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Introduction: Diabetic nephropathy develops in 40% of patients 10 years after the diagnosis of diabetes, with albuminuria >300mg/dl (>200µg/min) more than twice in 3-6 months. (1) Strict glycemic control reduces mortality by 48% (2) and macroalbuminuria by 50%. The Terrabrio SAPI de CV group developed the Elevaté® Body Balance phytoformula made with Shilajit (Asphaltum punjabianum), Chaga (Inonotus obliquus), Moringa (Moringa oleifera), Berberine (Berberina vulgaris, Coptis chinensis French) and Bayetilla (Hamelia patens) used in traditional herbal medicine. Objective: To evaluate changes in albuminuria in patients with DM2 treated with a phytoformula as adjuvant therapy. Methods: A controlled clinical trial was conducted in 269 patients with DM2 treated with oral hypoglycemic agents plus 1.5 g/day of the phytoformulation under treatment for 90 days; a sub analysis of 20 patients with albuminuria was performed. Results: In the 20 patients with albuminuria, age was 53.20 (49.25-58) years, 12(60%) women and 8(40%) men; time of diagnosis of DM2 was 7.41±4.36 years, treated with metformin 16(80%), sulfonylureas 19(95%) and insulin 3(15%); 3-month changes in waist from 95.85±9. 82 to 93.80±10.34 with p 0.044; HbA1c from 9.82±1.24 to 7.28±1.70 with p 0.0001; BUN from 10.44±3.43 to 12.30±5.53 with p 0.023; Albuminuria from 43.50±36.45 to 30±35.39 with p 0.0001; GFR from 93.57±14.54 to 93.85±18.56 with p 0.908, with no differences in BMI, blood pressure, urea, and creatinine. Correlation was 0.795 between HbAc1 and albuminuria. Conclusions: Phytoformula reduced waist, HbA1c and albuminuria at 3 months; no changes in BMI and GFR were present.

Background Although recent guidelines advocate for HbA1c target individualization, a comprehensive criterion for patient categorization remains absent. This study aimed to categorize HbA1c variability levels and explore the relationship between glycemic control, cardiovascular outcomes, and mortality across different degrees of variability. Methods Action to Control Cardiovascular Risk in Diabetes study data were used. HbA1c variability was measured using the HbA1c variability score (HVS) and standard deviation (SD). K-means and K-medians clustering were used to combine the HVS and SD. Results K-means clustering was the most stable algorithm with the lowest clustering similarities. In the low variability group, intensive glucose-lowering treatment significantly reduced the risk of adverse cardiovascular outcomes (HR: 0·78 [95% CI: 0·63, 0·97]) without increasing mortality risk (HR: 1·07 [0.81, 1·42]); the risk of adverse cardiovascular events (HR: 1·33 [1·14, 1·56]) and all-cause mortality (HR: 1·23 [1·01,1·51]) increased with increasing mean HbA1c. In the high variability group, treatment increased the risk of cardiovascular events (HR: 2.00 [1·54, 2·60]) and mortality (HR: 2·20 [1·66, 2·92]); a higher mean HbA1c (7·86%, [7·66%, 8·06%]) had the lowest mortality risk, when the mean HbA1c was < 7·86%, a higher mean HbA1c was associated with a lower mortality risk (HR: 0·63 [0·42, 0·95]). In the medium variability group, a mean HbA1c around 7·5% was associated with the lowest risk. Conclusions HbA1c variability can guide glycemic control targets for patients with type 2 diabetes. For patients with low variability, the lower the HbA1c, the lower the risk. For those with medium variability, controlling HbA1c at 7·5% provides the maximum benefit. For patients with high variability, a mean HbA1c of around 7·8% presents the lowest risk of all-cause mortality, a lower HbA1c did not provide cardiovascular benefits but instead increased the mortality risk. Further studies, especially those with patients that reflect the general population with type 2 diabetes undergoing the latest therapeutic approaches, are essential to validate the conclusions of this study.

Abstract On March 11, 2020, the World Health Organization (WHO) declared a new coronavirus infection caused by the SARS-CoV-2 virus as a pandemic, making it the 11th pandemic of the 20th and 21st centuries. This study investigated the clinical and laboratory results (D-dimer, conventional coagulation, and HbA1c biomarker concentrations) of 150 patients (75 male and 75 female) with confirmed COVID-19 pneumonia and 50 controls (25 male and 25 female). For disease diagnosis, all COVID-19 patients were given a Real-Time Reverse Transcription Polymerase Chain Reaction Assay (RT-PCR). The findings revealed that D-dimer and HbA1c levels in COVID-19 patients were significantly higher (P 0.001) at the time of admission; In COVID-19 patients, there was also a strong correlation between D-dimer levels and HbA1c levels (P 0.001). In conclusion, COVID-19 patients are more likely to have a poor prognosis if their D-dimer and HbA1c levels remain uncontrolled over a lengthy period. To lower the likelihood of a bad prognosis in COVID-19, patients with higher levels of D-dimer and HbA1c should be continuously monitored. Resumo Em 11 de março de 2020, a Organização Mundial da Saúde (OMS) declarou uma nova infecção por coronavírus causada pelo vírus SARS-CoV-2 como uma pandemia, tornando-a a 11ª pandemia dos séculos XX e XXI. Este estudo investigou os resultados clínicos e laboratoriais (D-dímero, coagulação convencional e concentrações de biomarcadores HbA1c) de 150 pacientes (75 homens e 75 mulheres) com pneumonia por COVID-19 confirmada e 50 controles (25 homens e 25 mulheres). Para o diagnóstico da doença, todos os pacientes com COVID-19 receberam um Ensaio de Reação em Cadeia da Polimerase com Transcrição Reversa em Tempo Real (RT-PCR). Os achados revelaram que os níveis de D-dímero e HbA1c em pacientes com COVID-19 foram significativamente maiores (P 0,001) no momento da admissão. Em pacientes com COVID-19, também houve uma forte correlação entre os níveis de D-dímero e os níveis de HbA1c (P 0,001). Em conclusão, os pacientes com COVID-19 têm maior probabilidade de ter um prognóstico ruim se seus níveis de D-dímero e HbA1c permanecerem descontrolados por um longo período. Para diminuir a probabilidade de um mau prognóstico na COVID-19, os pacientes com níveis mais altos de D-dímero e HbA1c devem ser monitorados continuamente.

Diabetes is a global endemic with rapidly increasing prevalence in both developing and developed countries. The American Diabetes Association has recommended glycated hemoglobin (HbA1c) as a possible substitute to fasting blood glucose for diagnosis of diabetes. HbA1c is an important indicator of long-term glycemic control with the ability to reflect the cumulative glycemic history of the preceding two to three months. HbA1c not only provides a reliable measure of chronic hyperglycemia but also correlates well with the risk of long-term diabetes complications. Elevated HbA1c has also been regarded as an independent risk factor for coronary heart disease and stroke in subjects with or without diabetes. The valuable information provided by a single HbA1c test has rendered it as a reliable biomarker for the diagnosis and prognosis of diabetes. This review highlights the role of HbA1c in diagnosis and prognosis of diabetes patients.

Background: Type 2 diabetes mellitus (T2DM) is a growing global health concern characterized by chronic hyperglycemia, insulin resistance and associated complications. Serum ferritin an iron storage protein and inflammatory marker has been linked to poor glycemic control and may provide insight into the interplay between iron metabolism, inflammation and diabetes management. Objective: To assess the correlation of glycemic control with serum ferritin levels. Methods: This comparative cross-sectional study was performed at the Department of Pathology and Endocrinology, Fauji Foundation Hospital, Rawalpindi from December 2023 to May 2024. Data of patients attending the diabetic clinic were included and distributed into two groups: Group A (patients with good glycemic control) and Group B (patients with poor glycemic control). Blood samples from patients included in this study were collected and analyzed for the levels of serum ferritin, CRP and HbA1C. Descriptive variables were presented as frequency, percentages and median accordingly. Results: In this study of 160 participants, divided based on glycemic control, group A had a lower median HbA1C of 5.70% compared to 8.10% in group B. Group B also had a higher median serum ferritin level (348.00 µg/L versus 161.5 µg/L). Additionally, group A had lower levels of C-reactive protein (0.3 mg/dL) compared to group B (0.4 mg/dL). Higher serum ferritin and CRP levels were strongly correlated to increased HbA1C levels. Conclusion: Increased ferritin and CRP levels are strongly related to poorly controlled T2DM. Monitoring ferritin levels in glycemic care can enhance patient outcomes by allowing for better risk assessment ... 

Cognitive impairment (CI) is a common complication in patients with type 2 diabetes mellitus (T2DM), but its relationship with long-term glycemic control remains unclear. This study aimed to investigate the associations between mean hemoglobin A1c (HbA1c) levels, HbA1c control status, HbA1c fluctuations, and CI in Chinese adults aged 45 years and older with T2DM using data from the China Health and Retirement Longitudinal Study (CHARLS). A total of 797 participants with HbA1c measurements from 2011 to 2015 and cognitive function assessments in 2018 were included. Logistic regression models and restricted cubic spline (RCS) analysis were applied, adjusting for potential confounders. Higher mean HbA1c levels (≥ 9%) were significantly associated with an increased risk of CI, particularly in global cognition and episodic memory (OR 4.03 (1.45–11.20) for global cognition; OR 2.92 (1.02–8.38) for episodic memory). RCS analysis revealed a U-shaped relationship between mean HbA1c and CI, indicating that both excessively low and high HbA1c levels elevate CI risk. Uncontrolled HbA1c levels (≥ 8%) were also linked to higher CI risk compared to stable HbA1c levels. Maintaining HbA1c levels below 8% may significantly reduce CI risk in T2DM patients, highlighting the importance of personalized glycemic management.

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.

Background Diabetic neuropathy is one of the complications of diabetes that occurs due to poor glycemic control. Brain-derived neurotrophic factor (BDNF) levels can increase when patients are first diagnosed with type 2 diabetes mellitus and can change in response to glycemic control conditions throughout the course of the disease. However, the correlation between glycemic control and BDNF remains unclear. The objective of this study was to investigate whether glycemic control can predict the BDNF levels in patients with diabetic neuropathy, based on diabetic duration. Methods A cross-sectional study was conducted on 8 patients with diabetic neuropathy who were treated at a clinic in Central Java. We use glycated hemoglobin (HbA1c) levels as a parameter of glycemic control, which were measured according to the National Glycohemoglobin Standardization Program. BDNF serum levels were evaluated using the Enzyme-Linked Immunosorbent Assay (ELISA) method in the laboratory. Analysis was performed using ANCOVA tests. Results Together, HbA1c levels, diabetic duration, and interactions between diabetic duration and HbA1c explained 9.9% of variability in BDNF levels (p=0.046). However, HbA1c levels explained 8.1% of the variability in BDNF levels (p = 0.011), with only minor contributions from diabetic duration and interaction them. Conclusions The HbA1c levels significantly explained the variability in BDNF levels in patients with diabetic neuropathy regardless of diabetic duration.

Blood glucose control is closely related to type 2 diabetes mellitus (T2DM) prognosis. This multicenter study aimed to investigate blood glucose control among patients with insulin-treated T2DM in North China and explore the application value of combining an elastic network (EN) with a machine-learning algorithm to predict glycemic control. Basic information, biochemical indices, and diabetes-related data were collected via questionnaire from 2787 consecutive participants recruited from 27 centers in six cities between January 2016 and December 2017. An EN regression was used to address variable collinearity. Then, three common machine learning algorithms (random forest [RF], support vector machine [SVM], and back propagation artificial neural network [BP-ANN]) were used to simulate and predict blood glucose status. Additionally, a stepwise logistic regression was performed to compare the machine learning models. The well-controlled blood glucose rate was 45.82% in North China. The multivariable analysis found that hypertension history, atherosclerotic cardiovascular disease history, exercise, and total cholesterol were protective factors in glycosylated hemoglobin (HbA1c) control, while central adiposity, family history, T2DM duration, complications, insulin dose, blood pressure, and hypertension were risk factors for elevated HbA1c. Before the dimensional reduction in the EN, the areas under the curve of RF, SVM, and BP were 0.73, 0.61, and 0.70, respectively, while these figures increased to 0.75, 0.72, and 0.72, respectively, after dimensional reduction. Moreover, the EN and machine learning models had higher sensitivity and accuracy than the logistic regression models (the sensitivity and accuracy of logistic were 0.52 and 0.56; RF: 0.79, 0.70; SVM: 0.84, 0.73; BP-ANN: 0.78, 0.73, respectively). More than half of T2DM patients in North China had poor glycemic control and were at a higher risk of developing diabetic complications. The EN and machine learning algorithms are alternative choices, in addition to the traditional logistic model, for building predictive models of blood glucose control in patients with T2DM.

Endometrial cancer is one of the most common cancers in women worldwide and its incidence is increasing. Epidemiological evidence shows a strong association between endometrial cancer and obesity, and multiple mechanisms linking obesity and cancer progression have been described. However, it remains unclear which factors are the main drivers of endometrial cancer development. Hyperglycemia and type 2 diabetes mellitus are common co-morbidities of obesity, and there is evidence that hyperglycemia is a risk factor for endometrial cancer independent of obesity. This review aims to explore the association between hyperglycemia and endometrial cancer, and discuss the evidence supporting a role for increased glucose metabolism in endometrial cancer and how this phenotype may contribute to endometrial cancer growth and progression. Finally, the potential role of blood glucose lowering strategies, including drugs and bariatric surgery, for the treatment of this malignancy will be discussed.