Correlation Between Hemoglobin Glycosylation Index and Nerve Conduction Velocity in Patients with Type 2 Diabetes Mellitus

An overview of diabetes-related foot ulcers

The escalating prevalence of diabetes mellitus presents concern due to its widespread organ damage, including the heart, kidneys, eyes, and nerves, leading to severe complications such as heart attacks, strokes, blindness, and diabetes-related foot ulcers (DFUs). Management in the community setting should be focused on prevention, assessment and patient-centred care. By understanding the complex aetiology, risk factors, and classification of DFUs, along with utilising evidence-based interventions like the Wound, Infection and Ischemia (WIfI) system, we can streamline care. Neuropathy, peripheral arterial disease and infection are major contributors to DFU development, highlighting the importance of early detection and intervention. Comprehensive care addressing vascular health, infection control, pressure offloading, wound management, metabolic control, and patient education is essential for successful DFU management. Ultimately, proactive prevention strategies and interdisciplinary collaboration are necessary in the management of DFUs and improving patient outcomes.

The Relationship Between UA/HDL and Diabetic Peripheral Neuropathy: A Cross-Sectional Analysis

Purpose

This study was designed to analyze correlations between the uric acid to high-density lipoprotein cholesterol ratio (UHR) and peripheral nerve conduction velocity (NCV) among type 2 diabetes mellitus (T2DM) patients.

Patients and Methods

This was a single-center cross-sectional analysis of 324 T2DM patients. All patients were separated into a group with normal NCV (NCVN) and a group with abnormal NCV (NCVA). Patients were also classified into groups with low and high UHR values based on the median UHR in this study cohort. Neurophysiological data including motor and sensory conduction velocity (MCV and SCV, respectively) were measured for all patients.

Results

Relative to patients with low UHR values, those in the high UHR group presented with greater NCVA prevalence (P = 0.002). UHR remained negatively correlated with bilateral superficial peroneal nerve SCV, bilateral common peroneal nerve MCV, bilateral ulnar nerve SCV, and bilateral right median nerve MCV even after adjustment for confounding factors. UHR was identified as an NCVA-related risk factor, with a 1.370-fold increase in NCVA prevalence for every unit rise in UHR (P < 0.001).

Conclusion

These results identify UHR as a risk factor associated with NCVA that was independently negatively associated with NCV among T2DM patients.

Correlation Between Fibrinogen/Albumin and Diabetic Peripheral Neuropathy

Purpose

This study aimed to examine the correlation between fibrinogen/albumin (FAR) and diabetic peripheral neuropathy (DPN).

Patients and Methods

A total of 342 patients were included and categorized into either the DPN group or the Non-DPN (NDPN) group based on their DPN status. The FAR index was determined by calculating the ratio of fibrinogen (FIB) to serum albumin (ALB), multiplied by 100. The participants were then divided into a High-FAR group and a Low-FAR group using the median FAR value as the threshold. Neurophysiological data were collected from the participants, which included motor conduction velocity (MCV) and sensory conduction velocity (SCV).

Results

The DPN group displayed higher FAR levels [(DPN vs NDPN:6.72 (5.89,7.74) vs 5.94±1.14], in addition to slower SCV and MCV data compared to the NDPN group. The high FAR group had a higher prevalence of DPN (78.9% vs 55.6%) (P<0.05). There was a negative correlation between FAR and NCV, including bilateral median nerve SCV, left ulnar nerve SCV, bilateral median nerve MCV, bilateral common peroneal nerve MCV, bilateral tibial nerve MCV, and left ulnar nerve MCV. FAR was revealed to be an independent risk factor for the development of DPN in patients and demonstrated a greater predictive value for DPN development in Type 2 diabetes mellitus (T2DM) compared with FIB, HbA1c.

Conclusion

The results suggest that monitoring FAR levels in patients with T2DM could identify those at higher risk for developing DPN, making the FAR index a valuable predictor of DPN development. Furthermore, since FAR has an inverse relationship with NCV, it stands to reason that high FAR levels may indicate nerve damage and slower conduction velocities. Thus, managing FAR could prove beneficial in both preventing and delaying the onset of DPN in T2DM patients.

Association of hemoglobin glycation index with prognosis of coronary artery disease after percutaneous coronary intervention: A retrospective cohort study

AIMS

To analyze the association between hemoglobin glycation index (HGI) and the long-term prognosis of patients with coronary artery disease (CAD) after percutaneous coronary intervention (PCI).

METHODS

Predicted glycated hemoglobin (HbA1c) level was calculated using an established formula and HGI represented the difference between laboratory measured HbA1c and predicted HbA1c. A total of 1780 patients were stratified into three subgroups (HGI < -0.4, -0.4 ≦ HGI < 0.12 and HGI ≧ 0.12). The primary endpoints included all-cause mortality (ACM) and cardiac mortality (CM). The secondary endpoints were major adverse cardiac events (MACEs) and major adverse cardiac and cerebrovascular events (MACCEs).

RESULTS

ACM occurred in 54 patients: 22 (3.7) in the low-HGI subgroup, 8 (1.3) in the moderate-HGI subgroup and 24 (4.1) in the high-HGI subgroup (p = .012). After adjusting for the traditional clinical prognostic factors, multivariate Cox regression analysis showed that patients in both the low and high HGI subgroups had significantly increased risk of ACM as compared with patients in the moderate HGI subgroup (hazard ratio [HR] = 4.979, 95% confidence interval [CI]: 1.865-13.297, p = .001 and HR = 2.918, 95% CI: 1.075-7.922, p = .036). However, we did not find significant differences in the incidence of CM, MACEs and MACCEs.

CONCLUSION

HGI can predicts risk for long-term mortality in patients undergoing PCI. This index could be helpful for the effective clinical management of the CAD population.

Factors associated with hemoglobin glycation index in adults with type 1 diabetes mellitus: The FGM-Japan study

AIMS/INTRODUCTION

The discrepancy between HbA1c and glucose exposure may have significant clinical implications; however, the association between the hemoglobin glycation index (HGI) and clinical parameters in type 1 diabetes remains controversial. This study aimed to find the factors associated with HGI (laboratory HbA1c - predicted HbA1c derived from the continuous glucose monitoring [CGM]).

MATERIALS AND METHODS

We conducted a cross-sectional study of adults with type 1 diabetes (n = 211, age 50.9 ± 15.2 years old, female sex = 59.2%, duration of CGM use = 2.1 ± 1.0 years). All subjects wore the CGM for 90 days before HbA1c measurement. Data derived from the FreeStyle Libre sensor were used to calculate the glucose management indicator (GMI) and glycemic variability (GV) parameters. HGI was defined as the difference between the GMI and the laboratory HbA1c levels. The participants were divided into three groups according to the HGI tertile (low, moderate, and high). Multivariate regression analyses were performed.

RESULTS

The female sex ratio, HbA1c, and % coefficient of variation (%CV) significantly increased over the HGI tertile, while eGFR and Hb decreased over the HGI tertile. In multivariate analysis, the factors associated with HGI were %CV and eGFR, after adjusting for HbA1c level and sex (R²  = 0.44).

CONCLUSIONS

This study demonstrated that HGI is associated with female sex, eGFR, and some glycemic variability indices, independently of HbA1c. Minimizing glycemic fluctuations might reduce HGI. This information provides diabetic health professionals and patients with personalized diabetes management for adults with type 1 diabetes.

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

Background

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

Methods

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

Results

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

Conclusion

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