A1c Variability Research Articles

Major adverse cardiovascular events' reduction and their association with glucose-lowering medications and glycemic control among patients with type 2 diabetes: A retrospective cohort study using electronic health records.

Cardiovascular diseases are a common cause of death among patients with type 2 diabetes (T2DM). Major adverse cardiovascular event (MACE) risks can be significantly reduced under adequate glycemic control (GC). This study aims to identify factors that influence MACE risk among patients with T2DM, including Hemoglobin A1c variability score (HVS) and early use of MACE-preventive glucose-lowering medications (GLMs). We conducted a longitudinal cohort study to retrospectively review electronic health records between 2011 and 2022. Patients with T2DM ≥18 years without previous stroke or acute myocardial infarction (AMI) were included. Cox regression was utilized to investigate MACE risk factors and compare MACE risk reduction associated with early use of MACE-preventive GLMs. A total of 19 685 subjects were included, with 5431 having MACE, including 4453 strokes, 977 AMI, and 1 death. There were 11 123 subjects with good baseline GC. Subjects with good baseline GC had 0.837 (confidence interval [CI]: 0.782-0.895) times lower MACE risk than their counterpart. Subjects with a single MACE-preventive GLM at baseline with continuous use >365 days showed a decreased MACE hazard ratio (0.681; CI: 0.635-0.731). Among all MACE-preventive GLMs, semaglutide provided a more significant MACE-preventive effect. This study identified that GLM, early GC, and HVS are MACE determinants among patients with T2DM. Novel GLM, adequate GC, and reduction of HVS can benefit MACE outcomes.

The association of haemoglobin A1c variability with adverse outcomes in patients with atrial fibrillation prescribed anticoagulants.

The association of haemoglobin A1c (HbA1c) variability with the risk of adverse outcomes in patients with atrial fibrillation (AF) prescribed anticoagulants remains unclear. This study aimed to evaluate the association of HbA1c variability with the risk of ischaemic stroke (IS)/systemic embolism (SE) and all-cause mortality among patients with non-valvular AF prescribed anticoagulants. Patients newly diagnosed with AF from 2013 to 2018 were included. Variability in HbA1c, indexed by the coefficient of variation (CV), was determined for those with at least three HbA1c measurements available from the time of study enrolment to the end of follow-up. To evaluate whether prevalent diabetes would modify the relationship between HbA1c variability and outcomes, participants were divided into diabetes and non-diabetes groups. The study included 8790 patients (mean age 72.7% and 48.5% female). Over a median follow-up of 5.5 years (interquartile range 5.2, 5.8), the incident rate was 3.74 per 100 person-years for IS/SE and 4.89 for all-cause mortality in the diabetes group. The corresponding incident rates in the non-diabetes group were 2.41 and 2.42 per 100 person-years. In the diabetes group, after adjusting for covariates including mean HbA1c, greater HbA1c variability was significantly associated with increased risk of IS/SE [hazard ratio (HR) = 1.65, 95% confidence interval (CI): 1.27-2.13) and all-cause mortality (HR = 1.24, 95% CI: 1.05-1.47) compared with the lowest CV tertile. A similar pattern was evident in the non-diabetes group (IS/SE: HR = 1.58, 95% CI: 1.23-2.02; all-cause mortality: HR = 1.35, 95% CI: 1.10-1.64). Greater HbA1c variability was independently associated with increased risk of IS/SE and all-cause mortality among patients with AF, regardless of diabetic status.

The Effect of Sodium Glucose Cotransporter-2 Inhibitors on Hemoglobin A1c Variability and Acute Kidney Injury: A Causal Mediation Analysis.

The role of lower hemoglobin A1c (HbA1c) variability in the effect of sodium glucose cotransporter-2 inhibitors (SGLT2i) on acute kidney injury (AKI) remains unclear. We compared AKI risk between SGLT2i and dipeptidyl peptidase 4 inhibitors (DPP4i) initiators. Additionally, we aimed to explore the extent to which SGLT2i's influence on AKI risk is mediated by reducing long-term HbA1c variability. Using 2018-2022 year data in Yinzhou Regional Health Care Database, we included adult, type 2 diabetes patients who were new users of SGLT2i or DPP4i. The effect of SGLT2i versus DPP4i on AKI, HbA1c variability, and AKI through HbA1c variability was compared using inverse probability of treatment weighted Cox proportional hazards models, median regression models, and causal mediation analysis. With a median follow-up of 1.76 years, 19 717 adults (for SGLT2i, n = 6008; for DPP4i, n = 13 709) with type 2 diabetes were included. The adjusted hazard ratio for SGLT2i versus DPP4i was 0.79 (95% confidence interval [CI] 0.64-0.98) for AKI. The adjusted differences in median HbA1c variability score (HVS) and HbA1c reduction were -16.67% (95% CI: -27.71% to -5.62%) and -1.98% (95% CI: -14.34% to 10.38%), respectively. Furthermore, lower AKI risk associated with SGLT2i was moderately mediated (22.77%) through HVS. The results remained consistent across various subgroups and sensitivity analyses. Compared to DPP4i, lower AKI risk associated with SGLT2i is moderately mediated through HbA1c variability. These findings enhance our understanding of the effect of SGLT2i on AKI and underscore the importance of considering HbA1c variability in diabetes treatment and management.

HbA1c variability associated with dementia risk in people with type 2 diabetes.

Although poor glycemic control is associated with dementia, it is unknown if variability in glycemic control, even in those with optimal glycosylated hemoglobin A1c (HbA1c) levels, increases dementia risk. Among 171,964 people with type 2 diabetes, we evaluated the hazard of dementia association with long-term HbA1c variability using five operationalizations, including standard deviation (SD), adjusting for demographics and comorbidities. The mean baseline age was 61 years (48% women). Greater HbA1c SD was associated with greater dementia hazard (adjusted hazard ratio=1.15 [95% confidence interval: 1.12, 1.17]). In stratified analyses, higher HbA1c SD quintiles were associated with greater dementia hazard among those with a mean HbA1c<6% (P=0.0004) or 6% to 8% (P<0.0001) but not among those with mean HbA1c ≥ 8% (P=0.42). Greater HbA1c variability is associated with greater dementia risk, even among those with HbA1c concentrations at ideal clinical targets. These findings add to the importance and clinical impact of recommendations to minimize glycemic variability. We observed a cohort of 171,964 people with type 2 diabetes (mean age 61 years). This cohort was based in Northern California between 1996 and 2018. We examined the association between glycosylated hemoglobin A1c (HbA1c) variability and dementia risk. Greater HbA1c variability was associated with greater dementia hazard. This was most evident among those with normal-low mean HbA1c concentrations.

Polygenic and socioeconomic contributions to nicotine use and cardiometabolic health in early mid-life.

Early mid-life is marked by accumulating risks for cardiometabolic illness linked to health-risk behaviors like nicotine use. Identifying polygenic indices (PGI) has enriched scientific understanding of the cumulative genetic contributions to behavioral and cardiometabolic health, though few studies have assessed these associations alongside socioeconomic (SES) and lifestyle factors. Drawing on data from 2,337 individuals from the United States participating in the National Longitudinal Study of Adolescent to Adult Health, the current study assesses the fraction of variance in five related outcomes - use of conventional and electronic cigarettes, body mass index (BMI), waist circumference, and glycosylated hemoglobin (A1c) - explained by PGI, SES, and lifestyle. Regression models on African ancestry (AA) and European ancestry (EA) subsamples reveal that the fraction of variance explained by PGI ranges across outcomes. While adjusting for sex and age, PGI explained 3.5%, 2.2%, and 0% in the AA subsample of variability in BMI, waist circumference, and A1c, respectively (in the EA subsample these figures were 7.7%, 9.4%, and 1.3%). The proportion of variance explained by PGI in nicotine-use outcomes is also variable. Results further indicate that PGI and SES are generally complementary, accounting for more variance in the outcomes when modeled together versus separately. PGI are gaining attention in population health surveillance, but polygenic variability might not align clearly with health differences in populations or surpass SES as a fundamental cause of health disparities. We discuss future steps in integrating PGI and SES to refine population health prediction rules. Study findings point to the complementary relationship of polygenic indices (PGI) and socioeconomic indicators in explaining population variance in nicotine outcomes and cardiometabolic wellness. Population health surveillance and prediction rules would benefit from the combination of information from both polygenic and socioeconomic risks. Additionally, the risk for electronic cigarette use among users of conventional cigarettes may have a genetic component tied to the cumulative genetic propensity for heavy smoking. Further research on PGI for vaping is needed.

FREE: A randomized controlled feasibility trial of a cognitive behavioral therapy and technology-assisted intervention to reduce fear of hypoglycemia in young adults with type 1 diabetes

ObjectiveThe purpose of this study was to test the preliminary effectiveness of a cognitive behavioral therapy intervention (Fear Reduction Efficacy Evaluation [FREE]) designed to reduce fear of hypoglycemia in young adults with type 1 diabetes. The primary outcome was fear of hypoglycemia, secondary outcomes were A1C, and glycemic variability. MethodsA randomized clinical trial was used to test an 8-week intervention (FREE) compared to an attention control (diabetes education) in 50 young adults with type 1 diabetes who experienced fear of hypoglycemia at baseline. All participants wore a continuous glucose monitor for the 8-week study period. Self-reported fear of hypoglycemia point-of-care A1C testing, continuous glucose monitor-derived glucose variability were measured at baseline, Week 8, and Week 12 (post-program). ResultsCompared to controls, those participating in the FREE intervention experienced a reduction in fear of hypoglycemia (SMD B = −8.52, p = 0.021), change in A1C (SMD B = 0.04, p = 0.841) and glycemic variability (glucose standard deviation SMD B = −2.5, p = 0.545) by the end of the intervention. This represented an 8.52% greater reduction in fear of hypoglycemia. ConclusionA cognitive behavioral therapy intervention (FREE) resulted in improvements in fear of hypoglycemia.Clinicaltrials.govNCT03549104

Glucose Lability and Complications in Diabetic Ankle Fractures

Introduction/Purpose: Rotational ankle fractures in diabetics have long posed difficult clinical challenges, with several observational studies noting increased risk of complications and amputation following treatment of unstable ankle fractures in diabetics. Several treatment options exist, including non-operative management, external fixation, open reduction internal fixation, staged fixation, and fusion. Hemoglobin A1C produces a mean blood glucose level over a three-month period. There is mounting evidence that glucose variability may be an alternative predictor of complication profile in non-orthopedic procedures, as well as in the total joint arthroplasty literature. The purpose of this investigation is to retrospectively analyze complication rates for diabetics with rotational ankle fractures at a single institution and assess their association with both the hemoglobin A1C and glucose variability. Methods: After obtaining Institutional Review Board approval, all patients from 2015-2022 with a diagnosis of diabetes and ankle fracture were retrospectively identified based on ICD-9/ICD-10 codes. These charts were manually reviewed to determine fracture classification and treatment. For operatively and non-operatively managed ankle fractures, the patient’s hemoglobin A1C was recorded when available within 3 months of the date of injury. Glucose variability was calculated using a coefficient of variation. Results: Two-hundred patients were included for analysis, of which 29% (n=58) were treated non-operatively. The majority of surgically treated patients underwent acute ORIF (58%, n=116). 8.5% were treated with staged fixation, 3.5% with acute hindfoot fusion, and 1% with external fixation alone. The overall complication rate was 27.5% (n=55). There were no statistically significant differences in both A1C (7.81 +/- 1.89 vs 7.73 +/- 1.96) and glucose variability, as measured by the covariance of variation (0.23 +/- 0.15 vs 0.20 +/- 0.13) in patients who did and did not experience postoperative complications. Higher glucose variability did trend towards predicting complications, though not in a statistically significant fashion (OR=1.57, p=0.35). Conclusion: Ankle fractures in patients with diabetes pose a challenge for orthopedic surgeons, as evidenced by the high medical and surgical complication rate observed in this study and others. No statistical significance was found between higher A1C and complication rate or between glucose variability and complication rate. Further investigation on the impact of glucose variability on complication rates in a larger cohort of this patient population is warranted. Univariable logistic Regression Results for the association of the odds of post-operative complication with patient characteristics.

Glycemic variability evaluated by HbA1c rather than fasting plasma glucose is associated with adverse cardiovascular events.

Although studies have shown that glycemic variability is positively associated with an increased risk of cardiovascular disease, few studies have compared hemoglobin A1c (HbA1c) and fasting plasma glucose (FPG) variability with adverse cardiovascular events in patients with type 2 diabetes mellitus (T2DM). This was a post hoc analysis of the Action to Control Cardiovascular Risk in Diabetes (ACCORD) study. Cox proportional hazards models were used to explore the relationship between HbA1c or FPG variability and the incidence of major adverse cardiovascular events (MACEs). In total, 9,547 patients with T2DM were enrolled in this study. During the median 4.6 ± 1.5 years follow-up period, 907 patients developed MACEs. The risk of MACEs increased in the HbA1c variability group in each higher quartile of HbA1c variability (P < 0.01). Compared with those in the first quartile of HbA1c variability, patients in the fourth quartile had a hazard ratio of 1.37 (Model 2, 95% confidence interval: 1.13-1.67) for MACEs. Higher FPG variability was not associated with a higher risk of MACEs in patients with T2DM (P for trend=0.28). A U-shaped relationship was observed between HbA1c and FPG variability, and MACEs. Glucose control therapy modified the relationship between HbA1c and MACEs; participants with higher HbA1c variability receiving intensive glucose control were more likely to develop MACEs (P for interaction <0.01). In adults with T2DM, the relationship between glycemic variability evaluated using HbA1c and FPG was U-shaped, and an increase in HbA1c variability rather than FPG variability was significantly associated with MACEs. The relationship between HbA1c variability and MACEs was affected by the glucose control strategy, and a higher HbA1c variability was more strongly associated with MACEs in patients receiving an intensive glucose control strategy.

Association of Long-Term HbA1c Variability with Anxiety and Depression in Patients with Type 2 Diabetes: A Cross-Sectional Retrospective Study.

To explore the relationship between long-term glycemic variability and anxiety and depression in patients with type 2 diabetes. A cohort comprising 214 individuals diagnosed with type 2 diabetes participated in this study. Comprehensive demographic and laboratory information was gathered for them. The evaluation of anxiety relied on the 7-item Generalized Anxiety Disorder Scale (GAD-7), while depression was assessed utilizing the 9-item Health Questionnaire (PHQ-9). Based on the presence or absence of anxiety and depression, participants were categorized into either the mood disorder or control groups. Subsequently, univariate and stepwise multiple binary logistic regression analyses were conducted to investigate the potential correlations between factors and the presence of anxiety and depression. The prevalence of anxiety disorders is 23%, and depression is 32%. The prevalence of smoking, diabetic autonomic neuropathy, stroke, and osteoporosis in the mood disorder group was significantly higher than that in the control group (P < 0.05), the glycated hemoglobin A1c variability score (HVS), mean hemoglobin A1c value, total cholesterol, urinary albumin/creatinine and systemic immune-inflammatory index (SII) were significantly higher in the control group (P < 0.05). The level of high-density lipoprotein in the mood disorder group was significantly lower than the control group (P < 0.05). In stepwise multiple binary logistic regression analyses, the main factors associated with anxiety were depression (P < 0.001, OR=117.581) and gender (P < 0.001, OR=9.466), and the main factors related to depression included anxiety (P < 0.001, OR=49.424), smoking (P=0.042, OR=2.728), HVS (P=0.004, OR=8.664), and SII (P=0.014, OR=1.002). Persistent fluctuations in blood glucose levels have been linked to anxiety and depression. Consequently, maintaining an optimal level of glycemic control and minimizing fluctuations becomes imperative in the comprehensive management of diabetes.

Correlation of peripheral neuropathy with serum vitamin D levels and HbA1C variations in type 2 diabetes mellitus patients

Introduction and Aim: The burden of diagnosed and undiagnosed diabetes will increase in the coming decade. Vitamin D deficiency and HbA1C variability are risk factors for diabetic neuropathy, and the association was studied in the present study. Materials and Methods: Data from 346 Type II diabetes patients reporting in a tertiary care Hospital in North India after approval of the ethics committee was analyzed. Michigan Neuropathy Screening Instrument (MNSI) was used to screen the patients for neuropathy. Vitamin D levels were measured. Four HbA1C levels (once every three months) done at least one-year preceding enrolment in the study were analyzed. A nerve conduction study was performed in the Neurophysiology lab. Data were analyzed using SPSS 21.0. Results: Diabetic peripheral neuropathy (DPN) was present in 54.91% of patients. They had a higher BMI, HbA1C %, fasting blood glucose, and low Vitamin D levels. Nerve conduction studies showed more pronounced changes in patients with severe DPN grades. Variability of HbA1C% correlated positively with the duration of diabetes and BMI, and the relation was negative with vitamin D levels. Vitamin D correlated negatively with duration, Hb A1C variability, and BMI. The correlation of HbA1C variability with motor and sensory conduction velocity and amplitude was negative, and with latency, it was positive. Vitamin D correlated positively with amplitude and conduction velocity and negatively with latency. Conclusion: Nerve conduction study, variability in HBA1C, and Vitamin D levels can act as tools to detect DPN. Vitamin D is a modifiable risk factor that needs monitoring in people with diabetes.

Long-term Visit-to-Visit Variability in Hemoglobin A1c and Kidney-Related Outcomes in Persons With Diabetes

To characterize associations between long-term visit-to-visit variability of hemoglobin A1c (HbA1c) and risk of adverse kidney outcomes in patients with diabetes. Observational study. 93,598 adults with diabetes undergoing routine care in Stockholm, Sweden. Categories of baseline and time-varying HbA1c variability score (HVS, the percentage of total HbA1c measures that vary by>0.5% [5.5mmol/mol] during a 3-year window): 0-20%, 21%-40%, 41%-60%, 61%-80%, and 81%-100%, with 0-20% as the reference group. Chronic kidney disease (CKD) progression (composite of>50% estimated glomerular filtration rate [eGFR] decline and kidney failure), acute kidney disease (AKI by clinical diagnosis or transient creatinine elevations according to KDIGO criteria), and worsening of albuminuria. Multivariable Cox proportional hazards regression. Compared with persons showing low HbA1c variability (HVS 0-20%), any increase in variability was associated with a higher risk of adverse kidney outcomes beyond mean HbA1c. For example, for patients with a baseline HbA1c variability of 81%-100%, the adjusted HR was 1.6 (95% CI, 1.47-1.74) for CKD progression, 1.23 [1.16-1.3] for AKI, and 1.28 [1.21-1.36] for worsening of albuminuria. The results were consistent across subgroups (diabetes subtypes, baseline eGFR, or albuminuria categories), in time-varying analyses and in sensitivity analyses including time-weighted average HbA1c or alternative metrics of variability. Observational study, limitations of claims data, lack of information on diet, body mass index, medication changes, and diabetes duration. Higher long-term visit-to-visit HbA1c variability is consistently associated with the risks of CKD progression, AKI, and worsening of albuminuria. The evidence for current guideline recommendations derives from clinical trials that focus on a single HbA1c as the definitive measure of efficacy of an intervention. However, long-term visit-to-visit fluctuations of HbA1c may provide additional value in the prediction of future kidney complications. We evaluated the long-term fluctuations in glycemic control in almost 100,000 persons with diabetes undergoing routine care in Stockholm, Sweden. We observed that higher long-term HbA1c fluctuation is consistently associated with the risks of chronic kidney disease progression, worsening of albuminuria and acute kidney injury. This finding supports a role for long-term glycemic variability in the development of kidney complications and illustrates the potential usefulness of this metric for risk stratification at the bedside beyond a single HbA1c test.

Association between hemoglobin glycation index and non-alcoholic fatty liver disease.

The hemoglobin glycation index (HGI) reflects biological variability in hemoglobin A1c. Even so, studies on the relationship between HGI and non-alcoholic fatty liver disease (NAFLD) are limited. Therefore, this study aimed to explore the relationship between HGI and NAFLD. In addition, the study also aimed to provide new methods to identify patients with a high risk for the development of NAFLD. This was a retrospective study based on physical examination data from Japan. Patients were divided into quartiles (Q1-Q4) according to their HGI level; the lowest quartile (Q1) was used as the reference group. Patents were also classified into two subgroups based on the presence or absence of NAFLD. Baseline characteristics between the groups were compared. Multivariate logistic regression analysis was used to investigate the association between the HGI and NAFLD. A mediation analysis examined the mediation relationship between HGI and NAFLD. Subgroup analyses were performed to the reliability of the results. A total of 14280 patients were eligible for inclusion in this study; 2515 had NAFLD. Patients in the NAFLD group had higher levels of HGI than patients in the non-NAFLD group. Increases in HGI correlated with an increased risk of NAFLD. After adjusting for confounding factors, the multivariate logistic regression analysis revealed that HGI was positively related to the prevalence of NAFLD. In addition, mediation analysis showed that body mass index (BMI) partly mediated the indirect impact of HGI on NAFLD preference. Subgroup analyses were performed according to age, sex, smoking status, and waist circumference. Our results indicated that HGI significantly correlated with NAFLD in patients with one of the following factors: age ≤60 years, BMI >28 kg/m2, female sex, a history of smoking, and abdominal obesity. HGI was an independent risk factor for NAFLD, and BMI partly mediated the association between HGI and NAFLD.

Long-Term Visit-to-Visit Glycemic Variability as a Predictor of Major Adverse Limb and Cardiovascular Events in Patients With Diabetes.

Background Peripheral arterial disease (PAD) is a severe complication in patients with type 2 diabetes. Glycemic variability (GV) is associated with increased risks of developing microvascular and macrovascular diseases. However, few studies have focused on the association between GV and PAD. Methods and Results This cohort study used a database maintained by the National Taiwan University Hospital, a tertiary medical center in Taiwan. For each individual, GV parameters were calculated, including fasting glucose coefficient of variability (FGCV) and hemoglobin A1c variability score (HVS). Multivariate Cox regression models were constructed to estimate the relationships between GV parameters and composite scores for major adverse limb events (MALEs) and major adverse cardiovascular events (MACEs). Between 2014 and 2019, a total of 45 436 adult patients with prevalent type 2 diabetes were enrolled for analysis, and GV was assessed during a median follow-up of 64.4 months. The average number of visits and time periods were 13.38 and 157.87 days for the HVS group and 14.27 and 146.59 days for the FGCV group, respectively. The incidence rates for cardiac mortality, PAD, and critical limb ischemia (CLI) were 5.38, 20.11, and 2.41 per 1000 person-years in the FGCV group and 5.35, 20.32, and 2.50 per 1000 person-years in HVS group, respectively. In the Cox regression model with full adjustment, the highest FGCV quartile was associated with significantly increased risks of MALEs (hazard ratio [HR], 1.57 [95% CI, 1.40-1.76]; P<0.001) and MACEs (HR, 1.40 [95% CI, 1.25-1.56]; P<0.001). Similarly, the highest HVS quartile was associated with significantly increased risks of MALEs (HR, 1.44 [95% CI, 1.28-1.62]; P<0.001) and MACEs (HR, 1.28 [95% CI, 1.14-1.43]; P<0.001). The highest FGCV and HVS quartiles were both associated with the development of PAD and CLI (FGCV: PAD [HR, 1.57; P<0.001], CLI [HR, 2.19; P<0.001]; HVS: PAD [HR, 1.44; P<0.001], CLI [HR, 1.67; P=0.003]). The Kaplan-Meier analysis showed significantly higher risks of MALEs and MACEs with increasing GV magnitude (log-rank P<0.001). Conclusions Among individuals with diabetes, increased GV is independently associated with the development of MALEs, including PAD and CLI, and MACEs. The benefit of maintaining stable glycemic levels for improving clinical outcomes warrants further studies.

Haemoglobin A1c variability is independently associated with carotid intima media thickness and plaque in type 2 diabetes: a retrospective, cross-sectional study.

Haemoglobin A1c (HbA1c) variability, a metric reflecting long-term glycaemic fluctuation, is associated with macrovascular events in type 2 diabetes. We aimed to investigate the impacts of HbA1c variability on preclinical atherosclerosis in patients without prior cardiovascular disease. We conducted a cross-sectional study on 564 participants with diabetes who underwent general health checkups from 2016-2022. At least three HbA1c measurements were conducted for each patient. Carotid intima-media thickness (CIMT) and plaque were evaluated by B-mode ultrasonography on bilateral carotid common arteries. The standard deviation (SD) and coefficient of variance (CV) of HbA1c were calculated. We found that each doubling in CV-HbA1c and SD-HbA1c was associated with a significant increment in CIMT. The effects were more pronounced in the groups with higher mean-HbA1c (mean-HbA1c ≥6.5%). The odds ratio (95% confidence interval) for the carotid plaque was 2.68 (1.57, 4.56) and 2.88 (1.16, 5.13) in the third tertile of CV-HbA1c and SD-HbA1c respectively after fully adjusting for all the conventional risk factors in the multivariable logistic regression analysis. High mean-HbA1c plus the third tertile of HbA1c variability sharply increased the prevalence of carotid plaques. In conclusion, HbA1c variability was independently associated with CIMT and plaques in populations with diabetes. CV-HbA1c and SD-HbA1c had more effects on subclinical atherosclerosis in patients with poorly-controlled blood glucose levels.

Identification of a novel link between adiposity and visuospatial perception.

Recent work has reported a negative association between BMI and performance on the Penn Line Orientation Task. To determine the reliability of this effect, a comprehensive assessment of visual function in individuals with healthy weight (HW) and those with overweight/obesity (OW/OB) was performed. Visual acuity/contrast, Penn Line Orientation Task, and higher-order visuospatial function were measured in 80 (40 with HW, 40 with OW/OB) case-control study participants. Adiposity, fasting glucose, hemoglobin A1c, diet, physical activity, and heart rate variability were also assessed. A subgroup of 22 participants plus 5 additional participants (n=27) underwent functional magnetic resonance imaging scanning. Compared with those with HW, individuals with OW/OB performed worse on tasks requiring judgments of line orientation. This effect was mediated by body fat percentage and was unrelated to other measures. Functional magnetic resonance imaging revealed a negative association between BMI and response in the primary visual cortex (V1) during line orientation judgment. Performance was unrelated to V1 response but positively correlated with response in a network of regions, including the lateral occipital cortex, when BMI was accounted for in the model. These results demonstrate a selective deficit in line orientation perception associated with adiposity and blunted activation in the V1 that cannot be attributed to visual acuity and does not generalize to other visuospatial tasks.

Glycated Haemoglobin A1c Variability Score Elicits Kidney Function Decline in Chinese People Living with Type 2 Diabetes.

Our aim was to investigate the association of glycated haemoglobin A1c (HbA1c) variability score (HVS) with estimated glomerular filtration rate (eGFR) slope in Chinese adults living with type 2 diabetes. This cohort study included adults with type 2 diabetes attending outpatient clinics between 2011 and 2019 from a large electronic medical record-based database of diabetes in China (WECODe). We estimated the individual-level visit-to-visit HbA1c variability using HVS, a proportion of changes in HbA1c of ≥0.5% (5.5 mmol/mol). We estimated the odds of people experiencing a rapid eGFR annual decline using a logistic regression and differences across HVS categories in the mean eGFR slope using a mixed-effect model. The analysis involved 2397 individuals and a median follow-up of 4.7 years. Compared with people with HVS ≤ 20%, those with HVS of 60% to 80% had 11% higher odds of experiencing rapid eGFR annual decline, with an extra eGFR decline of 0.93 mL/min/1.73 m2 per year on average; those with HVS &gt; 80% showed 26% higher odds of experiencing a rapid eGFR annual decline, with an extra decline of 1.83 mL/min/1.73 m2 per year on average. Chinese adults with type 2 diabetes and HVS &gt; 60% could experience a more rapid eGFR decline.

Changes of HbA1c variability after the switch to a longer-acting insulin analog in people with type 1 diabetes.

This study investigated whether longer-acting basal analogs (insulin degludec and insulin glargine U300) could reduce visit-to-visit hemoglobin A1c (HbA1c) variability in patients with type 1 diabetes. Ninety adults with type 1 diabetes for whom the basal insulin was switched to a longer-acting insulin analog were analyzed retrospectively. The coefficient of variation of HbA1c levels (CV-HbA1c) during the year before and after the switch was compared. The CV-HbA1c after the switch was not significantly different from that before the switch (4.39 ± 2.24% vs 4.25 ± 2.07%; P=0.506). The linear regression model revealed that CV-HbA1c before the switch was independently associated with the change of CV-HbA1c (regression coefficient per standard deviation=-0.568, P < 0.001), whereas the other variables were not (all P > 0.05). In conclusion, CV-HbA1c remained unchanged after the switch on average, but CV-HbA1c before the switch was associated with the decrease of CV-HbA1c in individuals with type 1 diabetes.

Diabete hi-tech

Recently, technological innovations have radically changed diabetes care. Insulin pumps and continuous glucose monitoring systems have significantly improved diabetes outcomes in both children and adults with Type 1 diabetes. For this reason, the major international associations recommend the use of technology in the management of Type 1 diabetes. The limitations of glycemic self-monitoring have prompted research to develop alternative techniques, favouring the expansion of continuous blood glucose monitoring systems (CGMs). CGM measures interstitial glucose through tiny sensors inserted in the subcutaneous tissue. Sensors can provide information in real time on the current glucose level and its trend. These data can be uploaded to the cloud and checked any time by doctors, patients and their caregivers. CGM can be combined with multiple daily injections (MDI) therapy or continuous subcutaneous insulin infusion (CSII). With CSII, basal insulin is supplied in the form of a continuous infusion and pre-meal bolus doses are calculated based on the meals’ carbohydrate content. A variety of insulin pumps is available, some of which can communicate with specific CGM devices, helping the patient to make better decisions about insulin dosing. This approach is known as sensor-augmented insulin pump therapy and it is the gold standard for the treatment of Type 1 diabetes in children and young adults, as recommended by the Italian Association of Paediatric Endocrinology and Diabetology. In the most recent systems, basal insulin delivery can be automatically modified through algorithms, based on CGM results, target glucose and the amount of active insulin, even though the patients still have to set the pre-meal insulin bolus manually. This system is defined as a partially hybrid closed-loop system, also known as artificial pancreas. Compared to MDI, CSII is associated with better control of blood glucose - measured by haemoglobin A1c and glycemic variability -, reduction of daily insulin requirement and an improvement of the quality of life. Psychophysical disabilities, language barriers or socio-economic disadvantage should not be considered limitations of CSII treatment as long as the caregiver is able to manage the therapy. Nowadays, MDI is recommended as first line therapy exclusively for those patients who do not want to use insulin pumps due to physical discomfort. The aim of this article is to provide updated information on the management of paediatric diabetes with modern technological devices for glucose monitoring and insulin delivery.

172-LB: Early-Onset Type 2 Diabetes Is Associated with Higher A1C and Glycemic Variability in a Cohort of Veterans followed Prospectively

Introduction: Early-onset type 2 diabetes in adults (eoT2D; T2D diagnosed between 18 and 39 years) is increasingly prevalent and is associated with high rates of diabetic complications. We hypothesized that eoT2D would be associated with adverse glycemic parameters (e.g., higher A1c and glycemic variability) . Methods: We used data from the Veterans Administration Diabetes Risk Cohort, a dynamic cohort of all U.S. Veterans enrolled in the Veterans Health Administration since 1/1/08 who were free from diabetes at enrollment. Among participants who developed T2D (n=936,596) , we fit linear regression models examining associations of age at detection with A1c-based measures of glycemia. Results: A ten-year younger age at detection was associated with a 0.24% higher A1c at T2D detection, a 0.29% higher mean A1c, a 0.02% higher A1c coefficient of variation (CV) , and a 0.02% higher A1c average real variability (ARV) . Compared to T2D with onset after 40 years, eoT2D was associated with a 0.73% higher A1c at T2D detection, a 0.82% higher mean A1c, a 0.05% higher CV, and a 0.04% higher ARV (all p&amp;lt;0.0001, Table 1) . Conclusions: eoT2D is associated with higher A1c and glycemic variability. Further study of glycemic trajectories, modifiable risk factors, and clinical course in this T2D subgroup are needed. Disclosure S. Avramovic: None. M. D. Schwartz: None. D. Enquobahrie: None. E. J. Boyko: None. P. Wander: None.

Effect of Hemoglobin A1c Trajectories on Future Outcomes in a 10-Year Cohort With Type 2 Diabetes Mellitus.

BackgroundHemoglobin A1c (HbA1c) variability may be a predictor of diabetic complications, but the predictive values of HbA1c trajectories remain unclear. We aimed to classify long-term HbA1c trajectories and to explore their effects on future clinical outcomes in a 10-year cohort with type 2 diabetes mellitus (T2DM).MethodsA total of 2,161 participants with T2DM from the Beijing Community Diabetes Study were included. The 10-year follow-up was divided into two stages for the present data analysis. Stage I (from 2008 to 2014) was used to identify the HbA1c trajectories and to calculate the adjusted SD of HbA1c (HbA1c-adjSD), or the coefficient of variation of HbA1c (HbA1c-CV). Stage II (from 2014 to 2018) was used to collect the records of the new occurrence of diabetes-related clinical outcomes. Latent growth mixture models were used to identify HbA1c trajectories. Cox proportional hazards models were used to explore the relationship between HbA1c trajectories, HbA1c-adjSD, or HbA1c-CV and the future outcomes.ResultsThree HbA1c trajectories were identified, including low stable (88.34%), gradual decreasing (5.83%), and pre-stable and post-increase (5.83%). Either the risk of death or the chronic complications were significantly higher in the latter two groups compared to the low stable group after adjustment for average HbA1c and other traditional risk factors, the adjusted hazard ratios (HRs) for renal events, composite endpoint, and all-cause death for the pre-stable and post-increase group were 2.83 [95%CI: 1.25–6.41, p = 0.013], 1.85 (95%CI: 1.10–3.10, p = 0.020), and 3.01 (95%CI: 1.13–8.07, p = 0.028), respectively, and the adjusted HR for renal events for the gradual decreasing group was 2.37 (95%CI: 1.08–5.21, p = 0.032). In addition, both univariate and multivariate Cox HR models indicated that participants in the fourth and third quartiles of HbA1c-CV or HbA1c-adjSD were at higher risk of renal events compared to participants in the first quartile.ConclusionsHbA1c trajectories, HbA1c-CV, and HbA1c-adjSD could all predict diabetes-related clinical outcomes. HbA1c trajectories could reflect long-term blood glucose fluctuation more intuitively, and non-stable HbA1c trajectories may predict increased risk of renal events, all-cause death, and composite endpoint events, independent of average HbA1c.