Poor Glycemic Control In Patients Research Articles

Factors Affecting Glycemic Control in Patients with Type 2 Diabetes in Kalasin Province, Thailand: An Analytical Cross-Sectional Study

Background: Optimal glycemic management is critical since it can predict all associated essential causes of death, even after accounting for other risk factors. Objectives: This study investigated the factors associated with glycemic control in patients with type 2 diabetes mellitus (T2DM), particularly self-care behaviors. Methods: An analytical cross-sectional study examined 385 patients with T2DM in 1 general and 17 community hospitals in Kalasin Province. The samples were collected using mixed-method sampling. Data were collected using a validated questionnaire with six parts and laboratory examination records between September 2021 and December 2022. Descriptive statistics (e.g., percentages and means ± SD) were used to analyze the data. The relationship between relevant factors and lack of glycemic control was analyzed by multivariate logistic regression using SPSS version 25. Results: Results showed that most patients were female (78.18%), and the mean age was 59.84 ± 9.05 years. Additionally, a significant proportion of the patients (79.74%) had poor glycemic control. Several factors are significantly associated with poor glycemic control in patients with T2DM. These factors included subjects under the age of 60 years (OR = 2.95, 95% CI: 1.57 to 5.54; p-value < 0.001), a diabetes duration of over 10 years (OR = 3.95, 95% CI: 1.90 to 8.22; p-value < 0.001), poor knowledge about diabetes (OR = 3.92, 95% CI: 1.59 to 9.67; p-value < 0.003), and inadequate self-care behaviors (OR = 6.12, 95% CI: 3.20 to 11.68; p-value < 0.001). Conclusions: In conclusion, proper interventions for controlling glycemic level behaviors are necessary to improve proper self-care behaviors in patients who have a low knowledge level of T2DM, have had diabetes for over ten years, and are aged < 60 years. This approach can reduce the likelihood of experiencing disabilities and economic hardship.

Platelet indices as predictors of poor glucoregulation in type 2 diabetes mellitus adults at Bishoftu General Hospital, Ethiopia.

Diabetes is a chronic metabolic syndrome that has become a global public health problem with significant morbidity and mortality. It is a pro-inflammatory and pro-thrombotic condition characterized by increased platelet activation and alterations in platelet indices. However, the use of platelet indices as predictors of poor glucoregulation has not been fully evaluated in this context, and evidence for their role as predictors of poor glycemic status in diabetic patients is limited. To evaluate platelet indices and determine their prognostic significance in relation to inadequate glucoregulation among individuals diagnosed with type 2 diabetes at Bishoftu General Hospital in Ethiopia, from June 15 to August 12, 2022. A comparative cross-sectional study was conducted in 261 participants including 174 individuals with type 2 diabetes mellitus (T2DM) and 87 non-diabetic controls. The systematic random sampling technique was used to select par-ticipants. Data were collected using structured questionnaires, physical measurements, checklists, and laboratory tests. Platelet parameters and fasting blood glucose levels were determined from blood samples using Sysmex-XN550 and CobasC311 analyzers, respectively. The hematology analyzer output was checked and participants were also screened for malaria parasites using a prepared blood smear. Collected data were entered into Epi-data version 3.1 and exported to SPSS version 25 for analysis. The χ 2 test, Mann-Whitney U test, Kruskal-Wallis test, post hoc test, Spearman correlation, and receiver operating characteristic curve were used for analysis. A P value < 0.05 was considered statistically significant. The results of our study indicate that diabetic patients have significantly higher levels of platelet distribution width (PDW), mean platelet volume (MPV), platelet large cell ratio (PLCR), and plateletcrit (PCT) compared to healthy individuals (P < 0.001). Furthermore, these indices were found to be significantly elevated in individuals with poor glycemic control in T2DM compared to those with good glycemic control and healthy controls. We also observed significant correlations between these indices and various anthropometric and clinical variables. Our findings suggest that PDW, with a cut-off value of 15.75 fL and an area under the curve (AUC) of 0.803, MPV, with a cut-off value of 12.25 fL and an AUC of 0.774, PLCR, with a cut-off value of 36.3% and an AUC of 0.775, and PCT, with a cut-off value of 0.24% and an AUC of 0.761, can serve as predictors of poor glycemic control in patients with diabetes mellitus. The observed correlation between diabetic patients and a significant increase in platelet indices has highlighted their potential as predictors of poor glycemic control in diabetes. Therefore, regular screening and profiling of platelet indices is recommended as part of the follow-up process for individuals with diabetes mellitus.

Proportion and Potential Risk Factors of Poor Glycemic Control among Patients with Type 2 Diabetes Mellitus: Experience of a Tertiary Center in Baghdad, Iraq, 2020

Background: Diabetes Mellitus is the most prevalent metabolic disorder worldwide. Effective diabetes self-management and keeping the Glycosylated hemoglobin level within the normal range could decrease the burden on the health system by reducing hospital admissions and diabetic complications, lowering the financial strain on the health system. Objective: To recognize the potential risk factors of poor glycemic control in patients having type 2 Diabetes Mellitus in Baghdad, Iraq. Patients and Methods: This cross-sectional study was conducted in the Diabetes and Endocrine Center at Al-Kindy Teaching Hospital, Baghdad, Iraq, from June to November 2020. The total number of the study's participants was 234. Based on the cutoff point of glycosylated hemoglobin of 7, the patients with glycosylated hemoglobin of ≥7 were considered uncontrolled, and those with glycosylated hemoglobin of &lt;7 were considered controlled. Results: The proportion of cases with uncontrolled diabetes was 68.4% (160). The remaining 74 patients had controlled diabetes. Age, sex, marital status, and employment status were not significantly associated with the control status (P&gt;0.05). The binary analysis showed a significant association observed between the control status and level of education (P=0.001), income (P=0.001), presence of comorbidities (P=0.028), positive family history (P=0.03), dyslipidemia (P=0.001), cholesterol level (P=0.002), high triglyceride level (P=0.001), and low-density lipoprotein-cholesterol level (P=0.025). The smoking status, body mass index, and high-density lipoprotein-cholesterol level were not significantly associated with the control status (P&gt;0.05). The disease characteristics, including the disease duration, fasting blood glucose, type of medication, self-monitoring, healthy diet, physical activity, and medication adherence, were significant factors (P&lt;0.001). The multivariate regression method showed that dyslipidemia, FBS≥130, physical inactivity, and poor medication adherence were significant predictors for uncontrolled DM (P-value = 0.03, 0.001, 0.03, and 0.043, respectively). Conclusion: The most important potentially modifiable risk factors for poor diabetes control were dyslipidemia, physical inactivity, and poor adherence to the management protocol. Enhancing the education of patients and their healthcare providers on these factors is crucial to improving the patients’ control status.

Associations of Area-Level and Parental Individual-Level Social Deprivation with Glycemic Control over Time among Children with Type 1 Diabetes in France: A Longitudinal Cohort Study

Background. Poor glycemic control in patients with type 1 diabetes (T1D) is associated with greater social deprivation. However, the evidence is inconsistent in terms of the type of social deprivation (individual-level or area-level) and whether glycemic control changes over time. Here, we investigated the impacts of individual-level and area-level social deprivation on the glycated hemoglobin (HbA1c) trajectory from the time of T1D diagnosis. Materials and Methods. We retrospectively analyzed a cohort of children who were diagnosed with T1D between 2017 and 2020 at Bordeaux University Hospital. Social deprivation was assessed using both parental individual indicator (EPICES score) and ecological indicator (European Deprivation Index (EDI) score). Piecewise linear mixed-effects models were used to estimate the effects of social deprivation on HbA1c trajectory. Results. We included 168 patients. The most-deprived group included 29% and 22% of all patients, as revealed by the respective EPICES and EDI scores. The two indicators were poorly correlated. The short-term decrease in HbA1c level tended to be smaller in the most-deprived patients over the first 4 months after diagnosis than in other patients (slope difference of 2.68% per year compared with the slope among the least-deprived patients, P=0.056). The long-term trajectory was influenced by area-level deprivation (EDI score); the least-deprived patients (quintile 1) exhibited more stable mean HbA1c levels. Conclusions. Social deprivation may partially explain poor glycemic control in some patients; both short-term individual deprivation and long-term area-level deprivation may be involved. Further research is needed to determine how to integrate this information into a therapeutic strategy.

Impact of diabetes mellitus and poor glycemic control on the prevalence of left atrial low-voltage areas and rhythm outcome in patients with atrial fibrillation ablation.

Left atrial low-voltage areas (LVAs) are known to be correlated with atrial scarring and atrial fibrillation (AF) recurrence after ablation. However, the association between LVAs and glycemic status before ablation has not been fully clarified. The purpose of this study was to investigate associations among the prevalence of diabetes mellitus (DM), glycemic control, and the prevalence of LVAs in patients with AF ablation. In total, 912 (age, 68 ± 10 years; female, 299 [33%]; persistent AF, 513 [56%]) consecutive patients who underwent initial AF ablation were included. A preprocedure glycated hemoglobin A1c (HbA1c)≥7% was set as the cutoff for poor glycemic control in patients with DM. LVAs were defined as areas with a bipolar voltage of <0.5 mV covering ≥5 cm2 of left atrium. LVAs existed in 208 (23%) patients, and 168 (18%) patients had DM. LVAs were found more frequently in patients with DM and poor glycemic control. On multivariate analysis, DM with HbA1c≥7% was an independent predictor of LVAs (odds ratio, 3.3; 95% confidence interval: 1.6-6.7; p = .001). In patients with LVAs, freedom from AF recurrence during the 24-month study period was significantly lower in patients who had DM with HbA1c≥7% than in those without DM (37.9% vs. 54.7%, p = .02). In patients with AF ablation, LVAs were found more frequently in patients with DM and poor glycemic control. DM with HbA1c≥7% was an independent predictor of LVAs.

Effects of a combination of dyslipidemia and hypertension on the glycemic control of patients with type 2 diabetes mellitus: a cross-sectional study

Objectives: Both dyslipidemia and hypertension contribute to poor glycemic control in patients with type 2 diabetes mellitus, but the combined effect of dyslipidemia and hypertension on glycemic control in patients with type 2 diabetes mellitus has not been evaluated. The aim of this study was to analyze the interaction effect between dyslipidemia and hypertension on glycemic control in patients with type 2 diabetes mellitus. Methods: A total of 2485 patients with type 2 diabetes mellitus were selected from the Xuzhou community of China by multi-stage cluster random sampling for a cross-sectional survey. Their glycated hemoglobin, dyslipidemia, and hypertension were assessed, and the interaction effects between dyslipidemia and hypertension on glycemic control were analyzed using relative excess risk due to the interaction, the synergy index, and the attributable proportion of the additive interaction. Results: Of the participants, 62.13% (1544/2485) had dyslipidemia and 55.01% (1367/2485) had hypertension. Of the participants, 76.66% (1905/2485) who had both dyslipidemia and hypertension also had poor glycemic control. The prevalence of poor glycemic control was higher in those with both dyslipidemia and hypertension (odds ratio 2.735, 95% confidence interval 2.117–3.532; p &lt; 0.001) compared with those who had normal blood lipids and without hypertension, after adjustment for confounders. The relative excess risk due to the interaction, the attributable proportion, and the synergy index were 1.077 (95% confidence interval 0.558–1.596), 2.637 (95% confidence interval 1.268–4.006), and 0.394 (95% confidence interval 0.230–0.558), respectively, for the interaction between dyslipidemia and hypertension. Conclusions: Dyslipidemia and hypertension have an additive interaction on poor glycemic control in patients with type 2 diabetes mellitus.

Frequency and correlates of poor glycemic control in patients with type 2 diabetes at Jimma Medical Centre, Ethiopia: a cross-sectional study.

the majority of studies in Ethiopia determine the prevalence of glycemic control employed by fasting blood sugar (FBS), which is impacted by a variety of factors. Hence, the purpose of this study was to assess the status of glycemic control using HbA1c and its correlates in patients with type 2 diabetes in Southwest Ethiopia. a cross-sectional study was employed among 124 T2 diabetes mellitus (DM) patients at Jimma Medical Center (JMC), Southwest Ethiopia. HbA1c and FBS were estimated using the Cobas 6000 analyzer. The body mass index (BMI) and waist-to-hip ratio were calculated as the standard formula. Data were analyzed by SPSS version 25. Logistic regression analysis was employed to identify independent risk factors associated with poor glycemic control of DM patients. males comprised 63.7% (n=79) of the total respondents. The mean age of aOR: 2.21, 95% CI 1.13, 4.34; p = 0.01f participants was 51.84 ± 11.6 years; 60.5% (n=75) of T2 DM patients were in poor glycemic control (HbA1c ≥ 7%). In multivariate logistic regression analysis, BMI of ≥ 30, (aOR: 2.21, 95% CI 1.13, 4.34) increased waist-to-hip ratio (aOR: 1.63, 95% CI 0.82, 2.18), high systolic blood pressure (aOR: 1.52, 95% CI 1.11, 6.23), high FBS (aOR: 1.61, 95% CI 1.00, 4.12), and longer duration of DM (aOR: 1.23, 95% CI 0.87, 1.88) were associated with poor glycemic control. the level of poor glycemic control in the study population is high. Obesity and/or overweight, central obesity, systolic hypertension, and fasting blood sugar levels were all associated with poor glycemic control in T2 DM patients.

Correlation of Neutrophil Lymphocyte ratio with HbA1c in patients of type 2 Diabetes Mellitus attending Medical Department of a Tertiary Care Centre

Background: Type 2 diabetes mellitus is associated with chronic low-grade inflammation. Neutrophil lymphocyte is an indicator of subclinical inflammation. One of the novel inflammatory markers is neutrophil to lymphocyte ratio. We find only less studies that are directly accessing the relation between NLR and HbA1c, in the present study we aim to correlate neutrophil lymphocyte ratio with glycemic control in patients having type 2 diabetes.&#x0D; Methods: Hospital based observational cross section study was carried out at department of medicine at Bharatpur Period August 2023 to December 2023. 176 patients with type 2 diabetes mellitus as per ADA criteria aged more than 18 years were included in the study. Microsoft excel and SPSS version 20.0 were used for collection and analysis of the data, p&lt;0.05 was considered as significant.&#x0D; Results: Out of 176 cases having mean age 55.78±9.111, Mean BMI of all participant was 28.37±4.061, mean absolute Neutrophil count was 7.36±7.123 10³/μl and Mean neutrophil lymphocyte ratio count was 4.03±1.545, in which 147 patients (83.5%) had NLR within normal limit, and remaining 29 patients (16.5%) had NLR within more than 3.53. In our study 123 patients (69.9%) had HbA1C more than 9.0. The association of NLR with HbA1c which was significant association (p=0.047) noted between two parameters indicating higher frequency of NLR had a positive correlation with HbA1c and was found to be an independent predictor of poor glycemic control in patients with type 2 diabetes mellitus.&#x0D; Conclusions: Our study concluded that the association of NLR with HbA1c which was significant association. The Association of HbA1c with absolute neutrophil count was which significant and association of HbA1c with absolute lymphocyte count was which significant. Type 2 DM patients with raised NLR should be evaluated for cardiovascular, renal and ocular complications of diabetes. NLR may be useful as an easily measurable, noninvasive, widely available and cost-effective parameter for the disease monitoring tool during follow up of diabetic patients.

Prevalence and the association between clinical factors and Diabetes-Related Distress (DRD) with poor glycemic control in patients with type 2 diabetes: A Northern Thai cross-sectional study.

Glycemic control is important to prevent diabetic complications. However, evidence linking factors such as diabetes-related distress (DRD) to poor glycemic outcomes is lacking in Thailand. Therefore, this study aimed to investigate the prevalence and associated factors of poor glycemic control type 2 diabetes. A cross-sectional study was conducted on 127 type 2 diabetic patients between December 2021 and March 2022 at Maharaj Nakorn Chiang Mai Hospital, Thailand. Data collection included demographic data, clinical data (duration of being type 2 diabetes, diabetic treatment modalities, weight, height, blood pressure, FBS, and HbA1c), behavioral data (self-care behavior, physical activity, dietary assessment, smoking, alcohol consumption, and sleep quality), and psycho-social data (depression and DRD). Poor glycemic control was defined as not achieving the target HbA1c based on the 2021 American Diabetes Association (ADA) Guideline. Multivariable logistic regression was used to explore the associations between potential factors including DRD, and poor glycemic control. The prevalence of poor glycemic control in patients with type 2 diabetes was 29.1%. Our analysis revealed that age under 65 years old (OR 6.40, 95% CI 2.07-19.77, p = 0.001), obesity (BMI ≥ 25 kg/m2) (OR 2.96, 95% CI 1.05-8.39, p = 0.041), and DRD (OR 14.20, 95% CI 3.76-53.64, p<0.001) were significantly associated with poor glycemic control. Three dimensions of DRD were associated with poor glycemic control, including emotional distress (OR 4.23, 95% CI 1.51-11.85, p = 0.006), regimen-related distress (OR 6.00, 95% CI 1.88-19.18, p = 0.003), and interpersonal distress (OR 5.25, 95% CI 1.39-20.02, p = 0.015). Age, obesity, and DRD are associated with poor glycemic control. A holistic approach that includes addressing DRD is crucial for improving glycemic outcomes in patients with type 2 diabetes. Further studies in broader populations using a cohort design are recommended.

Poor glycemic control is associated with the prevalence of left atrial low-voltage areas in patients with atrial fibrillation ablation

Abstract Background Although glycemic control before atrial fibrillation (AF) ablation is associated with AF recurrence after the procedure, the mechanism has not been fully clarified. Left atrial low-voltage areas (LVAs) were known to be correlated with atrial scarring and AF recurrence after ablation, and a preliminary study showed that the past history of diabetes mellitus (DM) was an independent predictor of LVAs. However, the association between LVAs and glycemic control before ablation has not been elucidated. Purpose The purpose of this study was to investigate the association between glycemic control before AF ablation and the prevalence of LVAs in patients with AF ablation. Methods The study design was a single center, retrospective observational study. Between December 2014 and December 2018, 782 (age, 68 ± 10 years; female, 262 [34%]; persistent AF, 414 [53%]) consecutive patients who underwent initial AF ablation were included. Glycated hemoglobin A1c (HbA1c) levels were measured before the procedure, and HbA1c ≥ 7% was set as the cut-off value of poor glycemic control in patients with DM. The definition of LVAs was sites with a left atrial bipolar amplitude &amp;lt; 0.5 mV covering as ≥ 5 cm². Rhythm outcome after the procedure was followed for 24 months. Results Of 782 patients, 162 (21%) patients had LVAs, and 135 (17%) patients had past history of DM. LVAs were found more frequently in patients with poor glycemic control (Figure 1). On multivariate analysis, DM with HbA1c ≥7% was an independent predictor of LVAs (odds ratio, 3.6; 95% confidence interval: 1.8–7.0; p &amp;lt; 0.001). In patients with LVAs, freedom from AF recurrence during the study periods tended to be lower in patients who had DM with HbA1c ≥7% than in those with non-DM (40.3% versus 51.6%, p = 0.06) (Figure 2). Conclusions In patients with AF ablation, LVAs were found more frequently in patients with poor glycemic control. In particular, DM with HbA1c ≥7% was an independent predictor of LVAs.Prevalence of LVAs and glycemic controlAF recurrence and glycemic control

THU152 HbA1c Control Of Pediatric Patients With Diabetes In The Urban Community Hospital Representing Underserved, Minority Population: Can The Frequent Encounters With Multi-Disciplinary Diabetes Team Overcome Patient-Related Adverse Variables In Improving Their Glycemic Control?

Abstract Disclosure: S. Velasquez: None. O. Ozcan: None. S. Dave-Sharma: None. Introduction: The American Diabetes association’s established goal for HbA1C across all age groups is as &amp;lt;7%. As the prevalence of diabetes continues to increase, its control in the pediatric population has become more challenging. Studies have shown controlling diabetes is especially challenging in minority ethnic groups of low income neighborhoods. Our hospital, located in the Mott Haven community, serves the minority population of mostly Hispanics and Blacks with an average household income of &amp;lt; $ 20 000 (poverty rate 39%). Objectives: This study aimed to identify different variables among patients with controlled and uncontrolled HbA1c in our unique pediatric population. Our goal was to learn from the group of patients with good diabetes control to improve the control of patients with poor glycemic control by changing the protocols involving our multi-disciplinary care team. Results: We collected data from 86 patients with diabetes (type 1 and 2) from our pediatric endocrinology clinic and divided them into 3 groups based on their HbA1c levels. 1. Good control: HbA1c &amp;lt;7.5 % in 33/86 (38%) patients, 2. Poor control: HbA1C 7.5%-10% with 34/86 (40%), and 3. Very poor control: HbA1C &amp;gt;10% with 19/86 (22%) patients. We studied the data for variables such as gender, age, ethnicity, insurance, type of diabetes, duration of diabetes, BMI, frequency of encounters with the multi-disciplinary team, and more. Of 86 patients, the average age was 15.65 (51% were females, and 49% were males). 43% had type 1 diabetes, and 57% had type 2 diabetes. The type 1 vs. Type 2 diabetes ratio among different groups was 12% Type 1 vs. 88% type 2 in the group with good control, 73.5% Type 1 vs. 26.5% Type 2 in the poor control group, and 42% type 1 vs. 58% type 2 in the group with very poor control. Obesity was found in 50% of our patients; in the good control group, it was 57%. In the poor control group, 38%, and in the very poor control group, 58%. A correlation study run between the HbA1c levels and duration of diabetes found significantly correlated (R 0.38) in the good control group. Other groups were found positively correlated yet found statistically insignificant. Conclusion: We found that Type 2 Diabetes with Obesity was more prevalent than Type 1 in our pediatric population. Patients with poor and very poor diabetes control had a longer duration of diabetes compared to patients with good control. Frequent visits with the diabetes team improved HbA1C in our patients. 84% of patients in the good control group were seen by the diabetes team more frequently (at least every 3-6 months) compared to patients with higher HbA1c. The group with very poor glycemic control patients were more obese with Type 2 Diabetes and were less frequently coming for the scheduled appointments. More efforts must be made to design the protocols to overcome patient-related obstacles to improve diabetes control. Presentation: Thursday, June 15, 2023

Magnitude and predictors of poor glycemic control in patients with diabetes at Jimma Medical Center, Ethiopia

Despite the development of new medications over the past decade to aid in the control of blood glucose, most diabetic patients often do not reach recommended glycemic targets of glycated hemoglobin (HbA1C) < 7% in daily clinical practice because of many contributing factors. This study was designed to assess the magnitude and predictors of poor glycemic control among adult diabetic patients on ambulatory chronic care follow-up at Jimma Medical Center. A cross sectional study was conducted on 307 adult diabetic patients between January 2 and April 30, 2022. Representative samples were selected using a systematic random sampling technique. Predictors of poor glycemic control were assessed using a binary and multi variable logistic regression. Data analysis was performed using Statistical Package for Social Science version 25 and R in the R studio environment. A total of 307 adult diabetic patients were included in the study making a response rate of 93%. Out of 307 adult diabetic patients, majority (62.5%) were males. Mean age of the patients was 48.91 ± 15.68 years. The majority, 221 (72%), of patients had poor glycemic control. Non-adherence of patients to medications (AOR = 3.36, 95% CI 1.16–9.72, p = 0.04), no formal education (AOR = 3.84, 95% CI (1.06–13.93, p = 0.04), therapeutic inertia (AOR = 3.16, 95% CI 1.61–6.20, p = 0.001) and poor diabetic knowledge (AOR = 4.79, 95% CI 1.56–14.68, p = 0.006) were found to be independent predictors of poor glycemic control. Nearly three fourth of diabetic patients in the present study had poor glycemic control and were at higher risk of developing diabetic complications or already developed it. These results highlight the need for appropriate management of patients focusing on adherence to medications, education, therapeutic inertia and diabetic knowledge to maintain good glycemic control and improve adverse outcomes of the disease in this study setting.

Relationship between daytime napping with the occurrence and development of diabetes: a systematic review and meta-analysis

ObjectiveTo determine the relationship of napping with incident diabetes risk and glycaemic control in people with diabetes.DesignSystematic review and meta-analysis.Data sourcesMEDLINE (PubMed), EMBASE, Web of Science and the Cochrane Library...

Impaired Removal of the Damaged Mitochondria in the Metabolic Memory Phenomenon Associated with Continued Progression of Diabetic Retinopathy.

Retinopathy fails to halt even after diabetic patients in poor glycemic control try to institute tight glycemic control, suggesting a "metabolic memory" phenomenon, and the experimental models have demonstrated that mitochondria continue to be damaged/dysfunctional, fueling into the vicious cycle of free radicals. Our aim was to investigate the role of removal of the damaged mitochondria in the metabolic memory. Using human retinal endothelial cells (HRECs), incubated in 20mM D-glucose for 4days, followed by 5mM D-glucose for 4 additional days, mitochondrial turnover, formation of mitophagosome, and mitophagy flux were evaluated. Mitophagy was confirmed in a rat model of metabolic memory where the rats were kept in poor glycemic control (blood glucose ~ 400mg/dl) for 3months soon after induction of streptozotocin-induced diabetes, followed by 3 additional months of good control (BG < 150mg/dl). Reversal of high glucose by normal glucose had no effect on mitochondrial turnover and mitophagosome formation, and mitophagy flux remained compromised. Similarly, 3months of good glycemic control in rats, which had followed 3months of poor glycemic control, had no effect on mitophagy flux. Thus, poor turnover/removal of the damaged mitochondria, initiated during poor glycemic control, does not benefit from the termination of hyperglycemic insult, and the damaged mitochondria continue to produce free radicals, suggesting the importance of mitophagy in the metabolic memory phenomenon associated with the continued progression of diabetic retinopathy.

Development and economic assessment of machine learning models to predict glycosylated hemoglobin in type 2 diabetes.

Background: Glycosylated hemoglobin (HbA1c) is recommended for diagnosing and monitoring type 2 diabetes. However, the monitoring frequency in real-world applications has not yet reached the recommended frequency in the guidelines. Developing machine learning models to screen patients with poor glycemic control in patients with T2D could optimize management and decrease medical service costs. Methods: This study was carried out on patients with T2D who were examined for HbA1c at the Sichuan Provincial People's Hospital from April 2018 to December 2019. Characteristics were extracted from interviews and electronic medical records. The data (excluded FBG or included FBG) were randomly divided into a training dataset and a test dataset with a radio of 8:2 after data pre-processing. Four imputing methods, four screening methods, and six machine learning algorithms were used to optimize data and develop models. Models were compared on the basis of predictive performance metrics, especially on the model benefit (MB, a confusion matrix combined with economic burden associated with therapeutic inertia). The contributions of features were interpreted using SHapley Additive exPlanation (SHAP). Finally, we validated the sample size on the best model. Results: The study included 980 patients with T2D, of whom 513 (52.3%) were defined as positive (need to perform the HbA1c test). The results indicated that the model trained in the data (included FBG) presented better forecast performance than the models that excluded the FBG value. The best model used modified random forest as the imputation method, ElasticNet as the feature screening method, and the LightGBM algorithms and had the best performance. The MB, AUC, and AUPRC of the best model, among a total of 192 trained models, were 43475.750 (¥), 0.972, 0.944, and 0.974, respectively. The FBG values, previous HbA1c values, having a rational and reasonable diet, health status scores, type of manufacturers of metformin, interval of measurement, EQ-5D scores, occupational status, and age were the most significant contributors to the prediction model. Conclusion: We found that MB could be an indicator to evaluate the model prediction performance. The proposed model performed well in identifying patients with T2D who need to undergo the HbA1c test and could help improve individualized T2D management.

Well-controlled versus poorly controlled diabetes in patients with obesity: differences in MRI-evaluated pancreatic fat content.

Patients with obesity and poorly controlled type 2 diabetes (T2D) are at high risk of diabetic complications. This study aimed to determine the associations of visceral adipose tissue (VAT), hepatic proton-density fat fraction (PDFF), and pancreatic PDFF with poor glycemic control in patients with obesity and T2D and to evaluate the metabolic effect of bariatric surgery in patients with obesity and poorly controlled diabetes. In this retrospective cross-sectional study, from July 2019 to March 2021, 151 consecutive obese patients with new-onset T2D (n=28), well-controlled T2D (n=17), poorly controlled T2D (n=32), prediabetes (n=20), or normal glucose tolerance (NGT; n=54) were included. A total of 18 patients with poorly controlled T2D were evaluated before and 12 months after bariatric surgery, and 18 non-obese healthy individuals served as controls. VAT, hepatic PDFF, and pancreatic PDFF were quantified by magnetic resonance imaging (MRI) using a chemical shift-encoded sequence [iterative decomposition of water and fat with echo asymmetry and least-squares estimation quantitation (IDEAL-IQ)]. Univariate analysis and multivariate regression analysis were performed. There were significant differences in VAT, hepatic PDFF, and all pancreatic PDFF between the new-onset T2D, prediabetes, and NGT groups (all P<0.05). Pancreatic tail PDFF was significantly higher in the poorly controlled T2D group than in the well-controlled T2D group (P=0.001). In the multivariate analysis, only pancreatic tail PDFF was significantly associated with increased odds of poor glycemic control [odds ratio (OR) =2.09; 95% confidence interval (CI): 1.11-3.94; P=0.022]. The glycated hemoglobin (HbA1c), hepatic PDFF, and pancreatic PDFF significantly decreased (all P<0.01) after bariatric surgery, and the values were statistically similar to those observed in the non-obese healthy controls. Increased fat in the pancreatic tail is strongly associated with poor glycemic control in patients with obesity and T2D. Bariatric surgery is an effective therapy for poorly controlled diabetes and obesity, which improves glycemic control and decreases ectopic fat deposits.

Glycemic control and its associated factors in hypertensive patients with type 2 diabetes.

Inadequate glycemic control among patients with type 2 diabetes is growing worldwide. Earlier research studies investigated the predictors of poor glycemic control among patients with diabetes, but not among hypertensive patients who have type 2 diabetes as a comorbid disease. The aim of this study was to explore the factors associated with poor glycemic control in patients with type 2 diabetes and hypertension. In the present retrospective study, the medical records of two major hospitals were used to collect sociodemographic, biomedical, disease and medication-related information about patients with hypertension and type 2 diabetes. Binary regression analysis was conducted to find the predictors of the study outcome. The data from 522 patients were collected. High physical activity (OR=2.232; 95% CI: 1.368-3.640; p<0.01), receiving insulin (OR=5.094; 95% CI: 3.213-8.076; p <0.01) or GLP1 receptor agonist (OR=2.057; 95% CI: 1.309-3.231; p<0.01) increased the odds of having controlled blood glucose. Increased age (OR=1.041; 95% CI: 1.013-1.070; p<0.01), elevated high-density lipoprotein (HDL) levels (OR=3.727; 95% CI: 1.959-7.092; p<0.01), and lower triglycerides (TGs) levels (OR=0.918; 95% CI: 0.874-0.965; p<0.01) were also associated with improved glycemic control among the study participants. Most of the current study participants showed uncontrolled type 2 diabetes. Low physical activity, not receiving insulin or GLP1 receptor agonist, younger age, low HDL and high TG levels were independently associated with poor glycemic control. Future interventions should place a strong emphasis on the value of consistent physical activity and a stable lipid profile in enhancing glycemic control, particularly in younger patients and those who are not receiving insulin or GLP1 receptor agonist therapy.

Coagulopathies among Patients of Diabetes Mellitus in Gujrat, Pakistan

Objective: To evaluate the behavior of coagulation-related tests like prothrombin time (PT) and activated partial thromboplastin time (APTT) in chronic diabetic patients. Study Design: Cross-sectional study Place and Duration of Study: Department of Hematology, Aziz Bhatti Hospital, Gujrat, Pakistan from 1st July 2022 to 31stDecember 2022. Methodology: One hundred and seventy nine patients were enrolled. Prothrombin time and activated partial Thromboplastin time were performed from the patient’s samples. Results: Forty four patients were with good glycemic control and 135 were with poor glycemic control. It was revealed that patients with type 2 diabetes with poor glycemic control had lower levels of PT concentration 10.95±1.67 and prothrombin time was 11.50±1.331 in good glycemic control patients (P=0.04).APTT result was insignificant with 33.40±2.889 in poor glycemic control patients and while in good glycemic control APTT 34.08±1.348 (p=0.389). Conclusion: Prothrombin time was low if diabetes is uncontrolled. Early detection of lower PT may indicate that micro or macrovascular coagulopathies are in progress. APTT analysis shows insignificant results in diabetic patients and did not find a special role in coagulopathies disorders. Keywords: Coagulopathies, Prothrombin time, Activated partial thromboplastin time, Chronic diabetic patients

Type 2 diabetes with obesity and hypertension: prevalence and sociodemographic risk factors in Yemen

BACKGROUND: Diabetes Mellitus is a rapidly growing and challenging health issue of the 21st century. Type 2 diabetes (T2D) is a major risk factor for obesity and hypertension (HTN). The prevalence of HTN and obesity in T2D patients is not well documented in the country of Yemen. AIM: The focus of this study was to assess the prevalence of hypertension and obesity in patients with type 2 diabetes mellitus in Yemen.METHODS: A cross-sectional study was carried out in Dhamar city hospitals. Patients diagnosed with diabetes (300 males and females: 30 years old and/or above) visiting an endocrinology and diabetes clinic in Dhamar city hospital for the first time were examined and evaluated for blood glucose levels, mode of treatment, duration of the disorder and body mass index. Socio-demographic and clinical data were collected using pretested questionnaires. Ethical approval was obtained from corresponding ethical committees from Dhamar University and hospitals. Data were analyzed using SPSS 23 version.RESULTS: The prevalence of overweight and/or obesity in T2D was 60.67%, and hypertension was 35.66%. The occurrence of T2D was predominant in females (56.33%), patients with an age over 50 years (61%), and rural dwellers (74%). The mode of treatment was mainly through oral antihyperglycemic and hypoglycemic agents (79.3%). A large population (66.3%) showed poor control of blood glucose levels ranging from more than 131 to 500 mg/dL and the type of work these patients were involved in was mostly physically inactive (64%). The body mass index revealed that more than 35% were found to be overweight and 23% to be obese. Patients with hypertension and undergoing treatment for the same showed a significant (P &lt; 0.001) increase in blood glucose level compared to those who were not diagnosed with HTN.CONCLUSION: The prevalence of obesity, HTN, and poor glycemic control in patients with T2D is high, especially in females.

Neutrophil to Lymphocyte Ratio as a Denotative of Type II Diabetes Mellitus

Introduction: The neutrophil-lymphocyte ratio (NLR) is an economically accessible tool useful in cases of Type II Diabetes Mellitus (T2DM). It can be utilized as a laboratory index, indicating systemic inflammation. The sensitivity of NLR to HbA1c must be validated to utilize for its prognostic potential. Correlation of HbA1c with NLR and other parameters have been analyzed to assess the utility of NLR in chronic cases of T2DM, thereby evaluating the utility of NLR as a marker of systemic inflammation in diabetes. Place and Duration of study: Department of Pathology, Saveetha Medical College, Chennai, One year. Objectives: 1. To compare levels of NLR and other parameters in diabetics with those of healthy people and observe the nature of correlation between HbA1c and other parameters.2. To find the linear relationship between HbA1c and other parameters in the diabetic population. Material and methods: An observational study conducted at our Institute from January to October 2020. Blood profiles of 240 T2DM patients and 100 healthy controls were recorded. Results: The NLR (5.35) of T2DM group was significantly elevated (p 0.001) compared to NLR of control group (2.16). Urea and creatinine levels had positive regression coefficients with HbA1c indicating susceptibility of diabetic patients to diabetic nephropathy. Conclusion: NLR showed significance in T2DM. Increased NLR is associated with elevated HbA1c and poor glycemic control in patients with T2DM. It can be used as a prognostic tool during the follow up of diabetic patients.