Background Iron deficiency anemia (IDA) and diabetes are prevalent health concerns, especially in regions like India. While previous studies have explored the relationship between glycated hemoglobin (HbA1c) levels and IDA, there is still inconsistency in the findings, particularly in the Indian population. Understanding this association is crucial for accurate diagnosis and management of both conditions. Materials and methods A case-control study was conducted at the Department of General Medicine at Acharya Vinoba Bhave Rural Hospital (AVBRH), Wardha, India, from May 2022 to October 2022. A total of 141 non-diabetic patients with IDA (study group) and 141 age- and gender-matched non-anemic controls were included. HbA1c levels were measured at baseline and after three months of IDA treatment. Statistical analysis was performed using SPSS software, including the Kolmogorov-Smirnov test, Chi-square test, Mann-Whitney test, and Pearson correlation coefficient. Results In the study group, HbA1c levels significantly increased from a mean of 4.63% at baseline to 5.82% after IDA treatment (p < 0.0001). However, there was no significant correlation between changes in hemoglobin (Hb) levels and HbA1c levels post-correction (r = 0.056, p = 0.510). In addition, all cases and controls were labeled non-diabetic based on a cutoff HbA1c level of 6%. After three months of IDA treatment, 80.85% of cases recovered from IDA. Conclusion The study highlights that HbA1c levels are lower in patients with IDA and may increase with the correction of IDA. However, there is no significant direct correlation between IDA correction and HbA1c increase. Therefore, when interpreting HbA1c levels, clinicians must consider the presence of IDA, especially in regions with high prevalence rates of both IDA and diabetes, like India. This understanding can improve management strategies for both conditions, ensuring better patient health outcomes.

BACKGROUND: Left ventricular hypertrophy (LVH) is a critical factor in heart failure and cardiovascular event-related mortality. While the prevalence of LVH in diabetic patients is well-documented, its occurrence and risk factors in non-diabetic populations remain largely unexplored. This study addresses this issue by investigating the independent risk factors of LVH in non-diabetic individuals.
METHODS: This cross-sectional study, conducted meticulously, utilized data from a robust and comprehensive source, DATADRYAD, in the Sierra Leone database, collected between October 2019 and October 2021, including LVH and various variables. All variables were described and screened using univariate analysis, Spearman correlation, and principal component analysis (PCA). The lipid profile, including total cholesterols (TC), triglycerides (TG), high-density lipoprotein (HDL-C), non-high-density lipoprotein (Non-HDL-C), and low-density lipoprotein cholesterol (LDL-C), TC/HDL-C ratio, TG/HDL-C ratio, Non-HDL-C /HDL-C ratio and LDL-C/HDL-C ratio, which quartiles were treated as categorical variables, with the lowest quartile serving as the reference category. Three adjusted models were constructed to mitigate the influence of other variables. To ensure the robustness of the model, receiver operating characteristic (ROC) curves were used to calculate the cutoff values by analyzing the ROC curves. A sensitivity analysis was performed to validate the findings further.
RESULTS: The dataset encompasses information from 2092 individuals. After adjusting for potential factors that could influence the results, we found that TC (OR = 2.773, 95%CI: 1.805-4.26), Non-HDL-C (OR = 2.74, 95%CI: 1.7723-4.236), TC/HDL-C ratio (OR = 2.237, 95%CI: 1.445-3.463), Non-HDL-C/HDL-C ratio (OR = 2.357, 95%CI: 1.548-3.588), TG/HDL-C ratio (OR = 1.513, 95%CI: 1.02-2.245) acts as independent risk factors of LVH. ROC curve analysis revealed the predictive ability of blood lipids for LVH, with Non-HDL-C exhibiting area under the curve (AUC = 0.6109), followed by TC (AUC = 0.6084).
CONCLUSIONS: TC, non-HDL-C, TC/HDL-C ratio, Non-HDL-C/HDL-C ratio, and TG/HDL-C ratio were independent risk factors of LVH in non-diabetic people. Non-HDL-C and TC were found to be essential indicators for predicting the prevalence of LVH.

Mucormycosis is a rare, opportunistic fungal infection prevalent in the elderly, especially in patients with uncontrolled diabetes or a compromised immune system. However, the outbreak of the pandemic declared by the World Health Organization in late 2019 increased the incidence of mucormycosis cases in COVID-19 patients due to increased steroid usage that resulted in an immunocompromised state. The current report presents two cases of oral mucormycotic osteomyelitis in patients who developed diabetes post-COVID infection, along with a review and relevant literature.

BACKGROUND: Finerenone, a non-steroidal mineralocorticoid receptor antagonist, has previously demonstrated its efficacy and safety in chronic kidney disease (CKD) associated with diabetes mellitus. Given its therapeutic potential, finerenone has been preliminarily explored in clinical practice for non-diabetic CKD patients. The effectiveness and safety in this population require further investigation in a real-world setting.
METHODS: This retrospective, real-world analysis included non-diabetic CKD patients receiving finerenone. The main clinical outcomes assessed were changes in urinary albumin-to-creatinine ratio (UACR) and estimated glomerular filtration rate (eGFR). Serum potassium (sK+) levels were also monitored. Data were collected at baseline, and then at 1 month and 3 months following treatment initiation.
RESULTS: Totally, 16 patients were included. There was a notable decrease in UACR from 1-month post-treatment, with a further reduction at 3 months, resulting in a median reduction of 200.41 mg/g (IQR, 84.04-1057.10 mg/g; P = 0.028; percent change, 44.52% [IQR, 31.79-65.42%]). The average eGFR at baseline was 80.16 ml/min/1.73m2, with no significant change after 1 month (80.72 ml/min/1.73m2, P = 0.594) and a slight numerical increase to 83.45 ml/min/1.73m2 (P = 0.484) after 3 months. During the 3-month follow-up, sK+ levels showed only minor fluctuations, with no significant differences compared to baseline, and remained within the normal range throughout the treatment period. No treatment discontinuation or hospitalization due to hyperkalemia was observed.
CONCLUSIONS: In non-diabetic CKD patients, finerenone showed good effectiveness and safety within a 3-month follow-up period. This study provides valuable real-world evidence supporting the use of finerenone in non-diabetic CKD and highlights the need for future large-scale prospective research to further validate its efficacy.

This case report presents a 9-year-old child without underlying pathology, with a severe life-threatening non-diabetic metabolic ketoacidosis occurring less than 48 h after the onset of fasting and vomiting. The patient was admitted to the pediatric intensive care unit. He received volume expansion and maintenance fluid therapy which allowed a favorable evolution. Because of the unusual rapid onset of intense ketonemia and acidosis, a hereditary metabolic disease was investigated. The association between short fasting period and severe metabolic ketoacidosis has never been described in children outside of the neonatal period. This clinical case emphasizes urgent recognition, rigorous diagnostic and appropriate management in clinical practice.

UNASSIGNED: This study aims to investigate comorbidities, clinical features, laboratory values, and diagnoses in non-diabetic patients experiencing hypoglycemic episodes.
UNASSIGNED: A retrospective observational study was conducted at Shariati Hospital in Iran from 2016 to 2023. Seventy-four non-diabetic patients admitted with a diagnosis of hypoglycemia were included, while patients with diabetes were excluded. Demographic data, symptoms, and biochemical assessments were obtained from the hospital information system. Hypoglycemic episodes were identified based on low measured blood glucose, recorded medications for hypoglycemia treatment, or recorded codes indicating hypoglycemia. Hypoglycemia was defined as blood glucose below 70 mg/dL (3.9 mmol/L) along with two other criteria of the Whipple triad. Statistical analysis was performed using SPSS software (version 26).
UNASSIGNED: Among the enrolled patients, 63.5% were female, and 13.5% were elderly (≥ 65 years). The most common comorbidities observed were cardiovascular disease (20.3%), psychological disorders (20.3%), hypothyroidism (14.9%), and hypertension (8.1%). The prevalent symptoms included weakness, loss of consciousness, sweating, palpitations, dizziness, and tremors. Non-diabetic hypoglycemia was caused by factitious disorders, insulinoma, organ failure, and infection, respectively.
UNASSIGNED: Due to the diverse range of clinical symptoms, hypoglycemia in non-diabetic patients may be diagnosed late, leading to misdiagnoses such as psychological disorders or seizures. It is crucial to consider the possibility of hypoglycemia in non-diabetic patients and determine its underlying cause. Given the poor prognosis associated with hypoglycemia, timely interventions are essential.

BACKGROUND: Type 2 diabetes mellitus represents a multifaceted disorder characterized by intricate pathophysiological mechanisms, encompassing diminished insulin secretion, augmented hepatic glucose production, and heightened insulin resistance. This study aims to assess the sex (Male and Female only) and family history-based differences in the prevalence of T2DM and explore the determinants contributing to this disparity among clinical patients.
METHODS: The study encompassed a diverse pool of clinical patients, encompassing both individuals with diabetes and those without the condition, who had previously sought medical attention for clinical checkups at healthcare centers. The collected data included essential parameters such as blood pressure, weight, height, smoking habits, educational background, and physical activity levels. To ensure methodological rigor and data accuracy, blood pressure measurements adhered to the stringent guidelines set forth by the World Health Organization.
RESULTS: Participants of the present study reported diabetes, among which notable findings emerged regarding health indicators. It was observed that the prevalence of high blood pressure, obesity, and high blood cholesterol exhibited a statistically significant increase among the female participants, underscoring the sex-based disparities in these health parameters. The male population aged 60 or older, the presence of a family history of DM accentuated this risk, resulting in a striking 3.1 times higher prevalence compared to females, who exhibited a 2.4 times higher risk (OR = 2.4, p = 0.0008). This intriguing relationship between diabetes and cholesterol levels was not limited to sex. Both male (OR = 2.47) and female (OR = 2.1) diabetes patients displayed highly significant associations with cholesterol levels. The risk of T2DM was significantly associated with triglycerides in both sexes (1.58 times higher in males, and 1.71 times higher in females).
CONCLUSIONS: The significance of hypertension as a comorbidity in T2DM, highlighting sex-specific associations and the potential impact of a family history of diabetes on blood pressure. Our findings emphasize the importance of considering lipid profiles, obesity, and their sex-specific associations when assessing and managing diabetes risk. Comprehensive diabetes care should include strategies for lipid control, weight management, and cardiovascular risk reduction, tailored to the individual\'s sex and specific risk profile.

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Disruptions in glucose metabolism are frequently observed among patients undergoing peritoneal dialysis (PD) who utilize glucose-containing dialysis solutions. We aimed to investigate the relationship between glucometabolic indices, including fasting glucose, insulin resistance, advanced glycation end products (AGEs), PD-related glucose load, and icodextrin usage, and aortic stiffness in PD patients with and without diabetic mellitus (DM). This study involved 172 PD patients (mean age 58.3 ± 13.5 years), consisting of 110 patients without DM and 62 patients with DM. Aortic stiffness was assessed using the carotid-femoral pulse wave velocity (cfPWV). Impaired fasting glucose was defined as a fasting glucose level ≥ 100 mg/dL. Homeostatic model assessment for insulin resistance (HOMA-IR) scores, serum AGEs, dialysate glucose load, and icodextrin usage were assessed. Patients with DM exhibited the highest cfPWV (9.9 ± 1.9 m/s), followed by those with impaired fasting glucose (9.1 ± 1.4 m/s), whereas patients with normal fasting glucose had the lowest cfPWV (8.3 ± 1.3 m/s), which demonstrated a significant trend. In non-DM patients, impaired fasting glucose (β = 0.52, 95% confidence interval [CI] = 0.01-1.03, p = 0.046), high HOMA-IR (β = 0.60, 95% CI = 0.12-1.08, p = 0.015), and a high PD glucose load (β = 0.58, 95% CI = 0.08-1.08, p = 0.023) were independently associated with increased cfPWV. In contrast, none of the glucometabolic factors contributed to differences in cfPWV in DM patients. In conclusion, among PD patients without DM, impaired fasting glucose, insulin resistance, and PD glucose load were closely associated with aortic stiffness.

Although adult spontaneous (non-diabetic) hypoglycaemia is rare, its recognition is important for the preventative or curative treatment of the underlying cause. Establishing Whipple\'s triad-low blood glucose, neuroglycopaenia and resolution of neuroglycopaenia on increasing blood glucose levels to normal or above-is essential to verify hypoglycaemia. Awareness that hypoglycaemia may occur in severely ill patients is important for its prevention. Further investigation in such cases is unnecessary unless another cause of hypoglycaemia is suspected. Patients are often asymptomatic and normoglycaemic at review. Their history of medication, self-medication, access to hypoglycaemic drugs, alcohol use and comorbidity may provide aetiological clues. The investigation involves obtaining blood samples during symptoms for laboratory glucose measurement or provoking fasting or postprandial hypoglycaemia as directed by symptoms. If confirmed, insulin, c-peptide, proinsulin and beta-hydroxybutyrate are analysed in hypoglycaemic samples. These will classify hypoglycaemia due to non-ketotic hyperinsulinaemia, non-ketotic hypoinsulinaemia and ketotic hypoinsulinaemia, and direct investigations to identify the underlying cause. There are, however, many pitfalls that may mislabel healthy individuals as \"hypoglycaemic\" or misdiagnose treatable or preventable causes of spontaneous hypoglycaemia. Clinical acumen and appropriate investigations will mostly identify hypoglycaemia and its cause.