatment, recurrent infection was uncommon and the majority of UTIs responded to treatment with standard antibiotics. Moreover, interruption or discontinuation of SGLT-2 inhibitor as a result of UTI was rare and SGLT-2 inhibitors did not increase the risk of severe infections such as urosepsis and pyelonephritis.

Development and validation of a clinical prediction model for detecting coronary heart disease in middle-aged and elderly people: a diagnostic study

OBJECTIVE

To develop and validate a multivariate prediction model to estimate the risk of coronary heart disease (CHD) in middle-aged and elderly people and to provide a feasible method for early screening and diagnosis in middle-aged and elderly CHD patients.

METHODS

This study was a single-center, retrospective, case-control study. Admission data of 932 consecutive patients with suspected CHD were retrospectively assessed from September 1, 2020 to December 31, 2021 in the Department of Integrative Cardiology at China-Japan Friendship Hospital. A total of 839 eligible patients were included in this study, and 588 patients were assigned to the derivation set and 251 as the validation set at a 7:3 ratio. Clinical characteristics of included patients were compared between derivation set and validation set by univariate analysis. The least absolute shrinkage and selection operator (Lasso) regression analysis method was performed to avoid collinearity and identify key potential predictors. Multivariate logistic regression analysis was used to construct a clinical prediction model with identified predictors for clinical practice. Bootstrap validation was used to test performance and eventually we obtained the actual model. And the Hosmer-Lemeshow test was carried out to evaluate the goodness-fit of the constructed model. The area under curve (AUC) of receiver operating characteristic (ROC), calibration curve, decision curve analysis (DCA), and clinical impact curve (CIC) were plotted and utilized with validation set to comprehensively evaluate the predictive accuracy and clinical value of the model.

RESULTS

A total of eight indicators were identified as risk factors for the development of CHD in middle-aged and elderly people by univariate analysis. Of these candidate predictors, four key parameters were defined to be significantly related to CHD by Lasso regression analysis, including age (OR 1.034, 95% CI 1.002 ~ 1.067, P = 0.040), hemoglobin A1c (OR 1.380, 95% CI 1.078 ~ 1.768, P = 0.011), ankle-brachial index (OR 0.078, 95% CI 0.012 ~ 0.522, P = 0.009), and brachial artery flow-mediated vasodilatation (OR 0.848, 95% CI 0.726 ~ 0.990, P = 0.037). The Hosmer-Lemeshow test showed a good calibration performance of the clinical prediction model (derivation set, χ2 = 7.865, P = 0.447; validation set, χ2 = 11.132, P = 0.194). The ROCs of the nomogram in the derivation set and validation set were 0.722 and 0.783, respectively, suggesting excellent predictive power and suitable performance. The clinical prediction model presented a greater net benefit and clinical impact based on DCA and CIC analysis.

CONCLUSION

Overall, the development and validation of the multivariate model combined the laboratory and clinical parameters of patients with CHD, which could be beneficial to the individualized prediction of middle-aged and elderly people, and helped to facilitate clinical assessments and decisions during treatment and management of CHD.

Long-term glycemic variability predicts compromised development of heart failure with improved ejection fraction: a cohort study

Background

A substantial portion of heart failure (HF) patients adherent to guideline-directed medical therapies have experienced improved ejection fraction (EF), termed HFimpEF. Glycemic variability (GV) has emerged as a critical cardiometabolic factor. However, the relation between long-term GV and the incidence of HFimpEF is still unclear.

Methods

A total of 591 hospitalized HF patients with reduced EF (HFrEF, EF≤ 40%) admitted from January 2013 to December 2020 were consecutively enrolled. Repeat echocardiograms were performed at baseline and after around 12 months. The incidence of HFimpEF, defined as (1) an absolute EF improvement ≥10% and (2) a second EF > 40% and its association with long-term fasting plasma glucose (FPG) variability were analyzed.

Results

During a mean follow-up of 12.2 ± 0.6 months, 218 (42.0%) patients developed HFimpEF. Multivariate analysis showed FPG variability was independently associated with the incidence of HFimpEF after adjustment for baseline HbA1c, mean FPG during follow-up and other traditional risk factors (odds ratio [OR] for highest vs. lowest quartile of CV of FPG: 0.487 [95% CI 0.257~0.910]). Evaluation of GV by alternative measures yielded similar results. Subgroup analysis revealed that long-term GV was associated with HFimpEF irrespective of glycemic levels and diabetic conditions.

Conclusions

This study reveals that greater FPG variability is associated with compromised development of HFimpEF. A more stable control of glycemic levels might provide favorable effects on myocardial functional recovery in HF patients even without diabetes.

Visit to Visit Hemoglobin A1c Variation and Long-term Risk of Major Adverse Limb Events in Patients With Type 2 Diabetes

CONTEXT

Glycemic variation had been demonstrated to be associated with several complications of diabetes.

OBJECTIVE

Investigation of the association between visit to visit hemoglobin A1c (HbA1c) variation and the long-term risk of major adverse limb events (MALEs).

METHODS

Retrospective database study. Average real variability was used to represent glycemic variations with all the HbA1c measurements during the 4 following years after the initial diagnosis of type 2 diabetes. Participants were followed from the beginning of the fifth year until death or the end of the follow-up. The association between HbA1c variations and MALEs was evaluated after adjusting for mean HbA1c and baseline characteristics. Included were 56 872 patients at the referral center with a first diagnosis of type 2 diabetes, no lower extremity arterial disease, and at least 1 HbA1c measurement in each of the 4 following years were identified from a multicenter database. The main outcome measure was incidence of a MALE, which was defined as the composite of revascularization, foot ulcers, and lower limb amputations.

RESULTS

The average number of HbA1c measurements was 12.6. The mean follow-up time was 6.1 years. The cumulative incidence of MALEs was 9.25 per 1000 person-years. Visit to visit HbA1c variations were significantly associated with MALEs and lower limb amputation after multivariate adjustment. People in the highest quartile of variations had increased risks for MALEs (HR 1.25, 95% CI 1.10-1.41) and lower limb amputation (HR 3.05, 95% CI 1.97-4.74).

CONCLUSION

HbA1c variation was independently associated with a long-term risk of MALEs and lower limb amputations in patients with type 2 diabetes.

Visit-to-Visit Variability in Fasting Blood Glucose Predicts the New-Onset Heart Failure: Results From Two Large Chinese Cohorts

Previous studies have hypothesized an association between higher fasting blood glucose (FBG) and heart failure (HF). However, FBG values fluctuate continuously over time, the association between FBG variability and the risk of HF is uncertain. We investigated the relationship between visit-to-visit variability in FBG and the risk of new-onset HF. This cohort study used data from a prospective Kailuan cohort (recruited during 2006-2007) and a retrospective cohort of family medicine patients from Hong Kong (recruited during 2000-2003) were followed up until December 31st, 2016, and December 31st, 2019, respectively, for the outcome of incident HF. Four indexes of variability were used, including standard deviation (SD), coefficient of variation (CV), variability independent of the mean (VIM), and average real variability (ARV). Cox regression was used to identify HF. In total, 98,554 and 22,217 subjects without preexisting HF from the Kailuan and Hong Kong cohorts were analyzed, respectively, with 1218 cases of incident HF in the former and 4,041 in the latter. Subjects in the highest quartile of FBG-CV had the highest risk of incident HF in both cohorts (Kailuan: HR 1.245, 95% CI 1.055-1.470); Hong Kong: HR 1.362, 95%CI 1.145-1.620; compared with the lowest quartile). Similar results were observed when using FBG-ARV, FBG-VIM, and FBG-SD. Meta-analysis showed similar results (highest versus lowest quartile: HR 1.30, 95%CI 1.15-1.47, P < 0.0001). As observed from 2 large, geographically distinct Chinese cohorts, a higher FBG variability was independently associated with higher risk of incident HF.

A study of factors influencing long-term glycemic variability in patients with type 2 diabetes: a structural equation modeling approach

Aim

The present study aims to utilize structural equation modeling (SEM) to investigate the factors impacting long-term glycemic variability among patients afflicted with type 2 diabetes.

Method

The present investigation is a retrospective cohort study that involved the collection of data on patients with type 2 diabetes mellitus who received care at a hospital located in Chengdu, Sichuan Province, over a period spanning from January 1, 2013, to October 30, 2022. Inclusion criteria required patients to have had at least three laboratory test results available. Pertinent patient-related information encompassing general demographic characteristics and biochemical indicators was gathered. Variability in the dataset was defined by standard deviation (SD) and coefficient of variation (CV), with glycosylated hemoglobin variation also considering variability score (HVS). Linear regression analysis was employed to establish the structural equation models for statistically significant influences on long-term glycemic variability. Structural equation modeling was employed to analyze effects and pathways.

Results

Diabetes outpatient special disease management, uric acid variability, mean triglyceride levels, mean total cholesterol levels, total cholesterol variability, LDL variability, baseline glycated hemoglobin, and recent glycated hemoglobin were identified as significant factors influencing long-term glycemic variability. The overall fit of the structural equation model was found to be satisfactory and it was able to capture the relationship between outpatient special disease management, biochemical indicators, and glycated hemoglobin variability. According to the total effect statistics, baseline glycated hemoglobin and total cholesterol levels exhibited the strongest impact on glycated hemoglobin variability.

Conclusion

The factors that have a significant impact on the variation of glycosylated hemoglobin include glycosylated hemoglobin itself, lipids, uric acid, and outpatient special disease management for diabetes. The identification and management of these associated factors can potentially mitigate long-term glycemic variability, thereby delaying the onset of complications and enhancing patients' quality of life.

Correlation Between Glycemic Variability and Diabetic Complications: A Narrative Review

Diabetes mellitus is a metabolic disorder with a complex etiology in which glycemic dynamics are disturbed and the body is unable to maintain the process of glucose homeostasis through the pancreas. Persistent symptoms of high blood glucose or low blood glucose may lead to diabetic complications, such as neuropathy, nephropathy, retinopathy, and cardiovascular diseases. Glycemic variability which can represent the presence of excessive glycemic excursions is an indicator for evaluating glucose homoeostasis. Limiting glycemic variability has gradually become an emerging therapeutic target in improve diabetes metabolism and prevent associated complications. This article reviews the progress of research on the various quantifiable parameters of glycemic variability and their relationships with vascular lesions and mechanisms.

Intensified glycemic control by HbA1c for patients with coronary heart disease and Type 2 diabetes: a review of findings and conclusions

The occurrence and development of coronary heart disease (CHD) are closely linked to fluctuations in blood glucose levels. While the efficacy of intensified treatment guided by HbA1c levels remains uncertain for individuals with diabetes and CHD, this review summarizes the findings and conclusions regarding HbA1c in the context of CHD. Our review showed a curvilinear correlation between regulated level of HbA1c and therapeutic effectiveness of intensified glycemic control among patients with type 2 diabetes and coronary heart disease. It is necessary to optimize the dynamic monitoring indicators of HbA1c, combine genetic profiles, haptoglobin phenotypes for example and select more suitable hypoglycemic drugs to establish more appropriate glucose-controlling guideline for patients with CHD at different stage of diabetes.

Hemoglobin A1c Variability Metrics Predict Coronary Artery Calcium and Cardiovascular Events in Type 1 Diabetes: The CACTI Study

CONTEXT

Interventions that decrease mean glucose have reduced rates of micro- and macrovascular complications in type 1 diabetes (T1D). However, the difference in cardiovascular risk between people with T1D and the general population endures, suggesting that factors beyond hemoglobin A1C (HbA1c) normalization drive cardiovascular outcomes.

OBJECTIVE

To determine whether various HbA1c metrics predict anatomic cardiovascular disease (CVD) risk factors and/or CVD events in people with T1D.

METHODS

We used linear regression to analyze the relationship of several HbA1c metrics to anatomic CVD risk factors and then used Cox regression to model their relationship to incident CVD events in the CACTI Study (ClinicalTrials.gov Identifier: NCT00005754).

RESULTS

In linear regression models adjusted for age, sex, and T1D duration, baseline Hba1c (b = 0.3998, P = 0.0236), mean HbA1c (b = 0.5385, P = 0.0109), and HbA1c SD (b = 1.1521, P = 0.0068) were each positively associated with square root transformed coronary artery calcium volume. Conversely, only mean HbA1c (b = 1.659, P = 0.0048) positively associated with pericardial adipose tissue volume. In survival models adjusted for age, sex, and T1D duration, baseline HbA1c [hazard ratio (HR): 1.471, 95% CI: 1.257-1.721], mean HbA1c (HR: 1.850, 95% CI: 1.511-2.264), time-varying HbA1c (HR: 1.500, 95% CI: 1.236-1.821), and HbA1c SD (HR: 1.665, 95% CI: 1.022-2.711) each independently predicted CVD events over 14.3 ± 5.2 person-years of follow-up.

CONCLUSIONS/INTERPRETATION

We found that various HbA1c metrics positively correlated with CAC volume and independently predicted incident CVD events in the CACTI T1D cohort. These associations with CVD events persisted for baseline HbA1c, mean HbA1c, and time-varying HbA1c even after adjustment for numerous CVD risk factors.

Higher glucose fluctuation is associated with a higher risk of cardiovascular disease: Insights from pooled results among patients with diabetes

BACKGROUND

The relationship between glucose fluctuation and the risk of cardiovascular disease (CVD) in patients with diabetes remains elusive. Glycated hemoglobin (HbA1c) variability is a key parameter of glucose fluctuation.

METHODS

PubMed, Cochrane Library, Web of Science, and Embase were searched up to 1 July 2022. Studies reporting associations of HbA1c variability (HbA1c-SD), coefficient of variation of HbA1c (HbA1c-CV), and HbA1c variability score [HVS] with the risk of CVD among patients with diabetes were included. We used three different insights (a high-low value meta-analysis, a study-specific meta-analysis, and a non-linear dose-response meta-analysis) to explore the relationship between HbA1c variability and CVD risk. A subgroup analysis was also performed to screen the potential confounding factors.

RESULTS

A total of 14 studies with 254 017 patients with diabetes were eligible. The highest HbA1c variability was significantly associated with increased risks of CVD (HbA1c-SD, risk ratio [RR] 1.45; HbA1c-CV, RR 1.74; HVS, RR 2.46; all p < .001) compared to the lowest HbA1c variability. The RRs of CVD for per HbA1c variability were significantly >1 (all p < .001). The subgroup analysis for per HbA1c-SD found a significant exposure-covariate interaction in the types of diabetes (p = .003 for interaction). The dose-response analysis showed a positive association between HbA1c-CV and CVD risk (P for nonlinearity <.001).

CONCLUSIONS

Our study suggests that the higher glucose fluctuation is significantly associated with the higher CVD risk in diabetes patients based on HbA1c variability. The CVD risk associated with per HbA1c-SD might be higher among patients type 1 diabetes than patients with type 2 diabetes.

Increased Two-Hour Post-Load Plasma Glucose Fluctuation Corresponds with Worse Clinical Prognoses among Acute Ischemic Stroke Patients without a History of Diabetes Mellitus

INTRODUCTION

This study aimed to evaluate the relationship between 2-h post-load minus fasting plasma glucose (2hPG-FPG) and 1-year clinical outcomes, such as death, stroke recurrence, and modified Rankin Scale (mRS) ≥2-3 among acute ischemic stroke (AIS) patients without diabetes mellitus (DM) history.

METHODS

1,214 AIS patients without DM history, obtained from ACROSS-China, were divided into 4 quartiles, based on 2hPG-FPG measurements obtained 14 days post-admission. Four models were constructed using multivariate Cox and logistic regression analyses, based on the inclusion of age, gender, trial of ORG 10172 in acute stroke treatment, NIH Stroke Scale scores (model 1), plus 10 other clinical parameters (model 2), plus newly diagnosed DM (NDDM) post-admission (model 3), plus 2hPG and FPG (model 4). Associations found from those 4 models between 2hPG-FPG and 1-year clinical outcomes were confirmed via stratification, multiplicative interaction, sensitivity, and restricted cubic spline analyses.

RESULTS

The highest quartile of 2hPG-FPG, after adjusting for variables, such as stroke severity (model 2), was independently associated with death, stroke recurrence, and mRS ≥2-3 (odds ratio [OR] = 3.95, 2.96, 4.15, and 4.83, respectively, all p < 0.0001). Increased 2hPG-FPG remained independently associated with mRS ≥2-3 in models 3-4, as well as increased mRS ≥2 under stratification analyses among both non-NDDM and NDDM patients.

CONCLUSION

2hPG-FPG is a relatively specific indicator of poorer 1-year clinical prognoses among AIS patients, independent of NDDM, 2hPG, and FPG post-hospital admission. Therefore, the oral glucose tolerance test could be a useful approach for detecting a higher likelihood for developing poorer prognoses among patients without DM history.

Hyperglycemia and Glycemic Variability Associated with Glucocorticoids in Women without Pre-Existing Diabetes Undergoing Neoadjuvant or Adjuvant Taxane Chemotherapy for Early-Stage Breast Cancer

Glucocorticoids, which are administered with chemotherapy, cause hyperglycemia. Glycemic variability among breast cancer patients without diabetes is not well known. A retrospective cohort study was conducted involving early-stage breast cancer patients without diabetes who received dexamethasone prior to neoadjuvant or adjuvant taxane chemotherapy between August 2017-December 2019. Random blood glucose levels were analyzed, and steroid-induced hyperglycemia (SIH) was defined as a random glucose level of >140 mg/dL. A multivariate proportional hazards model was used to identify the risk factors of SIH. Out of 100 patients, the median age was 53 years (IQR: 45-63.5). A total of 45% of patients were non-Hispanic White, 28% Hispanic, 19% Asian, and 5% African American. The incidence of SIH was 67%, and glycemic fluctuations were highest in those with glucose levels of >200 mg/dL. Non-Hispanic White patients represented a significant predictor for time to SIH, with a hazard ratio of 2.5 (95% CI: 1.04, 5.95, p = 0.039). SIH was transient in over 90% of the patients, and only seven patients remained hyperglycemic after glucocorticoid and chemotherapy completion. Pretaxane dexamethasone-induced hyperglycemia was observed in 67% of the patients, with the greatest glycemic lability in those patients with blood glucose levels of >200 mg/dL. The non-Hispanic White patients had a higher risk of developing SIH.